U.S. patent application number 14/350052 was filed with the patent office on 2014-08-28 for methods and processes for non-invasive assessment of genetic variations.
This patent application is currently assigned to SEQUENOM, INC. The applicant listed for this patent is SEQUENOM, INC.. Invention is credited to Charles R. Cantor, Cosmin Deciu, Zeljko Dzakula, Sung Kyun Kim, Dirk Johannes Van Den Boom.
Application Number | 20140242588 14/350052 |
Document ID | / |
Family ID | 47073531 |
Filed Date | 2014-08-28 |
United States Patent
Application |
20140242588 |
Kind Code |
A1 |
Van Den Boom; Dirk Johannes ;
et al. |
August 28, 2014 |
METHODS AND PROCESSES FOR NON-INVASIVE ASSESSMENT OF GENETIC
VARIATIONS
Abstract
Technology provided herein relates in part to methods, processes
and apparatuses for non-invasive assessment of genetic
variations.
Inventors: |
Van Den Boom; Dirk Johannes;
(Encinitas, CA) ; Cantor; Charles R.; (Del Mar,
CA) ; Kim; Sung Kyun; (Glendale, CA) ;
Dzakula; Zeljko; (San Diego, CA) ; Deciu; Cosmin;
(San Diego, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SEQUENOM, INC. |
San Diego |
CA |
US |
|
|
Assignee: |
SEQUENOM, INC
San Diego
CA
|
Family ID: |
47073531 |
Appl. No.: |
14/350052 |
Filed: |
October 5, 2012 |
PCT Filed: |
October 5, 2012 |
PCT NO: |
PCT/US2012/059114 |
371 Date: |
April 4, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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61544251 |
Oct 6, 2011 |
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61545053 |
Oct 7, 2011 |
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61663477 |
Jun 22, 2012 |
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61709899 |
Oct 4, 2012 |
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Current U.S.
Class: |
435/6.11 ;
702/20 |
Current CPC
Class: |
G16B 20/00 20190201;
C12Q 1/6869 20130101; G16B 30/00 20190201; C12Q 1/6827 20130101;
G16B 40/00 20190201; C12Q 2537/16 20130101; C12Q 1/6827 20130101;
C12Q 2537/165 20130101 |
Class at
Publication: |
435/6.11 ;
702/20 |
International
Class: |
C12Q 1/68 20060101
C12Q001/68 |
Claims
1. A method for detecting the presence or absence of a chromosome
aneuploidy, comprising: (a) obtaining counts of partial nucleotide
sequence reads mapped to genomic sections of a reference genome,
which partial nucleotide sequence reads are reads of circulating
cell-free nucleic acid from a test sample from a pregnant female
bearing a fetus, wherein at least some of the partial nucleotide
sequence reads comprise: i) multiple nucleobase gaps between
identified nucleobases, or ii) one or more nucleobase classes,
wherein each nucleobase class comprises a subset of nucleobases
present in the sample nucleic acid, or a combination of (i) and
(ii), (b) normalizing the counts of the partial nucleotide sequence
reads, thereby providing normalized counts, and (c) detecting the
presence or absence of a chromosome aneuploidy based on the
normalized counts.
2-10. (canceled)
11. The method of claim 10, wherein the normalizing in (b)
comprises normalizing according to guanine and cytosine (GC)
content of the genomic sections, and providing calculated genomic
section levels.
12. The method of claim 11, wherein the normalizing in (b)
comprises: (i) determining a guanine and cytosine (GC) bias
coefficient for the test sample based on a fitted relation between
(1) the counts of the partial nucleotide sequence reads mapped to
each of the genomic sections, and (2) GC content for each of the
genomic sections, wherein the GC bias coefficient is a slope for a
linear fitted relation or a curvature estimation for a non-linear
fitted relation; and (ii) calculating a genomic section level for
each of the genomic sections based on the counts of the partial
nucleotide sequence reads, the GC bias coefficient of (i), and a
fitted relation, for each of the portions, between (1) the GC bias
coefficient for each of multiple samples, and (2) the counts of the
partial nucleotide sequence reads mapped to each of the genomic
sections for the multiple samples, thereby providing calculated
genomic section levels, whereby bias in the counts of the partial
nucleotide sequence reads mapped to each of the portions of the
reference genome is reduced in the calculated genomic section
levels, and wherein the normalized counts in (b) comprise the
calculated genomic section levels.
13. The method of claim 12, wherein the normalized counts in (b)
are the calculated genomic section levels.
14-16. (canceled)
17. The method of claim 1, wherein the normalizing in (b) comprises
performing a local regression on the counts of the partial
nucleotide sequence reads or the calculated genomic section levels,
or the counts of the partial nucleotide sequence reads and the
calculated genomic section levels.
18. The method of claim 17, wherein the local regression comprises
a weighted least squares fit.
19. The method of claim 18, wherein the local regression comprises
a LOESS regression.
20. The method of claim 1, wherein the partial nucleotide sequence
reads are unary partial reads, for which unary partial reads one
nucleotide species is known at known positions and the other
positions can be any one of three other nucleotide species.
21. The method of claim 20, wherein the partial nucleotide sequence
reads are about 30 base pairs or more.
22. The method of claim 1, wherein the partial nucleotide sequence
reads are binary partial reads, for which binary partial reads a
first nucleotide class consisting of two possible bases is known at
known positions and a second nucleotide class consisting of two
possible bases is known at known positions, wherein the bases of
the first nucleotide class are different than the bases of the
second nucleotide class.
23. The method of claim 22, wherein the partial nucleotide sequence
reads are about 30 base pairs or more.
24. The method of claim 1, wherein the partial nucleotide sequence
reads are ternary partial reads, for which ternary partial reads a
first nucleotide species is known at known positions, a second
nucleotide species is known at other known positions and the other
positions are any one of two nucleotide species other than the
first nucleotide species and the second nucleotide species.
25. The method of claim 24, wherein the partial nucleotide sequence
reads are about 20 base pairs or more.
26. The method of claim 1, comprising sequencing the circulating
cell-free nucleic acid and determining the partial nucleotide
sequence reads.
27. The method of claim 26, which partial nucleotide sequence reads
are determined using a method comprising a massively parallel
sequencing (MPS) process or a nanopore process, or a massively
parallel sequencing (MPS) process and a nanopore process.
28. The method of claim 26, comprising mapping the partial
nucleotide sequence reads to genomic sections of the reference
genome.
29-31. (canceled)
32. The method of claim 1, wherein the test sample is blood plasma,
blood serum or urine.
33-97. (canceled)
98. The method of claim 1, wherein the chromosome aneuploidy is a
fetal chromosome trisomy.
99. The method of claim 98, wherein the chromosome trisomy is a
chromosome 13 trisomy, chromosome 18 trisomy and/or chromosome 21
trisomy.
100. The method of claim 99, wherein the chromosome trisomy is a
chromosome 21 trisomy.
Description
RELATED PATENT APPLICATIONS
[0001] This patent application is a national stage application of
international patent application no. PCT/US2012/059114, filed on
Oct. 5, 2012, entitled METHODS AND PROCESSES FOR NON-INVASIVE
ASSESSMENT OF GENETIC VARIATIONS, naming Dirk Johannes VAN DEN
BOOM, Charles R. CANTOR, Sung Kyun KIM, Zeljko DZAKULA, and Cosmin
DECIU as inventors, and having attorney docket no. SEQ-6036-PC,
which claims the benefit of U.S. Provisional Patent Application No.
61/545,053 filed on Oct. 7, 2011, entitled METHODS AND PROCESSES
FOR NON-INVASIVE ASSESSMENT OF GENETIC VARIATIONS, naming Dirk
Johannes Van Den Boom and Charles R. Cantor as inventors, and
designated by Attorney Docket No. SEQ-6036-PV. This patent
application also claims the benefit of U.S. Provisional Patent
Application No. 61/709,899 filed on Oct. 4, 2012, entitled METHODS
AND PROCESSES FOR NON-INVASIVE ASSESSMENT OF GENETIC VARIATIONS,
naming Cosmin Deciu, Zeljko Dzakula, Mathias Ehrich and Sung Kyun
Kim as inventors, and designated by Attorney Docket No.
SEQ-6034-PV3; U.S. Provisional Patent Application No. 61/663,477
filed on Jun. 22, 2012, entitled METHODS AND PROCESSES FOR
NON-INVASIVE ASSESSMENT OF GENETIC VARIATIONS, naming Zeljko
Dzakula and Mathias Ehrich as inventors, and designated by Attorney
Docket No. SEQ-6034-PV2; and U.S. Provisional Patent Application
No. 61/544,251 filed on Oct. 6, 2011, entitled METHODS AND
PROCESSES FOR NON-INVASIVE ASSESSMENT OF GENETIC VARIATIONS, naming
Zeljko Dzakula and Mathias Ehrich as inventors, and designated by
Attorney Docket No. SEQ-6034-PV. The entire content of the
foregoing provisional applications are incorporated herein by
reference, including all text, tables and drawings.
FIELD
[0002] Technology provided herein relates in part to methods,
processes and apparatuses for non-invasive assessment of genetic
variations.
BACKGROUND
[0003] Genetic information of living organisms (e.g., animals,
plants and microorganisms) and other forms o Genetic information of
living organisms (e.g., animals, plants and microorganisms) and
other forms of replicating genetic information (e.g., viruses) is
encoded in deoxyribonucleic acid (DNA) or ribonucleic acid (RNA).
Genetic information is a succession of nucleotides or modified
nucleotides representing the primary structure of chemical or
hypothetical nucleic acids. In humans, the complete genome contains
about 30,000 genes located on twenty-four (24) chromosomes (see The
Human Genome, T. Strachan, BIOS Scientific Publishers, 1992). Each
gene encodes a specific protein, which after expression via
transcription and translation fulfills a specific biochemical
function within a living cell.
[0004] Many medical conditions are caused by one or more genetic
variations. Certain genetic variations cause medical conditions
that include, for example, hemophilia, thalassemia, Duchenne
Muscular Dystrophy (DMD), Huntington's Disease (HD), Alzheimer's
Disease and Cystic Fibrosis (CF) (Human Genome Mutations, D. N.
Cooper and M. Krawczak, BIOS Publishers, 1993). Such genetic
diseases can result from an addition, substitution, or deletion of
a single nucleotide in DNA of a particular gene. Certain birth
defects are caused by a chromosomal abnormality, also referred to
as an aneuploidy, such as Trisomy 21 (Down's Syndrome), Trisomy 13
(Patau Syndrome), Trisomy 18 (Edward's Syndrome), Monosomy X
(Turner's Syndrome) and certain sex chromosome aneuploidies such as
Klinefelter's Syndrome (XXY), for example. Another genetic
variation is fetal gender, which can often be determined based on
sex chromosomes X and Y. Some genetic variations may predispose an
individual to, or cause, any of a number of diseases such as, for
example, diabetes, arteriosclerosis, obesity, various autoimmune
diseases and cancer (e.g., colorectal, breast, ovarian, lung).
[0005] Identifying one or more genetic variations or variances can
lead to diagnosis of, or determining predisposition to, a
particular medical condition. Identifying a genetic variance can
result in facilitating a medical decision and/or employing a
helpful medical procedure. In some cases, identification of one or
more genetic variations or variances involves the analysis of
cell-free DNA.
[0006] Cell-free DNA (CF-DNA) is composed of DNA fragments that
originate from cell death and circulate in peripheral blood. High
concentrations of CF-DNA can be indicative of certain clinical
conditions such as cancer, trauma, burns, myocardial infarction,
stroke, sepsis, infection, and other illnesses. Additionally,
cell-free fetal DNA (CFF-DNA) can be detected in the maternal
bloodstream and used for various noninvasive prenatal
diagnostics.
[0007] The presence of fetal nucleic acid in maternal plasma allows
for non-invasive prenatal diagnosis through the analysis of a
maternal blood sample. For example, quantitative abnormalities of
fetal DNA in maternal plasma can be associated with a number of
pregnancy-associated disorders, including preeclampsia, preterm
labor, antepartum hemorrhage, invasive placentation, fetal Down
syndrome, and other fetal chromosomal aneuploidies. Hence, fetal
nucleic acid analysis in maternal plasma can be a useful mechanism
for the monitoring of fetomaternal well-being. Early detection of
pregnancy-related conditions, including complications during
pregnancy and genetic defects of the fetus is important, as it
allows early medical intervention necessary for the safety of both
the mother and the fetus. Prenatal diagnosis traditionally has been
conducted using cells isolated from the fetus through procedures
such as chorionic villus sampling (CVS) or amniocentesis. However,
these conventional methods are invasive and present an appreciable
risk to both the mother and the fetus. The National Health Service
currently cites a miscarriage rate of between 1 and 2 percent
following the invasive amniocentesis and chorionic villus sampling
(CVS) tests. The use of non-invasive screening techniques that
utilize circulating CFF-DNA can be an alternative to these invasive
approaches.
SUMMARY
[0008] Provided in some aspects are methods for determining the
presence or absence of a genetic variation and methods for
determining the presence or absence of a fetal aneuploidy using
partial nucleotide sequence reads, and computer program products
and systems for implementing methods discussed herein.
[0009] Also provided, in some aspects, are methods for detecting
the presence or absence of a genetic variation, comprising: (a)
obtaining counts of partial nucleotide sequence reads mapped to
genomic sections of a reference genome, which partial nucleotide
sequence reads are reads of circulating cell-free nucleic acid from
a test sample, where at least some of the partial nucleotide
sequence reads comprise: i) multiple nucleobase gaps between
identified nucleobases, or ii) one or more nucleobase classes,
where each nucleobase class comprises a subset of nucleobases
present in the sample nucleic acid, or a combination of (i) and
(ii), (b) normalizing the counts of the partial nucleotide sequence
reads, thereby providing normalized counts, and (c) detecting the
presence or absence of a genetic variation based on the normalized
counts. In some cases, the genetic variation is a nucleic acid
sequence variation. In some cases, the genetic variation is a copy
number variation. In some embodiments, the test sample is from a
pregnant female and the genetic variation is a fetal
aneuploidy.
[0010] In some embodiments, a method comprises comparing the
normalized counts to a reference, thereby making a comparison,
where determining the presence or absence of the genetic variation
in (c) is based on the normalized counts and the comparison. In
some cases, the reference is counts of sequence reads mapped to a
reference chromosome or segment thereof. In some cases, the
reference chromosome is chromosome 1, chromosome 14, chromosome 19
or combination thereof.
[0011] In some embodiments, the counts of the partial nucleotide
sequence reads obtained in (a) comprise counts of partial
nucleotide sequence reads mapped to a test chromosome or segment
thereof. In some cases, the test chromosome is chromosome 13,
chromosome 18, chromosome 21 or combination thereof. In some cases,
the counts are expressed as a ratio of counts for genomic sections
in a test chromosome or segment thereof to counts for genomic
sections in autosomes or segment thereof, thereby providing a count
representation.
[0012] In some embodiments, the normalizing in (b) comprises
normalizing according to guanine and cytosine (GC) content of the
genomic sections, and providing calculated genomic section levels.
In some embodiments, the normalizing in (b) comprises: (i)
determining a guanine and cytosine (GC) bias for each of the
genomic sections for multiple samples from a fitted relation for
each sample between (1) the counts of the partial nucleotide
sequence reads mapped to each of the genomic sections, and (2) GC
content for each of the genomic sections; and (ii) calculating a
genomic section level for each of the genomic sections from a
fitted relation between (1) the GC bias and (2) the counts of the
partial nucleotide sequence reads mapped to each of the genomic
sections, thereby providing calculated genomic section levels,
whereby bias in the counts of the partial nucleotide sequence reads
mapped to each of the portions of the reference genome is reduced
in the calculated genomic section levels, and where the normalized
counts in (b) comprise the calculated genomic section levels. In
some cases, the normalized counts in (b) are the calculated genomic
section levels.
[0013] In some embodiments, the normalized counts are adjusted for
a first level for a first set of genomic sections which first level
is significantly different than a second level for a second set of
genomic sections, thereby providing adjusted normalized counts,
where determining the presence or absence of the genetic variation
in (c) is based on the adjusted normalized counts.
[0014] In some embodiments, a method comprises (i) identifying a
first elevation of the normalized counts significantly different
than a second elevation of the normalized counts in a normalized
counts profile, which first elevation is for a first set of genomic
sections, and which second elevation is for a second set of genomic
sections; (ii) determining an expected elevation range for a
homozygous and heterozygous copy number variation according to an
uncertainty value for a segment of the genome; and (iii) adjusting
the first elevation by a predetermined value, or adjusting the
first elevation to the second elevation, when the first elevation
is within one of the expected elevation ranges, thereby providing
an adjustment of the first elevation. In some cases, the segment of
the genome comprises the first elevation or the second elevation,
or the first elevation and the second elevation.
[0015] In some embodiments, the normalizing in (b) comprises
performing a local regression on the counts of the partial
nucleotide sequence reads or the calculated genomic section levels,
or the counts of the partial nucleotide sequence reads and the
calculated genomic section levels. Sometimes the local regression
comprises a weighted least squares fit. Sometimes the local
regression comprises a LOESS regression.
[0016] In some embodiments, the partial nucleotide sequence reads
are unary partial reads, for which unary partial reads one
nucleotide is known at known positions and the other positions can
be any one of three other nucleotides. Sometimes the partial
nucleotide sequence reads are about 30 base pairs or more.
[0017] In some embodiments, the partial nucleotide sequence reads
are binary partial reads, for which binary partial reads a first
nucleotide class consisting of two possible bases is known at known
positions and a second nucleotide class consisting of two possible
bases is known at known positions, where the bases of the first
nucleotide class are different than the bases of the second
nucleotide class. Sometimes the partial nucleotide sequence reads
are about 30 base pairs or more.
[0018] In some embodiments, the partial nucleotide sequence reads
are ternary partial reads, for which ternary partial reads a first
nucleotide is known at known positions, a second nucleotide is
known at other known positions and the other positions are any one
of two nucleotides other than the first nucleotide and the second
nucleotide. Sometimes the partial nucleotide sequence reads are
about 20 base pairs or more.
[0019] In some embodiments, a method comprises determining partial
nucleotide sequence reads of the nucleic acid from the test sample.
In some cases, the partial nucleotide sequence reads are determined
using a method comprising a massively parallel sequencing (MPS)
process or a nanopore process, or a massively parallel sequencing
(MPS) process and a nanopore process. In some embodiments, a method
comprises mapping the partial nucleotide sequence reads to genomic
sections of the reference genome.
[0020] In some embodiments, a method comprises isolating the
nucleic acid from the test sample. In some embodiments, a method
comprises obtaining the test sample. In some cases, the test sample
is obtained from a pregnant female. The test sample sometimes is
blood plasma, blood serum or urine.
[0021] Also provided, in some aspects, are systems comprising one
or more processors and memory, which memory comprises instructions
executable by the one or more processors and which memory comprises
counts of partial nucleotide sequence reads mapped to genomic
sections of a reference genome, which partial nucleotide sequence
reads are reads of circulating cell-free nucleic acid from a test
sample, where at least some of the partial nucleotide sequence
reads comprise: i) multiple nucleobase gaps between identified
nucleobases, or ii) one or more nucleobase classes, where each
nucleobase class comprises a subset of nucleobases present in the
sample nucleic acid, or a combination of (i) and (ii); and which
instructions executable by the one or more processors are
configured to: (a) normalize the counts of the partial nucleotide
sequence reads, thereby providing normalized counts, and (b) detect
the presence or absence of a genetic variation based on the
normalized counts.
[0022] Also provided, in some aspects, are apparatuses comprising
one or more processors and memory, which memory comprises
instructions executable by the one or more processors and which
memory comprises counts of partial nucleotide sequence reads mapped
to genomic sections of a reference genome, which partial nucleotide
sequence reads are reads of circulating cell-free nucleic acid from
a test sample, where at least some of the partial nucleotide
sequence reads comprise: i) multiple nucleobase gaps between
identified nucleobases, or ii) one or more nucleobase classes,
where each nucleobase class comprises a subset of nucleobases
present in the sample nucleic acid, or a combination of (i) and
(ii); and which instructions executable by the one or more
processors are configured to: (a) normalize the counts of the
partial nucleotide sequence reads, thereby providing normalized
counts, and (b) detect the presence or absence of a genetic
variation based on the normalized counts.
[0023] Also provided, in some aspects, are computer program
products tangibly embodied on a computer-readable medium,
comprising instructions that when executed by one or more
processors are configured to: (a) access counts of partial
nucleotide sequence reads mapped to genomic sections of a reference
genome, which partial nucleotide sequence reads are reads of
circulating cell-free nucleic acid from a test sample, where at
least some of the partial nucleotide sequence reads comprise: i)
multiple nucleobase gaps between identified nucleobases, or ii) one
or more nucleobase classes, where each nucleobase class comprises a
subset of nucleobases present in the sample nucleic acid, or a
combination of (i) and (ii), (b) normalize the counts of the
partial nucleotide sequence reads, thereby providing normalized
counts, and (c) detect the presence or absence of a genetic
variation based on the normalized counts.
[0024] Also provided, in some aspects, are methods for detecting
the presence or absence of a fetal aneuploidy comprising: (a)
obtaining partial nucleotide sequence reads from a sample
comprising circulating, cell-free nucleic acid from a pregnant
female, where at least some partial nucleotide sequence reads
comprise: i) multiple nucleobase gaps between identified
nucleobases, or ii) one or more nucleobase classes, where each
nucleobase class comprises a subset of nucleobases present in the
sample nucleic acid, or combination of (i) and (ii), (b) mapping
the partial nucleotide sequence reads to reference genome sections,
(c) counting the number of partial nucleotide sequence reads mapped
to each reference genome section, (d) comparing the number of
counts of the partial nucleotide sequence reads mapped in (c), or
derivative thereof, to a reference, thereby making a comparison,
and (e) determining the presence or absence of a fetal aneuploidy
based on the comparison.
[0025] Also provided, in some aspects, are methods for detecting
the presence or absence of a fetal aneuploidy comprising: (a)
mapping partial nucleotide sequence reads that have been obtained
from a sample comprising circulating, cell-free nucleic acid from a
pregnant female, to reference genome sections, where at least some
partial nucleotide sequence reads comprise: i) multiple nucleobase
gaps between identified nucleobases, or ii) one or more nucleobase
classes, where each nucleobase class comprises a subset of
nucleobases present in the sample nucleic acid, or combination of
(i) and (ii), (b) counting the number of partial nucleotide
sequence reads mapped to each reference genome section, (c)
comparing the number of counts of the partial nucleotide sequence
reads mapped in (b), or derivative thereof, to a reference, thereby
making a comparison, and (d) determining the presence or absence of
a fetal aneuploidy based on the comparison.
[0026] Also provided, in some aspects, are methods for detecting
the presence or absence of a fetal aneuploidy comprising: (a)
obtaining a sample comprising circulating, cell-free nucleic acid
from a pregnant female, (b) isolating sample nucleic acid from the
sample, (c) obtaining partial nucleotide sequence reads from the
sample nucleic acid, where at least some partial nucleotide
sequence reads comprise: i) multiple nucleobase gaps between
identified nucleobases, or ii) one or more nucleobase classes,
where each nucleobase class comprises a subset of nucleobases
present in the sample nucleic acid, or combination of (i) and (ii),
(d) mapping the partial nucleotide sequence reads to reference
genome sections, (e) counting the number of partial nucleotide
sequence reads mapped to each reference genome section, (f)
comparing the number of counts of the partial nucleotide sequence
reads mapped in (e), or derivative thereof, to a reference, thereby
making a comparison, and (g) determining the presence or absence of
a fetal aneuploidy based on the comparison.
[0027] Also provided, in some aspects, are methods for detecting
the presence or absence of a genetic variation comprising: (a)
obtaining partial nucleotide sequence reads from a sample
comprising nucleic acid from a subject, where at least some partial
nucleotide sequence reads comprise: i) multiple nucleobase gaps
between identified nucleobases, or ii) one or more nucleobase
classes, where each nucleobase class comprises a subset of
nucleobases present in the sample nucleic acid, or combination of
(i) and (ii), (b) mapping the partial nucleotide sequence reads to
reference genome sections, (c) comparing the partial nucleotide
sequence reads mapped in (b) to a reference, thereby making a
comparison, and (d) determining the presence or absence of a
genetic variation based on the comparison.
[0028] Also provided, in some aspects, are methods for detecting
the presence or absence of a genetic variation comprising: (a)
mapping partial nucleotide sequence reads that have been obtained
from a sample comprising nucleic acid from a subject, to reference
genome sections, where at least some partial nucleotide sequence
reads comprise: i) multiple nucleobase gaps between identified
nucleobases, or ii) one or more nucleobase classes, where each
nucleobase class comprises a subset of nucleobases present in the
sample nucleic acid, or combination of (i) and (ii), (b) comparing
the partial nucleotide sequence reads mapped in (a) to a reference,
thereby making a comparison, and (c) determining the presence or
absence of a genetic variation based on the comparison.
[0029] Also provided, in some aspects, are methods for detecting
the presence or absence of a genetic variation comprising: (a)
obtaining a sample comprising nucleic acid from a subject, (b)
isolating sample nucleic acid from the sample, (c) obtaining
partial nucleotide sequence reads from the sample nucleic acid,
where at least some partial nucleotide sequence reads comprise: i)
multiple nucleobase gaps between identified nucleobases, or ii) one
or more nucleobase classes, where each nucleobase class comprises a
subset of nucleobases present in the sample nucleic acid, or
combination of (i) and (ii), (d) mapping the partial nucleotide
sequence reads to reference genome sections, (e) comparing the
partial nucleotide sequence reads mapped in (d) to a reference,
thereby making a comparison, and (f) determining the presence or
absence of a genetic variation based on the comparison.
[0030] In some embodiments, the genetic variation is a nucleic acid
sequence variation. In some cases, nucleotide sequences of the
partial nucleotide sequence reads are compared to a reference and
sometimes a sequence match or mismatch is determined.
[0031] In some embodiments, the genetic variation is a nucleic acid
copy number variation. In some embodiments, a method further
comprises after the mapping of partial nucleotide sequence reads,
counting the number of partial nucleotide sequence reads mapped to
each reference genome section. Often, the number of counts of the
partial nucleotide sequence reads, or derivative thereof, are
compared to a reference.
[0032] In some embodiments, the subject is a fetus and the sample
is from a pregnant female that bears a fetus. In some cases, the
sample comprises circulating, cell-free nucleic acid and sometimes
the sample nucleic acid comprises maternal and fetal nucleic
acid.
[0033] In some embodiments, the sample is blood, urine, saliva,
cervical swab, serum, and/or plasma.
[0034] In some embodiments, the partial nucleotide sequence reads
comprise relative positional information for one or more nucleobase
species. In some cases, the partial nucleotide sequence reads
contain relative positional information for adenine. In some cases,
the partial nucleotide sequence reads contain relative positional
information for guanine. In some cases, the partial nucleotide
sequence reads contain relative positional information for thymine.
In some cases, the partial nucleotide sequence reads contain
relative positional information for cytosine. In some cases, the
partial nucleotide sequence reads contain relative positional
information for methyl-cytosine.
[0035] In some embodiments, the partial nucleotide sequence reads
contain relative positional information for two nucleobase species
selected from the group consisting of adenine, guanine, thymine,
cytosine or methyl-cytosine. In some embodiments, the partial
nucleotide sequence reads contain relative positional information
for three nucleobase species selected from the group consisting of
adenine, guanine, thymine, cytosine or methyl-cytosine.
[0036] In some embodiments, the one or more nucleobase species
comprise one or more detectable labels. In some embodiments, the
partial nucleotide sequence reads contain relative positional
information for sequences complementary to one or more hybridized
probe species. In some cases, the one or more hybridized probe
species comprise one or more detectable labels.
[0037] In some embodiments, the nucleobase class is purine. In some
embodiments, the nucleobase class is pyrimidine. In some cases,
purines are distinguished from pyrimidines in the partial
nucleotide sequence reads.
[0038] In some embodiments, the sample nucleic acid comprises
single stranded nucleic acid. In some embodiments, the sample
nucleic acid comprises double stranded nucleic acid. In some cases,
the nucleobase class is a nucleobase pair species in a duplex
nucleic acid.
[0039] In some embodiments, the obtaining partial nucleotide
sequence reads includes subjecting the sample nucleic acid to a
sequencing process using a sequencing device. In some cases, the
partial nucleotide sequence reads are obtained by nanopore
sequencing. In some cases, the partial nucleotide sequence reads
are obtained by reversible terminator-based sequencing. In some
cases, the partial nucleotide sequence reads are obtained by
pyrosequencing. In some cases, the partial nucleotide sequence
reads are obtained by real time sequencing. In some cases, the
partial nucleotide sequence reads are obtained by oligonucleotide
probe ligation sequencing. In some cases, the partial nucleotide
sequence reads are obtained by sequencing by hybridization.
[0040] In some embodiments, the partial nucleotide sequence reads
comprise a number of discrete position identities sufficient to map
to a reference genome section. In some embodiments, the partial
nucleotide sequence read is of sufficient length to map to a
reference genome section. In some cases, the partial nucleotide
sequence read length is at least about 36 nucleobases. In some
cases, the partial nucleotide sequence read length is at least
about 72 nucleobases. In some cases, the partial nucleotide
sequence read length is at least about 108 nucleobases. In some
embodiments, the nucleobase gaps in each partial nucleotide
sequence read independently are about 1 to about 100 sequential
nucleobases.
[0041] In some embodiments, a method comprises obtaining full
nucleotide sequence reads, which nucleotide sequence reads do not
contain nucleobase gaps between identified nucleobases or a
nucleobase class comprising a subset of nucleobases present in the
sample nucleic acid.
[0042] In some embodiments, the genetic variation is associated
with a medical condition. In some cases, the medical condition is
cancer. In some cases, the medical condition is an aneuploidy and
is sometimes a fetal aneuploidy. In some embodiments, the fetal
aneuploidy is trisomy 13, trisomy 18 or trisomy 21.
[0043] Also provided, in some aspects, are computer program
products, comprising a computer usable medium having a computer
readable program code embodied therein, the computer readable
program code comprising distinct software modules comprising a
sequence receiving module, a logic processing module, and a data
display organization module, the computer readable program code
adapted to be executed to implement a method for identifying the
presence or absence of a fetal aneuploidy, the method comprising:
(a) obtaining, by the sequence receiving module, partial nucleotide
sequence reads from a sample comprising circulating, cell-free
nucleic acid from a pregnant female, where at least some partial
nucleotide sequence reads comprise: i) multiple nucleobase gaps
between identified nucleobases, or ii) one or more nucleobase
classes, where each nucleobase class comprises a subset of
nucleobases present in the sample nucleic acid, or combination of
(i) and (ii); (b) receiving, by the logic processing module, the
partial nucleotide sequence reads; (c) mapping, by the logic
processing module, the partial nucleotide sequence reads to
reference genome sections; (d) counting, by the logic processing
module, the number of partial nucleotide sequence reads mapped to
each reference genome section; (e) comparing, by the logic
processing module, the number of counts of the partial nucleotide
sequence reads, or derivative thereof, to a reference, or portion
thereof, thereby making a comparison; (f) providing, by the logic
processing module, an outcome determinative of the presence or
absence of a fetal aneuploidy based on the comparison; and (g)
organizing, by the data display organization module in response to
being determined by the logic processing module, a data display
indicating the presence or absence of a fetal aneuploidy.
[0044] In some embodiments, a computer program product is stored in
an apparatus comprising memory. In some cases, the apparatus
comprises a processor that implements one or more functions of the
computer program product specified in any of the above
embodiments.
[0045] Also provided, in some aspects, are systems comprising a
nucleic acid sequencing apparatus and a processing apparatus, where
the sequencing apparatus obtains sequence reads from a sample, and
the processing apparatus obtains the sequence reads from the
sequencing apparatus and carries out a method comprising: (a)
mapping partial nucleotide sequence reads from the sequencing
apparatus that have been obtained from a sample comprising
circulating, cell-free nucleic acid from a pregnant female, to
reference genome sections, where at least some partial nucleotide
sequence reads comprise: i) multiple nucleobase gaps between
identified nucleobases, or ii) one or more nucleobase classes,
where each nucleobase class comprises a subset of nucleobases
present in the sample nucleic acid, or combination of (i) and (ii),
(b) counting the number of partial nucleotide sequence reads mapped
to each reference genome section, (c) comparing the number of
counts of the partial nucleotide sequence reads mapped in (b), or
derivative thereof, to a reference, or portion thereof, thereby
making a comparison, and (d) providing an outcome determinative of
the presence or absence of a fetal aneuploidy based on the
comparison.
[0046] Certain aspects of the technology are described further in
the following description, examples, claims and drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0047] The drawings illustrate embodiments of the technology and
are not limiting. For clarity and ease of illustration, the
drawings are not made to scale and, in some instances, various
aspects may be shown exaggerated or enlarged to facilitate an
understanding of particular embodiments.
[0048] FIG. 1 graphically illustrates how increased uncertainty in
bin counts within a genomic region sometimes reduces gaps between
euploid and trisomy Z-values.
[0049] FIG. 2 graphically illustrates how decreased differences
between triploid and euploid number of counts within a genomic
region sometimes reduces predictive power of Z-scores. See Example
1 for experimental details and results.
[0050] FIG. 3 graphically illustrates the dependence of p-values on
the position of genomic bins within chromosome 21.
[0051] FIG. 4 schematically represents a bin filtering procedure. A
large number of euploid samples are lined up, bin count
uncertainties (SD or MAD values) are evaluated, and bins with
largest uncertainties sometimes are filtered out.
[0052] FIG. 5 graphically illustrates count profiles for chromosome
21 in two patients.
[0053] FIG. 6 graphically illustrates count profiles for patients
used to filter out uninformative bins from chromosome 18. In FIG.
6, the two bottom traces show a patient with a large deletion in
chromosome 18. See Example 1 for experimental details and
results.
[0054] FIG. 7 graphically illustrates the dependence of p-values on
the position of genomic bins within chromosome 18.
[0055] FIG. 8 schematically represents bin count normalization. The
procedure first lines up known euploid count profiles, from a data
set, and normalizes them with respect to total counts. For each
bin, the median counts and deviations from the medians are
evaluated. Bins with too much variability (exceeding 3 mean
absolute deviations (e.g., MAD)) sometimes are eliminated. The
remaining bins are normalized again with respect to residual total
counts, and medians are re-evaluated following the renormalization,
in some embodiments. Finally, the resulting reference profile (see
bottom trace, left panel) is used to normalize bin counts in test
samples (see top trace, left panel), smoothing the count contour
(see trace on the right) and leaving gaps where uninformative bins
have been excluded from consideration.
[0056] FIG. 9 graphically illustrates the expected behavior of
normalized count profiles. The majority of normalized bin counts
often will center on 1, with random noise superimposed. Deletions
and duplications (e.g., maternal or fetal, or maternal and fetal,
deletions and duplications) sometimes shifts the elevation to an
integer multiple of 0.5. Profile elevations corresponding to a
triploid fetal chromosome often shifts upward in proportion to the
fetal fraction. See Example 1 for experimental details and
results.
[0057] FIG. 10 graphically illustrates a normalized T18 count
profile with a heterozygous maternal deletion in chromosome 18. The
light gray segment of the graph tracing shows a higher average
elevation than the black segment of the graph tracing. See Example
1 for experimental details and results.
[0058] FIG. 11 graphically illustrates normalized binwise count
profiles for two samples collected from the same patient with
heterozygous maternal deletion in chromosome 18. The substantially
identical tracings can be used to determine if two samples are from
the same donor.
[0059] FIG. 12 graphically illustrates normalized binwise count
profiles of a sample from one study, compared with two samples from
a previous study. The duplication in chromosome 22 unambiguously
points out the patient's identity.
[0060] FIG. 13 graphically illustrates normalized binwise count
profiles of chromosome 4 in the same three patients presented in
FIG. 12. The duplication in chromosome 4 confirms the patient's
identity established in FIG. 12. See Example 1 for experimental
details and results.
[0061] FIG. 14 graphically illustrates the distribution of
normalized bin counts in chromosome 5 from a euploid sample.
[0062] FIG. 15 graphically illustrates two samples with different
levels of noise in their normalized count profiles.
[0063] FIG. 16 schematically represents factors determining the
confidence in peak elevation: noise standard deviation (e.g.,
.sigma.) and average deviation from the reference baseline (e.g.,
.DELTA.). See Example 1 for experimental details and results.
[0064] FIG. 17 graphically illustrates the results of applying a
correlation function to normalized bin counts. The correlation
function shown in FIG. 17 was used to normalize bin counts in
chromosome 5 of an arbitrarily chosen euploid patient.
[0065] FIG. 18 graphically illustrates the standard deviation for
the average stretch elevation in chromosome 5, evaluated as a
sample estimate (square data points) and compared with the standard
error of the mean (triangle data points) and with the estimate
corrected for auto-correlation .rho.=0.5 (circular data points).
The aberration depicted in FIG. 18 is about 18 bins long. See
Example 1 for experimental details and results.
[0066] FIG. 19 graphically illustrates Z-values calculated for
average peak elevation in chromosome 4. The patient has a
heterozygous maternal duplication in chromosome 4 (see FIG.
13).
[0067] FIG. 20 graphically illustrates p-values for average peak
elevation, based on a t-test and the Z-values from FIG. 19. The
order of the t-distribution is determined by the length of the
aberration. See Example 1 for experimental details and results.
[0068] FIG. 21 schematically represents edge comparisons between
matching aberrations from different samples. Illustrated in FIG. 21
are overlaps, containment, and neighboring deviations.
[0069] FIG. 22 graphically illustrates matching heterozygous
duplications in chromosome 4 (top trace and bottom trace),
contrasted with a marginally touching aberration in an unrelated
sample (middle trace). See Example 1 for experimental details and
results.
[0070] FIG. 23 schematically represents edge detection by means of
numerically evaluated first derivatives of count profiles.
[0071] FIG. 24 graphically illustrates that first derivative of
count profiles, obtained from real data, are difficult to
distinguish from noise.
[0072] FIG. 25 graphically illustrates the third power of the count
profile, shifted by 1 to suppress noise and enhance signal (see top
trace). Also illustrated in FIG. 25 (see bottom trace) is a first
derivative of the top trace. Edges are unmistakably detectable. See
Example 1 for experimental details and results.
[0073] FIG. 26 graphically illustrates histograms of median
chromosome 21 elevations for various patients. The dotted histogram
illustrates median chromosome 21 elevations for 86 euploid
patients. The hatched histogram illustrates median chromosome 21
elevations for 35 trisomy 21 patients. The count profiles were
normalized with respect to a euploid reference set prior to
evaluating median elevations.
[0074] FIG. 27 graphically illustrates a distribution of normalized
counts for chromosome 21 in a trisomy sample.
[0075] FIG. 28 graphically represents area ratios for various
patients. The dotted histogram illustrates chromosome 21 area
ratios for 86 euploid patients. The hatched histogram illustrates
chromosome 21 area ratios for 35 trisomy 21 patients. The count
profiles were normalized with respect to a euploid reference set
prior to evaluating area ratios. See Example 1 for experimental
details and results.
[0076] FIG. 29 graphically illustrates area ratio in chromosome 21
plotted against median normalized count elevations. The open
circles represent about 86 euploid samples. The filled circles
represent about 35 trisomy patients. See Example 1 for experimental
details and results.
[0077] FIG. 30 graphically illustrates relationships among 9
different classification criteria, as evaluated for a set of
trisomy patients. The criteria involve Z-scores, median normalized
count elevations, area ratios, measured fetal fractions, fitted
fetal fractions, the ratio between fitted and measured fetal
fractions, sum of squared residuals for fitted fetal fractions, sum
of squared residuals with fixed fetal fractions and fixed ploidy,
and fitted ploidy values. See Example 1 for experimental details
and results.
[0078] FIG. 31 graphically illustrates simulated functional Phi
profiles for trisomy (dashed line) and euploid cases (solid line,
bottom).
[0079] FIG. 32 graphically illustrates functional Phi values
derived from measured trisomy (filled circles) and euploid data
sets (open circles). See Example 2 for experimental details and
results.
[0080] FIG. 33 graphically illustrates linearized sum of squared
differences as a function of measured fetal fraction.
[0081] FIG. 34 graphically illustrates fetal fraction estimates
based on Y-counts plotted against values obtained from a fetal
quantifier assay (e.g., FQA) fetal fraction values.
[0082] FIG. 35 graphically illustrates Z-values for T21 patients
plotted against FQA fetal fraction measurements. For FIG. 33-35 see
Example 2 for experimental details and results.
[0083] FIG. 36 graphically illustrates fetal fraction estimates
based on chromosome Y plotted against measured fetal fractions.
[0084] FIG. 37 graphically illustrates fetal fraction estimates
based on chromosome 21 (Chr21) plotted against measured fetal
fractions.
[0085] FIG. 38 graphically illustrates fetal fraction estimates
derived from chromosome X counts plotted against measured fetal
fractions.
[0086] FIG. 39 graphically illustrates medians of normalized bin
counts for T21 cases plotted against measured fetal fractions. For
FIG. 36-39 see Example 2 for experimental details and results.
[0087] FIG. 40 graphically illustrates simulated profiles of fitted
triploid ploidy (e.g., X) as a function of F.sub.0 with fixed
errors .DELTA.F=+/-0.2%.
[0088] FIG. 41 graphically illustrates fitted triploid ploidy
values as a function of measured fetal fractions. For FIGS. 40 and
41 see Example 2 for experimental details and results.
[0089] FIG. 42 graphically illustrates probability distributions
for fitted ploidy at different levels of errors in measured fetal
fractions. The top panel in FIG. 42 sets measured fetal fraction
error to 0.2%. The middle panel in FIG. 42 sets measured fetal
fraction error to 0.4%. The bottom panel in FIG. 42 sets measured
fetal fraction error to 0.6%. See Example 2 for experimental
details and results.
[0090] FIG. 43 graphically illustrates euploid and trisomy
distributions of fitted ploidy values for a data set derived from
patient samples.
[0091] FIG. 44 graphically illustrates fitted fetal fractions
plotted against measured fetal fractions. For FIGS. 43 and 44 see
Example 2 for experimental details and results.
[0092] FIG. 45 schematically illustrates the predicted difference
between euploid and trisomy sums of squared residuals for fitted
fetal fraction as a function of the measured fetal fraction.
[0093] FIG. 46 graphically illustrates the difference between
euploid and trisomy sums of squared residuals as a function of the
measured fetal fraction using a data set derived from patient
samples. The data points are obtained by fitting fetal fraction
values assuming fixed uncertainties in fetal fraction
measurements.
[0094] FIG. 47 graphically illustrates the difference between
euploid and trisomy sums of squared residuals as a function of the
measured fetal fraction. The data points are obtained by fitting
fetal fraction values assuming that uncertainties in fetal fraction
measurements are proportional to fetal fractions:
.DELTA.F=2/3+F.sub.0/6. For FIG. 45-47 see Example 2 for
experimental details and results.
[0095] FIG. 48 schematically illustrates the predicted dependence
of the fitted fetal fraction plotted against measured fetal
fraction profiles on systematic offsets in reference counts. The
lower and upper branches represent euploid and triploids cases,
respectively.
[0096] FIG. 49 graphically represents the effects of simulated
systematic errors .DELTA. artificially imposed on actual data. The
main diagonal in the upper panel and the upper diagonal in the
lower right panel represent ideal agreement. The dark gray line in
all panels represents equations (51) and (53) for euploid and
triploid cases, respectively. The data points represent actual
measurements incorporating various levels of artificial systematic
shifts. The systematic shifts are given as the offset above each
panel. For FIGS. 48 and 49 see Example 2 for experimental details
and results.
[0097] FIG. 50 graphically illustrates fitted fetal fraction as a
function of the systematic offset, obtained for a euploid and for a
triploid data set.
[0098] FIG. 51 graphically illustrates simulations based on
equation (61), along with fitted fetal fractions for actual data.
Black lines represent two standard deviations (obtained as square
root of equation (61)) above and below equation (40). .DELTA.F is
set to 2/3+F.sub.0/6. For FIGS. 50 and 51 see Example 2 for
experimental details and results.
[0099] Example 3 addresses FIGS. 52 to 61F. FIG. 52 graphically
illustrates an example of application of the cumulative sum
algorithm to a heterozygous maternal microdeletion in chromosome
12, bin 1457. The difference between the intercepts associated with
the left and the right linear models is 2.92, indicating that the
heterozygous deletion is 6 bins wide.
[0100] FIG. 53 graphically illustrates a hypothetical heterozygous
deletion, approximately 2 genomic sections wide, and its associated
cumulative sum profile. The difference between the left and the
right intercepts is -1.
[0101] FIG. 54 graphically illustrates a hypothetical homozygous
deletion, approximately 2 genomic sections wide, and its associated
cumulative sum profile. The difference between the left and the
right intercepts is -2.
[0102] FIG. 55 graphically illustrates a hypothetical heterozygous
deletion, approximately 6 genomic sections wide, and its associated
cumulative sum profile. The difference between the left and the
right intercepts is -3.
[0103] FIG. 56 graphically illustrates a hypothetical homozygous
deletion, approximately 6 genomic sections wide, and its associated
cumulative sum profile. The difference between the left and the
right intercepts is -6.
[0104] FIG. 57 graphically illustrates a hypothetical heterozygous
duplication, approximately 2 genomic sections wide, and its
associated cumulative sum profile. The difference between the left
and the right intercepts is 1.
[0105] FIG. 58 graphically illustrates a hypothetical homozygous
duplication, approximately 2 genomic sections wide, and its
associated cumulative sum profile. The difference between the left
and the right intercepts is 2.
[0106] FIG. 59 graphically illustrates a hypothetical heterozygous
duplication, approximately 6 genomic sections wide, and its
associated cumulative sum profile. The difference between the left
and the right intercepts is 3.
[0107] FIG. 60 graphically illustrates a hypothetical homozygous
duplication, approximately 6 genomic sections wide, and its
associated cumulative sum profile. The difference between the left
and the right intercepts is 6.
[0108] FIG. 61A-F graphically illustrate candidates for fetal
heterozygous duplications in data obtained from women and infant
clinical studies with high fetal fraction values (40-50%). To rule
out the possibility that the aberrations originate from the mother
and not the fetus, independent maternal profiles were used. The
profile elevation in the affected regions is approximately 1.25, in
accordance with the fetal fraction estimates.
[0109] FIG. 62 to FIG. 111 are described in Example 4 herein.
[0110] FIG. 112A-C illustrates padding of a normalized autosomal
profile for a euploid WI sample. FIG. 112A is an example of an
unpadded profile. FIG. 112B is an example of a padded profile. FIG.
112C is an example of a padding correction (e.g., an adjusted
profile, an adjusted elevation).
[0111] FIG. 113A-C illustrates padding of a normalized autosomal
profile for a euploid WI sample. FIG. 113A is an example of an
unpadded profile. FIG. 113B is an example of a padded profile. FIG.
113C is an example of a padding correction (e.g., an adjusted
profile, an adjusted elevation).
[0112] FIG. 114A-C illustrates padding of a normalized autosomal
profile for a trisomy 13 WI sample. FIG. 114A is an example of an
unpadded profile. FIG. 114B is an example of a padded profile. FIG.
114C is an example of a padding correction (e.g., an adjusted
profile, an adjusted elevation).
[0113] FIG. 115A-C illustrates padding of a normalized autosomal
profile for a trisomy 18 WI sample. FIG. 115A is an example of an
unpadded profile. FIG. 115B is an example of a padded profile. FIG.
115C is an example of a padding correction (e.g., an adjusted
profile, an adjusted elevation).
[0114] FIGS. 116-120, 122, 123, 126, 128, 129 and 131 show a
maternal duplication within a profile.
[0115] FIGS. 121, 124, 125, 127 and 130 show a maternal deletion
within a profile.
[0116] FIG. 132 shows GC coefficient for a binary genome.
[0117] FIG. 133 shows GC curve for a binary genome.
[0118] FIG. 134 shows relative coverage for a binary genome.
[0119] FIG. 135 shows standard deviation of counts per bin for a
binary genome.
[0120] FIG. 136 shows total autosomal counts for a binary
genome.
[0121] FIG. 137 presents a representative counts profile
illustrating the observed aligned reads per 50 kb bin and their
respective GC content. Top row represents the original four base
genome and the bottom row represents a binary genome. Raw (i.e. no
normalization); GCN (i.e. a multiplicative normalization that
rescales each counts per bin according to an expectation derived
from its GC content); RM (i.e. repeat masked); GCRM (i.e. GCN on RM
data); and PERUN (i.e. PERUN with secondary LOESS with Padding)
[0122] FIG. 138 shows raw chromosome 21 representation for a binary
genome.
[0123] FIG. 139 shows PERUN chromosome 21 representation for a
binary genome.
[0124] FIG. 140 shows raw chromosome 13 representation for a binary
genome.
[0125] FIG. 141 shows PERUN chromosome 13 representation for a
binary genome.
[0126] FIG. 142 shows raw chromosome 18 representation for a binary
genome.
[0127] FIG. 143 shows PERUN chromosome 18 representation for a
binary genome.
[0128] FIG. 144 shows WGSIM raw chromosome 21 representation for a
binary genome.
[0129] FIG. 145 shows WGSIM PERUN chromosome 21 representation for
a binary genome.
[0130] FIG. 146 shows logarithmic number of unique sequences
possible for a 4 base genome and for a 2 base genome as functions
of read length. The horizontal line is the length of the human
reference genome.
[0131] FIG. 147 shows GC coefficient for unary genome A.
[0132] FIG. 148 shows GC coefficient for unary genome C.
[0133] FIG. 149 shows GC coefficient for unary genome G.
[0134] FIG. 150 shows GC coefficient for unary genome T.
[0135] FIG. 151 shows total autosomal counts for unary genome
A.
[0136] FIG. 152 shows total autosomal counts for unary genome
C.
[0137] FIG. 153 shows total autosomal counts for unary genome
G.
[0138] FIG. 154 shows total autosomal counts for unary genome
T.
[0139] FIG. 155 shows raw chromosome 21 representation for unary
genome A.
[0140] FIG. 156 shows raw chromosome 21 representation for unary
genome C.
[0141] FIG. 157 shows raw chromosome 21 representation for unary
genome G.
[0142] FIG. 158 shows raw chromosome 21 representation for unary
genome T.
[0143] FIG. 159 shows PERUN chromosome 21 representation for unary
genome A.
[0144] FIG. 160 shows PERUN chromosome 21 representation for unary
genome C.
[0145] FIG. 161 shows PERUN chromosome 21 representation for unary
genome G.
[0146] FIG. 162 shows PERUN chromosome 21 representation for unary
genome T.
[0147] FIG. 163 shows PERUN chromosome 13 representation for unary
genome A.
[0148] FIG. 164 shows PERUN chromosome 13 representation for unary
genome C.
[0149] FIG. 165 shows PERUN chromosome 13 representation for unary
genome G.
[0150] FIG. 166 shows PERUN chromosome 13 representation for unary
genome T.
[0151] FIG. 167 shows PERUN chromosome 18 representation for unary
genome A.
[0152] FIG. 168 shows PERUN chromosome 18 representation for unary
genome C.
[0153] FIG. 169 shows PERUN chromosome 18 representation for unary
genome G.
[0154] FIG. 170 shows PERUN chromosome 18 representation for unary
genome T.
[0155] FIG. 171 presents a representative counts profile
illustrating the observed aligned reads per 50 kb bin and their
respective GC content for unary genome guanine. Top row represents
the original four base genome and the bottom row represents unary
genome guanine. Raw (i.e. no normalization); GCN (i.e. a
multiplicative normalization that rescales each counts per bin
according to an expectation derived from its GC content); RM (i.e.
repeat masked); GCRM (i.e. GCN on RM data); and PERUN (i.e. PERUN
with secondary LOESS with Padding)
[0156] FIG. 172 presents a representative counts profile
illustrating the observed aligned reads per 50 kb bin and their
respective GC content for unary genome cytosine. Top row represents
the original four base genome and the bottom row represents unary
genome cytosine. Raw (i.e. no normalization); GCN (i.e. a
multiplicative normalization that rescales each counts per bin
according to an expectation derived from its GC content); RM (i.e.
repeat masked); GCRM (i.e. GCN on RM data); and PERUN (i.e. PERUN
with secondary LOESS with Padding)
[0157] FIG. 173 shows WGSIM total autosomal counts for unary genome
A.
[0158] FIG. 174 shows WGSIM total autosomal counts for unary genome
C.
[0159] FIG. 175 shows WGSIM total autosomal counts for unary genome
G.
[0160] FIG. 176 shows WGSIM total autosomal counts for unary genome
T.
[0161] FIG. 177 shows WGSIM raw chromosome 21 representation for
unary genome A.
[0162] FIG. 178 shows WGSIM raw chromosome 21 representation for
unary genome C.
[0163] FIG. 179 shows WGSIM raw chromosome 21 representation for
unary genome G.
[0164] FIG. 180 shows WGSIM raw chromosome 21 representation for
unary genome T.
[0165] FIG. 181 shows WGSIM PERUN chromosome 21 representation for
unary genome A.
[0166] FIG. 182 shows WGSIM PERUN chromosome 21 representation for
unary genome C.
[0167] FIG. 183 shows WGSIM PERUN chromosome 21 representation for
unary genome G.
[0168] FIG. 184 shows WGSIM PERUN chromosome 21 representation for
unary genome T.
DETAILED DESCRIPTION
[0169] Provided are methods, processes and apparatuses useful for
identifying a genetic variation. Identifying a genetic variation
sometimes comprises detecting a copy number variation and/or
sometimes comprises adjusting an elevation comprising a copy number
variation. In some embodiments, an elevation is adjusted providing
an identification of one or more genetic variations or variances
with a reduced likelihood of a false positive or false negative
diagnosis. In some embodiments, identifying a genetic variation by
a method described herein can lead to a diagnosis of, or
determining a predisposition to, a particular medical condition.
Identifying a genetic variance can result in facilitating a medical
decision and/or employing a helpful medical procedure.
[0170] Also provided herein are methods for determining the
presence or absence of a genetic variation and methods for
determining the presence or absence of a fetal aneuploidy using
partial nucleotide sequence reads, and computer program products
and systems for implementing the methods discussed herein.
Assessment of a genetic variation, such as, for example, a fetal
aneuploidy, from a maternal sample typically involves sequencing of
the nucleic acid present in the sample, mapping sequence reads to
certain regions in the genome, quantifying the sequence reads for
the sample, and analyzing the quantification. Sequencing using
existing methods which generate complete nucleotide sequence reads,
however, can be expensive and often is the rate-limiting step in
the assessment of genetic variations. Generation of partial
nucleotide sequence reads can be faster and less expensive than
generation of complete sequence reads. Provided herein are methods
for rapidly assessing the presence or absence of a genetic
variation using partial nucleotide sequence reads.
[0171] Samples
[0172] Provided herein are methods and compositions for analyzing
nucleic acid. In some embodiments, nucleic acid fragments in a
mixture of nucleic acid fragments are analyzed. A mixture of
nucleic acids can comprise two or more nucleic acid fragment
species having different nucleotide sequences, different fragment
lengths, different origins (e.g., genomic origins, fetal vs.
maternal origins, cell or tissue origins, sample origins, subject
origins, and the like), or combinations thereof.
[0173] Nucleic acid or a nucleic acid mixture utilized in methods
and apparatuses described herein often is isolated from a sample
obtained from a subject. A subject can be any living or non-living
organism, including but not limited to a human, a non-human animal,
a plant, a bacterium, a fungus or a protist. Any human or non-human
animal can be selected, including but not limited to mammal,
reptile, avian, amphibian, fish, ungulate, ruminant, bovine (e.g.,
cattle), equine (e.g., horse), caprine and ovine (e.g., sheep,
goat), swine (e.g., pig), camelid (e.g., camel, llama, alpaca),
monkey, ape (e.g., gorilla, chimpanzee), ursid (e.g., bear),
poultry, dog, cat, mouse, rat, fish, dolphin, whale and shark. A
subject may be a male or female (e.g., woman).
[0174] Nucleic acid may be isolated from any type of suitable
biological specimen or sample (e.g., a test sample). A sample or
test sample can be any specimen that is isolated or obtained from a
subject (e.g., a human subject, a pregnant female). Non-limiting
examples of specimens include fluid or tissue from a subject,
including, without limitation, umbilical cord blood, chorionic
villi, amniotic fluid, cerebrospinal fluid, spinal fluid, lavage
fluid (e.g., bronchoalveolar, gastric, peritoneal, ductal, ear,
arthroscopic), biopsy sample (e.g., from pre-implantation embryo),
celocentesis sample, fetal nucleated cells or fetal cellular
remnants, washings of female reproductive tract, urine, feces,
sputum, saliva, nasal mucous, prostate fluid, lavage, semen,
lymphatic fluid, bile, tears, sweat, breast milk, breast fluid,
embryonic cells and fetal cells (e.g. placental cells). In some
embodiments, a biological sample is a cervical swab from a subject.
In some embodiments, a biological sample may be blood and sometimes
plasma or serum. As used herein, the term "blood" encompasses whole
blood or any fractions of blood, such as serum and plasma as
conventionally defined, for example. Blood or fractions thereof
often comprise nucleosomes (e.g., maternal and/or fetal
nucleosomes). Nucleosomes comprise nucleic acids and are sometimes
cell-free or intracellular. Blood also comprises buffy coats. Buffy
coats are sometimes isolated by utilizing a ficoll gradient. Buffy
coats can comprise white blood cells (e.g., leukocytes, T-cells,
B-cells, platelets, and the like). Sometimes buffy coats comprise
maternal and/or fetal nucleic acid. Blood plasma refers to the
fraction of whole blood resulting from centrifugation of blood
treated with anticoagulants. Blood serum refers to the watery
portion of fluid remaining after a blood sample has coagulated.
Fluid or tissue samples often are collected in accordance with
standard protocols hospitals or clinics generally follow. For
blood, an appropriate amount of peripheral blood (e.g., between
3-40 milliliters) often is collected and can be stored according to
standard procedures prior to or after preparation. A fluid or
tissue sample from which nucleic acid is extracted may be acellular
(e.g., cell-free). In some embodiments, a fluid or tissue sample
may contain cellular elements or cellular remnants. In some
embodiments fetal cells or cancer cells may be included in the
sample.
[0175] A sample often is heterogeneous, by which is meant that more
than one type of nucleic acid species is present in the sample. For
example, heterogeneous nucleic acid can include, but is not limited
to, (i) fetal derived and maternal derived nucleic acid, (ii)
cancer and non-cancer nucleic acid, (iii) pathogen and host nucleic
acid, and more generally, (iv) mutated and wild-type nucleic acid.
A sample may be heterogeneous because more than one cell type is
present, such as a fetal cell and a maternal cell, a cancer and
non-cancer cell, or a pathogenic and host cell. In some
embodiments, a minority nucleic acid species and a majority nucleic
acid species is present.
[0176] For prenatal applications of technology described herein,
fluid or tissue sample may be collected from a female at a
gestational age suitable for testing, or from a female who is being
tested for possible pregnancy. Suitable gestational age may vary
depending on the prenatal test being performed. In certain
embodiments, a pregnant female subject sometimes is in the first
trimester of pregnancy, at times in the second trimester of
pregnancy, or sometimes in the third trimester of pregnancy. In
certain embodiments, a fluid or tissue is collected from a pregnant
female between about 1 to about 45 weeks of fetal gestation (e.g.,
at 1-4, 4-8, 8-12, 12-16, 16-20, 20-24, 24-28, 28-32, 32-36, 36-40
or 40-44 weeks of fetal gestation), and sometimes between about 5
to about 28 weeks of fetal gestation (e.g., at 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27
weeks of fetal gestation). Sometimes a fluid or tissue sample is
collected from a pregnant female during or just after (e.g., 0 to
72 hours after) giving birth (e.g., vaginal or non-vaginal birth
(e.g., surgical delivery)).
[0177] Nucleic Acid Isolation and Processing
[0178] Nucleic acid may be derived from one or more sources (e.g.,
cells, serum, plasma, buffy coat, lymphatic fluid, skin, soil, and
the like) by methods known in the art. Cell lysis procedures and
reagents are known in the art and may generally be performed by
chemical (e.g., detergent, hypotonic solutions, enzymatic
procedures, and the like, or combination thereof), physical (e.g.,
French press, sonication, and the like), or electrolytic lysis
methods. Any suitable lysis procedure can be utilized. For example,
chemical methods generally employ lysing agents to disrupt cells
and extract the nucleic acids from the cells, followed by treatment
with chaotropic salts. Physical methods such as freeze/thaw
followed by grinding, the use of cell presses and the like also are
useful. High salt lysis procedures also are commonly used. For
example, an alkaline lysis procedure may be utilized. The latter
procedure traditionally incorporates the use of phenol-chloroform
solutions, and an alternative phenol-chloroform-free procedure
involving three solutions can be utilized. In the latter
procedures, one solution can contain 15 mM Tris, pH 8.0; 10 mM EDTA
and 100 ug/ml Rnase A; a second solution can contain 0.2N NaOH and
1% SDS; and a third solution can contain 3M KOAc, pH 5.5. These
procedures can be found in Current Protocols in Molecular Biology,
John Wiley & Sons, N.Y., 6.3.1-6.3.6 (1989), incorporated
herein in its entirety.
[0179] The terms "nucleic acid" and "nucleic acid molecule" are
used interchangeably. The terms refer to nucleic acids of any
composition form, such as deoxyribonucleic acid (DNA, e.g.,
complementary DNA (cDNA), genomic DNA (gDNA) and the like),
ribonucleic acid (RNA, e.g., message RNA (mRNA), short inhibitory
RNA (siRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), microRNA,
RNA highly expressed by the fetus or placenta, and the like),
and/or DNA or RNA analogs (e.g., containing base analogs, sugar
analogs and/or a non-native backbone and the like), RNA/DNA hybrids
and polyamide nucleic acids (PNAs), all of which can be in single-
or double-stranded form. Unless otherwise limited, a nucleic acid
can comprise known analogs of natural nucleotides, some of which
can function in a similar manner as naturally occurring
nucleotides. A nucleic acid can be in any form useful for
conducting processes herein (e.g., linear, circular, supercoiled,
single-stranded, double-stranded and the like). A nucleic acid may
be, or may be from, a plasmid, phage, autonomously replicating
sequence (ARS), centromere, artificial chromosome, chromosome, or
other nucleic acid able to replicate or be replicated in vitro or
in a host cell, a cell, a cell nucleus or cytoplasm of a cell in
certain embodiments. A nucleic acid in some embodiments can be from
a single chromosome or fragment thereof (e.g., a nucleic acid
sample may be from one chromosome of a sample obtained from a
diploid organism). Sometimes nucleic acids comprise nucleosomes,
fragments or parts of nucleosomes or nucleosome-like structures.
Nucleic acids sometimes comprise protein (e.g., histones, DNA
binding proteins, and the like). Nucleic acids analyzed by
processes described herein sometimes are substantially isolated and
are not substantially associated with protein or other molecules.
Nucleic acids also include derivatives, variants and analogs of RNA
or DNA synthesized, replicated or amplified from single-stranded
("sense" or "antisense", "plus" strand or "minus" strand, "forward"
reading frame or "reverse" reading frame) and double-stranded
polynucleotides. Deoxyribonucleotides include deoxyadenosine,
deoxycytidine, deoxyguanosine and deoxythymidine. For RNA, the base
cytosine is replaced with uracil and the sugar 2' position includes
a hydroxyl moiety. A nucleic acid may be prepared using a nucleic
acid obtained from a subject as a template.
[0180] Nucleic acid may be isolated at a different time point as
compared to another nucleic acid, where each of the samples is from
the same or a different source. A nucleic acid may be from a
nucleic acid library, such as a cDNA or RNA library, for example. A
nucleic acid may be a result of nucleic acid purification or
isolation and/or amplification of nucleic acid molecules from the
sample. Nucleic acid provided for processes described herein may
contain nucleic acid from one sample or from two or more samples
(e.g., from 1 or more, 2 or more, 3 or more, 4 or more, 5 or more,
6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more,
12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or
more, 18 or more, 19 or more, or 20 or more samples).
[0181] Nucleic acids can include extracellular nucleic acid in
certain embodiments. The term "extracellular nucleic acid" as used
herein can refer to nucleic acid isolated from a source having
substantially no cells and also is referred to as "cell-free"
nucleic acid and/or "cell-free circulating" nucleic acid.
Extracellular nucleic acid can be present in and obtained from
blood (e.g., from the blood of a pregnant female). Extracellular
nucleic acid often includes no detectable cells and may contain
cellular elements or cellular remnants. Non-limiting examples of
acellular sources for extracellular nucleic acid are blood, blood
plasma, blood serum and urine. As used herein, the term "obtain
cell-free circulating sample nucleic acid" includes obtaining a
sample directly (e.g., collecting a sample, e.g., a test sample) or
obtaining a sample from another who has collected a sample. Without
being limited by theory, extracellular nucleic acid may be a
product of cell apoptosis and cell breakdown, which provides basis
for extracellular nucleic acid often having a series of lengths
across a spectrum (e.g., a "ladder").
[0182] Extracellular nucleic acid can include different nucleic
acid species, and therefore is referred to herein as
"heterogeneous" in certain embodiments. For example, blood serum or
plasma from a person having cancer can include nucleic acid from
cancer cells and nucleic acid from non-cancer cells. In another
example, blood serum or plasma from a pregnant female can include
maternal nucleic acid and fetal nucleic acid. In some instances,
fetal nucleic acid sometimes is about 5% to about 50% of the
overall nucleic acid (e.g., about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
48, or 49% of the total nucleic acid is fetal nucleic acid). In
some embodiments, the majority of fetal nucleic acid in nucleic
acid is of a length of about 500 base pairs or less (e.g., about
80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of fetal
nucleic acid is of a length of about 500 base pairs or less). In
some embodiments, the majority of fetal nucleic acid in nucleic
acid is of a length of about 250 base pairs or less (e.g., about
80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of fetal
nucleic acid is of a length of about 250 base pairs or less). In
some embodiments, the majority of fetal nucleic acid in nucleic
acid is of a length of about 200 base pairs or less (e.g., about
80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of fetal
nucleic acid is of a length of about 200 base pairs or less). In
some embodiments, the majority of fetal nucleic acid in nucleic
acid is of a length of about 150 base pairs or less (e.g., about
80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of fetal
nucleic acid is of a length of about 150 base pairs or less). In
some embodiments, the majority of fetal nucleic acid in nucleic
acid is of a length of about 100 base pairs or less (e.g., about
80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of fetal
nucleic acid is of a length of about 100 base pairs or less). In
some embodiments, the majority of fetal nucleic acid in nucleic
acid is of a length of about 50 base pairs or less (e.g., about 80,
85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of fetal nucleic
acid is of a length of about 50 base pairs or less). In some
embodiments, the majority of fetal nucleic acid in nucleic acid is
of a length of about 25 base pairs or less (e.g., about 80, 85, 90,
91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of fetal nucleic acid is
of a length of about 25 base pairs or less).
[0183] Nucleic acid may be provided for conducting methods
described herein without processing of the sample(s) containing the
nucleic acid, in certain embodiments. In some embodiments, nucleic
acid is provided for conducting methods described herein after
processing of the sample(s) containing the nucleic acid. For
example, a nucleic acid can be extracted, isolated, purified,
partially purified or amplified from the sample(s). The term
"isolated" as used herein refers to nucleic acid removed from its
original environment (e.g., the natural environment if it is
naturally occurring, or a host cell if expressed exogenously), and
thus is altered by human intervention (e.g., "by the hand of man")
from its original environment. The term "isolated nucleic acid" as
used herein can refer to a nucleic acid removed from a subject
(e.g., a human subject). An isolated nucleic acid can be provided
with fewer non-nucleic acid components (e.g., protein, lipid) than
the amount of components present in a source sample. A composition
comprising isolated nucleic acid can be about 50% to greater than
99% free of non-nucleic acid components. A composition comprising
isolated nucleic acid can be about 90%, 91%, 92%, 93%, 94%, 95%,
96%, 97%, 98%, 99% or greater than 99% free of non-nucleic acid
components. The term "purified" as used herein can refer to a
nucleic acid provided that contains fewer non-nucleic acid
components (e.g., protein, lipid, carbohydrate) than the amount of
non-nucleic acid components present prior to subjecting the nucleic
acid to a purification procedure. A composition comprising purified
nucleic acid may be about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%,
88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or
greater than 99% free of other non-nucleic acid components. The
term "purified" as used herein can refer to a nucleic acid provided
that contains fewer nucleic acid species than in the sample source
from which the nucleic acid is derived. A composition comprising
purified nucleic acid may be about 90%, 91%, 92%, 93%, 94%, 95%,
96%, 97%, 98%, 99% or greater than 99% free of other nucleic acid
species. For example, fetal nucleic acid can be purified from a
mixture comprising maternal and fetal nucleic acid. In certain
examples, nucleosomes comprising small fragments of fetal nucleic
acid can be purified from a mixture of larger nucleosome complexes
comprising larger fragments of maternal nucleic acid.
[0184] The term "amplified" as used herein refers to subjecting a
target nucleic acid in a sample to a process that linearly or
exponentially generates amplicon nucleic acids having the same or
substantially the same nucleotide sequence as the target nucleic
acid, or segment thereof. The term "amplified" as used herein can
refer to subjecting a target nucleic acid (e.g., in a sample
comprising other nucleic acids) to a process that selectively and
linearly or exponentially generates amplicon nucleic acids having
the same or substantially the same nucleotide sequence as the
target nucleic acid, or segment thereof. The term "amplified" as
used herein can refer to subjecting a population of nucleic acids
to a process that non-selectively and linearly or exponentially
generates amplicon nucleic acids having the same or substantially
the same nucleotide sequence as nucleic acids, or portions thereof,
that were present in the sample prior to amplification. Sometimes
the term "amplified" refers to a method that comprises a polymerase
chain reaction (PCR).
[0185] Nucleic acid also may be processed by subjecting nucleic
acid to a method that generates nucleic acid fragments, in certain
embodiments, before providing nucleic acid for a process described
herein. In some embodiments, nucleic acid subjected to
fragmentation or cleavage may have a nominal, average or mean
length of about 5 to about 10,000 base pairs, about 100 to about
1,000 base pairs, about 100 to about 500 base pairs, or about 10,
15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95,
100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000,
4000, 5000, 6000, 7000, 8000 or 9000 base pairs. Fragments can be
generated by a suitable method known in the art, and the average,
mean or nominal length of nucleic acid fragments can be controlled
by selecting an appropriate fragment-generating procedure. In
certain embodiments, nucleic acid of a relatively shorter length
can be utilized to analyze sequences that contain little sequence
variation and/or contain relatively large amounts of known
nucleotide sequence information. In some embodiments, nucleic acid
of a relatively longer length can be utilized to analyze sequences
that contain greater sequence variation and/or contain relatively
small amounts of nucleotide sequence information.
[0186] Nucleic acid fragments may contain overlapping nucleotide
sequences, and such overlapping sequences can facilitate
construction of a nucleotide sequence of the non-fragmented
counterpart nucleic acid, or a segment thereof. For example, one
fragment may have subsequences x and y and another fragment may
have subsequences y and z, where x, y and z are nucleotide
sequences that can be 5 nucleotides in length or greater. Overlap
sequence y can be utilized to facilitate construction of the x-y-z
nucleotide sequence in nucleic acid from a sample in certain
embodiments. Nucleic acid may be partially fragmented (e.g., from
an incomplete or terminated specific cleavage reaction) or fully
fragmented in certain embodiments.
[0187] Nucleic acid can be fragmented by various methods known in
the art, which include without limitation, physical, chemical and
enzymatic processes. Non-limiting examples of such processes are
described in U.S. Patent Application Publication No. 20050112590
(published on May 26, 2005, entitled "Fragmentation-based methods
and systems for sequence variation detection and discovery," naming
Van Den Boom et al.). Certain processes can be selected to generate
non-specifically cleaved fragments or specifically cleaved
fragments. Non-limiting examples of processes that can generate
non-specifically cleaved fragment nucleic acid include, without
limitation, contacting nucleic acid with apparatus that expose
nucleic acid to shearing force (e.g., passing nucleic acid through
a syringe needle; use of a French press); exposing nucleic acid to
irradiation (e.g., gamma, x-ray, UV irradiation; fragment sizes can
be controlled by irradiation intensity); boiling nucleic acid in
water (e.g., yields about 500 base pair fragments) and exposing
nucleic acid to an acid and base hydrolysis process.
[0188] As used herein, "fragmentation" or "cleavage" refers to a
procedure or conditions in which a nucleic acid molecule, such as a
nucleic acid template gene molecule or amplified product thereof,
may be severed into two or more smaller nucleic acid molecules.
Such fragmentation or cleavage can be sequence specific, base
specific, or nonspecific, and can be accomplished by any of a
variety of methods, reagents or conditions, including, for example,
chemical, enzymatic, physical fragmentation.
[0189] As used herein, "fragments", "cleavage products", "cleaved
products" or grammatical variants thereof, refers to nucleic acid
molecules resultant from a fragmentation or cleavage of a nucleic
acid template gene molecule or amplified product thereof. While
such fragments or cleaved products can refer to all nucleic acid
molecules resultant from a cleavage reaction, typically such
fragments or cleaved products refer only to nucleic acid molecules
resultant from a fragmentation or cleavage of a nucleic acid
template gene molecule or the segment of an amplified product
thereof containing the corresponding nucleotide sequence of a
nucleic acid template gene molecule. For example, an amplified
product can contain one or more nucleotides more than the amplified
nucleotide region of a nucleic acid template sequence (e.g., a
primer can contain "extra" nucleotides such as a transcriptional
initiation sequence, in addition to nucleotides complementary to a
nucleic acid template gene molecule, resulting in an amplified
product containing "extra" nucleotides or nucleotides not
corresponding to the amplified nucleotide region of the nucleic
acid template gene molecule). Accordingly, fragments can include
fragments arising from portions of amplified nucleic acid molecules
containing, at least in part, nucleotide sequence information from
or based on the representative nucleic acid template molecule.
[0190] As used herein, the term "complementary cleavage reactions"
refers to cleavage reactions that are carried out on the same
nucleic acid using different cleavage reagents or by altering the
cleavage specificity of the same cleavage reagent such that
alternate cleavage patterns of the same target or reference nucleic
acid or protein are generated. In certain embodiments, nucleic acid
may be treated with one or more specific cleavage agents (e.g., 1,
2, 3, 4, 5, 6, 7, 8, 9, 10 or more specific cleavage agents) in one
or more reaction vessels (e.g., nucleic acid is treated with each
specific cleavage agent in a separate vessel).
[0191] Nucleic acid may be specifically cleaved or non-specifically
cleaved by contacting the nucleic acid with one or more enzymatic
cleavage agents (e.g., nucleases, restriction enzymes). The term
"specific cleavage agent" as used herein refers to an agent,
sometimes a chemical or an enzyme that can cleave a nucleic acid at
one or more specific sites. Specific cleavage agents often cleave
specifically according to a particular nucleotide sequence at a
particular site. Non-specific cleavage agents often cleave nucleic
acids at non-specific sites or degrade nucleic acids. Non-specific
cleavage agents often degrade nucleic acids by removal of
nucleotides from the end (either the 5' end, 3' end or both) of a
nucleic acid strand.
[0192] Any suitable non-specific or specific enzymatic cleavage
agent can be used to cleave or fragment nucleic acids. A suitable
restriction enzyme can be used to cleave nucleic acids, in some
embodiments. Examples of enzymatic cleavage agents include without
limitation endonucleases (e.g., DNase (e.g., DNase I, II); RNase
(e.g., RNase E, F, H, P); Cleavase.TM. enzyme; Taq DNA polymerase;
E. coli DNA polymerase I and eukaryotic structure-specific
endonucleases; murine FEN-1 endonucleases; type I, II or III
restriction endonucleases such as Acc I, Afl III, Alu I, Alw44 I,
Apa I, Asn I, Ava I, Ava II, BamH I, Ban II, Bcl I, Bgl I. Bgl II,
Bln I, Bsm I, BssH II, BstE II, Cfo I, Cla I, Dde I, Dpn I, Dra I,
EcIX I, EcoR I, EcoR I, EcoR II, EcoR V, Hae II, Hae II, Hind III,
Hind III, Hpa I, Hpa II, Kpn I, Ksp I, Mlu I, MluN I, Msp I, Nci I,
Nco I, Nde I, Nde II, Nhe I, Not I, Nru I, Nsi I, Pst I, Pvu I, Pvu
II, Rsa I, Sac I, Sal I, Sau3A I, Sca I, ScrF I, Sfi I, Sma I, Spe
I, Sph I, Ssp I, Stu I, Sty I, Swa I, Taq I, Xba I, Xho I;
glycosylases (e.g., uracil-DNA glycosylase (UDG), 3-methyladenine
DNA glycosylase, 3-methyladenine DNA glycosylase II, pyrimidine
hydrate-DNA glycosylase, FaPy-DNA glycosylase, thymine mismatch-DNA
glycosylase, hypoxanthine-DNA glycosylase, 5-Hydroxymethyluracil
DNA glycosylase (HmUDG), 5-Hydroxymethylcytosine DNA glycosylase,
or 1,N6-etheno-adenine DNA glycosylase); exonucleases (e.g.,
exonuclease III); ribozymes, and DNAzymes. Nucleic acid may be
treated with a chemical agent, and the modified nucleic acid may be
cleaved. In non-limiting examples, nucleic acid may be treated with
(i) alkylating agents such as methylnitrosourea that generate
several alkylated bases, including N3-methyladenine and
N3-methylguanine, which are recognized and cleaved by alkyl purine
DNA-glycosylase; (ii) sodium bisulfite, which causes deamination of
cytosine residues in DNA to form uracil residues that can be
cleaved by uracil N-glycosylase; and (iii) a chemical agent that
converts guanine to its oxidized form, 8-hydroxyguanine, which can
be cleaved by formamidopyrimidine DNA N-glycosylase. Examples of
chemical cleavage processes include without limitation alkylation,
(e.g., alkylation of phosphorothioate-modified nucleic acid);
cleavage of acid lability of P3'-N5'-phosphoroamidate-containing
nucleic acid; and osmium tetroxide and piperidine treatment of
nucleic acid.
[0193] Nucleic acid also may be exposed to a process that modifies
certain nucleotides in the nucleic acid before providing nucleic
acid for a method described herein. A process that selectively
modifies nucleic acid based upon the methylation state of
nucleotides therein can be applied to nucleic acid, for example. In
addition, conditions such as high temperature, ultraviolet
radiation, x-radiation, can induce changes in the sequence of a
nucleic acid molecule. Nucleic acid may be provided in any form
useful for conducting a sequence analysis or manufacture process
described herein, such as solid or liquid form, for example. In
certain embodiments, nucleic acid may be provided in a liquid form
optionally comprising one or more other components, including
without limitation one or more buffers or salts.
[0194] Nucleic acid may be single or double stranded. Single
stranded DNA, for example, can be generated by denaturing double
stranded DNA by heating or by treatment with alkali, for example.
In some cases, nucleic acid is in a D-loop structure, formed by
strand invasion of a duplex DNA molecule by an oligonucleotide or a
DNA-like molecule such as peptide nucleic acid (PNA). D loop
formation can be facilitated by addition of E. Coli RecA protein
and/or by alteration of salt concentration, for example, using
methods known in the art.
[0195] Determining Fetal Nucleic Acid Content
[0196] The amount of fetal nucleic acid (e.g., concentration,
relative amount, absolute amount, copy number, and the like) in
nucleic acid is determined in some embodiments. In some cases, the
amount of fetal nucleic acid in a sample is referred to as "fetal
fraction". Sometimes "fetal fraction" refers to the fraction of
fetal nucleic acid in circulating cell-free nucleic acid in a
sample (e.g., a blood sample, a serum sample, a plasma sample)
obtained from a pregnant female. In certain embodiments, the amount
of fetal nucleic acid is determined according to markers specific
to a male fetus (e.g., Y-chromosome STR markers (e.g., DYS 19, DYS
385, DYS 392 markers); RhD marker in RhD-negative females), allelic
ratios of polymorphic sequences, or according to one or more
markers specific to fetal nucleic acid and not maternal nucleic
acid (e.g., differential epigenetic biomarkers (e.g., methylation;
described in further detail below) between mother and fetus, or
fetal RNA markers in maternal blood plasma (see e.g., Lo, 2005,
Journal of Histochemistry and Cytochemistry 53 (3): 293-296)).
[0197] Determination of fetal nucleic acid content (e.g., fetal
fraction) sometimes is performed using a fetal quantifier assay
(FQA) as described, for example, in U.S. Patent Application
Publication No. 2010/0105049, which is hereby incorporated by
reference. This type of assay allows for the detection and
quantification of fetal nucleic acid in a maternal sample based on
the methylation status of the nucleic acid in the sample. In some
cases, the amount of fetal nucleic acid from a maternal sample can
be determined relative to the total amount of nucleic acid present,
thereby providing the percentage of fetal nucleic acid in the
sample. In some cases, the copy number of fetal nucleic acid can be
determined in a maternal sample. In some cases, the amount of fetal
nucleic acid can be determined in a sequence-specific (or
locus-specific) manner and sometimes with sufficient sensitivity to
allow for accurate chromosomal dosage analysis (for example, to
detect the presence or absence of a fetal aneuploidy).
[0198] A fetal quantifier assay (FQA) can be performed in
conjunction with any of the methods described herein. Such an assay
can be performed by any method known in the art and/or described in
U.S. Patent Application Publication No. 2010/0105049, such as, for
example, by a method that can distinguish between maternal and
fetal DNA based on differential methylation status, and quantify
(i.e. determine the amount of) the fetal DNA. Methods for
differentiating nucleic acid based on methylation status include,
but are not limited to, methylation sensitive capture, for example,
using a MBD2-Fc fragment in which the methyl binding domain of MBD2
is fused to the Fc fragment of an antibody (MBD-FC) (Gebhard et al.
(2006) Cancer Res. 66(12):6118-28); methylation specific
antibodies; bisulfite conversion methods, for example, MSP
(methylation-sensitive PCR), COBRA, methylation-sensitive single
nucleotide primer extension (Ms-SNuPE) or Sequenom MassCLEAVE.TM.
technology; and the use of methylation sensitive restriction
enzymes (e.g., digestion of maternal DNA in a maternal sample using
one or more methylation sensitive restriction enzymes thereby
enriching the fetal DNA). Methyl-sensitive enzymes also can be used
to differentiate nucleic acid based on methylation status, which,
for example, can preferentially or substantially cleave or digest
at their DNA recognition sequence if the latter is non-methylated.
Thus, an unmethylated DNA sample will be cut into smaller fragments
than a methylated DNA sample and a hypermethylated DNA sample will
not be cleaved. Except where explicitly stated, any method for
differentiating nucleic acid based on methylation status can be
used with the compositions and methods of the technology herein.
The amount of fetal DNA can be determined, for example, by
introducing one or more competitors at known concentrations during
an amplification reaction. Determining the amount of fetal DNA also
can be done, for example, by RT-PCR, primer extension, sequencing
and/or counting. In certain instances, the amount of nucleic acid
can be determined using BEAMing technology as described in U.S.
Patent Application Publication No. 2007/0065823. In some cases, the
restriction efficiency can be determined and the efficiency rate is
used to further determine the amount of fetal DNA.
[0199] In some cases, a fetal quantifier assay (FQA) can be used to
determine the concentration of fetal DNA in a maternal sample, for
example, by the following method: a) determine the total amount of
DNA present in a maternal sample; b) selectively digest the
maternal DNA in a maternal sample using one or more methylation
sensitive restriction enzymes thereby enriching the fetal DNA; c)
determine the amount of fetal DNA from step b); and d) compare the
amount of fetal DNA from step c) to the total amount of DNA from
step a), thereby determining the concentration of fetal DNA in the
maternal sample. In some cases, the absolute copy number of fetal
nucleic acid in a maternal sample can be determined, for example,
using mass spectrometry and/or a system that uses a competitive PCR
approach for absolute copy number measurements. See for example,
Ding and Cantor (2003) Proc Natl Acad Sci USA 100:3059-3064, and
U.S. Patent Application Publication No. 2004/0081993, both of which
are hereby incorporated by reference.
[0200] In some cases, fetal fraction can be determined based on
allelic ratios of polymorphic sequences (e.g., single nucleotide
polymorphisms (SNPs)), such as, for example, using a method
described in U.S. Patent Application Publication No. 2011/0224087,
which is hereby incorporated by reference. In such a method,
nucleotide sequence reads are obtained for a maternal sample and
fetal fraction is determined by comparing the total number of
nucleotide sequence reads that map to a first allele and the total
number of nucleotide sequence reads that map to a second allele at
an informative polymorphic site (e.g., SNP) in a reference genome.
In some cases, fetal alleles are identified, for example, by their
relative minor contribution to the mixture of fetal and maternal
nucleic acids in the sample when compared to the major contribution
to the mixture by the maternal nucleic acids. Accordingly, the
relative abundance of fetal nucleic acid in a maternal sample can
be determined as a parameter of the total number of unique sequence
reads mapped to a target nucleic acid sequence on a reference
genome for each of the two alleles of a polymorphic site.
[0201] The amount of fetal nucleic acid in extracellular nucleic
acid can be quantified and used in conjunction with a method
provided herein. Thus, in certain embodiments, methods of the
technology described herein comprise an additional step of
determining the amount of fetal nucleic acid. The amount of fetal
nucleic acid can be determined in a nucleic acid sample from a
subject before or after processing to prepare sample nucleic acid.
In certain embodiments, the amount of fetal nucleic acid is
determined in a sample after sample nucleic acid is processed and
prepared, which amount is utilized for further assessment. In some
embodiments, an outcome comprises factoring the fraction of fetal
nucleic acid in the sample nucleic acid (e.g., adjusting counts,
removing samples, making a call or not making a call).
[0202] The determination step can be performed before, during, at
any one point in a method described herein, or after certain (e.g.,
aneuploidy detection, fetal gender determination) methods described
herein. For example, to achieve a fetal gender or aneuploidy
determination method with a given sensitivity or specificity, a
fetal nucleic acid quantification method may be implemented prior
to, during or after fetal gender or aneuploidy determination to
identify those samples with greater than about 2%, 3%, 4%, 5%, 6%,
7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%,
21%, 22%, 23%, 24%, 25% or more fetal nucleic acid. In some
embodiments, samples determined as having a certain threshold
amount of fetal nucleic acid (e.g., about 15% or more fetal nucleic
acid; about 4% or more fetal nucleic acid) are further analyzed for
fetal gender or aneuploidy determination, or the presence or
absence of aneuploidy or genetic variation, for example. In certain
embodiments, determinations of, for example, fetal gender or the
presence or absence of aneuploidy are selected (e.g., selected and
communicated to a patient) only for samples having a certain
threshold amount of fetal nucleic acid (e.g., about 15% or more
fetal nucleic acid; about 4% or more fetal nucleic acid).
[0203] In some embodiments, the determination of fetal fraction or
determining the amount of fetal nucleic acid is not required or
necessary for identifying the presence or absence of a chromosome
aneuploidy. In some embodiments, identifying the presence or
absence of a chromosome aneuploidy does not require the sequence
differentiation of fetal versus maternal DNA. In some cases this is
because the summed contribution of both maternal and fetal
sequences in a particular chromosome, chromosome portion or segment
thereof is analyzed. In some embodiments, identifying the presence
or absence of a chromosome aneuploidy does not rely on a priori
sequence information that would distinguish fetal DNA from maternal
DNA.
[0204] Enriching for a Subpopulation of Nucleic Acid
[0205] In some embodiments, nucleic acid (e.g., extracellular
nucleic acid) is enriched or relatively enriched for a
subpopulation or species of nucleic acid. Nucleic acid
subpopulations can include, for example, fetal nucleic acid,
maternal nucleic acid, nucleic acid comprising fragments of a
particular length or range of lengths, or nucleic acid from a
particular genome region (e.g., single chromosome, set of
chromosomes, and/or certain chromosome regions). Such enriched
samples can be used in conjunction with a method provided herein.
Thus, in certain embodiments, methods of the technology comprise an
additional step of enriching for a subpopulation of nucleic acid in
a sample, such as, for example, fetal nucleic acid. In some cases,
a method for determining fetal fraction described above also can be
used to enrich for fetal nucleic acid. In certain embodiments,
maternal nucleic acid is selectively removed (partially,
substantially, almost completely or completely) from the sample. In
some cases, enriching for a particular low copy number species
nucleic acid (e.g., fetal nucleic acid) may improve quantitative
sensitivity. Methods for enriching a sample for a particular
species of nucleic acid are described, for example, in U.S. Pat.
No. 6,927,028, International Patent Application Publication No.
WO2007/140417, International Patent Application Publication No.
WO2007/147063, International Patent Application Publication No.
WO2009/032779, International Patent Application Publication No.
WO2009/032781, International Patent Application Publication No.
WO2010/033639, International Patent Application Publication No.
WO2011/034631, International Patent Application Publication No.
WO2006/056480, and International Patent Application Publication No.
WO2011/143659, all of which are incorporated by reference
herein.
[0206] In some embodiments, nucleic acid is enriched for certain
target fragment species and/or reference fragment species. In some
cases, nucleic acid is enriched for a specific nucleic acid
fragment length or range of fragment lengths using one or more
length-based separation methods described below. In some cases,
nucleic acid is enriched for fragments from a select genomic region
(e.g., chromosome) using one or more sequence-based separation
methods described herein and/or known in the art. Certain methods
for enriching for a nucleic acid subpopulation (e.g., fetal nucleic
acid) in a sample are described in detail below.
[0207] Some methods for enriching for a nucleic acid subpopulation
(e.g., fetal nucleic acid) that can be used with a method described
herein include methods that exploit epigenetic differences between
maternal and fetal nucleic acid. For example, fetal nucleic acid
can be differentiated and separated from maternal nucleic acid
based on methylation differences. Methylation-based fetal nucleic
acid enrichment methods are described in U.S. Patent Application
Publication No. 2010/0105049, which is incorporated by reference
herein. Such methods sometimes involve binding a sample nucleic
acid to a methylation-specific binding agent (methyl-CpG binding
protein (MBD), methylation specific antibodies, and the like) and
separating bound nucleic acid from unbound nucleic acid based on
differential methylation status. Such methods also can include the
use of methylation-sensitive restriction enzymes (as described
above; e.g., HhaI and HpaII), which allow for the enrichment of
fetal nucleic acid regions in a maternal sample by selectively
digesting nucleic acid from the maternal sample with an enzyme that
selectively and completely or substantially digests the maternal
nucleic acid to enrich the sample for at least one fetal nucleic
acid region.
[0208] Another method for enriching for a nucleic acid
subpopulation (e.g., fetal nucleic acid) that can be used with a
method described herein is a restriction endonuclease enhanced
polymorphic sequence approach, such as a method described in U.S.
Patent Application Publication No. 2009/0317818, which is
incorporated by reference herein. Such methods include cleavage of
nucleic acid comprising a non-target allele with a restriction
endonuclease that recognizes the nucleic acid comprising the
non-target allele but not the target allele; and amplification of
uncleaved nucleic acid but not cleaved nucleic acid, where the
uncleaved, amplified nucleic acid represents enriched target
nucleic acid (e.g., fetal nucleic acid) relative to non-target
nucleic acid (e.g., maternal nucleic acid). In some cases, nucleic
acid may be selected such that it comprises an allele having a
polymorphic site that is susceptible to selective digestion by a
cleavage agent, for example.
[0209] Some methods for enriching for a nucleic acid subpopulation
(e.g., fetal nucleic acid) that can be used with a method described
herein include selective enzymatic degradation approaches. Such
methods involve protecting target sequences from exonuclease
digestion thereby facilitating the elimination in a sample of
undesired sequences (e.g., maternal DNA). For example, in one
approach, sample nucleic acid is denatured to generate single
stranded nucleic acid, single stranded nucleic acid is contacted
with at least one target-specific primer pair under suitable
annealing conditions, annealed primers are extended by nucleotide
polymerization generating double stranded target sequences, and
digesting single stranded nucleic acid using a nuclease that
digests single stranded (i.e. non-target) nucleic acid. In some
cases, the method can be repeated for at least one additional
cycle. In some cases, the same target-specific primer pair is used
to prime each of the first and second cycles of extension, and in
some cases, different target-specific primer pairs are used for the
first and second cycles.
[0210] Some methods for enriching for a nucleic acid subpopulation
(e.g., fetal nucleic acid) that can be used with a method described
herein include massively parallel signature sequencing (MPSS)
approaches. MPSS typically is a solid phase method that uses
adapter (i.e. tag) ligation, followed by adapter decoding, and
reading of the nucleic acid sequence in small increments. Tagged
PCR products are typically amplified such that each nucleic acid
generates a PCR product with a unique tag. Tags are often used to
attach the PCR products to microbeads. After several rounds of
ligation-based sequence determination, for example, a sequence
signature can be identified from each bead. Each signature sequence
(MPSS tag) in a MPSS dataset is analyzed, compared with all other
signatures, and all identical signatures are counted.
[0211] In some cases, certain MPSS-based enrichment methods can
include amplification (e.g., PCR)-based approaches. In some cases,
loci-specific amplification methods can be used (e.g., using
loci-specific amplification primers). In some cases, a multiplex
SNP allele PCR approach can be used. In some cases, a multiplex SNP
allele PCR approach can be used in combination with uniplex
sequencing. For example, such an approach can involve the use of
multiplex PCR (e.g., MASSARRAY system) and incorporation of capture
probe sequences into the amplicons followed by sequencing using,
for example, the Illumina MPSS system. In some cases, a multiplex
SNP allele PCR approach can be used in combination with a
three-primer system and indexed sequencing. For example, such an
approach can involve the use of multiplex PCR (e.g., MASSARRAY
system) with primers having a first capture probe incorporated into
certain loci-specific forward PCR primers and adapter sequences
incorporated into loci-specific reverse PCR primers, to thereby
generate amplicons, followed by a secondary PCR to incorporate
reverse capture sequences and molecular index barcodes for
sequencing using, for example, the Illumina MPSS system. In some
cases, a multiplex SNP allele PCR approach can be used in
combination with a four-primer system and indexed sequencing. For
example, such an approach can involve the use of multiplex PCR
(e.g., MASSARRAY system) with primers having adaptor sequences
incorporated into both loci-specific forward and loci-specific
reverse PCR primers, followed by a secondary PCR to incorporate
both forward and reverse capture sequences and molecular index
barcodes for sequencing using, for example, the Illumina MPSS
system. In some cases, a microfluidics approach can be used. In
some cases, an array-based microfluidics approach can be used. For
example, such an approach can involve the use of a microfluidics
array (e.g., Fluidigm) for amplification at low plex and
incorporation of index and capture probes, followed by sequencing.
In some cases, an emulsion microfluidics approach can be used, such
as, for example, digital droplet PCR.
[0212] In some cases, universal amplification methods can be used
(e.g., using universal or non-loci-specific amplification primers).
In some cases, universal amplification methods can be used in
combination with pull-down approaches. In some cases, a method can
include biotinylated ultramer pull-down (e.g., biotinylated
pull-down assays from Agilent or IDT) from a universally amplified
sequencing library. For example, such an approach can involve
preparation of a standard library, enrichment for selected regions
by a pull-down assay, and a secondary universal amplification step.
In some cases, pull-down approaches can be used in combination with
ligation-based methods. In some cases, a method can include
biotinylated ultramer pull down with sequence specific adapter
ligation (e.g., HALOPLEX PCR, Halo Genomics). For example, such an
approach can involve the use of selector probes to capture
restriction enzyme-digested fragments, followed by ligation of
captured products to an adaptor, and universal amplification
followed by sequencing. In some cases, pull-down approaches can be
used in combination with extension and ligation-based methods. In
some cases, a method can include molecular inversion probe (MIP)
extension and ligation. For example, such an approach can involve
the use of molecular inversion probes in combination with sequence
adapters followed by universal amplification and sequencing. In
some cases, complementary DNA can be synthesized and sequenced
without amplification.
[0213] In some cases, extension and ligation approaches can be
performed without a pull-down component. In some cases, a method
can include loci-specific forward and reverse primer hybridization,
extension and ligation. Such methods can further include universal
amplification or complementary DNA synthesis without amplification,
followed by sequencing. Such methods can reduce or exclude
background sequences during analysis, in some cases.
[0214] In some cases, pull-down approaches can be used with an
optional amplification component or with no amplification
component. In some cases, a method can include a modified pull-down
assay and ligation with full incorporation of capture probes
without universal amplification. For example, such an approach can
involve the use of modified selector probes to capture restriction
enzyme-digested fragments, followed by ligation of captured
products to an adaptor, optional amplification, and sequencing. In
some cases, a method can include a biotinylated pull-down assay
with extension and ligation of adaptor sequence in combination with
circular single stranded ligation. For example, such an approach
can involve the use of selector probes to capture regions of
interest (i.e. target sequences), extension of the probes, adaptor
ligation, single stranded circular ligation, optional
amplification, and sequencing. In some cases, the analysis of the
sequencing result can separate target sequences form
background.
[0215] In some embodiments, nucleic acid is enriched for fragments
from a select genomic region (e.g., chromosome) using one or more
sequence-based separation methods described herein. Sequence-based
separation generally is based on nucleotide sequences present in
the fragments of interest (e.g., target and/or reference fragments)
and substantially not present in other fragments of the sample or
present in an insubstantial amount of the other fragments (e.g., 5%
or less). In some embodiments, sequence-based separation can
generate separated target fragments and/or separated reference
fragments. Separated target fragments and/or separated reference
fragments typically are isolated away from the remaining fragments
in the nucleic acid sample. In some cases, the separated target
fragments and the separated reference fragments also are isolated
away from each other (e.g., isolated in separate assay
compartments). In some cases, the separated target fragments and
the separated reference fragments are isolated together (e.g.,
isolated in the same assay compartment). In some embodiments,
unbound fragments can be differentially removed or degraded or
digested.
[0216] In some embodiments, a selective nucleic acid capture
process is used to separate target and/or reference fragments away
from the nucleic acid sample. Commercially available nucleic acid
capture systems include, for example, Nimblegen sequence capture
system (Roche NimbleGen, Madison, Wis.); Illumina BEADARRAY
platform (Illumina, San Diego, Calif.); Affymetrix GENECHIP
platform (Affymetrix, Santa Clara, Calif.); Agilent SureSelect
Target Enrichment System (Agilent Technologies, Santa Clara,
Calif.); and related platforms. Such methods typically involve
hybridization of a capture oligonucleotide to a segment or all of
the nucleotide sequence of a target or reference fragment and can
include use of a solid phase (e.g., solid phase array) and/or a
solution based platform. Capture oligonucleotides (sometimes
referred to as "bait") can be selected or designed such that they
preferentially hybridize to nucleic acid fragments from selected
genomic regions or loci (e.g., one of chromosomes 21, 18, 13, X or
Y, or a reference chromosome).
[0217] In some embodiments, nucleic acid is enriched for a
particular nucleic acid fragment length, range of lengths, or
lengths under or over a particular threshold or cutoff using one or
more length-based separation methods. Nucleic acid fragment length
typically refers to the number of nucleotides in the fragment.
Nucleic acid fragment length also is sometimes referred to as
nucleic acid fragment size. In some embodiments, a length-based
separation method is performed without measuring lengths of
individual fragments. In some embodiments, a length based
separation method is performed in conjunction with a method for
determining length of individual fragments. In some embodiments,
length-based separation refers to a size fractionation procedure
where all or part of the fractionated pool can be isolated (e.g.,
retained) and/or analyzed. Size fractionation procedures are known
in the art (e.g., separation on an array, separation by a molecular
sieve, separation by gel electrophoresis, separation by column
chromatography (e.g., size-exclusion columns), and
microfluidics-based approaches). In some cases, length-based
separation approaches can include fragment circularization,
chemical treatment (e.g., formaldehyde, polyethylene glycol (PEG)),
mass spectrometry and/or size-specific nucleic acid amplification,
for example.
[0218] Certain length-based separation methods that can be used
with methods described herein employ a selective sequence tagging
approach, for example. The term "sequence tagging" refers to
incorporating a recognizable and distinct sequence into a nucleic
acid or population of nucleic acids. The term "sequence tagging" as
used herein has a different meaning than the term "sequence tag"
described later herein. In such sequence tagging methods, a
fragment size species (e.g., short fragments) nucleic acids are
subjected to selective sequence tagging in a sample that includes
long and short nucleic acids. Such methods typically involve
performing a nucleic acid amplification reaction using a set of
nested primers which include inner primers and outer primers. In
some cases, one or both of the inner can be tagged to thereby
introduce a tag onto the target amplification product. The outer
primers generally do not anneal to the short fragments that carry
the (inner) target sequence. The inner primers can anneal to the
short fragments and generate an amplification product that carries
a tag and the target sequence. Typically, tagging of the long
fragments is inhibited through a combination of mechanisms which
include, for example, blocked extension of the inner primers by the
prior annealing and extension of the outer primers. Enrichment for
tagged fragments can be accomplished by any of a variety of
methods, including for example, exonuclease digestion of single
stranded nucleic acid and amplification of the tagged fragments
using amplification primers specific for at least one tag.
[0219] Another length-based separation method that can be used with
methods described herein involves subjecting a nucleic acid sample
to polyethylene glycol (PEG) precipitation. Examples of methods
include those described in International Patent Application
Publication Nos. WO2007/140417 and WO2010/115016. This method in
general entails contacting a nucleic acid sample with PEG in the
presence of one or more monovalent salts under conditions
sufficient to substantially precipitate large nucleic acids without
substantially precipitating small (e.g., less than 300 nucleotides)
nucleic acids.
[0220] Another size-based enrichment method that can be used with
methods described herein involves circularization by ligation, for
example, using circligase. Short nucleic acid fragments typically
can be circularized with higher efficiency than long fragments.
Non-circularized sequences can be separated from circularized
sequences, and the enriched short fragments can be used for further
analysis.
[0221] Obtaining Sequence Reads
[0222] In some embodiments, nucleic acids (e.g., nucleic acid
fragments, sample nucleic acid, cell-free nucleic acid) may be
sequenced. In some cases, a full or substantially full sequence is
obtained and sometimes a partial sequence is obtained. Sequencing,
mapping and related analytical methods are known in the art (e.g.,
United States Patent Application Publication US2009/0029377,
incorporated by reference). Certain aspects of such processes are
described hereafter.
[0223] As used herein, "reads" (i.e., "a read", "a sequence read")
are short nucleotide sequences produced by any sequencing process
described herein or known in the art. Reads can be generated from
one end of nucleic acid fragments ("single-end reads"), and
sometimes are generated from both ends of nucleic acids (e.g.,
paired-end reads, double-end reads).
[0224] In some embodiments the nominal, average, mean or absolute
length of single-end reads sometimes is about 20 contiguous
nucleotides to about 50 contiguous nucleotides, sometimes about 30
contiguous nucleotides to about 40 contiguous nucleotides, and
sometimes about 35 contiguous nucleotides or about 36 contiguous
nucleotides. Sometimes the nominal, average, mean or absolute
length of single-end reads is about 20 to about 30 bases in length.
Sometimes the nominal, average, mean or absolute length of
single-end reads is about 24 to about 28 bases in length. Sometimes
the nominal, average, mean or absolute length of single-end reads
is about 21, 22, 23, 24, 25, 26, 27, 28 or about 29 bases in
length.
[0225] In certain embodiments, the nominal, average, mean or
absolute length of the paired-end reads sometimes is about 10
contiguous nucleotides to about 25 contiguous nucleotides (e.g.,
about 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 or 24
nucleotides in length), sometimes is about 15 contiguous
nucleotides to about 20 contiguous nucleotides, and sometimes is
about 17 contiguous nucleotides or about 18 contiguous
nucleotides.
[0226] Reads generally are representations of nucleotide sequences
in a physical nucleic acid. For example, in a read containing an
ATGC depiction of a sequence, "A" represents an adenine nucleotide,
"T" represents a thymine nucleotide, "G" represents a guanine
nucleotide and "C" represents a cytosine nucleotide, in a physical
nucleic acid. Sequence reads obtained from the blood of a pregnant
female can be reads from a mixture of fetal and maternal nucleic
acid. A mixture of relatively short reads can be transformed by
processes described herein into a representation of a genomic
nucleic acid present in the pregnant female and/or in the fetus. A
mixture of relatively short reads can be transformed into a
representation of a copy number variation (e.g., a maternal and/or
fetal copy number variation), genetic variation or an aneuploidy,
for example. Reads of a mixture of maternal and fetal nucleic acid
can be transformed into a representation of a composite chromosome
or a segment thereof comprising features of one or both maternal
and fetal chromosomes. In certain embodiments, "obtaining" nucleic
acid sequence reads of a sample from a subject and/or "obtaining"
nucleic acid sequence reads of a biological specimen from one or
more reference persons can involve directly sequencing nucleic acid
to obtain the sequence information. In some embodiments,
"obtaining" can involve receiving sequence information obtained
directly from a nucleic acid by another.
[0227] Sequence reads can be mapped and the number of reads or
sequence tags mapping to a specified nucleic acid region (e.g., a
chromosome, a bin, a genomic section) are referred to as counts. In
some embodiments, counts can be manipulated or transformed (e.g.,
normalized, combined, added, filtered, selected, averaged, derived
as a mean, the like, or a combination thereof). In some
embodiments, counts can be transformed to produce normalized
counts. Normalized counts for multiple genomic sections can be
provided in a profile (e.g., a genomic profile, a chromosome
profile, a profile of a segment or portion of a chromosome). One or
more different elevations in a profile also can be manipulated or
transformed (e.g., counts associated with elevations can be
normalized) and elevations can be adjusted.
[0228] In some embodiments, one nucleic acid sample from one
individual is sequenced. In certain embodiments, nucleic acid
samples from two or more biological samples, where each biological
sample is from one individual or two or more individuals, are
pooled and the pool is sequenced. In the latter embodiments, a
nucleic acid sample from each biological sample often is identified
by one or more unique identification tags.
[0229] In some embodiments, a fraction of the genome is sequenced,
which sometimes is expressed in the amount of the genome covered by
the determined nucleotide sequences (e.g., "fold" coverage less
than 1). When a genome is sequenced with about 1-fold coverage,
roughly 100% of the nucleotide sequence of the genome is
represented by reads. A genome also can be sequenced with
redundancy, where a given region of the genome can be covered by
two or more reads or overlapping reads (e.g., "fold" coverage
greater than 1). In some embodiments, a genome is sequenced with
about 0.1-fold to about 100-fold coverage, about 0.2-fold to
20-fold coverage, or about 0.2-fold to about 1-fold coverage (e.g.,
about 0.2-, 0.3-, 0.4-, 0.5-, 0.6-, 0.7-, 0.8-, 0.9-, 1-, 2-, 3-,
4-, 5-, 6-, 7-, 8-, 9-, 10-, 15-, 20-, 30-, 40-, 50-, 60-, 70-,
80-, 90-fold coverage).
[0230] In certain embodiments, a fraction of a nucleic acid pool
that is sequenced in a run is further sub-selected prior to
sequencing. In certain embodiments, hybridization-based techniques
(e.g., using oligonucleotide arrays) can be used to first
sub-select for nucleic acid sequences from certain chromosomes
(e.g., a potentially aneuploid chromosome and other chromosome(s)
not involved in the aneuploidy tested). In some embodiments,
nucleic acid can be fractionated by size (e.g., by gel
electrophoresis, size exclusion chromatography or by
microfluidics-based approach) and in certain instances, fetal
nucleic acid can be enriched by selecting for nucleic acid having a
lower molecular weight (e.g., less than 300 base pairs, less than
200 base pairs, less than 150 base pairs, less than 100 base
pairs). In some embodiments, fetal nucleic acid can be enriched by
suppressing maternal background nucleic acid, such as by the
addition of formaldehyde. In some embodiments, a portion or subset
of a pre-selected pool of nucleic acids is sequenced randomly. In
some embodiments, the nucleic acid is amplified prior to
sequencing. In some embodiments, a portion or subset of the nucleic
acid is amplified prior to sequencing.
[0231] In some cases, a sequencing library is prepared prior to or
during a sequencing process. Methods for preparing a sequencing
library are known in the art and commercially available platforms
may be used for certain applications. Certain commercially
available library platforms may be compatible with certain
nucleotide sequencing processes described herein. For example, one
or more commercially available library platforms may be compatible
with a sequencing by synthesis process. In some cases, a
ligation-based library preparation method is used (e.g., ILLUMINA
TRUSEQ, Illumina, San Diego Calif.). Ligation-based library
preparation methods typically use a methylated adaptor design which
can incorporate an index sequence at the initial ligation step and
often can be used to prepare samples for single-read sequencing,
paired-end sequencing and multiplexed sequencing. In some cases, a
transposon-based library preparation method is used (e.g.,
EPICENTRE NEXTERA, Epicentre, Madison Wis.). Transposon-based
methods typically use in vitro transposition to simultaneously
fragment and tag DNA in a single-tube reaction (often allowing
incorporation of platform-specific tags and optional barcodes), and
prepare sequencer-ready libraries.
[0232] Any sequencing method suitable for conducting methods
described herein can be utilized. In some embodiments, a
high-throughput sequencing method is used. High-throughput
sequencing methods generally involve clonally amplified DNA
templates or single DNA molecules that are sequenced in a massively
parallel fashion within a flow cell (e.g. as described in Metzker M
Nature Rev 11:31-46 (2010); Volkerding et al. Clin Chem 55:641-658
(2009)). Such sequencing methods also can provide digital
quantitative information, where each sequence read is a countable
"sequence tag" or "count" representing an individual clonal DNA
template, a single DNA molecule, bin or chromosome. Next generation
sequencing techniques capable of sequencing DNA in a massively
parallel fashion are collectively referred to herein as "massively
parallel sequencing" (MPS). High-throughput sequencing technologies
include, for example, sequencing-by-synthesis with reversible dye
terminators, sequencing by oligonucleotide probe ligation,
pyrosequencing and real time sequencing. Non-limiting examples of
MPS include Massively Parallel Signature Sequencing (MPSS), Polony
sequencing, Pyrosequencing, Illumina (Solexa) sequencing, SOLiD
sequencing, Ion semiconductor sequencing, DNA nanoball sequencing,
Helioscope single molecule sequencing, single molecule real time
(SMRT) sequencing, nanopore sequencing, ION Torrent and RNA
polymerase (RNAP) sequencing.
[0233] Systems utilized for high-throughput sequencing methods are
commercially available and include, for example, the Roche 454
platform, the Applied Biosystems SOLID platform, the Helicos True
Single Molecule DNA sequencing technology, the
sequencing-by-hybridization platform from Affymetrix Inc., the
single molecule, real-time (SMRT) technology of Pacific
Biosciences, the sequencing-by-synthesis platforms from 454 Life
Sciences, Illumina/Solexa and Helicos Biosciences, and the
sequencing-by-ligation platform from Applied Biosystems. The ION
TORRENT technology from Life technologies and nanopore sequencing
also can be used in high-throughput sequencing approaches.
[0234] In some embodiments, first generation technology, such as,
for example, Sanger sequencing including the automated Sanger
sequencing, can be used in a method provided herein. Additional
sequencing technologies that include the use of developing nucleic
acid imaging technologies (e.g. transmission electron microscopy
(TEM) and atomic force microscopy (AFM)), also are contemplated
herein. Examples of various sequencing technologies are described
below.
[0235] A nucleic acid sequencing technology that may be used in a
method described herein is sequencing-by-synthesis and reversible
terminator-based sequencing (e.g. Illumina's Genome Analyzer;
Genome Analyzer II; HISEQ 2000; HISEQ 2500 (Illumina, San Diego
Calif.)). With this technology, millions of nucleic acid (e.g. DNA)
fragments can be sequenced in parallel. In one example of this type
of sequencing technology, a flow cell is used which contains an
optically transparent slide with 8 individual lanes on the surfaces
of which are bound oligonucleotide anchors (e.g., adaptor primers).
A flow cell often is a solid support that can be configured to
retain and/or allow the orderly passage of reagent solutions over
bound analytes. Flow cells frequently are planar in shape,
optically transparent, generally in the millimeter or
sub-millimeter scale, and often have channels or lanes in which the
analyte/reagent interaction occurs.
[0236] In certain sequencing by synthesis procedures, for example,
template DNA (e.g., circulating cell-free DNA (ccfDNA)) sometimes
can be fragmented into lengths of several hundred base pairs in
preparation for library generation. In some embodiments, library
preparation can be performed without further fragmentation or size
selection of the template DNA (e.g., ccfDNA). Sample isolation and
library generation may be performed using automated methods and
apparatus, in certain embodiments. Briefly, template DNA is end
repaired by a fill-in reaction, exonuclease reaction or a
combination of a fill-in reaction and exonuclease reaction. The
resulting blunt-end repaired template DNA is extended by a single
nucleotide, which is complementary to a single nucleotide overhang
on the 3' end of an adapter primer, and often increases ligation
efficiency. Any complementary nucleotides can be used for the
extension/overhang nucleotides (e.g., NT, C/G), however adenine
frequently is used to extend the end-repaired DNA, and thymine
often is used as the 3' end overhang nucleotide.
[0237] In certain sequencing by synthesis procedures, for example,
adapter oligonucleotides are complementary to the flow-cell
anchors, and sometimes are utilized to associate the modified
template DNA (e.g., end-repaired and single nucleotide extended)
with a solid support, such as the inside surface of a flow cell,
for example. In some embodiments, the adapter also includes
identifiers (i.e., indexing nucleotides, or "barcode" nucleotides
(e.g., a unique sequence of nucleotides usable as an identifier to
allow unambiguous identification of a sample and/or chromosome)),
one or more sequencing primer hybridization sites (e.g., sequences
complementary to universal sequencing primers, single end
sequencing primers, paired end sequencing primers, multiplexed
sequencing primers, and the like), or combinations thereof (e.g.,
adapter/sequencing, adapter/identifier,
adapter/identifier/sequencing). Identifiers or nucleotides
contained in an adapter often are six or more nucleotides in
length, and frequently are positioned in the adaptor such that the
identifier nucleotides are the first nucleotides sequenced during
the sequencing reaction. In certain embodiments, identifier
nucleotides are associated with a sample but are sequenced in a
separate sequencing reaction to avoid compromising the quality of
sequence reads. Subsequently, the reads from the identifier
sequencing and the DNA template sequencing are linked together and
the reads de-multiplexed. After linking and de-multiplexing the
sequence reads and/or identifiers can be further adjusted or
processed as described herein.
[0238] In certain sequencing by synthesis procedures, utilization
of identifiers allows multiplexing of sequence reactions in a flow
cell lane, thereby allowing analysis of multiple samples per flow
cell lane. The number of samples that can be analyzed in a given
flow cell lane often is dependent on the number of unique
identifiers utilized during library preparation and/or probe
design. Non limiting examples of commercially available multiplex
sequencing kits include Illumina's multiplexing sample preparation
oligonucleotide kit and multiplexing sequencing primers and PhiX
control kit (e.g., Illumina's catalog numbers PE-400-1001 and
PE-400-1002, respectively). A method described herein can be
performed using any number of unique identifiers (e.g., 4, 8, 12,
24, 48, 96, or more). The greater the number of unique identifiers,
the greater the number of samples and/or chromosomes, for example,
that can be multiplexed in a single flow cell lane. Multiplexing
using 12 identifiers, for example, allows simultaneous analysis of
96 samples (e.g., equal to the number of wells in a 96 well
microwell plate) in an 8 lane flow cell. Similarly, multiplexing
using 48 identifiers, for example, allows simultaneous analysis of
384 samples (e.g., equal to the number of wells in a 384 well
microwell plate) in an 8 lane flow cell.
[0239] In certain sequencing by synthesis procedures,
adapter-modified, single-stranded template DNA is added to the flow
cell and immobilized by hybridization to the anchors under
limiting-dilution conditions. In contrast to emulsion PCR, DNA
templates are amplified in the flow cell by "bridge" amplification,
which relies on captured DNA strands "arching" over and hybridizing
to an adjacent anchor oligonucleotide. Multiple amplification
cycles convert the single-molecule DNA template to a clonally
amplified arching "cluster," with each cluster containing
approximately 1000 clonal molecules. Approximately
50.times.10.sup.6 separate clusters can be generated per flow cell.
For sequencing, the clusters are denatured, and a subsequent
chemical cleavage reaction and wash leave only forward strands for
single-end sequencing. Sequencing of the forward strands is
initiated by hybridizing a primer complementary to the adapter
sequences, which is followed by addition of polymerase and a
mixture of four differently colored fluorescent reversible dye
terminators. The terminators are incorporated according to sequence
complementarity in each strand in a clonal cluster. After
incorporation, excess reagents are washed away, the clusters are
optically interrogated, and the fluorescence is recorded. With
successive chemical steps, the reversible dye terminators are
unblocked, the fluorescent labels are cleaved and washed away, and
the next sequencing cycle is performed. This iterative,
sequencing-by-synthesis process sometimes requires approximately
2.5 days to generate read lengths of 36 bases. With
50.times.10.sup.6 clusters per flow cell, the overall sequence
output can be greater than 1 billion base pairs (Gb) per analytical
run.
[0240] Another nucleic acid sequencing technology that may be used
with a method described herein is 454 sequencing (Roche). 454
sequencing uses a large-scale parallel pyrosequencing system
capable of sequencing about 400-600 megabases of DNA per run. The
process typically involves two steps. In the first step, sample
nucleic acid (e.g. DNA) is sometimes fractionated into smaller
fragments (300-800 base pairs) and polished (made blunt at each
end). Short adaptors are then ligated onto the ends of the
fragments. These adaptors provide priming sequences for both
amplification and sequencing of the sample-library fragments. One
adaptor (Adaptor B) contains a 5'-biotin tag for immobilization of
the DNA library onto streptavidin-coated beads. After nick repair,
the non-biotinylated strand is released and used as a
single-stranded template DNA (sstDNA) library. The sstDNA library
is assessed for its quality and the optimal amount (DNA copies per
bead) needed for emPCR is determined by titration. The sstDNA
library is immobilized onto beads. The beads containing a library
fragment carry a single sstDNA molecule. The bead-bound library is
emulsified with the amplification reagents in a water-in-oil
mixture. Each bead is captured within its own microreactor where
PCR amplification occurs. This results in bead-immobilized,
clonally amplified DNA fragments.
[0241] In the second step of 454 sequencing, single-stranded
template DNA library beads are added to an incubation mix
containing DNA polymerase and are layered with beads containing
sulfurylase and luciferase onto a device containing pico-liter
sized wells. Pyrosequencing is performed on each DNA fragment in
parallel. Addition of one or more nucleotides generates a light
signal that is recorded by a CCD camera in a sequencing instrument.
The signal strength is proportional to the number of nucleotides
incorporated. Pyrosequencing exploits the release of pyrophosphate
(PPi) upon nucleotide addition. PPi is converted to ATP by ATP
sulfurylase in the presence of adenosine 5' phosphosulfate.
Luciferase uses ATP to convert luciferin to oxyluciferin, and this
reaction generates light that is discerned and analyzed (see, for
example, Margulies, M. et al. Nature 437:376-380 (2005)).
[0242] Another nucleic acid sequencing technology that may be used
in a method provided herein is Applied Biosystems' SOLiD.TM.
technology. In SOLiD.TM. sequencing-by-ligation, a library of
nucleic acid fragments is prepared from the sample and is used to
prepare clonal bead populations. With this method, one species of
nucleic acid fragment will be present on the surface of each bead
(e.g. magnetic bead). Sample nucleic acid (e.g. genomic DNA) is
sheared into fragments, and adaptors are subsequently attached to
the 5' and 3' ends of the fragments to generate a fragment library.
The adapters are typically universal adapter sequences so that the
starting sequence of every fragment is both known and identical.
Emulsion PCR takes place in microreactors containing all the
necessary reagents for PCR. The resulting PCR products attached to
the beads are then covalently bound to a glass slide. Primers then
hybridize to the adapter sequence within the library template. A
set of four fluorescently labeled di-base probes compete for
ligation to the sequencing primer. Specificity of the di-base probe
is achieved by interrogating every 1st and 2nd base in each
ligation reaction. Multiple cycles of ligation, detection and
cleavage are performed with the number of cycles determining the
eventual read length. Following a series of ligation cycles, the
extension product is removed and the template is reset with a
primer complementary to the n-1 position for a second round of
ligation cycles. Often, five rounds of primer reset are completed
for each sequence tag. Through the primer reset process, each base
is interrogated in two independent ligation reactions by two
different primers. For example, the base at read position 5 is
assayed by primer number 2 in ligation cycle 2 and by primer number
3 in ligation cycle 1.
[0243] Another nucleic acid sequencing technology that may be used
in a method described herein is the Helicos True Single Molecule
Sequencing (tSMS). In the tSMS technique, a polyA sequence is added
to the 3' end of each nucleic acid (e.g. DNA) strand from the
sample. Each strand is labeled by the addition of a fluorescently
labeled adenosine nucleotide. The DNA strands are then hybridized
to a flow cell, which contains millions of oligo-T capture sites
that are immobilized to the flow cell surface. The templates can be
at a density of about 100 million templates/cm.sup.2. The flow cell
is then loaded into a sequencing apparatus and a laser illuminates
the surface of the flow cell, revealing the position of each
template. A CCD camera can map the position of the templates on the
flow cell surface. The template fluorescent label is then cleaved
and washed away. The sequencing reaction begins by introducing a
DNA polymerase and a fluorescently labeled nucleotide. The oligo-T
nucleic acid serves as a primer. The polymerase incorporates the
labeled nucleotides to the primer in a template directed manner.
The polymerase and unincorporated nucleotides are removed. The
templates that have directed incorporation of the fluorescently
labeled nucleotide are detected by imaging the flow cell surface.
After imaging, a cleavage step removes the fluorescent label, and
the process is repeated with other fluorescently labeled
nucleotides until the desired read length is achieved. Sequence
information is collected with each nucleotide addition step (see,
for example, Harris T. D. et al., Science 320:106-109 (2008)).
[0244] Another nucleic acid sequencing technology that may be used
in a method provided herein is the single molecule, real-time
(SMRT.TM.) sequencing technology of Pacific Biosciences. With this
method, each of the four DNA bases is attached to one of four
different fluorescent dyes. These dyes are phospholinked. A single
DNA polymerase is immobilized with a single molecule of template
single stranded DNA at the bottom of a zero-mode waveguide (ZMW). A
ZMW is a confinement structure which enables observation of
incorporation of a single nucleotide by DNA polymerase against the
background of fluorescent nucleotides that rapidly diffuse in an
out of the ZMW (in microseconds). It takes several milliseconds to
incorporate a nucleotide into a growing strand. During this time,
the fluorescent label is excited and produces a fluorescent signal,
and the fluorescent tag is cleaved off. Detection of the
corresponding fluorescence of the dye indicates which base was
incorporated. The process is then repeated.
[0245] Another nucleic acid sequencing technology that may be used
in a method described herein is ION TORRENT (Life Technologies)
single molecule sequencing which pairs semiconductor technology
with a simple sequencing chemistry to directly translate chemically
encoded information (A, C, G, T) into digital information (0, 1) on
a semiconductor chip. ION TORRENT uses a high-density array of
micro-machined wells to perform nucleic acid sequencing in a
massively parallel way. Each well holds a different DNA molecule.
Beneath the wells is an ion-sensitive layer and beneath that an ion
sensor. Typically, when a nucleotide is incorporated into a strand
of DNA by a polymerase, a hydrogen ion is released as a byproduct.
If a nucleotide, for example a C, is added to a DNA template and is
then incorporated into a strand of DNA, a hydrogen ion will be
released. The charge from that ion will change the pH of the
solution, which can be detected by an ion sensor. A sequencer can
call the base, going directly from chemical information to digital
information. The sequencer then sequentially floods the chip with
one nucleotide after another. If the next nucleotide that floods
the chip is not a match, no voltage change will be recorded and no
base will be called. If there are two identical bases on the DNA
strand, the voltage will be double, and the chip will record two
identical bases called. Because this is direct detection (i.e.
detection without scanning, cameras or light), each nucleotide
incorporation is recorded in seconds.
[0246] Another nucleic acid sequencing technology that may be used
in a method described herein is the chemical-sensitive field effect
transistor (CHEMFET) array. In one example of this sequencing
technique, DNA molecules are placed into reaction chambers, and the
template molecules can be hybridized to a sequencing primer bound
to a polymerase. Incorporation of one or more triphosphates into a
new nucleic acid strand at the 3' end of the sequencing primer can
be detected by a change in current by a CHEMFET sensor. An array
can have multiple CHEMFET sensors. In another example, single
nucleic acids are attached to beads, and the nucleic acids can be
amplified on the bead, and the individual beads can be transferred
to individual reaction chambers on a CHEMFET array, with each
chamber having a CHEMFET sensor, and the nucleic acids can be
sequenced (see, for example, U.S. Patent Application Publication
No. 2009/0026082).
[0247] Another nucleic acid sequencing technology that may be used
in a method described herein is electron microscopy. In one example
of this sequencing technique, individual nucleic acid (e.g. DNA)
molecules are labeled using metallic labels that are
distinguishable using an electron microscope. These molecules are
then stretched on a flat surface and imaged using an electron
microscope to measure sequences (see, for example, Moudrianakis E.
N. and Beer M. Proc Natl Acad Sci USA. 1965 March; 53:564-71). In
some cases, transmission electron microscopy (TEM) is used (e.g.
Halcyon Molecular's TEM method). This method, termed Individual
Molecule Placement Rapid Nano Transfer (IMPRNT), includes utilizing
single atom resolution transmission electron microscope imaging of
high-molecular weight (e.g. about 150 kb or greater) DNA
selectively labeled with heavy atom markers and arranging these
molecules on ultra-thin films in ultra-dense (3 nm
strand-to-strand) parallel arrays with consistent base-to-base
spacing. The electron microscope is used to image the molecules on
the films to determine the position of the heavy atom markers and
to extract base sequence information from the DNA (see, for
example, International Patent Application No. WO 2009/046445).
[0248] Other sequencing methods that may be used to conduct methods
herein include digital PCR and sequencing by hybridization. Digital
polymerase chain reaction (digital PCR or dPCR) can be used to
directly identify and quantify nucleic acids in a sample. Digital
PCR can be performed in an emulsion, in some embodiments. For
example, individual nucleic acids are separated, e.g., in a
microfluidic chamber device, and each nucleic acid is individually
amplified by PCR. Nucleic acids can be separated such that there is
no more than one nucleic acid per well. In some embodiments,
different probes can be used to distinguish various alleles (e.g.
fetal alleles and maternal alleles). Alleles can be enumerated to
determine copy number. In sequencing by hybridization, the method
involves contacting a plurality of polynucleotide sequences with a
plurality of polynucleotide probes, where each of the plurality of
polynucleotide probes can be optionally tethered to a substrate.
The substrate can be a flat surface with an array of known
nucleotide sequences, in some embodiments. The pattern of
hybridization to the array can be used to determine the
polynucleotide sequences present in the sample. In some
embodiments, each probe is tethered to a bead, e.g., a magnetic
bead or the like. Hybridization to the beads can be identified and
used to identify the plurality of polynucleotide sequences within
the sample.
[0249] In some embodiments, nanopore sequencing can be used in a
method described herein. Nanopore sequencing is a single-molecule
sequencing technology whereby a single nucleic acid molecule (e.g.
DNA) is sequenced directly as it passes through a nanopore. A
nanopore is a small hole or channel, of the order of 1 nanometer in
diameter. Certain transmembrane cellular proteins can act as
nanopores (e.g. alpha-hemolysin). In some cases, nanopores can be
synthesized (e.g. using a silicon platform). Immersion of a
nanopore in a conducting fluid and application of a potential
across it results in a slight electrical current due to conduction
of ions through the nanopore. The amount of current which flows is
sensitive to the size of the nanopore. As a DNA molecule passes
through a nanopore, each nucleotide on the DNA molecule obstructs
the nanopore to a different degree and generates characteristic
changes to the current. The amount of current which can pass
through the nanopore at any given moment therefore varies depending
on whether the nanopore is blocked by an A, a C, a G, a T, or in
some cases, methyl-C. The change in the current through the
nanopore as the DNA molecule passes through the nanopore represents
a direct reading of the DNA sequence. In some cases a nanopore can
be used to identify individual DNA bases as they pass through the
nanopore in the correct order (see, for example, Soni G V and
Meller A. Clin Chem 53: 1996-2001 (2007); International Patent
Application No. WO2010/004265).
[0250] There are a number of ways that nanopores can be used to
sequence nucleic acid molecules. In some embodiments, an
exonuclease enzyme, such as a deoxyribonuclease, is used. In this
case, the exonuclease enzyme is used to sequentially detach
nucleotides from a nucleic acid (e.g. DNA) molecule. The
nucleotides are then detected and discriminated by the nanopore in
order of their release, thus reading the sequence of the original
strand. For such an embodiment, the exonuclease enzyme can be
attached to the nanopore such that a proportion of the nucleotides
released from the DNA molecule is capable of entering and
interacting with the channel of the nanopore. The exonuclease can
be attached to the nanopore structure at a site in close proximity
to the part of the nanopore that forms the opening of the channel.
In some cases, the exonuclease enzyme can be attached to the
nanopore structure such that its nucleotide exit trajectory site is
orientated towards the part of the nanopore that forms part of the
opening.
[0251] In some embodiments, nanopore sequencing of nucleic acids
involves the use of an enzyme that pushes or pulls the nucleic acid
(e.g. DNA) molecule through the pore. In this case, the ionic
current fluctuates as a nucleotide in the DNA molecule passes
through the pore. The fluctuations in the current are indicative of
the DNA sequence. For such an embodiment, the enzyme can be
attached to the nanopore structure such that it is capable of
pushing or pulling the target nucleic acid through the channel of a
nanopore without interfering with the flow of ionic current through
the pore. The enzyme can be attached to the nanopore structure at a
site in close proximity to the part of the structure that forms
part of the opening. The enzyme can be attached to the subunit, for
example, such that its active site is orientated towards the part
of the structure that forms part of the opening.
[0252] In some embodiments, nanopore sequencing of nucleic acids
involves detection of polymerase bi-products in close proximity to
a nanopore detector. In this case, nucleoside phosphates
(nucleotides) are labeled so that a phosphate labeled species is
released upon the addition of a polymerase to the nucleotide strand
and the phosphate labeled species is detected by the pore.
Typically, the phosphate species contains a specific label for each
nucleotide. As nucleotides are sequentially added to the nucleic
acid strand, the bi-products of the base addition are detected. The
order that the phosphate labeled species are detected can be used
to determine the sequence of the nucleic acid strand.
[0253] The length of the sequence read is often associated with the
particular sequencing technology. High-throughput methods, for
example, provide sequence reads that can vary in size from tens to
hundreds of base pairs (bp). Nanopore sequencing, for example, can
provide sequence reads that can vary in size from tens to hundreds
to thousands of base pairs. In some embodiments, the sequence reads
are of a mean, median or average length of about 15 bp to 900 bp
long (e.g. about 20 bp, about 25 bp, about 30 bp, about 35 bp,
about 40 bp, about 45 bp, about 50 bp, about 55 bp, about 60 bp,
about 65 bp, about 70 bp, about 75 bp, about 80 bp, about 85 bp,
about 90 bp, about 95 bp, about 100 bp, about 110 bp, about 120 bp,
about 130, about 140 bp, about 150 bp, about 200 bp, about 250 bp,
about 300 bp, about 350 bp, about 400 bp, about 450 bp, or about
500 bp. In some embodiments, the sequence reads are of a mean,
median or average length of about 1000 bp or more.
[0254] In some embodiments, chromosome-specific sequencing is
performed. In some embodiments, chromosome-specific sequencing is
performed utilizing DANSR (digital analysis of selected regions).
Digital analysis of selected regions enables simultaneous
quantification of hundreds of loci by cfDNA-dependent catenation of
two locus-specific oligonucleotides via an intervening `bridge`
oligo to form a PCR template. In some embodiments,
chromosome-specific sequencing is performed by generating a library
enriched in chromosome-specific sequences. In some embodiments,
sequence reads are obtained only for a selected set of chromosomes.
In some embodiments, sequence reads are obtained only for
chromosomes 21, 18 and 13.
[0255] In some embodiments, nucleic acids may include a fluorescent
signal or sequence tag information. Quantification of the signal or
tag may be used in a variety of techniques such as, for example,
flow cytometry, quantitative polymerase chain reaction (qPCR), gel
electrophoresis, gene-chip analysis, microarray, mass spectrometry,
cytofluorimetric analysis, fluorescence microscopy, confocal laser
scanning microscopy, laser scanning cytometry, affinity
chromatography, manual batch mode separation, electric field
suspension, sequencing, and combination thereof.
[0256] Partial Nucleotide Sequence Reads
[0257] In some embodiments, nucleotide sequence reads obtained from
a sample are partial nucleotide sequence reads. As used herein,
"partial nucleotide sequence reads" refers to sequence reads of any
length with incomplete sequence information, also referred to as
sequence ambiguity. Partial nucleotide sequence reads may lack
information regarding nucleobase identity and/or nucleobase
position or order. Partial nucleotide sequence reads generally do
not include sequence reads in which the only incomplete sequence
information (or in which less than all of the bases are sequenced
or determined) is from inadvertent or unintentional sequencing
errors. Such sequencing errors can be inherent to certain
sequencing processes and include, for example, incorrect calls for
nucleobase identity, and missing or extra nucleobases. Thus, for
partial nucleotide sequence reads herein, certain information about
the sequence is often deliberately excluded. That is, one
deliberately obtains sequence information with respect to less than
all of the nucleobases or which might otherwise be characterized as
or be a sequencing error. In some embodiments, a partial nucleotide
sequence read can span a portion of a nucleic acid fragment. In
some embodiments, a partial nucleotide sequence read can span the
entire length of a nucleic acid fragment.
[0258] A non-limiting example of a partial nucleotide sequence read
is a sequence where the nucleotide species at less than all
positions present in the nucleic acid are determined. Another
non-limiting example is a sequence in which a subset of nucleobase
species in the nucleic acid are determined. As used herein,
"nucleobase species" refers to a type of nucleobase present in a
sample nucleic acid. Non-modified nucleobase species typically
include adenine (A), guanine (G), cytosine (C), thymine (T),
methyl-cytosine (met-C), uracil (U, for RNA) or inosine (I). As
used herein, "relative positional information" refers to the
location of a nucleobase within a nucleotide sequence in relation
to surrounding known nucleobases and/or nucleobase gaps. As used
herein, "nucleobase gaps" refers to one or more nucleobase
positions where the identity of the nucleobase at each position is
unknown. Such gaps exist between known nucleobases in the sequence
read, and can be, independently, 1 to 100 sequential nucleobase
positions (e.g., 5, 10, 20, 30, 40, 50, 60, 70, 80, or 90
sequential nucleobase positions). In some cases, one nucleotide
species of a sequence read is decipherable while the remaining
nucleotide species are indeterminate. For example, a partial
nucleotide sequence read in which only the adenine nucleobases are
identified may contain nucleobase gaps of varying length (e.g.
A.sub.----A_A.sub.------A). In the example sequence, the gaps
comprise 2, 1, and 3 nucleobase positions. Such reads may sometimes
be referred to as unary sequence reads (i.e., one nucleotide
species is known at known positions and the other positions can be
any one of three other nucleotides). In some cases, two or three
nucleobase species of a sequence read are decipherable while the
remaining nucleotide species are indeterminate (i.e., two or three
nucleotide species are known at known positions and the other
positions can be any one of three other nucleotide species).
[0259] Another non-limiting example of a partial nucleotide
sequence read is a nucleic acid for which the composition of all or
some nucleobase species is determined (i.e., base composition), but
the order and relative positions of the nucleobases is not
determined. For example, a nucleic acid may be digested e.g., by an
endonuclease, into individual nucleobases prior to sequencing. In
such cases, the number of nucleobases present in the nucleic acid
can be determined for each nucleobase species (e.g., A, G, T, C,
met-C, I, U) by an appropriate method (e.g., mass spectrometry;
nanopore sequencing). For example, a nucleic acid may contain 30%
A, 30% T, 20% G, and 20% C. Such partial nucleotide sequence reads
can be mapped to one or more locations in a reference genome that
comprise similar nucleobase compositions. In some cases, knowledge
of some nucleotide dispersion within a given sequence read is
combined with base composition data. In some cases, information
pertaining to the variance, skewness and/or kurtosis of the
nucleotide bases may provide further information pertaining to the
uniqueness (e.g., mappability, identity) of a sequence.
[0260] Another non-limiting example of a partial nucleotide
sequence read is a sequence where the nucleobase class (but not
exact nucleobase identity) is known throughout all or part of the
sequence read. As used herein, "nucleobase class" refers to a
grouping of a nucleobase species subset present in the sample
nucleic acid such as, for example, purines (Pu; e.g. A, G) and
pyrimidines (Py; e.g. T, C). An example of a partial nucleotide
sequence read comprising one or more nucleobase classes is:
Pu-Pu-Py-Pu-Py-Py-Pu-Pu (i.e.
purine-purine-pyrimidine-purine-pyrimidine-pyrimidine-purine-purine).
This example sequence read would represent a nucleotide sequence
comprising the following nucleobase possibilities for each
position: (A or G)-(A or G)-(T or C)-(A or G)-(T or C)-(T or C)-(A
or G)-(A or G). Partial nucleotide sequence reads comprising one or
more nucleobase classes can be contiguous reads of nucleobase
classes or can contain nucleobase gaps, as described above. Such
reads sometimes are referred to as binary sequence reads or binary
partial reads (e.g., a first nucleotide class consisting of two
possible bases is known at known positions and a second nucleotide
class consisting of two possible bases is known at known positions,
where the bases of the first nucleotide class are different than
the bases of the second nucleotide class).
[0261] In some embodiments, partial nucleotide sequence reads
contain relative positional information for one or more nucleobase
species. In some embodiments, the partial nucleotide sequence reads
can contain relative positional information for one or more, but
not all, of adenine (A), guanine (G), cytosine (C), thymine (T),
methyl-cytosine (met-C), or inosine (I). In some cases, the partial
nucleotide sequence reads contain relative positional information
for adenine. In some cases, the partial nucleotide sequence reads
contain relative positional information for guanine. In some cases,
the partial nucleotide sequence reads contain relative positional
information for cytosine. In some cases, the partial nucleotide
sequence reads contain relative positional information for thymine.
In some cases, the partial nucleotide sequence reads contain
relative positional information for methyl-cytosine.
[0262] In some embodiments, partial nucleotide sequence reads
contain relative positional information for two or more nucleobase
species. In some embodiments, the partial nucleotide sequence reads
contain relative positional information for two or more nucleobase
species which can include adenine (A), guanine (G), cytosine (C),
thymine (T) and/or methyl-cytosine (met-C). In some embodiments,
the partial nucleotide sequence reads contain relative positional
information for two nucleobase species which can include adenine
(A), guanine (G), cytosine (C), thymine (T) and/or methyl-cytosine
(met-C). In some embodiments, the partial nucleotide sequence reads
contain relative positional information for three nucleobase
species which can include adenine (A), guanine (G), cytosine (C),
thymine (T) and/or methyl-cytosine (met-C). In some embodiments,
the partial nucleotide sequence reads contain relative positional
information for four nucleobase species which can include adenine
(A), guanine (G), cytosine (C), thymine (T) and/or methyl-cytosine
(met-C). In some cases, partial nucleotide sequence reads are
ternary partial reads (e.g., a first nucleotide species is known at
known positions, a second nucleotide species is known at other
known positions and the other positions are any one of two
nucleotide species other than the first nucleotide species and the
second nucleotide species).
[0263] Nucleobase Classes
[0264] In some embodiments, partial nucleotide sequence reads
contain one or more nucleobase classes. Such nucleobase classes
contain a subset of nucleobases present in the sample nucleic acid.
The subset of nucleobases can be any designation or combination of
nucleobases known in the art. For example, a nucleobase class can
be a designation of purine nucleobases (e.g. adenine, guanine) or
pyrimidine nucleobases (e.g. thymine, cytosine, uracil). Thus a
partial nucleotide sequence read may contain information about the
purine and/or pyrimidine content, and often includes relative
positional information for each purine and/or pyrimidine class. In
some cases, nanopore sequencing can be used to identify the
relative positions of purines and/or pyrimidines in a sample
nucleic acid to generate partial nucleotide sequence reads. Other
examples of nucleobase classes include, without limitation, groups
of adjacent and consecutive nucleobases (e.g., nucleobase dimers,
trimers, multimers), and nucleobase pair species in a duplex
nucleic acid. In some embodiments, nucleobase classes can be
homodimers, heterodimers, homotrimers, heterotrimers,
homomultimers, or heteromultimers. For example, a nucleobase class
can be a homodimer (i.e. two identical adjacent nucleobases) such
as, for example, AA, TT, CC, GG; or a heterodimer (i.e. two
different adjacent nucleobases), such as, for example AT, AG, AC,
TA, TG, TC, CG, CA, CT, GT, GA, GC. Such homodimers and/or
heterodimers, for example, can possess distinguishable
characteristics that can be deciphered by any sequencing technology
suitable to identify such nucleobase classes to generate partial
nucleotide sequence reads. In some embodiments, the nucleobase
class is one or more nucleobase pair species in a duplex nucleic
acid. Such nucleobase pair species are typically the Watson and
Crick nucleobase pairs found in double stranded nucleic acid (i.e.
A-T and G-C pairs). In some cases, the identity of each nucleobase
pair is determined for a length of duplex DNA, but the orientation
(e.g., A-T vs. T-A) is not. Such pairs can each possess their own
characteristics that can be deciphered with any sequencing
technology suitable to identify such nucleobase classes and
generate partial nucleotide sequence reads.
[0265] Labeled Nucleobase Species or Class
[0266] In some embodiments, the nucleic acid to be sequenced can
contain one or more nucleobase species and/or nucleobase classes
which are in a form capable of interacting with an agent or station
in an apparatus to produce a signal characteristic of that
interaction. In such cases, the nucleobase species or class is
considered as labeled. Some of the sequencing technologies in the
art make use of labeled nucleobases to generate nucleotide sequence
reads. In some embodiments, sequencing technologies in the art make
use of one or more labeled nucleobase species or nucleobase classes
to generate partial nucleotide sequence reads. Such labeling can be
instrinsic or extrinsic, as described in further detail below.
[0267] If a native nucleobase species or class can undergo an
interaction to produce a characteristic signal, then the nucleic
acid is considered as intrinsically labeled. In such embodiments,
it is not necessary that an extrinsic label be added to the nucleic
acid. If a non-native molecule, however, is attached to a native
nucleobase species or class to generate an interaction that
produces the characteristic signal, then the nucleic acid is
considered as extrinsically labeled. In some embodiments, the
"label" can be, for example, light emitting, light absorbing, light
diffracting, light scattering, energy accepting, energy absorbing,
fluorescent, radioactive, or quenching. Non-limiting examples of
"labels" are provided hereafter.
[0268] Many naturally occurring units of a nucleic acid are light
emitting compounds or quenchers. For instance, nucleobases of
native nucleic acid molecules have distinct absorption spectra,
e.g., A, G, T, C, and U have absorption maximums at 259 nm, 252 nm,
267 nm, 271 nm, and 258 nm respectively. Modified nucleobases which
include intrinsic labels also may be incorporated into nucleic
acids. A nucleic acid molecule may include, for example, any of the
following modified nucleobases which have characteristic energy
emission patterns of a light emitting compound or a quenching
compound: 2,4-dithiouracil, 2,4-Diselenouracil, hypoxanthine,
mercaptopurine, 2-aminopurine, and selenopurine.
[0269] A nucleobase also may be considered as intrinsically labeled
when a property of the nucleobase, other than a light emitting,
quenching or radioactive property, provides information about the
identity of the nucleobase without the addition of an extrinsic
label, in certain embodiments. For example, the shape and charge of
the nucleobase provides information about the nucleobase species
which can result in a specific characteristic signal, such as a
change in conductance arising from the blockage of a conductance
path by the nucleobase, which can, for example, be detected and
deciphered using a nanopore sequencing technology.
[0270] In some embodiments, one or more nucleobase species or
nucleobase classes can be extrinsically labeled. In some cases, a
nucleobase species or nucleobase class is labeled with one or more
detectable labels. In some cases, each nucleobase species or
nucleobase class is labeled with a unique detectable label. In some
embodiments, one or more nucleobase species or nucleobase classes
are each labeled with a unique detectable label and one or more
nucleobase species or nucleobase classes are unlabeled. Examples of
detectable labels include, without limitation, sugars, peptides,
proteins, antibodies, chemical compounds, conducting polymers,
binding moieties such as biotin, mass tags, colorimetric agents,
light emitting agents, chemiluminescent agents, light scattering
agents, fluorescent tags, radioactive tags, charge tags (electrical
or magnetic charge), volatile tags and hydrophobic tags,
biomolecules (e.g., members of a binding pair antibody/antigen,
antibody/antibody, antibody/antibody fragment, antibody/antibody
receptor, antibody/protein A or protein G, hapten/anti-hapten,
biotin/avidin, biotin/streptavidin, folic acid/folate binding
protein, vitamin B12/intrinsic factor, chemical reactive
group/complementary chemical reactive group (e.g.,
sulfhydryl/maleimide, sulfhydryl/haloacetyl derivative,
amine/isotriocyanate, amine/succinimidyl ester, and amine/sulfonyl
halides) and the like.
[0271] Hybridized Probe Species
[0272] Hybridizing one or more probes to a sample nucleic acid can
facilitate determining relative positional information for
nucleobase species and/or classes in partial nucleotide sequence
reads. In some embodiments, one or more probe or oligonucleotide
species can be hybridized to the sample nucleic acid. Such
hybridization often occurs prior to sequencing, in some
embodiments. As used herein, a probe or oligonucleotide "species"
refers to a first probe or oligonucleotide having a nucleotide
sequence that differs by one nucleotide base or more from the
nucleotide sequence of a second probe or oligonucleotide when the
nucleotide sequences of the first and second probes or
oligonucleotides are aligned. Thus one probe or oligonucleotide
species may differ from a second probe or oligonucleotide species
by one or more nucleotides when the nucleotide sequences of the
first and second probe or oligonucleotide are aligned with one
another (e.g., about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45,
50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 or more than 100
nucleotide differences).
[0273] In certain cases, the obtained partial nucleotide sequence
reads can contain relative positional information for sequences
complementary to the probe or oligonucleotide species. Probes can
be of any length and sequence suitable for obtaining partial
nucleotide sequence reads. In some cases, a probe is at least 2 to
about 30 nucleotides in length. For example, a probe can be 2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, or 30 nucleotides in length. Any
number of probe species and any combination of probe species with
various lengths and/or sequences can be used to generate partial
sequence reads. In some embodiments, random probes are used. In
some embodiments, probes with specific sequences identical and/or
complementary to certain genome sections are used. In certain
embodiments where nanopore sequencing is used to generate partial
nucleotide sequence reads, a probe or oligonucleotide species may
contain a signal-generating moiety that hybridizes to a sample
nucleic acid and alters the passage of the nucleic acid through a
nanopore, and/or generates a signal when released from the target
nucleic acid when it passes through the nanopore.
[0274] Full Sequence Reads
[0275] In some embodiments, full nucleotide sequence reads are
obtained in addition to or in lieu of the partial nucleotide
sequence reads described above. Such "full" nucleotide sequence
reads can be of any length and substantially contain no nucleobase
gaps between identified nucleobases or a nucleobase class. Full
nucleotide sequence reads can contain all nucleobases, all
nucleobase classes, or a combination thereof. Thus, a full
nucleotide sequence read generally is a run of continuous
nucleobases where each nucleobase is identified (i.e. as A, G, C,
T, etc.).
[0276] Sequencing Module
[0277] Sequencing and obtaining sequencing reads can be provided by
a sequencing module or by an apparatus comprising a sequencing
module. A "sequence receiving module" as used herein is the same as
a "sequencing module". An apparatus comprising a sequencing module
can be any apparatus that determines the sequence of a nucleic acid
from a sequencing technology known in the art. In certain
embodiments, an apparatus comprising a sequencing module performs a
sequencing reaction known in the art. A sequencing module generally
provides a nucleic acid sequence read according to data from a
sequencing reaction (e.g., signals generated from a sequencing
apparatus). In some embodiments, a sequencing module or an
apparatus comprising a sequencing module is required to provide
sequencing reads. In some embodiments a sequencing module can
receive, obtain, access or recover sequence reads from another
sequencing module, computer peripheral, operator, server, hard
drive, apparatus or from a suitable source. Sometimes a sequencing
module can manipulate sequence reads. For example, a sequencing
module can align, assemble, fragment, complement, reverse
complement, error check, or error correct sequence reads. An
apparatus comprising a sequencing module can comprise at least one
processor. In some embodiments, sequencing reads are provided by an
apparatus that includes a processor (e.g., one or more processors)
which processor can perform and/or implement one or more
instructions (e.g., processes, routines and/or subroutines) from
the sequencing module. In some embodiments, sequencing reads are
provided by an apparatus that includes multiple processors, such as
processors coordinated and working in parallel. In some
embodiments, a sequencing module operates with one or more external
processors (e.g., an internal or external network, server, storage
device and/or storage network (e.g., a cloud)). Sometimes a
sequencing module gathers, assembles and/or receives data and/or
information from another module, apparatus, peripheral, component
or specialized component (e.g., a sequencer). In some embodiments,
sequencing reads are provided by an apparatus comprising one or
more of the following: one or more flow cells, a camera, a photo
detector, a photo cell, fluid handling components, a printer, a
display (e.g., an LED, LCT or CRT) and the like. Often a sequencing
module receives, gathers and/or assembles sequence reads. Sometimes
a sequencing module accepts and gathers input data and/or
information from an operator of an apparatus. For example,
sometimes an operator of an apparatus provides instructions, a
constant, a threshold value, a formula or a predetermined value to
a module. Sometimes a sequencing module can transform data and/or
information that it receives into a contiguous nucleic acid
sequence. In some embodiments, a nucleic acid sequence provided by
a sequencing module is printed or displayed. In some embodiments,
sequence reads are provided by a sequencing module and transferred
from a sequencing module to an apparatus or an apparatus comprising
any suitable peripheral, component or specialized component. In
some embodiments, data and/or information are provided from a
sequencing module to an apparatus that includes multiple
processors, such as processors coordinated and working in parallel.
In some cases, data and/or information related to sequence reads
can be transferred from a sequencing module to any other suitable
module. A sequencing module can transfer sequence reads to a
mapping module or counting module, in some embodiments.
[0278] Mapping Reads
[0279] Mapping nucleotide sequence reads (i.e., sequence
information from a fragment whose physical genomic position is
unknown) can be performed in a number of ways, and often comprises
alignment of the obtained sequence reads with a matching sequence
in a reference genome (e.g., Li et al., "Mapping short DNA
sequencing reads and calling variants using mapping quality score,"
Genome Res., 2008 Aug. 19). In such alignments, sequence reads
generally are aligned to a reference sequence and those that align
are designated as being "mapped" or a "sequence tag." In some
cases, a mapped sequence read is referred to as a "hit" or a
"count". In some embodiments, mapped sequence reads are grouped
together according to various parameters and assigned to particular
genomic sections, which are discussed in further detail below.
[0280] As used herein, the terms "aligned", "alignment", or
"aligning" refer to two or more nucleic acid sequences that can be
identified as a match (e.g., 100% identity) or partial match.
Alignments can be done manually or by a computer algorithm,
examples including the Efficient Local Alignment of Nucleotide Data
(ELAND) computer program distributed as part of the Illumina
Genomics Analysis pipeline. The alignment of a sequence read can be
a 100% sequence match. In come cases, an alignment is less than a
100% sequence match (i.e., non-perfect match, partial match,
partial alignment). In some embodiments an alignment is about a
99%, 98%, 97%, 96%, 95%, 94%, 93%, 92%, 91%, 90%, 89%, 88%, 87%,
86%, 85%, 84%, 83%, 82%, 81%, 80%, 79%, 78%, 77%, 76% or 75% match.
In some embodiments, an alignment comprises a mismatch. In some
embodiments, an alignment comprises 1, 2, 3, 4 or 5 mismatches. Two
or more sequences can be aligned using either strand. In some cases
a nucleic acid sequence is aligned with the reverse complement of
another nucleic acid sequence.
[0281] Various computational methods can be used to map each
sequence read to a genomic section. Non-limiting examples of
computer algorithms that can be used to align sequences include,
without limitation, BLAST, BLITZ, FASTA, BOWTIE 1, BOWTIE 2, ELAND,
MAQ, PROBEMATCH, SOAP or SEQMAP, or variations thereof or
combinations thereof. In some embodiments, sequence reads can be
aligned with sequences in a reference genome. In some embodiments,
the sequence reads can be found and/or aligned with sequences in
nucleic acid databases known in the art including, for example, Gen
Bank, dbEST, dbSTS, EMBL (European Molecular Biology Laboratory)
and DDBJ (DNA Databank of Japan). BLAST or similar tools can be
used to search the identified sequences against a sequence
database. Search hits can then be used to sort the identified
sequences into appropriate genomic sections (described hereafter),
for example.
[0282] The term "sequence tag" is herein used interchangeably with
the term "mapped sequence tag" to refer to a sequence read that has
been specifically assigned i.e. mapped, to a larger sequence e.g. a
reference genome, by alignment. Mapped sequence tags are uniquely
mapped to a reference genome i.e. they are assigned to a single
location to the reference genome. Tags that can be mapped to more
than one location on a reference genome i.e. tags that do not map
uniquely, are not included in the analysis. A "sequence tag" can be
a nucleic acid (e.g. DNA) sequence (i.e. read) assigned
specifically to a particular genomic section and/or chromosome
(i.e. one of chromosomes 1-22, X or Y for a human subject). A
sequence tag may be repetitive or non-repetitive within a single
segment of the reference genome (e.g., a chromosome). In some
embodiments, repetitive sequence tags are eliminated from further
analysis (e.g. quantification). In some embodiments, a read may
uniquely or non-uniquely map to portions in the reference genome. A
read is considered to be "uniquely mapped" if it aligns with a
single sequence in the reference genome. A read is considered to be
"non-uniquely mapped" if it aligns with two or more sequences in
the reference genome. In some embodiments, non-uniquely mapped
reads are eliminated from further analysis (e.g. quantification). A
certain, small degree of mismatch (0-1) may be allowed to account
for single nucleotide polymorphisms that may exist between the
reference genome and the reads from individual samples being
mapped, in certain embodiments. In some embodiments, no degree of
mismatch is allowed for a read to be mapped to a reference
sequence.
[0283] As used herein, the term "reference genome" can refer to any
particular known, sequenced or characterized genome, whether
partial or complete, of any organism or virus which may be used to
reference identified sequences from a subject. For example, a
reference genome used for human subjects as well as many other
organisms can be found at the National Center for Biotechnology
Information at www.ncbi.nlm.nih.gov. A "genome" refers to the
complete genetic information of an organism or virus, expressed in
nucleic acid sequences. As used herein, a reference sequence or
reference genome often is an assembled or partially assembled
genomic sequence from an individual or multiple individuals. In
some embodiments, a reference genome is an assembled or partially
assembled genomic sequence from one or more human individuals. In
some embodiments, a reference genome comprises sequences assigned
to chromosomes.
[0284] In certain embodiments, where a sample nucleic acid is from
a pregnant female, a reference sequence sometimes is not from the
fetus, the mother of the fetus or the father of the fetus, and is
referred to herein as an "external reference." A maternal reference
may be prepared and used in some embodiments. When a reference from
the pregnant female is prepared ("maternal reference sequence")
based on an external reference, reads from DNA of the pregnant
female that contains substantially no fetal DNA often are mapped to
the external reference sequence and assembled. In certain
embodiments the external reference is from DNA of an individual
having substantially the same ethnicity as the pregnant female. A
maternal reference sequence may not completely cover the maternal
genomic DNA (e.g., it may cover about 50%, 60%, 70%, 80%, 90% or
more of the maternal genomic DNA), and the maternal reference may
not perfectly match the maternal genomic DNA sequence (e.g., the
maternal reference sequence may include multiple mismatches).
[0285] In some cases, mappability is assessed for a genomic region
(e.g., genomic section, genomic portion, bin). Mappability is the
ability to unambiguously align a nucleotide sequence read to a
portion of a reference genome, typically up to a specified number
of mismatches, including, for example, 0, 1, 2 or more mismatches.
For a given genomic region, the expected mappability can be
estimated using a sliding-window approach of a preset read length
and averaging the resulting read-level mappability values. Genomic
regions comprising stretches of unique nucleotide sequence
sometimes have a high mappability value.
[0286] Mapping Partial Sequence Reads
[0287] In some embodiments, partial nucleotide sequence reads are
mapped to a reference genome using methods described above. In such
cases, the information contained in the partial nucleotide sequence
read is sufficient to map the read to one or more locations in a
reference genome. In some cases, the information contained in the
partial nucleotide sequence read is sufficient to map the read to
one specific location in a reference genome. Such a location in a
reference genome is often part or all of a genome section as
described above. The information contained in the partial
nucleotide sequence read can be a certain number of nucleobase or
nucleobase class identities, or can be nucleobase or nucleobase
class identities over a specific length of read, as discussed in
further detail below.
[0288] In some embodiments, partial nucleotide sequence reads
contain a number of discrete position identities sufficient to map
to a reference genome section. For example, certain genomic
sequences have a distinct pattern of nucleobases or nucleobase
classes, such that a specific minimum number of one or more
nucleobases must be known to uniquely match a partial nucleotide
sequence read to such a genomic sequence. The minimum number of
position identities for each nucleobase that is required for a
positive and unique match can be calculated, often varies from
sequence to sequence, and can vary among nucleobases for a given
sequence. The minimum number of position identities also varies,
for example, depending on whether 1, 2, 3, or 4 of the nucleobase
species or a certain number of nucleobase classes are identified in
the partial nucleotide sequence read.
[0289] In some embodiments, a partial nucleotide sequence read is
of sufficient length to map to a reference genome section. As used
herein, the length of a partial nucleotide sequence read refers to
the length of sequence inclusive of identified nucleobases or
nucleobase classes and unidentified nucleobase positions (i.e.
positions located in one or more nucleotide sequence gaps). For
example, certain genomic sequences have a distinct pattern of
nucleobases or nucleobase classes, such that a specific length of
sequence must be known to uniquely match a partial nucleotide
sequence read to such a genomic sequence. The minimum length for
each partial nucleotide sequence read that is required for a
positive and unique match can be calculated and often varies from
sequence to sequence and can vary depending on which of the
nucleobase species or nucleobase classes are identified. The
minimum length of sequence read also varies, for example, depending
on whether 1, 2, 3, or 4 of the nucleobase species are identified
in the read. In some embodiments, the partial nucleotide sequence
read length is at least about 10 nucleobases (e.g., nucleobase
positions) to about 150 nucleobases (e.g., nucleobase positions).
In some embodiments, the partial nucleotide sequence read length is
at least about 15 nucleobases to about 40 nucleobases. For example,
a partial nucleotide sequence read length can be at least about 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38 or 39 nucleobases (e.g., nucleobase positions).
In some embodiments, the partial nucleotide sequence read length is
at least about 50 nucleobases to about 60 nucleobases. In some
embodiments, the partial nucleotide sequence read length is at
least about 70 nucleobases to about 80 nucleobases. In some
embodiments, the partial nucleotide sequence read length is at
least about 100 nucleobases to about 110 nucleobases. In some
embodiments, the partial nucleotide sequence read length is at
least about 140 nucleobases to about 150 nucleobases. In some
embodiments, the partial nucleotide sequence read length is about
20 nucleobases. In some embodiments, the partial nucleotide
sequence read length is about 30 nucleobases. In some embodiments,
the partial nucleotide sequence read length is about 36
nucleobases. In some embodiments, the partial nucleotide sequence
read length is about 54 nucleobases. In some embodiments, the
partial nucleotide sequence read length is about 72 nucleobases. In
some embodiments, the partial nucleotide sequence read length is
about 108 nucleobases. In some embodiments, the partial nucleotide
sequence read length is about 144 nucleobases.
[0290] In some embodiments, the minimum number of identified
nucleobase species or nucleobase classes, or the minimum length of
partial nucleotide sequence read, required for a positive and
unique match to a reference genome can vary depending on the size
of the reference genome. For example, a partial nucleotide sequence
read may be required to contain more information for obtaining a
positive and unique match to a full genome (e.g. human genome)
versus obtaining a positive and unique match to a partial genome
(i.e. a chromosome or portion thereof).
[0291] In some embodiments, the reference genome is an assembly of
partial sequence information, sometimes referred to herein as a
nucleotide barcode reference or nucleotide signature reference.
Such barcodes or signatures can be representations of a complete
reference genome, or portion thereof, where the complete nucleotide
sequence or sequences are known. In some cases, the barcodes or
signatures, and the corresponding sequences that they represent,
are stored in a database. In embodiments where partial nucleotide
sequence reads are mapped, the pattern of identified nucleobases or
nucleobase classes in each partial nucleotide sequence read can be
matched to a particular barcode or signature reference. Since the
barcode or signature reference often represents a known sequence in
a reference genome, the partial sequence reads can thus be mapped
to a particular locus or genome section in a reference genome.
[0292] Mapping Module
[0293] Sequence reads can be mapped by a mapping module or by an
apparatus comprising a mapping module, which mapping module
generally maps reads to a reference genome or segment thereof. A
mapping module can map sequencing reads by a suitable method known
in the art. In some embodiments, a mapping module or an apparatus
comprising a mapping module is required to provide mapped sequence
reads. An apparatus comprising a mapping module can comprise at
least one processor. In some embodiments, mapped sequencing reads
are provided by an apparatus that includes a processor (e.g., one
or more processors) which processor can perform and/or implement
one or more instructions (e.g., processes, routines and/or
subroutines) from the mapping module. In some embodiments,
sequencing reads are mapped by an apparatus that includes multiple
processors, such as processors coordinated and working in parallel.
In some embodiments, a mapping module operates with one or more
external processors (e.g., an internal or external network, server,
storage device and/or storage network (e.g., a cloud)). An
apparatus may comprise a mapping module and a sequencing module. In
some embodiments, sequence reads are mapped by an apparatus
comprising one or more of the following: one or more flow cells, a
camera, fluid handling components, a printer, a display (e.g., an
LED, LCT or CRT) and the like. A mapping module can receive
sequence reads from a sequencing module, in some embodiments.
Mapped sequencing reads can be transferred from a mapping module to
a counting module or a normalization module, in some
embodiments.
[0294] Genomic Sections
[0295] In some embodiments, mapped sequence reads (i.e. sequence
tags) are grouped together according to various parameters and
assigned to particular genomic sections. Often, the individual
mapped sequence reads can be used to identify an amount of a
genomic section present in a sample. In some embodiments, the
amount of a genomic section can be indicative of the amount of a
larger sequence (e.g. a chromosome) in the sample. The term
"genomic section" can also be referred to herein as a "sequence
window", "section", "bin", "locus", "region", "partition" or
"portion". In some embodiments, a genomic section is an entire
chromosome, segment of a chromosome, segment of a reference genome,
multiple chromosome portions, multiple chromosomes, portions from
multiple chromosomes, and/or combinations thereof. Sometimes a
genomic section is predefined based on specific parameters.
Sometimes a genomic section is arbitrarily defined based on
partitioning of a genome (e.g., partitioned by size, segments,
contiguous regions, contiguous regions of an arbitrarily defined
size, and the like). In some cases, a genomic section is delineated
based on one or more parameters which include, for example, length
or a particular feature or features of the sequence. Genomic
sections can be selected, filtered and/or removed from
consideration using any suitable criteria know in the art or
described herein. In some embodiments, a genomic section is based
on a particular length of genomic sequence. In some embodiments, a
method can include analysis of multiple mapped sequence reads to a
plurality of genomic sections. The genomic sections can be
approximately the same length or the genomic sections can be
different lengths. Sometimes genomic sections are of about equal
length. In some cases genomic sections of different lengths are
adjusted or weighted. In some embodiments, a genomic section is
about 10 kilobases (kb) to about 100 kb, about 20 kb to about 80
kb, about 30 kb to about 70 kb, about 40 kb to about 60 kb, and
sometimes about 50 kb. In some embodiments, a genomic section is
about 10 kb to about 20 kb. A genomic section is not limited to
contiguous runs of sequence. Thus, genomic sections can be made up
of contiguous and/or non-contiguous sequences. A genomic section is
not limited to a single chromosome. In some embodiments, a genomic
section includes all or part of one chromosome or all or part of
two or more chromosomes. In some cases, genomic sections may span
one, two, or more entire chromosomes. In addition, the genomic
sections may span joint or disjointed portions of multiple
chromosomes.
[0296] In some embodiments, genomic sections can be particular
chromosome segments in a chromosome of interest, such as, for
example, chromosomes where a genetic variation is assessed (e.g. an
aneuploidy of chromosomes 13, 18 and/or 21 or a sex chromosome). A
genomic section can also be a pathogenic genome (e.g. bacterial,
fungal or viral) or fragment thereof. Genomic sections can be
genes, gene fragments, regulatory sequences, introns, exons, and
the like.
[0297] In some embodiments, a genome (e.g. human genome) is
partitioned into genomic sections based on the information content
of the regions. The resulting genomic regions may contain sequences
for multiple chromosomes and/or may contain sequences for portions
of multiple chromosomes. In some cases, the partitioning may
eliminate similar locations across the genome and only keep unique
regions. The eliminated regions may be within a single chromosome
or may span multiple chromosomes. The resulting genome is thus
trimmed down and optimized for faster alignment, often allowing for
focus on uniquely identifiable sequences. In some cases, the
partitioning may down weight similar regions. The process for down
weighting a genomic section is discussed in further detail below.
In some embodiments, the partitioning of the genome into regions
transcending chromosomes may be based on information gain produced
in the context of classification. For example, the information
content may be quantified using the p-value profile measuring the
significance of particular genomic locations for distinguishing
between groups of confirmed normal and abnormal subjects (e.g.
euploid and trisomy subjects, respectively). In some embodiments,
the partitioning of the genome into regions transcending
chromosomes may be based on any other criterion, such as, for
example, speed/convenience while aligning tags, high or low GC
content, uniformity of GC content, other measures of sequence
content (e.g. fraction of individual nucleotides, fraction of
pyrimidines or purines, fraction of natural vs. non-natural nucleic
acids, fraction of methylated nucleotides, and CpG content),
methylation state, duplex melting temperature, amenability to
sequencing or PCR, uncertainty value assigned to individual bins,
and/or a targeted search for particular features.
[0298] Sequence Tag Density
[0299] "Sequence tag density" refers to the normalized value of
sequence tags or reads for a defined genomic section where the
sequence tag density is used for comparing different samples and
for subsequent analysis. The value of the sequence tag density
often is normalized within a sample. In some embodiments,
normalization can be performed by counting the number of tags
falling within each genomic section; obtaining a median value of
the total sequence tag count for each chromosome; obtaining a
median value of all of the autosomal values; and using this value
as a normalization constant to account for the differences in total
number of sequence tags obtained for different samples. A sequence
tag density sometimes is about 1 for a disomic chromosome. Sequence
tag densities can vary according to sequencing artifacts, most
notably G/C bias, which can be corrected by use of an external
standard or internal reference (e.g., derived from substantially
all of the sequence tags (genomic sequences), which may be, for
example, a single chromosome or a calculated value from all
autosomes, in some embodiments). Thus, dosage imbalance of a
chromosome or chromosomal regions can be inferred from the
percentage representation of the locus among other mappable
sequenced tags of the specimen. Dosage imbalance of a particular
chromosome or chromosomal regions therefore can be quantitatively
determined and be normalized. Methods for sequence tag density
normalization and quantification are discussed in further detail
below.
[0300] In some embodiments, a proportion of all of the sequence
reads are from a chromosome involved in an aneuploidy (e.g.,
chromosome 13, chromosome 18, chromosome 21), and other sequence
reads are from other chromosomes. By taking into account the
relative size of the chromosome involved in the aneuploidy (e.g.,
"target chromosome": chromosome 21) compared to other chromosomes,
one could obtain a normalized frequency, within a reference range,
of target chromosome-specific sequences, in some embodiments. If
the fetus has an aneuploidy in a target chromosome, then the
normalized frequency of the target chromosome-derived sequences is
statistically greater than the normalized frequency of non-target
chromosome-derived sequences, thus allowing the detection of the
aneuploidy. The degree of change in the normalized frequency will
be dependent on the fractional concentration of fetal nucleic acids
in the analyzed sample, in some embodiments.
[0301] Counts
[0302] Sequence reads that are mapped or partitioned based on a
selected feature or variable can be quantified to determine the
number of reads that are mapped to a genomic section (e.g., bin,
partition, genomic portion, portion of a reference genome, portion
of a chromosome and the like), in some embodiments. Sometimes the
quantity of sequence reads that are mapped to a genomic section are
termed counts (e.g., a count). Often a count is associated with a
genomic section. Sometimes counts for two or more genomic sections
(e.g., a set of genomic sections) are mathematically manipulated
(e.g., averaged, added, normalized, the like or a combination
thereof). In some embodiments a count is determined from some or
all of the sequence reads mapped to (i.e., associated with) a
genomic section. In certain embodiments, a count is determined from
a pre-defined subset of mapped sequence reads. Pre-defined subsets
of mapped sequence reads can be defined or selected utilizing any
suitable feature or variable. In some embodiments, pre-defined
subsets of mapped sequence reads can include from 1 to n sequence
reads, where n represents a number equal to the sum of all sequence
reads generated from a test subject or reference subject
sample.
[0303] Sometimes a count is derived from sequence reads that are
processed or manipulated by a suitable method, operation or
mathematical process known in the art. Sometimes a count is derived
from sequence reads associated with a genomic section where some or
all of the sequence reads are weighted, removed, filtered,
normalized, adjusted, averaged, derived as a mean, added, or
subtracted or processed by a combination thereof. In some
embodiments, a count is derived from raw sequence reads and or
filtered sequence reads. A count (e.g., counts) can be determined
by a suitable method, operation or mathematical process. Sometimes
a count value is determined by a mathematical process. Sometimes a
count value is an average, mean or sum of sequence reads mapped to
a genomic section. Often a count is a mean number of counts. In
some embodiments, a count is associated with an uncertainty value.
Counts can be processed (e.g., normalized) by a method known in the
art and/or as described herein (e.g., bin-wise normalization,
normalization by GC content, linear and nonlinear least squares
regression, GC LOESS, LOWESS, PERUN, RM, GCRM, cQn and/or
combinations thereof).
[0304] Counts (e.g., raw, filtered and/or normalized counts) can be
processed and normalized to one or more elevations. Elevations and
profiles are described in greater detail hereafter. Sometimes
counts can be processed and/or normalized to a reference elevation.
Reference elevations are addressed later herein. Counts processed
according to an elevation (e.g., processed counts) can be
associated with an uncertainty value (e.g., a calculated variance,
an error, standard deviation, p-value, mean absolute deviation,
etc.). An uncertainty value typically defines a range above and
below an elevation. A value for deviation can be used in place of
an uncertainty value, and non-limiting examples of measures of
deviation include standard deviation, average absolute deviation,
median absolute deviation, standard score (e.g., Z-score, Z-value,
normal score, standardized variable) and the like.
[0305] Counts are often obtained from a nucleic acid sample from a
pregnant female bearing a fetus. Counts of nucleic acid sequence
reads mapped to a genomic section often are counts representative
of both the fetus and the mother of the fetus (e.g., a pregnant
female subject). Sometimes some of the counts mapped to a genomic
section are from a fetal genome and some of the counts mapped to
the same genomic section are from the maternal genome.
[0306] Counting Module
[0307] Counts can be provided by a counting module or by an
apparatus comprising a counting module. A counting module can
determine, assemble, and/or display counts according to a counting
method known in the art. A counting module generally determines or
assembles counts according to counting methodology known in the
art. In some embodiments, a counting module or an apparatus
comprising a counting module is required to provide counts. An
apparatus comprising a counting module can comprise at least one
processor. In some embodiments, counts are provided by an apparatus
that includes a processor (e.g., one or more processors) which
processor can perform and/or implement one or more instructions
(e.g., processes, routines and/or subroutines) from the counting
module. In some embodiments, reads are counted by an apparatus that
includes multiple processors, such as processors coordinated and
working in parallel. In some embodiments, a counting module
operates with one or more external processors (e.g., an internal or
external network, server, storage device and/or storage network
(e.g., a cloud)). In some embodiments, reads are counted by an
apparatus comprising one or more of the following: a sequencing
module, a mapping module, one or more flow cells, a camera, fluid
handling components, a printer, a display (e.g., an LED, LCT or
CRT) and the like. A counting module can receive data and/or
information from a sequencing module and/or a mapping module,
transform the data and/or information and provide counts (e.g.,
counts mapped to genomic sections). A counting module can receive
mapped sequence reads from a mapping module. A counting module can
receive normalized mapped sequence reads from a mapping module or
from a normalization module. A counting module can transfer data
and/or information related to counts (e.g., counts, assembled
counts and/or displays of counts) to any other suitable apparatus,
peripheral, or module. Sometimes data and/or information related to
counts are transferred from a counting module to a normalization
module, a plotting module, a categorization module and/or an
outcome module.
[0308] Data Processing
[0309] Mapped sequence reads that have been counted are referred to
herein as raw data, since the data represents unmanipulated counts
(e.g., raw counts). In some embodiments, sequence read data in a
data set can be processed further (e.g., mathematically and/or
statistically manipulated) and/or displayed to facilitate providing
an outcome. In some cases, processed counts can be referred to as a
derivative of counts, and may include for example, normalized
counts, levels, elevations, profiles, and the like. In certain
embodiments, data sets, including larger data sets, may benefit
from pre-processing to facilitate further analysis. Pre-processing
of data sets sometimes involves removal of redundant and/or
uninformative genomic sections or bins (e.g., bins with
uninformative data, redundant mapped reads, genomic sections or
bins with zero median counts, over represented or under represented
sequences). Without being limited by theory, data processing and/or
preprocessing may (i) remove noisy data, (ii) remove uninformative
data, (iii) remove redundant data, (iv) reduce the complexity of
larger data sets, and/or (v) facilitate transformation of the data
from one form into one or more other forms. The terms
"pre-processing" and "processing" when utilized with respect to
data or data sets are collectively referred to herein as
"processing". Processing can render data more amenable to further
analysis, and can generate an outcome in some embodiments.
[0310] The term "noisy data" as used herein refers to (a) data that
has a significant variance between data points when analyzed or
plotted, (b) data that has a significant standard deviation (e.g.,
greater than 3 standard deviations), (c) data that has a
significant standard error of the mean, the like, and combinations
of the foregoing. Noisy data sometimes occurs due to the quantity
and/or quality of starting material (e.g., nucleic acid sample),
and sometimes occurs as part of processes for preparing or
replicating DNA used to generate sequence reads. In certain
embodiments, noise results from certain sequences being over
represented when prepared using PCR-based methods.
[0311] Methods described herein can reduce or eliminate the
contribution of noisy data, and therefore reduce the effect of
noisy data on the provided outcome.
[0312] The terms "uninformative data", "uninformative bins", and
"uninformative genomic sections" as used herein refer to genomic
sections, or data derived therefrom, having a numerical value that
is significantly different from a predetermined threshold value or
falls outside a predetermined cutoff range of values. The terms
"threshold" and "threshold value" herein refer to any number that
is calculated using a qualifying data set and serves as a limit of
diagnosis of a genetic variation (e.g. a copy number variation, an
aneuploidy, a chromosomal aberration, and the like). Sometimes a
threshold is exceeded by results obtained by methods described
herein and a subject is diagnosed with a genetic variation (e.g.
trisomy 21). A threshold value or range of values often is
calculated by mathematically and/or statistically manipulating
sequence read data (e.g., from a reference and/or subject), in some
embodiments, and in certain embodiments, sequence read data
manipulated to generate a threshold value or range of values is
sequence read data (e.g., from a reference and/or subject). In some
embodiments, an uncertainty value is determined. An uncertainty
value generally is a measure of variance or error and can be any
suitable measure of variance or error. An uncertainty value can be
a standard deviation, standard error, calculated variance, p-value,
or mean absolute deviation (MAD), in some embodiments. In some
embodiments an uncertainty value can be calculated according to a
formula in Example 6.
[0313] Any suitable procedure can be utilized for processing data
sets described herein. Non-limiting examples of procedures suitable
for use for processing data sets include filtering, normalizing,
weighting, monitoring peak heights, monitoring peak areas,
monitoring peak edges, determining area ratios, mathematical
processing of data, statistical processing of data, application of
statistical algorithms, analysis with fixed variables, analysis
with optimized variables, plotting data to identify patterns or
trends for additional processing, the like and combinations of the
foregoing. In some embodiments, data sets are processed based on
various features (e.g., GC content, redundant mapped reads,
centromere regions, telomere regions, the like and combinations
thereof) and/or variables (e.g., fetal gender, maternal age,
maternal ploidy, percent contribution of fetal nucleic acid, the
like or combinations thereof). In certain embodiments, processing
data sets as described herein can reduce the complexity and/or
dimensionality of large and/or complex data sets. A non-limiting
example of a complex data set includes sequence read data generated
from one or more test subjects and a plurality of reference
subjects of different ages and ethnic backgrounds. In some
embodiments, data sets can include from thousands to millions of
sequence reads for each test and/or reference subject.
[0314] Data processing can be performed in any number of steps, in
certain embodiments. For example, data may be processed using only
a single processing procedure in some embodiments, and in certain
embodiments data may be processed using 1 or more, 5 or more, 10 or
more or 20 or more processing steps (e.g., 1 or more processing
steps, 2 or more processing steps, 3 or more processing steps, 4 or
more processing steps, 5 or more processing steps, 6 or more
processing steps, 7 or more processing steps, 8 or more processing
steps, 9 or more processing steps, 10 or more processing steps, 11
or more processing steps, 12 or more processing steps, 13 or more
processing steps, 14 or more processing steps, 15 or more
processing steps, 16 or more processing steps, 17 or more
processing steps, 18 or more processing steps, 19 or more
processing steps, or 20 or more processing steps). In some
embodiments, processing steps may be the same step repeated two or
more times (e.g., filtering two or more times, normalizing two or
more times), and in certain embodiments, processing steps may be
two or more different processing steps (e.g., filtering,
normalizing; normalizing, monitoring peak heights and edges;
filtering, normalizing, normalizing to a reference, statistical
manipulation to determine p-values, and the like), carried out
simultaneously or sequentially. In some embodiments, any suitable
number and/or combination of the same or different processing steps
can be utilized to process sequence read data to facilitate
providing an outcome. In certain embodiments, processing data sets
by the criteria described herein may reduce the complexity and/or
dimensionality of a data set.
[0315] In some embodiments, one or more processing steps can
comprise one or more filtering steps. The term "filtering" as used
herein refers to removing genomic sections or bins from
consideration. Bins can be selected for removal based on any
suitable criteria, including but not limited to redundant data
(e.g., redundant or overlapping mapped reads), non-informative data
(e.g., bins with zero median counts), bins with over represented or
under represented sequences, noisy data, the like, or combinations
of the foregoing. A filtering process often involves removing one
or more bins from consideration and subtracting the counts in the
one or more bins selected for removal from the counted or summed
counts for the bins, chromosome or chromosomes, or genome under
consideration. In some embodiments, bins can be removed
successively (e.g., one at a time to allow evaluation of the effect
of removal of each individual bin), and in certain embodiments all
bins marked for removal can be removed at the same time. In some
embodiments, genomic sections characterized by a variance above or
below a certain level are removed, which sometimes is referred to
herein as filtering "noisy" genomic sections. In certain
embodiments, a filtering process comprises obtaining data points
from a data set that deviate from the mean profile elevation of a
genomic section, a chromosome, or segment of a chromosome by a
predetermined multiple of the profile variance, and in certain
embodiments, a filtering process comprises removing data points
from a data set that do not deviate from the mean profile elevation
of a genomic section, a chromosome or segment of a chromosome by a
predetermined multiple of the profile variance. In some
embodiments, a filtering process is utilized to reduce the number
of candidate genomic sections analyzed for the presence or absence
of a genetic variation. Reducing the number of candidate genomic
sections analyzed for the presence or absence of a genetic
variation (e.g., micro-deletion, micro-duplication) often reduces
the complexity and/or dimensionality of a data set, and sometimes
increases the speed of searching for and/or identifying genetic
variations and/or genetic aberrations by two or more orders of
magnitude.
[0316] In some embodiments, one or more processing steps can
comprise one or more normalization steps. Normalization can be
performed by a suitable method known in the art. Sometimes
normalization comprises adjusting values measured on different
scales to a notionally common scale. Sometimes normalization
comprises a sophisticated mathematical adjustment to bring
probability distributions of adjusted values into alignment. In
some cases normalization comprises aligning distributions to a
normal distribution. Sometimes normalization comprises mathematical
adjustments that allow comparison of corresponding normalized
values for different datasets in a way that eliminates the effects
of certain gross influences (e.g., error and anomalies). Sometimes
normalization comprises scaling. Normalization sometimes comprises
division of one or more data sets by a predetermined variable or
formula. Non-limiting examples of normalization methods include
bin-wise normalization, normalization by GC content, linear and
nonlinear least squares regression, LOESS, GC LOESS, LOWESS
(locally weighted scatterplot smoothing), PERUN, repeat masking
(RM), GC-normalization and repeat masking (GCRM), cQn and/or
combinations thereof. In some embodiments, the determination of a
presence or absence of a genetic variation (e.g., an aneuploidy)
utilizes a normalization method (e.g., bin-wise normalization,
normalization by GC content, linear and nonlinear least squares
regression, LOESS, GC LOESS, LOWESS (locally weighted scatterplot
smoothing), PERUN, repeat masking (RM), GC-normalization and repeat
masking (GCRM), cQn, a normalization method known in the art and/or
a combination thereof).
[0317] For example, LOESS is a regression modeling method known in
the art that combines multiple regression models in a
k-nearest-neighbor-based meta-model. LOESS is sometimes referred to
as a locally weighted polynomial regression. GC LOESS, in some
embodiments, applies an LOESS model to the relation between
fragment count (e.g., sequence reads, counts) and GC composition
for genomic sections. Plotting a smooth curve through a set of data
points using LOESS is sometimes called an LOESS curve, particularly
when each smoothed value is given by a weighted quadratic least
squares regression over the span of values of the y-axis
scattergram criterion variable. For each point in a data set, the
LOESS method fits a low-degree polynomial to a subset of the data,
with explanatory variable values near the point whose response is
being estimated. The polynomial is fitted using weighted least
squares, giving more weight to points near the point whose response
is being estimated and less weight to points further away. The
value of the regression function for a point is then obtained by
evaluating the local polynomial using the explanatory variable
values for that data point. The LOESS fit is sometimes considered
complete after regression function values have been computed for
each of the data points. Many of the details of this method, such
as the degree of the polynomial model and the weights, are
flexible.
[0318] Any suitable number of normalizations can be used. In some
embodiments, data sets can be normalized 1 or more, 5 or more, 10
or more or even 20 or more times. Data sets can be normalized to
values (e.g., normalizing value) representative of any suitable
feature or variable (e.g., sample data, reference data, or both).
Non-limiting examples of types of data normalizations that can be
used include normalizing raw count data for one or more selected
test or reference genomic sections to the total number of counts
mapped to the chromosome or the entire genome on which the selected
genomic section or sections are mapped; normalizing raw count data
for one or more selected genomic sections to a median reference
count for one or more genomic sections or the chromosome on which a
selected genomic section or segments is mapped; normalizing raw
count data to previously normalized data or derivatives thereof;
and normalizing previously normalized data to one or more other
predetermined normalization variables. Normalizing a data set
sometimes has the effect of isolating statistical error, depending
on the feature or property selected as the predetermined
normalization variable. Normalizing a data set sometimes also
allows comparison of data characteristics of data having different
scales, by bringing the data to a common scale (e.g., predetermined
normalization variable). In some embodiments, one or more
normalizations to a statistically derived value can be utilized to
minimize data differences and diminish the importance of outlying
data. Normalizing genomic sections, or bins, with respect to a
normalizing value sometimes is referred to as "bin-wise
normalization".
[0319] In certain embodiments, a processing step comprising
normalization includes normalizing to a static window, and in some
embodiments, a processing step comprising normalization includes
normalizing to a moving or sliding window. The term "window" as
used herein refers to one or more genomic sections chosen for
analysis, and sometimes used as a reference for comparison (e.g.,
used for normalization and/or other mathematical or statistical
manipulation). The term "normalizing to a static window" as used
herein refers to a normalization process using one or more genomic
sections selected for comparison between a test subject and
reference subject data set. In some embodiments the selected
genomic sections are utilized to generate a profile. A static
window generally includes a predetermined set of genomic sections
that do not change during manipulations and/or analysis. The terms
"normalizing to a moving window" and "normalizing to a sliding
window" as used herein refer to normalizations performed to genomic
sections localized to the genomic region (e.g., immediate genetic
surrounding, adjacent genomic section or sections, and the like) of
a selected test genomic section, where one or more selected test
genomic sections are normalized to genomic sections immediately
surrounding the selected test genomic section. In certain
embodiments, the selected genomic sections are utilized to generate
a profile. A sliding or moving window normalization often includes
repeatedly moving or sliding to an adjacent test genomic section,
and normalizing the newly selected test genomic section to genomic
sections immediately surrounding or adjacent to the newly selected
test genomic section, where adjacent windows have one or more
genomic sections in common. In certain embodiments, a plurality of
selected test genomic sections and/or chromosomes can be analyzed
by a sliding window process.
[0320] In some embodiments, normalizing to a sliding or moving
window can generate one or more values, where each value represents
normalization to a different set of reference genomic sections
selected from different regions of a genome (e.g., chromosome). In
certain embodiments, the one or more values generated are
cumulative sums (e.g., a numerical estimate of the integral of the
normalized count profile over the selected genomic section, domain
(e.g., part of chromosome), or chromosome). The values generated by
the sliding or moving window process can be used to generate a
profile and facilitate arriving at an outcome. In some embodiments,
cumulative sums of one or more genomic sections can be displayed as
a function of genomic position. Moving or sliding window analysis
sometimes is used to analyze a genome for the presence or absence
of micro-deletions and/or micro-insertions. In certain embodiments,
displaying cumulative sums of one or more genomic sections is used
to identify the presence or absence of regions of genetic variation
(e.g., micro-deletions, micro-duplications). In some embodiments,
moving or sliding window analysis is used to identify genomic
regions containing micro-deletions and in certain embodiments,
moving or sliding window analysis is used to identify genomic
regions containing micro-duplications.
[0321] A particularly useful normalization methodology for reducing
error associated with nucleic acid indicators is referred to herein
as Parameterized Error Removal and Unbiased Normalization (PERUN).
PERUN methodology can be applied to a variety of nucleic acid
indicators (e.g., nucleic acid sequence reads) for the purpose of
reducing effects of error that confound predictions based on such
indicators.
[0322] For example, PERUN methodology can be applied to nucleic
acid sequence reads from a sample and reduce the effects of error
that can impair nucleic acid elevation determinations (e.g.,
genomic section elevation determinations). Such an application is
useful for using nucleic acid sequence reads to assess the presence
or absence of a genetic variation in a subject manifested as a
varying elevation of a nucleotide sequence (e.g., genomic section).
Non-limiting examples of variations in genomic sections are
chromosome aneuploidies (e.g., trisomy 21, trisomy 18, trisomy 13)
and presence or absence of a sex chromosome (e.g., XX in females
versus XY in males). A trisomy of an autosome (e.g., a chromosome
other than a sex chromosome) can be referred to as an affected
autosome. Other non-limiting examples of variations in genomic
section elevations include microdeletions, microinsertions,
duplications and mosaicism.
[0323] In certain applications, PERUN methodology can reduce
experimental bias by normalizing nucleic acid indicators for
particular genomic groups, the latter of which are referred to as
bins. Bins include a suitable collection of nucleic acid
indicators, a non-limiting example of which includes a length of
contiguous nucleotides, which is referred to herein as a genomic
section or portion of a reference genome. Bins can include other
nucleic acid indicators as described herein. In such applications,
PERUN methodology generally normalizes nucleic acid indicators at
particular bins across a number of samples in three dimensions. A
detailed description of particular PERUN applications is described
in Example 4 and Example 5 herein.
[0324] In certain embodiments, PERUN methodology includes
calculating a genomic section elevation for each bin from a fitted
relation between (i) experimental bias for a bin of a reference
genome to which sequence reads are mapped and (ii) counts of
sequence reads mapped to the bin. Experimental bias for each of the
bins can be determined across multiple samples according to a
fitted relation for each sample between (i) the counts of sequence
reads mapped to each of the bins, and (ii) a mapping feature fore
each of the bins. This fitted relation for each sample can be
assembled for multiple samples in three dimensions. The assembly
can be ordered according to the experimental bias in certain
embodiments (e.g., FIG. 82, Example 4), although PERUN methodology
may be practiced without ordering the assembly according to the
experimental bias.
[0325] A relation can be generated by a method known in the art. A
relation in two dimensions can be generated for each sample in
certain embodiments, and a variable probative of error, or possibly
probative of error, can be selected for one or more of the
dimensions. A relation can be generated, for example, using
graphing software known in the art that plots a graph using values
of two or more variables provided by a user. A relation can be
fitted using a method known in the art (e.g., graphing software).
Certain relations can be fitted by linear regression, and the
linear regression can generate a slope value and intercept value.
Certain relations sometimes are not linear and can be fitted by a
non-linear function, such as a parabolic, hyperbolic or exponential
function, for example.
[0326] In PERUN methodology, one or more of the fitted relations
may be linear. For an analysis of cell-free circulating nucleic
acid from pregnant females, where the experimental bias is GC bias
and the mapping feature is GC content, the fitted relation for a
sample between the (i) the counts of sequence reads mapped to each
bin, and (ii) GC content for each of the bins, can be linear. For
the latter fitted relation, the slope pertains to GC bias, and a GC
bias coefficient can be determined for each bin when the fitted
relations are assembled across multiple samples. In such
embodiments, the fitted relation for multiple samples and a bin
between (i) GC bias coefficient for the bin, and (ii) counts of
sequence reads mapped to bin, also can be linear. An intercept and
slope can be obtained from the latter fitted relation. In such
applications, the slope addresses sample-specific bias based on
GC-content and the intercept addresses a bin-specific attenuation
pattern common to all samples. PERUN methodology can significantly
reduce such sample-specific bias and bin-specific attenuation when
calculating genomic section elevations for providing an outcome
(e.g., presence or absence of genetic variation; determination of
fetal sex).
[0327] Thus, application of PERUN methodology to sequence reads
across multiple samples in parallel can significantly reduce error
caused by (i) sample-specific experimental bias (e.g., GC bias) and
(ii) bin-specific attenuation common to samples. Other methods in
which each of these two sources of error are addressed separately
or serially often are not able to reduce these as effectively as
PERUN methodology. Without being limited by theory, it is expected
that PERUN methodology reduces error more effectively in part
because its generally additive processes do not magnify spread as
much as generally multiplicative processes utilized in other
normalization approaches (e.g., GC-LOESS).
[0328] Additional normalization and statistical techniques may be
utilized in combination with PERUN methodology. An additional
process can be applied before, after and/or during employment of
PERUN methodology. Non-limiting examples of processes that can be
used in combination with PERUN methodology are described
hereafter.
[0329] In some embodiments, a secondary normalization or adjustment
of a genomic section elevation for GC content can be utilized in
conjunction with PERUN methodology. A suitable GC content
adjustment or normalization procedure can be utilized (e.g.,
GC-LOESS, GCRM). In certain embodiments, a particular sample can be
identified for application of an additional GC normalization
process. For example, application of PERUN methodology can
determine GC bias for each sample, and a sample associated with a
GC bias above a certain threshold can be selected for an additional
GC normalization process. In such embodiments, a predetermined
threshold elevation can be used to select such samples for
additional GC normalization.
[0330] In certain embodiments, a bin filtering or weighting process
can be utilized in conjunction with PERUN methodology. A suitable
bin filtering or weighting process can be utilized and non-limiting
examples are described herein. Examples 4 and 5 describe
utilization of R-factor measures of error for bin filtering.
[0331] GC Bias Module
[0332] Determining GC bias (e.g., determining GC bias for each of
the portions of a reference genome (e.g., genomic sections)) can be
provided by a GC bias module (e.g., by an apparatus comprising a GC
bias module). In some embodiments, a GC bias module is required to
provide a determination of GC bias. Sometimes a GC bias module
provides a determination of GC bias from a fitted relationship
(e.g., a fitted linear relationship) between counts of sequence
reads mapped to each of the portions of a reference genome and GC
content of each portion. An apparatus comprising a GC bias module
can comprise at least one processor. In some embodiments, GC bias
determinations (i.e., GC bias data) are provided by an apparatus
that includes a processor (e.g., one or more processors) which
processor can perform and/or implement one or more instructions
(e.g., processes, routines and/or subroutines) from the GC bias
module. In some embodiments, GC bias data is provided by an
apparatus that includes multiple processors, such as processors
coordinated and working in parallel. In some embodiments, a GC bias
module operates with one or more external processors (e.g., an
internal or external network, server, storage device and/or storage
network (e.g., a cloud)). In some embodiments, GC bias data is
provided by an apparatus comprising one or more of the following:
one or more flow cells, a camera, fluid handling components, a
printer, a display (e.g., an LED, LCT or CRT) and the like. A GC
bias module can receive data and/or information from a suitable
apparatus or module. Sometimes a GC bias module can receive data
and/or information from a sequencing module, a normalization
module, a weighting module, a mapping module or counting module. A
GC bias module sometimes is part of a normalization module (e.g.,
PERUN normalization module). A GC bias module can receive
sequencing reads from a sequencing module, mapped sequencing reads
from a mapping module and/or counts from a counting module, in some
embodiments. Often a GC bias module receives data and/or
information from an apparatus or another module (e.g., a counting
module), transforms the data and/or information and provides GC
bias data and/or information (e.g., a determination of GC bias, a
linear fitted relationship, and the like). GC bias data and/or
information can be transferred from a GC bias module to a level
module, filtering module, comparison module, a normalization
module, a weighting module, a range setting module, an adjustment
module, a categorization module, and/or an outcome module, in
certain embodiments.
[0333] Level Module
[0334] Determining levels (e.g., elevations) and/or calculating
genomic section levels (e.g., genomic section elevations) for
portions of a reference genome can be provided by a level module
(e.g., by an apparatus comprising a level module). In some
embodiments, a level module is required to provide a level or a
calculated genomic section level. Sometimes a level module provides
a level from a fitted relationship (e.g., a fitted linear
relationship) between a GC bias and counts of sequence reads mapped
to each of the portions of a reference genome. Sometimes a level
module calculates a genomic section level as part of PERUN. In some
embodiments, a level module provides a genomic section level (i.e.,
L.sub.i) according to equation
L.sub.i=(m.sub.i-G.sub.iS).GAMMA..sup.1 wherein G.sub.i is the GC
bias, m.sub.i is measured counts mapped to each portion of a
reference genome, i is a sample, and I is the intercept and S is
the slope of the a fitted relationship (e.g., a fitted linear
relationship) between a GC bias and counts of sequence reads mapped
to each of the portions of a reference genome. An apparatus
comprising a level module can comprise at least one processor. In
some embodiments, a level determination (i.e., level data) is
provided by an apparatus that includes a processor (e.g., one or
more processors) which processor can perform and/or implement one
or more instructions (e.g., processes, routines and/or subroutines)
from the level module. In some embodiments, level data is provided
by an apparatus that includes multiple processors, such as
processors coordinated and working in parallel. In some
embodiments, a level module operates with one or more external
processors (e.g., an internal or external network, server, storage
device and/or storage network (e.g., a cloud)). In some
embodiments, level data is provided by an apparatus comprising one
or more of the following: one or more flow cells, a camera, fluid
handling components, a printer, a display (e.g., an LED, LCT or
CRT) and the like. A level module can receive data and/or
information from a suitable apparatus or module. Sometimes a level
module can receive data and/or information from a GC bias module, a
sequencing module, a normalization module, a weighting module, a
mapping module or counting module. A level module can receive
sequencing reads from a sequencing module, mapped sequencing reads
from a mapping module and/or counts from a counting module, in some
embodiments. A level module sometimes is part of a normalization
module (e.g., PERUN normalization module). Often a level module
receives data and/or information from an apparatus or another
module (e.g., a GC bias module), transforms the data and/or
information and provides level data and/or information (e.g., a
determination of level, a linear fitted relationship, and the
like). Level data and/or information can be transferred from a
level module to a comparison module, a normalization module, a
weighting module, a range setting module, an adjustment module, a
categorization module, a module in a normalization module and/or an
outcome module, in certain embodiments.
[0335] Filtering Module
[0336] Filtering genomic sections can be provided by a filtering
module (e.g., by an apparatus comprising a filtering module). In
some embodiments, a filtering module is required to provide
filtered genomic section data (e.g., filtered genomic sections)
and/or to remove genomic sections from consideration. Sometimes a
filtering module removes counts mapped to a genomic section from
consideration. Sometimes a filtering module removes counts mapped
to a genomic section from a determination of an elevation or a
profile. A filtering module can filter data (e.g., counts, counts
mapped to genomic sections, genomic sections, genomic sections
elevations, normalized counts, raw counts, and the like) by one or
more filtering procedures known in the art or described herein.
[0337] An apparatus comprising a filtering module can comprise at
least one processor. In some embodiments, filtered data is provided
by an apparatus that includes a processor (e.g., one or more
processors) which processor can perform and/or implement one or
more instructions (e.g., processes, routines and/or subroutines)
from the filtering module. In some embodiments, filtered data is
provided by an apparatus that includes multiple processors, such as
processors coordinated and working in parallel. In some
embodiments, a filtering module operates with one or more external
processors (e.g., an internal or external network, server, storage
device and/or storage network (e.g., a cloud)). In some
embodiments, filtered data is provided by an apparatus comprising
one or more of the following: one or more flow cells, a camera,
fluid handling components, a printer, a display (e.g., an LED, LCT
or CRT) and the like. A filtering module can receive data and/or
information from a suitable apparatus or module. Sometimes a
filtering module can receive data and/or information from a
sequencing module, a normalization module, a weighting module, a
mapping module or counting module. A filtering module can receive
sequencing reads from a sequencing module, mapped sequencing reads
from a mapping module and/or counts from a counting module, in some
embodiments. Often a filtering module receives data and/or
information from another apparatus or module, transforms the data
and/or information and provides filtered data and/or information
(e.g., filtered counts, filtered values, filtered genomic sections,
and the like). Filtered data and/or information can be transferred
from a filtering module to a comparison module, a normalization
module, a weighting module, a range setting module, an adjustment
module, a categorization module, and/or an outcome module, in
certain embodiments.
[0338] Weighting Module
[0339] Weighting genomic sections can be provided by a weighting
module (e.g., by an apparatus comprising a weighting module). In
some embodiments, a weighting module is required to weight genomics
sections and/or provide weighted genomic section values. A
weighting module can weight genomic sections by one or more
weighting procedures known in the art or described herein. An
apparatus comprising a weighting module can comprise at least one
processor. In some embodiments, weighted genomic sections are
provided by an apparatus that includes a processor (e.g., one or
more processors) which processor can perform and/or implement one
or more instructions (e.g., processes, routines and/or subroutines)
from the weighting module. In some embodiments, weighted genomic
sections are provided by an apparatus that includes multiple
processors, such as processors coordinated and working in parallel.
In some embodiments, a weighting module operates with one or more
external processors (e.g., an internal or external network, server,
storage device and/or storage network (e.g., a cloud)). In some
embodiments, weighted genomic sections are provided by an apparatus
comprising one or more of the following: one or more flow cells, a
camera, fluid handling components, a printer, a display (e.g., an
LED, LCT or CRT) and the like. A weighting module can receive data
and/or information from a suitable apparatus or module. Sometimes a
weighting module can receive data and/or information from a
sequencing module, a normalization module, a filtering module, a
mapping module and/or a counting module. A weighting module can
receive sequencing reads from a sequencing module, mapped
sequencing reads from a mapping module and/or counts from a
counting module, in some embodiments. In some embodiments a
weighting module receives data and/or information from another
apparatus or module, transforms the data and/or information and
provides data and/or information (e.g., weighted genomic sections,
weighted values, and the like). Weighted genomic section data
and/or information can be transferred from a weighting module to a
comparison module, a normalization module, a filtering module, a
range setting module, an adjustment module, a categorization
module, and/or an outcome module, in certain embodiments.
[0340] In some embodiments, a normalization technique that reduces
error associated with insertions, duplications and/or deletions
(e.g., maternal and/or fetal copy number variations), is utilized
in conjunction with PERUN methodology.
[0341] Genomic section elevations calculated by PERUN methodology
can be utilized directly for providing an outcome. In some
embodiments, genomic section elevations can be utilized directly to
provide an outcome for samples in which fetal fraction is about 2%
to about 6% or greater (e.g., fetal fraction of about 4% or
greater). Genomic section elevations calculated by PERUN
methodology sometimes are further processed for the provision of an
outcome. In some embodiments, calculated genomic section elevations
are standardized. In certain embodiments, the sum, mean or median
of calculated genomic section elevations for a test genomic section
(e.g., chromosome 21) can be divided by the sum, mean or median of
calculated genomic section elevations for genomic sections other
than the test genomic section (e.g., autosomes other than
chromosome 21), to generate an experimental genomic section
elevation. An experimental genomic section elevation or a raw
genomic section elevation can be used as part of a standardization
analysis, such as calculation of a Z-score or Z-value. A Z-score
can be generated for a sample by subtracting an expected genomic
section elevation from an experimental genomic section elevation or
raw genomic section elevation and the resulting value may be
divided by a standard deviation for the samples. Resulting Z-scores
can be distributed for different samples and analyzed, or can be
related to other variables, such as fetal fraction and others, and
analyzed, to provide an outcome, in certain embodiments.
[0342] As noted herein, PERUN methodology is not limited to
normalization according to GC bias and GC content per se, and can
be used to reduce error associated with other sources of error. A
non-limiting example of a source of non-GC content bias is
mappability. When normalization parameters other than GC bias and
content are addressed, one or more of the fitted relations may be
non-linear (e.g., hyperbolic, exponential). Where experimental bias
is determined from a non-linear relation, for example, an
experimental bias curvature estimation may be analyzed in some
embodiments.
[0343] PERUN methodology can be applied to a variety of nucleic
acid indicators. Non-limiting examples of nucleic acid indicators
are nucleic acid sequence reads and nucleic acid elevations at a
particular location on a microarray. Non-limiting examples of
sequence reads include those obtained from cell-free circulating
DNA, cell-free circulating RNA, cellular DNA and cellular RNA.
PERUN methodology can be applied to sequence reads mapped to
suitable reference sequences, such as genomic reference DNA,
cellular reference RNA (e.g., transcriptome), and portions thereof
(e.g., part(s) of a genomic complement of DNA or RNA transcriptome,
part(s) of a chromosome).
[0344] Thus, in certain embodiments, cellular nucleic acid (e.g.,
DNA or RNA) can serve as a nucleic acid indicator. Cellular nucleic
acid reads mapped to reference genome portions can be normalized
using PERUN methodology.
[0345] Cellular nucleic acid sometimes is an association with one
or more proteins, and an agent that captures protein-associated
nucleic acid can be utilized to enrich for the latter, in some
embodiments. An agent in certain cases is an antibody or antibody
fragment that specifically binds to a protein in association with
cellular nucleic acid (e.g., an antibody that specifically binds to
a chromatin protein (e.g., histone protein)). Processes in which an
antibody or antibody fragment is used to enrich for cellular
nucleic acid bound to a particular protein sometimes are referred
to chromatin immunoprecipitation (ChIP) processes. ChIP-enriched
nucleic acid is a nucleic acid in association with cellular
protein, such as DNA or RNA for example. Reads of ChIP-enriched
nucleic acid can be obtained using technology known in the art.
Reads of ChIP-enriched nucleic acid can be mapped to one or more
portions of a reference genome, and results can be normalized using
PERUN methodology for providing an outcome.
[0346] Thus, provided in certain embodiments are methods for
calculating with reduced bias genomic section elevations for a test
sample, comprising: (a) obtaining counts of sequence reads mapped
to bins of a reference genome, which sequence reads are reads of
cellular nucleic acid from a test sample obtained by isolation of a
protein to which the nucleic acid was associated; (b) determining
experimental bias for each of the bins across multiple samples from
a fitted relation between (i) the counts of the sequence reads
mapped to each of the bins, and (ii) a mapping feature for each of
the bins; and (c) calculating a genomic section elevation for each
of the bins from a fitted relation between the experimental bias
and the counts of the sequence reads mapped to each of the bins,
thereby providing calculated genomic section elevations, whereby
bias in the counts of the sequence reads mapped to each of the bins
is reduced in the calculated genomic section elevations.
[0347] In certain embodiments, cellular RNA can serve as nucleic
acid indicators. Cellular RNA reads can be mapped to reference RNA
portions and normalized using PERUN methodology for providing an
outcome. Known sequences for cellular RNA, referred to as a
transcriptome, or a segment thereof, can be used as a reference to
which RNA reads from a sample can be mapped. Reads of sample RNA
can be obtained using technology known in the art. Results of RNA
reads mapped to a reference can be normalized using PERUN
methodology for providing an outcome.
[0348] Thus, provided in some embodiments are methods for
calculating with reduced bias genomic section elevations for a test
sample, comprising: (a) obtaining counts of sequence reads mapped
to bins of reference RNA (e.g., reference transcriptome or
segment(s) thereof), which sequence reads are reads of cellular RNA
from a test sample; (b) determining experimental bias for each of
the bins across multiple samples from a fitted relation between (i)
the counts of the sequence reads mapped to each of the bins, and
(ii) a mapping feature for each of the bins; and (c) calculating a
genomic section elevation for each of the bins from a fitted
relation between the experimental bias and the counts of the
sequence reads mapped to each of the bins, thereby providing
calculated genomic section elevations, whereby bias in the counts
of the sequence reads mapped to each of the bins is reduced in the
calculated genomic section elevations.
[0349] In some embodiments, microarray nucleic acid levels can
serve as nucleic acid indicators. Nucleic acid levels across
samples for a particular address, or hybridizing nucleic acid, on
an array can be analyzed using PERUN methodology, thereby
normalizing nucleic acid indicators provided by microarray
analysis. In this manner, a particular address or hybridizing
nucleic acid on a microarray is analogous to a bin for mapped
nucleic acid sequence reads, and PERUN methodology can be used to
normalize microarray data to provide an improved outcome.
[0350] Thus, provided in certain embodiments are methods for
reducing microarray nucleic acid level error for a test sample,
comprising: (a) obtaining nucleic acid levels in a microarray to
which test sample nucleic acid has been associated, which
microarray includes an array of capture nucleic acids; (b)
determining experimental bias for each of the capture nucleic acids
across multiple samples from a fitted relation between (i) the test
sample nucleic acid levels associated with each of the capture
nucleic acids, and (ii) an association feature for each of the
capture nucleic acids; and (c) calculating a test sample nucleic
acid level for each of the capture nucleic acids from a fitted
relation between the experimental bias and the levels of the test
sample nucleic acid associated with each of the capture nucleic
acids, thereby providing calculated levels, whereby bias in the
levels of test sample nucleic acid associated with each of the
capture nucleic acids is reduced in the calculated levels. The
association feature mentioned above can be any feature correlated
with hybridization of a test sample nucleic acid to a capture
nucleic acid that gives rise to, or may give rise to, error in
determining the level of test sample nucleic acid associated with a
capture nucleic acid.
[0351] Normalization Module
[0352] Normalized data (e.g., normalized counts) can be provided by
a normalization module (e.g., by an apparatus comprising a
normalization module). In some embodiments, a normalization module
is required to provide normalized data (e.g., normalized counts)
obtained from sequencing reads. A normalization module can
normalize data (e.g., counts, filtered counts, raw counts) by one
or more normalization procedures known in the art. An apparatus
comprising a normalization module can comprise at least one
processor. In some embodiments, normalized data is provided by an
apparatus that includes a processor (e.g., one or more processors)
which processor can perform and/or implement one or more
instructions (e.g., processes, routines and/or subroutines) from
the normalization module. In some embodiments, normalized data is
provided by an apparatus that includes multiple processors, such as
processors coordinated and working in parallel. In some
embodiments, a normalization module operates with one or more
external processors (e.g., an internal or external network, server,
storage device and/or storage network (e.g., a cloud)). In some
embodiments, normalized data is provided by an apparatus comprising
one or more of the following: one or more flow cells, a camera,
fluid handling components, a printer, a display (e.g., an LED, LCT
or CRT) and the like. A normalization module can receive data
and/or information from a suitable apparatus or module. Sometimes a
normalization module can receive data and/or information from a
sequencing module, a normalization module, a mapping module or
counting module. A normalization module can receive sequencing
reads from a sequencing module, mapped sequencing reads from a
mapping module and/or counts from a counting module, in some
embodiments. Often a normalization module receives data and/or
information from another apparatus or module, transforms the data
and/or information and provides normalized data and/or information
(e.g., normalized counts, normalized values, normalized reference
values (NRVs), and the like). Normalized data and/or information
can be transferred from a normalization module to a comparison
module, a normalization module, a range setting module, an
adjustment module, a categorization module, and/or an outcome
module, in certain embodiments. Sometimes normalized counts (e.g.,
normalized mapped counts) are transferred to an expected
representation module and/or to an experimental representation
module from a normalization module.
[0353] In some embodiments, a processing step comprises a
weighting. The terms "weighted", "weighting" or "weight function"
or grammatical derivatives or equivalents thereof, as used herein,
refer to a mathematical manipulation of a portion or all of a data
set sometimes utilized to alter the influence of certain data set
features or variables with respect to other data set features or
variables (e.g., increase or decrease the significance and/or
contribution of data contained in one or more genomic sections or
bins, based on the quality or usefulness of the data in the
selected bin or bins). A weighting function can be used to increase
the influence of data with a relatively small measurement variance,
and/or to decrease the influence of data with a relatively large
measurement variance, in some embodiments. For example, bins with
under represented or low quality sequence data can be "down
weighted" to minimize the influence on a data set, whereas selected
bins can be "up weighted" to increase the influence on a data set.
A non-limiting example of a weighting function is [1/(standard
deviation).sup.2]. A weighting step sometimes is performed in a
manner substantially similar to a normalizing step. In some
embodiments, a data set is divided by a predetermined variable
(e.g., weighting variable). A predetermined variable (e.g.,
minimized target function, Phi) often is selected to weigh
different parts of a data set differently (e.g., increase the
influence of certain data types while decreasing the influence of
other data types).
[0354] In certain embodiments, a processing step can comprise one
or more mathematical and/or statistical manipulations. Any suitable
mathematical and/or statistical manipulation, alone or in
combination, may be used to analyze and/or manipulate a data set
described herein. Any suitable number of mathematical and/or
statistical manipulations can be used. In some embodiments, a data
set can be mathematically and/or statistically manipulated 1 or
more, 5 or more, 10 or more or 20 or more times. Non-limiting
examples of mathematical and statistical manipulations that can be
used include addition, subtraction, multiplication, division,
algebraic functions, least squares estimators, curve fitting,
differential equations, rational polynomials, double polynomials,
orthogonal polynomials, z-scores, p-values, chi values, phi values,
analysis of peak elevations, determination of peak edge locations,
calculation of peak area ratios, analysis of median chromosomal
elevation, calculation of mean absolute deviation, sum of squared
residuals, mean, standard deviation, standard error, the like or
combinations thereof. A mathematical and/or statistical
manipulation can be performed on all or a portion of sequence read
data, or processed products thereof. Non-limiting examples of data
set variables or features that can be statistically manipulated
include raw counts, filtered counts, normalized counts, peak
heights, peak widths, peak areas, peak edges, lateral tolerances,
P-values, median elevations, mean elevations, count distribution
within a genomic region, relative representation of nucleic acid
species, the like or combinations thereof.
[0355] In some embodiments, a processing step can include the use
of one or more statistical algorithms. Any suitable statistical
algorithm, alone or in combination, may be used to analyze and/or
manipulate a data set described herein. Any suitable number of
statistical algorithms can be used. In some embodiments, a data set
can be analyzed using 1 or more, 5 or more, 10 or more or 20 or
more statistical algorithms. Non-limiting examples of statistical
algorithms suitable for use with methods described herein include
decision trees, counternulls, multiple comparisons, omnibus test,
Behrens-Fisher problem, bootstrapping, Fisher's method for
combining independent tests of significance, null hypothesis, type
I error, type II error, exact test, one-sample Z test, two-sample Z
test, one-sample t-test, paired t-test, two-sample pooled t-test
having equal variances, two-sample unpooled t-test having unequal
variances, one-proportion z-test, two-proportion z-test pooled,
two-proportion z-test unpooled, one-sample chi-square test,
two-sample F test for equality of variances, confidence interval,
credible interval, significance, meta analysis, simple linear
regression, robust linear regression, the like or combinations of
the foregoing. Non-limiting examples of data set variables or
features that can be analyzed using statistical algorithms include
raw counts, filtered counts, normalized counts, peak heights, peak
widths, peak edges, lateral tolerances, P-values, median
elevations, mean elevations, count distribution within a genomic
region, relative representation of nucleic acid species, the like
or combinations thereof.
[0356] In certain embodiments, a data set can be analyzed by
utilizing multiple (e.g., 2 or more) statistical algorithms (e.g.,
least squares regression, principle component analysis, linear
discriminant analysis, quadratic discriminant analysis, bagging,
neural networks, support vector machine models, random forests,
classification tree models, K-nearest neighbors, logistic
regression and/or loss smoothing) and/or mathematical and/or
statistical manipulations (e.g., referred to herein as
manipulations). The use of multiple manipulations can generate an
N-dimensional space that can be used to provide an outcome, in some
embodiments. In certain embodiments, analysis of a data set by
utilizing multiple manipulations can reduce the complexity and/or
dimensionality of the data set. For example, the use of multiple
manipulations on a reference data set can generate an N-dimensional
space (e.g., probability plot) that can be used to represent the
presence or absence of a genetic variation, depending on the
genetic status of the reference samples (e.g., positive or negative
for a selected genetic variation). Analysis of test samples using a
substantially similar set of manipulations can be used to generate
an N-dimensional point for each of the test samples. The complexity
and/or dimensionality of a test subject data set sometimes is
reduced to a single value or N-dimensional point that can be
readily compared to the N-dimensional space generated from the
reference data. Test sample data that fall within the N-dimensional
space populated by the reference subject data are indicative of a
genetic status substantially similar to that of the reference
subjects. Test sample data that fall outside of the N-dimensional
space populated by the reference subject data are indicative of a
genetic status substantially dissimilar to that of the reference
subjects. In some embodiments, references are euploid or do not
otherwise have a genetic variation or medical condition.
[0357] After data sets have been counted, optionally filtered and
normalized, the processed data sets can be further manipulated by
one or more filtering and/or normalizing procedures, in some
embodiments. A data set that has been further manipulated by one or
more filtering and/or normalizing procedures can be used to
generate a profile, in certain embodiments. The one or more
filtering and/or normalizing procedures sometimes can reduce data
set complexity and/or dimensionality, in some embodiments. An
outcome can be provided based on a data set of reduced complexity
and/or dimensionality.
[0358] Non-limiting examples of genomic section filtering is
provided herein in Example 4 with respect to PERUN methods. Genomic
sections may be filtered based on, or based on part on, a measure
of error. A measure of error comprising absolute values of
deviation, such as an R-factor, can be used for genomic section
removal or weighting in certain embodiments. An R-factor, in some
embodiments, is defined as the sum of the absolute deviations of
the predicted count values from the actual measurements divided by
the predicted count values from the actual measurements (e.g.,
Equation B herein). While a measure of error comprising absolute
values of deviation may be used, a suitable measure of error may be
alternatively employed. In certain embodiments, a measure of error
not comprising absolute values of deviation, such as a dispersion
based on squares, may be utilized. In some embodiments, genomic
sections are filtered or weighted according to a measure of
mappability (e.g., a mappability score; Example 5). A genomic
section sometimes is filtered or weighted according to a relatively
low number of sequence reads mapped to the genomic section (e.g.,
0, 1, 2, 3, 4, 5 reads mapped to the genomic section). Genomic
sections can be filtered or weighted according to the type of
analysis being performed. For example, for chromosome 13, 18 and/or
21 aneuploidy analysis, sex chromosomes may be filtered, and only
autosomes, or a subset of autosomes, may be analyzed.
[0359] In particular embodiments, the following filtering process
may be employed. The same set of genomic sections (e.g., bins)
within a given chromosome (e.g., chromosome 21) are selected and
the number of reads in affected and unaffected samples are
compared. The gap relates trisomy 21 and euploid samples and it
involves a set of genomic sections covering most of chromosome 21.
The set of genomic sections is the same between euploid and T21
samples. The distinction between a set of genomic sections and a
single section is not crucial, as a genomic section can be defined.
The same genomic region is compared in different patients. This
process can be utilized for a trisomy analysis, such as for T13 or
T18 in addition to, or instead of, T21.
[0360] After data sets have been counted, optionally filtered and
normalized, the processed data sets can be manipulated by
weighting, in some embodiments. One or more genomic sections can be
selected for weighting to reduce the influence of data (e.g., noisy
data, uninformative data) contained in the selected genomic
sections, in certain embodiments, and in some embodiments, one or
more genomic sections can be selected for weighting to enhance or
augment the influence of data (e.g., data with small measured
variance) contained in the selected genomic sections. In some
embodiments, a data set is weighted utilizing a single weighting
function that decreases the influence of data with large variances
and increases the influence of data with small variances. A
weighting function sometimes is used to reduce the influence of
data with large variances and augment the influence of data with
small variances (e.g., [1/(standard deviation).sup.2]). In some
embodiments, a profile plot of processed data further manipulated
by weighting is generated to facilitate classification and/or
providing an outcome. An outcome can be provided based on a profile
plot of weighted data
[0361] Filtering or weighting of genomic sections can be performed
at one or more suitable points in an analysis. For example, genomic
sections may be filtered or weighted before or after sequence reads
are mapped to portions of a reference genome. Genomic sections may
be filtered or weighted before or after an experimental bias for
individual genome portions is determined in some embodiments. In
certain embodiments, genomic sections may be filtered or weighted
before or after genomic section elevations are calculated.
[0362] After data sets have been counted, optionally filtered,
normalized, and optionally weighted, the processed data sets can be
manipulated by one or more mathematical and/or statistical (e.g.,
statistical functions or statistical algorithm) manipulations, in
some embodiments. In certain embodiments, processed data sets can
be further manipulated by calculating Z-scores for one or more
selected genomic sections, chromosomes, or portions of chromosomes.
In some embodiments, processed data sets can be further manipulated
by calculating P-values. Formulas for calculating Z-scores and
P-values are presented in Example 1. In certain embodiments,
mathematical and/or statistical manipulations include one or more
assumptions pertaining to ploidy and/or fetal fraction. In some
embodiments, a profile plot of processed data further manipulated
by one or more statistical and/or mathematical manipulations is
generated to facilitate classification and/or providing an outcome.
An outcome can be provided based on a profile plot of statistically
and/or mathematically manipulated data. An outcome provided based
on a profile plot of statistically and/or mathematically
manipulated data often includes one or more assumptions pertaining
to ploidy and/or fetal fraction.
[0363] In certain embodiments, multiple manipulations are performed
on processed data sets to generate an N-dimensional space and/or
N-dimensional point, after data sets have been counted, optionally
filtered and normalized. An outcome can be provided based on a
profile plot of data sets analyzed in N-dimensions.
[0364] In some embodiments, data sets are processed utilizing one
or more peak elevation analysis, peak width analysis, peak edge
location analysis, peak lateral tolerances, the like, derivations
thereof, or combinations of the foregoing, as part of or after data
sets have processed and/or manipulated. In some embodiments, a
profile plot of data processed utilizing one or more peak elevation
analysis, peak width analysis, peak edge location analysis, peak
lateral tolerances, the like, derivations thereof, or combinations
of the foregoing is generated to facilitate classification and/or
providing an outcome. An outcome can be provided based on a profile
plot of data that has been processed utilizing one or more peak
elevation analysis, peak width analysis, peak edge location
analysis, peak lateral tolerances, the like, derivations thereof,
or combinations of the foregoing.
[0365] In some embodiments, the use of one or more reference
samples known to be free of a genetic variation in question can be
used to generate a reference median count profile, which may result
in a predetermined value representative of the absence of the
genetic variation, and often deviates from a predetermined value in
areas corresponding to the genomic location in which the genetic
variation is located in the test subject, if the test subject
possessed the genetic variation. In test subjects at risk for, or
suffering from a medical condition associated with a genetic
variation, the numerical value for the selected genomic section or
sections is expected to vary significantly from the predetermined
value for non-affected genomic locations. In certain embodiments,
the use of one or more reference samples known to carry the genetic
variation in question can be used to generate a reference median
count profile, which may result in a predetermined value
representative of the presence of the genetic variation, and often
deviates from a predetermined value in areas corresponding to the
genomic location in which a test subject does not carry the genetic
variation. In test subjects not at risk for, or suffering from a
medical condition associated with a genetic variation, the
numerical value for the selected genomic section or sections is
expected to vary significantly from the predetermined value for
affected genomic locations.
[0366] In some embodiments, analysis and processing of data can
include the use of one or more assumptions. A suitable number or
type of assumptions can be utilized to analyze or process a data
set. Non-limiting examples of assumptions that can be used for data
processing and/or analysis include maternal ploidy, fetal
contribution, prevalence of certain sequences in a reference
population, ethnic background, prevalence of a selected medical
condition in related family members, parallelism between raw count
profiles from different patients and/or runs after GC-normalization
and repeat masking (e.g., GCRM), identical matches represent PCR
artifacts (e.g., identical base position), assumptions inherent in
a fetal quantifier assay (e.g., FQA), assumptions regarding twins
(e.g., if 2 twins and only 1 is affected the effective fetal
fraction is only 50% of the total measured fetal fraction
(similarly for triplets, quadruplets and the like)), fetal cell
free DNA (e.g., cfDNA) uniformly covers the entire genome, the like
and combinations thereof.
[0367] In those instances where the quality and/or depth of mapped
sequence reads does not permit an outcome prediction of the
presence or absence of a genetic variation at a desired confidence
level (e.g., 95% or higher confidence level), based on the
normalized count profiles, one or more additional mathematical
manipulation algorithms and/or statistical prediction algorithms,
can be utilized to generate additional numerical values useful for
data analysis and/or providing an outcome. The term "normalized
count profile" as used herein refers to a profile generated using
normalized counts. Examples of methods that can be used to generate
normalized counts and normalized count profiles are described
herein. As noted, mapped sequence reads that have been counted can
be normalized with respect to test sample counts or reference
sample counts. In some embodiments, a normalized count profile can
be presented as a plot.
[0368] Profiles
[0369] In some embodiments, a processing step can comprise
generating one or more profiles (e.g., profile plot) from various
aspects of a data set or derivation thereof (e.g., product of one
or more mathematical and/or statistical data processing steps known
in the art and/or described herein). The term "profile" as used
herein refers to a product of a mathematical and/or statistical
manipulation of data that can facilitate identification of patterns
and/or correlations in large quantities of data. A "profile" often
includes values resulting from one or more manipulations of data or
data sets, based on one or more criteria. A profile often includes
multiple data points. Any suitable number of data points may be
included in a profile depending on the nature and/or complexity of
a data set. In certain embodiments, profiles may include 2 or more
data points, 3 or more data points, 5 or more data points, 10 or
more data points, 24 or more data points, 25 or more data points,
50 or more data points, 100 or more data points, 500 or more data
points, 1000 or more data points, 5000 or more data points, 10,000
or more data points, or 100,000 or more data points.
[0370] In some embodiments, a profile is representative of the
entirety of a data set, and in certain embodiments, a profile is
representative of a portion or subset of a data set. That is, a
profile sometimes includes or is generated from data points
representative of data that has not been filtered to remove any
data, and sometimes a profile includes or is generated from data
points representative of data that has been filtered to remove
unwanted data. In some embodiments, a data point in a profile
represents the results of data manipulation for a genomic section.
In certain embodiments, a data point in a profile includes results
of data manipulation for groups of genomic sections. In some
embodiments, groups of genomic sections may be adjacent to one
another, and in certain embodiments, groups of genomic sections may
be from different parts of a chromosome or genome.
[0371] Data points in a profile derived from a data set can be
representative of any suitable data categorization. Non-limiting
examples of categories into which data can be grouped to generate
profile data points include: genomic sections based on size,
genomic sections based on sequence features (e.g., GC content, AT
content, position on a chromosome (e.g., short arm, long arm,
centromere, telomere), and the like), levels of expression,
chromosome, the like or combinations thereof. In some embodiments,
a profile may be generated from data points obtained from another
profile (e.g., normalized data profile renormalized to a different
normalizing value to generate a renormalized data profile). In
certain embodiments, a profile generated from data points obtained
from another profile reduces the number of data points and/or
complexity of the data set. Reducing the number of data points
and/or complexity of a data set often facilitates interpretation of
data and/or facilitates providing an outcome.
[0372] A profile often is a collection of normalized or
non-normalized counts for two or more genomic sections. A profile
often includes at least one elevation, and often comprises two or
more elevations (e.g., a profile often has multiple elevations). An
elevation generally is for a set of genomic sections having about
the same counts or normalized counts. Elevations are described in
greater detail herein. In some cases, a profile comprises one or
more genomic sections, which genomic sections can be weighted,
removed, filtered, normalized, adjusted, averaged, derived as a
mean, added, subtracted, processed or transformed by any
combination thereof. A profile often comprises normalized counts
mapped to genomic sections defining two or more elevations, where
the counts are further normalized according to one of the
elevations by a suitable method. Often counts of a profile (e.g., a
profile elevation) are associated with an uncertainty value.
[0373] A profile comprising one or more elevations can include a
first elevation and a second elevation. Sometimes a first elevation
is different (e.g., significantly different) than a second
elevation. In some embodiments a first elevation comprises a first
set of genomic sections, a second elevation comprises a second set
of genomic sections and the first set of genomic sections is not a
subset of the second set of genomic sections. In some cases, a
first set of genomic sections is different than a second set of
genomic sections from which a first and second elevation are
determined. Sometimes a profile can have multiple first elevations
that are different (e.g., significantly different, e.g., have a
significantly different value) than a second elevation within the
profile. Sometimes a profile comprises one or more first elevations
that are significantly different than a second elevation within the
profile and one or more of the first elevations are adjusted.
Sometimes a profile comprises one or more first elevations that are
significantly different than a second elevation within the profile,
each of the one or more first elevations comprise a maternal copy
number variation, fetal copy number variation, or a maternal copy
number variation and a fetal copy number variation and one or more
of the first elevations are adjusted. Sometimes a first elevation
within a profile is removed from the profile or adjusted (e.g.,
padded). A profile can comprise multiple elevations that include
one or more first elevations significantly different than one or
more second elevations and often the majority of elevations in a
profile are second elevations, which second elevations are about
equal to one another. Sometimes greater than 50%, greater than 60%,
greater than 70%, greater than 80%, greater than 90% or greater
than 95% of the elevations in a profile are second elevations.
[0374] A profile sometimes is displayed as a plot. For example, one
or more elevations representing counts (e.g., normalized counts) of
genomic sections can be plotted and visualized. Non-limiting
examples of profile plots that can be generated include raw count
(e.g., raw count profile or raw profile), normalized count,
bin-weighted, z-score, p-value, area ratio versus fitted ploidy,
median elevation versus ratio between fitted and measured fetal
fraction, principle components, the like, or combinations thereof.
Profile plots allow visualization of the manipulated data, in some
embodiments. In certain embodiments, a profile plot can be utilized
to provide an outcome (e.g., area ratio versus fitted ploidy,
median elevation versus ratio between fitted and measured fetal
fraction, principle components). The terms "raw count profile plot"
or "raw profile plot" as used herein refer to a plot of counts in
each genomic section in a region normalized to total counts in a
region (e.g., genome, genomic section, chromosome, chromosome bins
or a segment of a chromosome). In some embodiments, a profile can
be generated using a static window process, and in certain
embodiments, a profile can be generated using a sliding window
process.
[0375] A profile generated for a test subject sometimes is compared
to a profile generated for one or more reference subjects, to
facilitate interpretation of mathematical and/or statistical
manipulations of a data set and/or to provide an outcome. In some
embodiments, a profile is generated based on one or more starting
assumptions (e.g., maternal contribution of nucleic acid (e.g.,
maternal fraction), fetal contribution of nucleic acid (e.g., fetal
fraction), ploidy of reference sample, the like or combinations
thereof). In certain embodiments, a test profile often centers
around a predetermined value representative of the absence of a
genetic variation, and often deviates from a predetermined value in
areas corresponding to the genomic location in which the genetic
variation is located in the test subject, if the test subject
possessed the genetic variation. In test subjects at risk for, or
suffering from a medical condition associated with a genetic
variation, the numerical value for a selected genomic section is
expected to vary significantly from the predetermined value for
non-affected genomic locations. Depending on starting assumptions
(e.g., fixed ploidy or optimized ploidy, fixed fetal fraction or
optimized fetal fraction or combinations thereof) the predetermined
threshold or cutoff value or threshold range of values indicative
of the presence or absence of a genetic variation can vary while
still providing an outcome useful for determining the presence or
absence of a genetic variation. In some embodiments, a profile is
indicative of and/or representative of a phenotype.
[0376] By way of a non-limiting example, normalized sample and/or
reference count profiles can be obtained from raw sequence read
data by (a) calculating reference median counts for selected
chromosomes, genomic sections or segments thereof from a set of
references known not to carry a genetic variation, (b) removal of
uninformative genomic sections from the reference sample raw counts
(e.g., filtering); (c) normalizing the reference counts for all
remaining bins to the total residual number of counts (e.g., sum of
remaining counts after removal of uninformative bins) for the
reference sample selected chromosome or selected genomic location,
thereby generating a normalized reference subject profile; (d)
removing the corresponding genomic sections from the test subject
sample; and (e) normalizing the remaining test subject counts for
one or more selected genomic locations to the sum of the residual
reference median counts for the chromosome or chromosomes
containing the selected genomic locations, thereby generating a
normalized test subject profile. In certain embodiments, an
additional normalizing step with respect to the entire genome,
reduced by the filtered genomic sections in (b), can be included
between (c) and (d). A data set profile can be generated by one or
more manipulations of counted mapped sequence read data. Some
embodiments include the following. Sequence reads are mapped and
the number of sequence tags mapping to each genomic bin are
determined (e.g., counted). A raw count profile is generated from
the mapped sequence reads that are counted. An outcome is provided
by comparing a raw count profile from a test subject to a reference
median count profile for chromosomes, genomic sections or segments
thereof from a set of reference subjects known not to possess a
genetic variation, in certain embodiments.
[0377] In some embodiments, sequence read data is optionally
filtered to remove noisy data or uninformative genomic sections.
After filtering, the remaining counts typically are summed to
generate a filtered data set. A filtered count profile is generated
from a filtered data set, in certain embodiments.
[0378] After sequence read data have been counted and optionally
filtered, data sets can be normalized to generate elevations or
profiles. A data set can be normalized by normalizing one or more
selected genomic sections to a suitable normalizing reference
value. In some embodiments, a normalizing reference value is
representative of the total counts for the chromosome or
chromosomes from which genomic sections are selected. In certain
embodiments, a normalizing reference value is representative of one
or more corresponding genomic sections, portions of chromosomes or
chromosomes from a reference data set prepared from a set of
reference subjects known not to possess a genetic variation. In
some embodiments, a normalizing reference value is representative
of one or more corresponding genomic sections, portions of
chromosomes or chromosomes from a test subject data set prepared
from a test subject being analyzed for the presence or absence of a
genetic variation. In certain embodiments, the normalizing process
is performed utilizing a static window approach, and in some
embodiments the normalizing process is performed utilizing a moving
or sliding window approach. In certain embodiments, a profile
comprising normalized counts is generated to facilitate
classification and/or providing an outcome. An outcome can be
provided based on a plot of a profile comprising normalized counts
(e.g., using a plot of such a profile).
[0379] Elevations
[0380] In some embodiments, a value is ascribed to an elevation
(e.g., a number). An elevation can be determined by a suitable
method, operation or mathematical process (e.g., a processed
elevation). The term "level" as used herein is synonymous with the
term "elevation" as used herein. An elevation often is, or is
derived from, counts (e.g., normalized counts) for a set of genomic
sections. Sometimes an elevation of a genomic section is
substantially equal to the total number of counts mapped to a
genomic section (e.g., normalized counts). Often an elevation is
determined from counts that are processed, transformed or
manipulated by a suitable method, operation or mathematical process
known in the art. Sometimes an elevation is derived from counts
that are processed and non-limiting examples of processed counts
include weighted, removed, filtered, normalized, adjusted,
averaged, derived as a mean (e.g., mean elevation), added,
subtracted, transformed counts or combination thereof. Sometimes an
elevation comprises counts that are normalized (e.g., normalized
counts of genomic sections). An elevation can be for counts
normalized by a suitable process, non-limiting examples of which
include bin-wise normalization, normalization by GC content, linear
and nonlinear least squares regression, GC LOESS, LOWESS, PERUN,
RM, GCRM, cQn, the like and/or combinations thereof. An elevation
can comprise normalized counts or relative amounts of counts.
Sometimes an elevation is for counts or normalized counts of two or
more genomic sections that are averaged and the elevation is
referred to as an average elevation. Sometimes an elevation is for
a set of genomic sections having a mean count or mean of normalized
counts which is referred to as a mean elevation. Sometimes an
elevation is derived for genomic sections that comprise raw and/or
filtered counts. In some embodiments, an elevation is based on
counts that are raw. Sometimes an elevation is associated with an
uncertainty value. An elevation for a genomic section, or a
"genomic section elevation," is synonymous with a "genomic section
level" herein.
[0381] Normalized or non-normalized counts for two or more
elevations (e.g., two or more elevations in a profile) can
sometimes be mathematically manipulated (e.g., added, multiplied,
averaged, normalized, the like or combination thereof) according to
elevations. For example, normalized or non-normalized counts for
two or more elevations can be normalized according to one, some or
all of the elevations in a profile. Sometimes normalized or
non-normalized counts of all elevations in a profile are normalized
according to one elevation in the profile. Sometimes normalized or
non-normalized counts of a first elevation in a profile are
normalized according to normalized or non-normalized counts of a
second elevation in the profile.
[0382] Non-limiting examples of an elevation (e.g., a first
elevation, a second elevation) are an elevation for a set of
genomic sections comprising processed counts, an elevation for a
set of genomic sections comprising a mean, median or average of
counts, an elevation for a set of genomic sections comprising
normalized counts, the like or any combination thereof. In some
embodiments, a first elevation and a second elevation in a profile
are derived from counts of genomic sections mapped to the same
chromosome. In some embodiments, a first elevation and a second
elevation in a profile are derived from counts of genomic sections
mapped to different chromosomes.
[0383] In some embodiments an elevation is determined from
normalized or non-normalized counts mapped to one or more genomic
sections. In some embodiments, an elevation is determined from
normalized or non-normalized counts mapped to two or more genomic
sections, where the normalized counts for each genomic section
often are about the same. There can be variation in counts (e.g.,
normalized counts) in a set of genomic sections for an elevation.
In a set of genomic sections for an elevation there can be one or
more genomic sections having counts that are significantly
different than in other genomic sections of the set (e.g., peaks
and/or dips). Any suitable number of normalized or non-normalized
counts associated with any suitable number of genomic sections can
define an elevation.
[0384] Sometimes one or more elevations can be determined from
normalized or non-normalized counts of all or some of the genomic
sections of a genome. Often an elevation can be determined from all
or some of the normalized or non-normalized counts of a chromosome,
or segment thereof. Sometimes, two or more counts derived from two
or more genomic sections (e.g., a set of genomic sections)
determine an elevation. Sometimes two or more counts (e.g., counts
from two or more genomic sections) determine an elevation. In some
embodiments, counts from 2 to about 100,000 genomic sections
determine an elevation. In some embodiments, counts from 2 to about
50,000, 2 to about 40,000, 2 to about 30,000, 2 to about 20,000, 2
to about 10,000, 2 to about 5000, 2 to about 2500, 2 to about 1250,
2 to about 1000, 2 to about 500, 2 to about 250, 2 to about 100 or
2 to about 60 genomic sections determine an elevation. In some
embodiments counts from about 10 to about 50 genomic sections
determine an elevation. In some embodiments counts from about 20 to
about 40 or more genomic sections determine an elevation. In some
embodiments, an elevation comprises counts from about 2, 3, 4, 5,
6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
45, 50, 55, 60 or more genomic sections. In some embodiments, an
elevation corresponds to a set of genomic sections (e.g., a set of
genomic sections of a reference genome, a set of genomic sections
of a chromosome or a set of genomic sections of a segment of a
chromosome).
[0385] In some embodiments, an elevation is determined for
normalized or non-normalized counts of genomic sections that are
contiguous. Sometimes genomic sections (e.g., a set of genomic
sections) that are contiguous represent neighboring segments of a
genome or neighboring segments of a chromosome or gene. For
example, two or more contiguous genomic sections, when aligned by
merging the genomic sections end to end, can represent a sequence
assembly of a DNA sequence longer than each genomic section. For
example two or more contiguous genomic sections can represent of an
intact genome, chromosome, gene, intron, exon or segment thereof.
Sometimes an elevation is determined from a collection (e.g., a
set) of contiguous genomic sections and/or non-contiguous genomic
sections.
[0386] Significantly Different Elevations
[0387] In some embodiments, a profile of normalized counts
comprises an elevation (e.g., a first elevation) significantly
different than another elevation (e.g., a second elevation) within
the profile. A first elevation may be higher or lower than a second
elevation. In some embodiments, a first elevation is for a set of
genomic sections comprising one or more reads comprising a copy
number variation (e.g., a maternal copy number variation, fetal
copy number variation, or a maternal copy number variation and a
fetal copy number variation) and the second elevation is for a set
of genomic sections comprising reads having substantially no copy
number variation. In some embodiments, significantly different
refers to an observable difference. Sometimes significantly
different refers to statistically different or a statistically
significant difference. A statistically significant difference is
sometimes a statistical assessment of an observed difference. A
statistically significant difference can be assessed by a suitable
method in the art. Any suitable threshold or range can be used to
determine that two elevations are significantly different. In some
cases two elevations (e.g., mean elevations) that differ by about
0.01 percent or more (e.g., 0.01 percent of one or either of the
elevation values) are significantly different. Sometimes two
elevations (e.g., mean elevations) that differ by about 0.1 percent
or more are significantly different. In some cases, two elevations
(e.g., mean elevations) that differ by about 0.5 percent or more
are significantly different. Sometimes two elevations (e.g., mean
elevations) that differ by about 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5,
4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5 or more than about
10% are significantly different. Sometimes two elevations (e.g.,
mean elevations) are significantly different and there is no
overlap in either elevation and/or no overlap in a range defined by
an uncertainty value calculated for one or both elevations. In some
cases the uncertainty value is a standard deviation expressed as
sigma. Sometimes two elevations (e.g., mean elevations) are
significantly different and they differ by about 1 or more times
the uncertainty value (e.g., 1 sigma). Sometimes two elevations
(e.g., mean elevations) are significantly different and they differ
by about 2 or more times the uncertainty value (e.g., 2 sigma),
about 3 or more, about 4 or more, about 5 or more, about 6 or more,
about 7 or more, about 8 or more, about 9 or more, or about 10 or
more times the uncertainty value. Sometimes two elevations (e.g.,
mean elevations) are significantly different when they differ by
about 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2,
2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5,
3.6, 3.7, 3.8, 3.9, or 4.0 times the uncertainty value or more. In
some embodiments, the confidence level increases as the difference
between two elevations increases. In some cases, the confidence
level decreases as the difference between two elevations decreases
and/or as the uncertainty value increases. For example, sometimes
the confidence level increases with the ratio of the difference
between elevations and the standard deviation (e.g., MADs).
[0388] In some embodiments, a first set of genomic sections often
includes genomic sections that are different than (e.g.,
non-overlapping with) a second set of genomic sections. For
example, sometimes a first elevation of normalized counts is
significantly different than a second elevation of normalized
counts in a profile, and the first elevation is for a first set of
genomic sections, the second elevation is for a second set of
genomic sections and the genomic sections do not overlap in the
first set and second set of genomic sections. In some cases, a
first set of genomic sections is not a subset of a second set of
genomic sections from which a first elevation and second elevation
are determined, respectively. Sometimes a first set of genomic
sections is different and/or distinct from a second set of genomic
sections from which a first elevation and second elevation are
determined, respectively.
[0389] Sometimes a first set of genomic sections is a subset of a
second set of genomic sections in a profile. For example, sometimes
a second elevation of normalized counts for a second set of genomic
sections in a profile comprises normalized counts of a first set of
genomic sections for a first elevation in the profile and the first
set of genomic sections is a subset of the second set of genomic
sections in the profile. Sometimes an average, mean or median
elevation is derived from a second elevation where the second
elevation comprises a first elevation. Sometimes, a second
elevation comprises a second set of genomic sections representing
an entire chromosome and a first elevation comprises a first set of
genomic sections where the first set is a subset of the second set
of genomic sections and the first elevation represents a maternal
copy number variation, fetal copy number variation, or a maternal
copy number variation and a fetal copy number variation that is
present in the chromosome.
[0390] In some embodiments, a value of a second elevation is closer
to the mean, average or median value of a count profile for a
chromosome, or segment thereof, than the first elevation. In some
embodiments, a second elevation is a mean elevation of a
chromosome, a portion of a chromosome or a segment thereof. In some
embodiments, a first elevation is significantly different from a
predominant elevation (e.g., a second elevation) representing a
chromosome, or segment thereof. A profile may include multiple
first elevations that significantly differ from a second elevation,
and each first elevation independently can be higher or lower than
the second elevation. In some embodiments, a first elevation and a
second elevation are derived from the same chromosome and the first
elevation is higher or lower than the second elevation, and the
second elevation is the predominant elevation of the chromosome.
Sometimes, a first elevation and a second elevation are derived
from the same chromosome, a first elevation is indicative of a copy
number variation (e.g., a maternal and/or fetal copy number
variation, deletion, insertion, duplication) and a second elevation
is a mean elevation or predominant elevation of genomic sections
for a chromosome, or segment thereof.
[0391] In some cases, a read in a second set of genomic sections
for a second elevation substantially does not include a genetic
variation (e.g., a copy number variation, a maternal and/or fetal
copy number variation). Often, a second set of genomic sections for
a second elevation includes some variability (e.g., variability in
elevation, variability in counts for genomic sections). Sometimes,
one or more genomic sections in a set of genomic sections for an
elevation associated with substantially no copy number variation
include one or more reads having a copy number variation present in
a maternal and/or fetal genome. For example, sometimes a set of
genomic sections include a copy number variation that is present in
a small segment of a chromosome (e.g., less than 10 genomic
sections) and the set of genomic sections is for an elevation
associated with substantially no copy number variation. Thus a set
of genomic sections that include substantially no copy number
variation still can include a copy number variation that is present
in less than about 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1 genomic sections
of an elevation.
[0392] Sometimes a first elevation is for a first set of genomic
sections and a second elevation is for a second set of genomic
sections and the first set of genomic sections and second set of
genomic sections are contiguous (e.g., adjacent with respect to the
nucleic acid sequence of a chromosome or segment thereof).
Sometimes the first set of genomic sections and second set of
genomic sections are not contiguous.
[0393] Relatively short sequence reads from a mixture of fetal and
maternal nucleic acid can be utilized to provide counts which can
be transformed into an elevation and/or a profile. Counts,
elevations and profiles can be depicted in electronic or tangible
form and can be visualized. Counts mapped to genomic sections
(e.g., represented as elevations and/or profiles) can provide a
visual representation of a fetal and/or a maternal genome,
chromosome, or a portion or a segment of a chromosome that is
present in a fetus and/or pregnant female.
[0394] Comparison Module
[0395] A first elevation can be identified as significantly
different from a second elevation by a comparison module or by an
apparatus comprising a comparison module. In some embodiments, a
comparison module or an apparatus comprising a comparison module is
required to provide a comparison between two elevations. An
apparatus comprising a comparison module can comprise at least one
processor. In some embodiments, elevations are determined to be
significantly different by an apparatus that includes a processor
(e.g., one or more processors) which processor can perform and/or
implement one or more instructions (e.g., processes, routines
and/or subroutines) from the comparison module. In some
embodiments, elevations are determined to be significantly
different by an apparatus that includes multiple processors, such
as processors coordinated and working in parallel. In some
embodiments, a comparison module operates with one or more external
processors (e.g., an internal or external network, server, storage
device and/or storage network (e.g., a cloud)). In some
embodiments, elevations are determined to be significantly
different by an apparatus comprising one or more of the following:
one or more flow cells, a camera, fluid handling components, a
printer, a display (e.g., an LED, LCT or CRT) and the like. A
comparison module can receive data and/or information from a
suitable module. A comparison module can receive data and/or
information from a sequencing module, a mapping module, a counting
module, or a normalization module. A comparison module can receive
normalized data and/or information from a normalization module.
Data and/or information derived from, or transformed by, a
comparison module can be transferred from a comparison module to a
range setting module, a plotting module, an adjustment module, a
categorization module or an outcome module. A comparison between
two or more elevations and/or an identification of an elevation as
significantly different from another elevation can be transferred
from (e.g., provided to) a comparison module to a categorization
module, range setting module or adjustment module.
[0396] Reference Elevation and Normalized Reference Value
[0397] Sometimes a profile comprises a reference elevation (e.g.,
an elevation used as a reference). Often a profile of normalized
counts provides a reference elevation from which expected
elevations and expected ranges are determined (see discussion below
on expected elevations and ranges).
[0398] A reference elevation often is for normalized counts of
genomic sections comprising mapped reads from both a mother and a
fetus. A reference elevation is often the sum of normalized counts
of mapped reads from a fetus and a mother (e.g., a pregnant
female). Sometimes a reference elevation is for genomic sections
comprising mapped reads from a euploid mother and/or a euploid
fetus. Sometimes a reference elevation is for genomic sections
comprising mapped reads having a fetal genetic variation (e.g., an
aneuploidy (e.g., a trisomy)), and/or reads having a maternal
genetic variation (e.g., a copy number variation, insertion,
deletion). Sometimes a reference elevation is for genomic sections
that include substantially no maternal and/or fetal copy number
variations. Sometimes a second elevation is used as a reference
elevation. In some cases a profile comprises a first elevation of
normalized counts and a second elevation of normalized counts, the
first elevation is significantly different from the second
elevation and the second elevation is the reference elevation. In
some cases a profile comprises a first elevation of normalized
counts for a first set of genomic sections, a second elevation of
normalized counts for a second set of genomic sections, the first
set of genomic sections includes mapped reads having a maternal
and/or fetal copy number variation, the second set of genomic
sections comprises mapped reads having substantially no maternal
copy number variation and/or fetal copy number variation, and the
second elevation is a reference elevation.
[0399] In some embodiments counts mapped to genomic sections for
one or more elevations of a profile are normalized according to
counts of a reference elevation. In some embodiments, normalizing
counts of an elevation according to counts of a reference elevation
comprise dividing counts of an elevation by counts of a reference
elevation or a multiple or fraction thereof. Counts normalized
according to counts of a reference elevation often have been
normalized according to another process (e.g., PERUN) and counts of
a reference elevation also often have been normalized (e.g., by
PERUN). Sometimes the counts of an elevation are normalized
according to counts of a reference elevation and the counts of the
reference elevation are scalable to a suitable value either prior
to or after normalizing. The process of scaling the counts of a
reference elevation can comprise any suitable constant (i.e.,
number) and any suitable mathematical manipulation may be applied
to the counts of a reference elevation.
[0400] A normalized reference value (NRV) is often determined
according to the normalized counts of a reference elevation.
Determining an NRV can comprise any suitable normalization process
(e.g., mathematical manipulation) applied to the counts of a
reference elevation where the same normalization process is used to
normalize the counts of other elevations within the same
profile.
[0401] Determining an NRV often comprises dividing a reference
elevation by itself. Determining an NRV often comprises dividing a
reference elevation by a multiple of itself. Determining an NRV
often comprises dividing a reference elevation by the sum or
difference of the reference elevation and a constant (e.g., any
number).
[0402] An NRV is sometimes referred to as a null value. An NRV can
be any suitable value. In some embodiments, an NRV is any value
other than zero. Sometimes an NRV is a whole number. Sometimes an
NRV is a positive integer. In some embodiments, an NRV is 1, 10,
100 or 1000. Often, an NRV is equal to 1. Sometimes an NRV is equal
to zero. The counts of a reference elevation can be normalized to
any suitable NRV. In some embodiments, the counts of a reference
elevation are normalized to an NRV of zero. Often the counts of a
reference elevation are normalized to an NRV of 1.
[0403] Expected Elevations
[0404] An expected elevation is sometimes a pre-defined elevation
(e.g., a theoretical elevation, predicted elevation). An "expected
elevation" is sometimes referred to herein as a "predetermined
elevation value". In some embodiments, an expected elevation is a
predicted value for an elevation of normalized counts for a set of
genomic sections that include a copy number variation. In some
cases, an expected elevation is determined for a set of genomic
sections that include substantially no copy number variation. An
expected elevation can be determined for a chromosome ploidy (e.g.,
0, 1, 2 (i.e., diploid), 3 or 4 chromosomes) or a microploidy
(homozygous or heterozygous deletion, duplication, insertion or
absence thereof). Often an expected elevation is determined for a
maternal microploidy (e.g., a maternal and/or fetal copy number
variation).
[0405] An expected elevation for a genetic variation or a copy
number variation can be determined by any suitable manner. Often an
expected elevation is determined by a suitable mathematical
manipulation of an elevation (e.g., counts mapped to a set of
genomic sections for an elevation). Sometimes an expected elevation
is determined by utilizing a constant sometimes referred to as an
expected elevation constant. An expected elevation for a copy
number variation is sometimes calculated by multiplying a reference
elevation, normalized counts of a reference elevation or an NRV by
an expected elevation constant, adding an expected elevation
constant, subtracting an expected elevation constant, dividing by
an expected elevation constant, or by a combination thereof. Often
an expected elevation (e.g., an expected elevation of a maternal
and/or fetal copy number variation) determined for the same
subject, sample or test group is determined according to the same
reference elevation or NRV.
[0406] Often an expected elevation is determined by multiplying a
reference elevation, normalized counts of a reference elevation or
an NRV by an expected elevation constant where the reference
elevation, normalized counts of a reference elevation or NRV is not
equal to zero. Sometimes an expected elevation is determined by
adding an expected elevation constant to reference elevation,
normalized counts of a reference elevation or an NRV that is equal
to zero. In some embodiments, an expected elevation, normalized
counts of a reference elevation, NRV and expected elevation
constant are scalable. The process of scaling can comprise any
suitable constant (i.e., number) and any suitable mathematical
manipulation where the same scaling process is applied to all
values under consideration.
[0407] Expected Elevation Constant
[0408] An expected elevation constant can be determined by a
suitable method. Sometimes an expected elevation constant is
arbitrarily determined. Often an expected elevation constant is
determined empirically. Sometimes an expected elevation constant is
determined according to a mathematical manipulation. Sometimes an
expected elevation constant is determined according to a reference
(e.g., a reference genome, a reference sample, reference test
data). In some embodiments, an expected elevation constant is
predetermined for an elevation representative of the presence or
absence of a genetic variation or copy number variation (e.g., a
duplication, insertion or deletion). In some embodiments, an
expected elevation constant is predetermined for an elevation
representative of the presence or absence of a maternal copy number
variation, fetal copy number variation, or a maternal copy number
variation and a fetal copy number variation. An expected elevation
constant for a copy number variation can be any suitable constant
or set of constants.
[0409] In some embodiments, the expected elevation constant for a
homozygous duplication (e.g., a homozygous duplication) can be from
about 1.6 to about 2.4, from about 1.7 to about 2.3, from about 1.8
to about 2.2, or from about 1.9 to about 2.1. Sometimes the
expected elevation constant for a homozygous duplication is about
1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3 or about 2.4. Often the
expected elevation constant for a homozygous duplication is about
1.90, 1.92, 1.94, 1.96, 1.98, 2.0, 2.02, 2.04, 2.06, 2.08 or about
2.10. Often the expected elevation constant for a homozygous
duplication is about 2.
[0410] In some embodiments, the expected elevation constant for a
heterozygous duplication (e.g., a homozygous duplication) is from
about 1.2 to about 1.8, from about 1.3 to about 1.7, or from about
1.4 to about 1.6. Sometimes the expected elevation constant for a
heterozygous duplication is about 1.2, 1.3, 1.4, 1.5, 1.6, 1.7 or
about 1.8. Often the expected elevation constant for a heterozygous
duplication is about 1.40, 1.42, 1.44, 1.46, 1.48, 1.5, 1.52, 1.54,
1.56, 1.58 or about 1.60. In some embodiments, the expected
elevation constant for a heterozygous duplication is about 1.5.
[0411] In some embodiments, the expected elevation constant for the
absence of a copy number variation (e.g., the absence of a maternal
copy number variation and/or fetal copy number variation) is from
about 1.3 to about 0.7, from about 1.2 to about 0.8, or from about
1.1 to about 0.9. Sometimes the expected elevation constant for the
absence of a copy number variation is about 1.3, 1.2, 1.1, 1.0,
0.9, 0.8 or about 0.7. Often the expected elevation constant for
the absence of a copy number variation is about 1.09, 1.08, 1.06,
1.04, 1.02, 1.0, 0.98, 0.96, 0.94, or about 0.92. In some
embodiments, the expected elevation constant for the absence of a
copy number variation is about 1.
[0412] In some embodiments, the expected elevation constant for a
heterozygous deletion (e.g., a maternal, fetal, or a maternal and a
fetal heterozygous deletion) is from about 0.2 to about 0.8, from
about 0.3 to about 0.7, or from about 0.4 to about 0.6. Sometimes
the expected elevation constant for a heterozygous deletion is
about 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 or about 0.8. Often the expected
elevation constant for a heterozygous deletion is about 0.40, 0.42,
0.44, 0.46, 0.48, 0.5, 0.52, 0.54, 0.56, 0.58 or about 0.60. In
some embodiments, the expected elevation constant for a
heterozygous deletion is about 0.5.
[0413] In some embodiments, the expected elevation constant for a
homozygous deletion (e.g., a homozygous deletion) can be from about
-0.4 to about 0.4, from about -0.3 to about 0.3, from about -0.2 to
about 0.2, or from about -0.1 to about 0.1. Sometimes the expected
elevation constant for a homozygous deletion is about -0.4, -0.3,
-0.2, -0.1, 0.0, 0.1, 0.2, 0.3 or about 0.4. Often the expected
elevation constant for a homozygous deletion is about -0.1, -0.08,
-0.06, -0.04, -0.02, 0.0, 0.02, 0.04, 0.06, 0.08 or about 0.10.
Often the expected elevation constant for a homozygous deletion is
about 0.
[0414] Expected Elevation Range
[0415] Sometimes the presence or absence of a genetic variation or
copy number variation (e.g., a maternal copy number variation,
fetal copy number variation, or a maternal copy number variation
and a fetal copy number variation) is determined by an elevation
that falls within or outside of an expected elevation range. An
expected elevation range is often determined according to an
expected elevation. Sometimes an expected elevation range is
determined for an elevation comprising substantially no genetic
variation or substantially no copy number variation. A suitable
method can be used to determine an expected elevation range.
[0416] Sometimes, an expected elevation range is defined according
to a suitable uncertainty value calculated for an elevation.
Non-limiting examples of an uncertainty value are a standard
deviation, standard error, calculated variance, p-value, and mean
absolute deviation (MAD). Sometimes, an expected elevation range
for a genetic variation or a copy number variation is determined,
in part, by calculating the uncertainty value for an elevation
(e.g., a first elevation, a second elevation, a first elevation and
a second elevation). Sometimes an expected elevation range is
defined according to an uncertainty value calculated for a profile
(e.g., a profile of normalized counts for a chromosome or segment
thereof). In some embodiments, an uncertainty value is calculated
for an elevation comprising substantially no genetic variation or
substantially no copy number variation. In some embodiments, an
uncertainty value is calculated for a first elevation, a second
elevation or a first elevation and a second elevation. In some
embodiments an uncertainty value is determined for a first
elevation, a second elevation or a second elevation comprising a
first elevation.
[0417] An expected elevation range is sometimes calculated, in
part, by multiplying, adding, subtracting, or dividing an
uncertainty value by a constant (e.g., a predetermined constant) n.
A suitable mathematical procedure or combination of procedures can
be used. The constant n (e.g., predetermined constant n) is
sometimes referred to as a confidence interval. A selected
confidence interval is determined according to the constant n that
is selected. The constant n (e.g., the predetermined constant n,
the confidence interval) can be determined by a suitable manner.
The constant n can be a number or fraction of a number greater than
zero. The constant n can be a whole number. Often the constant n is
a number less than 10. Sometimes the constant n is a number less
than about 10, less than about 9, less than about 8, less than
about 7, less than about 6, less than about 5, less than about 4,
less than about 3, or less than about 2. Sometimes the constant n
is about 10, 9.5, 9, 8.5, 8, 7.5, 7, 6.5, 6, 5.5, 5, 4.5, 4, 3.5,
3, 2.5, 2 or 1. The constant n can be determined empirically from
data derived from subjects (a pregnant female and/or a fetus) with
a known genetic disposition.
[0418] Often an uncertainty value and constant n defines a range
(e.g., an uncertainty cutoff). For example, sometimes an
uncertainty value is a standard deviation (e.g., +/-5) and is
multiplied by a constant n (e.g., a confidence interval) thereby
defining a range or uncertainty cutoff (e.g., 5n to -5n).
[0419] In some embodiments, an expected elevation range for a
genetic variation (e.g., a maternal copy number variation, fetal
copy number variation, or a maternal copy number variation and
fetal copy number variation) is the sum of an expected elevation
plus a constant n times the uncertainty (e.g., n.times.sigma (e.g.,
6 sigma)). Sometimes the expected elevation range for a genetic
variation or copy number variation designated by k can be defined
by the formula:
(Expected Elevation Range).sub.k=(Expected
Elevation).sub.k+n.sigma. Formula R:
where .sigma. is an uncertainty value, n is a constant (e.g., a
predetermined constant) and the expected elevation range and
expected elevation are for the genetic variation k (e.g., k=a
heterozygous deletion, e.g., k=the absence of a genetic variation).
For example, for an expected elevation equal to 1 (e.g., the
absence of a copy number variation), an uncertainty value (i.e.
.sigma.) equal to +/-0.05, and n=3, the expected elevation range is
defined as 1.15 to 0.85. In some embodiments, the expected
elevation range for a heterozygous duplication is determined as
1.65 to 1.35 when the expected elevation for a heterozygous
duplication is 1.5, n=3, and the uncertainty value .sigma. is
+/-0.05. In some embodiments the expected elevation range for a
heterozygous deletion is determined as 0.65 to 0.35 when the
expected elevation for a heterozygous duplication is 0.5, n=3, and
the uncertainty value .sigma. is +/-0.05. In some embodiments the
expected elevation range for a homozygous duplication is determined
as 2.15 to 1.85 when the expected elevation for a heterozygous
duplication is 2.0, n=3 and the uncertainty value .sigma. is
+/-0.05. In some embodiments the expected elevation range for a
homozygous deletion is determined as 0.15 to -0.15 when the
expected elevation for a heterozygous duplication is 0.0, n=3 and
the uncertainty value .sigma. is +/-0.05.
[0420] Sometimes an expected elevation range for a homozygous copy
number variation (e.g., a maternal, fetal or maternal and fetal
homozygous copy number variation) is determined, in part, according
to an expected elevation range for a corresponding heterozygous
copy number variation. For example, sometimes an expected elevation
range for a homozygous duplication comprises all values greater
than an upper limit of an expected elevation range for a
heterozygous duplication. Sometimes an expected elevation range for
a homozygous duplication comprises all values greater than or equal
to an upper limit of an expected elevation range for a heterozygous
duplication. Sometimes an expected elevation range for a homozygous
duplication comprises all values greater than an upper limit of an
expected elevation range for a heterozygous duplication and less
than the upper limit defined by the formula R where .sigma. is an
uncertainty value and is a positive value, n is a constant and k is
a homozygous duplication. Sometimes an expected elevation range for
a homozygous duplication comprises all values greater than or equal
to an upper limit of an expected elevation range for a heterozygous
duplication and less than or equal to the upper limit defined by
the formula R where .sigma. is an uncertainty value, .sigma. is a
positive value, n is a constant and k is a homozygous
duplication.
[0421] In some embodiments, an expected elevation range for a
homozygous deletion comprises all values less than a lower limit of
an expected elevation range for a heterozygous deletion. Sometimes
an expected elevation range for a homozygous deletion comprises all
values less than or equal to a lower limit of an expected elevation
range for a heterozygous deletion. Sometimes an expected elevation
range for a homozygous deletion comprises all values less than a
lower limit of an expected elevation range for a heterozygous
deletion and greater than the lower limit defined by the formula R
where .sigma. is an uncertainty value, .sigma. is a negative value,
n is a constant and k is a homozygous deletion. Sometimes an
expected elevation range for a homozygous deletion comprises all
values less than or equal to a lower limit of an expected elevation
range for a heterozygous deletion and greater than or equal to the
lower limit defined by the formula R where .sigma. is an
uncertainty value, .sigma. is a negative value, n is a constant and
k is a homozygous deletion.
[0422] An uncertainty value can be utilized to determine a
threshold value. In some embodiments, a range (e.g., a threshold
range) is obtained by calculating the uncertainty value determined
from a raw, filtered and/or normalized counts. A range can be
determined by multiplying the uncertainty value for an elevation
(e.g. normalized counts of an elevation) by a predetermined
constant (e.g., 1, 2, 3, 4, 5, 6, etc.) representing the multiple
of uncertainty (e.g., number of standard deviations) chosen as a
cutoff threshold (e.g., multiply by 3 for 3 standard deviations),
whereby a range is generated, in some embodiments. A range can be
determined by adding and/or subtracting a value (e.g., a
predetermined value, an uncertainty value, an uncertainty value
multiplied by a predetermined constant) to and/or from an elevation
whereby a range is generated, in some embodiments. For example, for
an elevation equal to 1, a standard deviation of +/-0.2, where a
predetermined constant is 3, the range can be calculated as
(1+3(0.2)) to (1+3(-0.2)), or 1.6 to 0.4. A range sometimes can
define an expected range or expected elevation range for a copy
number variation. In certain embodiments, some or all of the
genomic sections exceeding a threshold value, falling outside a
range or falling inside a range of values, are removed as part of,
prior to, or after a normalization process. In some embodiments,
some or all of the genomic sections exceeding a calculated
threshold value, falling outside a range or falling inside a range
are weighted or adjusted as part of, or prior to the normalization
or classification process. Examples of weighting are described
herein. The terms "redundant data", and "redundant mapped reads" as
used herein refer to sample derived sequence reads that are
identified as having already been assigned to a genomic location
(e.g., base position) and/or counted for a genomic section.
[0423] In some embodiments an uncertainty value is determined
according to the formula below:
Z = L A - L o .sigma. A 2 N A + .sigma. o 2 N o ##EQU00001##
Where Z represents the standardized deviation between two
elevations, L is the mean (or median) elevation and sigma is the
standard deviation (or MAD). The subscript O denotes a segment of a
profile (e.g., a second elevation, a chromosome, an NRV, a "euploid
level", a level absent a copy number variation), and A denotes
another segment of a profile (e.g., a first elevation, an elevation
representing a copy number variation, an elevation representing an
aneuploidy (e.g., a trisomy). The variable N.sub.o represents the
total number of genomic sections in the segment of the profile
denoted by the subscript O. N.sub.A represents the total number of
genomic sections in the segment of the profile denoted by subscript
A.
[0424] Categorizing a Copy Number Variation
[0425] An elevation (e.g., a first elevation) that significantly
differs from another elevation (e.g., a second elevation) can often
be categorized as a copy number variation (e.g., a maternal and/or
fetal copy number variation, a fetal copy number variation, a
deletion, duplication, insertion) according to an expected
elevation range. In some embodiments, the presence of a copy number
variation is categorized when a first elevation is significantly
different from a second elevation and the first elevation falls
within the expected elevation range for a copy number variation.
For example, a copy number variation (e.g., a maternal and/or fetal
copy number variation, a fetal copy number variation) can be
categorized when a first elevation is significantly different from
a second elevation and the first elevation falls within the
expected elevation range for a copy number variation. Sometimes a
heterozygous duplication (e.g., a maternal or fetal, or maternal
and fetal, heterozygous duplication) or heterozygous deletion
(e.g., a maternal or fetal, or maternal and fetal, heterozygous
deletion) is categorized when a first elevation is significantly
different from a second elevation and the first elevation falls
within the expected elevation range for a heterozygous duplication
or heterozygous deletion, respectively. Sometimes a homozygous
duplication or homozygous deletion is categorized when a first
elevation is significantly different from a second elevation and
the first elevation falls within the expected elevation range for a
homozygous duplication or homozygous deletion, respectively.
[0426] Range Setting Module
[0427] Expected ranges (e.g., expected elevation ranges) for
various copy number variations (e.g., duplications, insertions
and/or deletions) or ranges for the absence of a copy number
variation can be provided by a range setting module or by an
apparatus comprising a range setting module. In some cases,
expected elevations are provided by a range setting module or by an
apparatus comprising a range setting module. In some embodiments, a
range setting module or an apparatus comprising a range setting
module is required to provide expected elevations and/or ranges.
Sometimes a range setting module gathers, assembles and/or receives
data and/or information from another module or apparatus. Sometimes
a range setting module or an apparatus comprising a range setting
module provides and/or transfers data and/or information to another
module or apparatus. Sometimes a range setting module accepts and
gathers data and/or information from a component or peripheral.
Often a range setting module gathers and assembles elevations,
reference elevations, uncertainty values, and/or constants.
Sometimes a range setting module accepts and gathers input data
and/or information from an operator of an apparatus. For example,
sometimes an operator of an apparatus provides a constant, a
threshold value, a formula or a predetermined value to a module. An
apparatus comprising a range setting module can comprise at least
one processor. In some embodiments, expected elevations and
expected ranges are provided by an apparatus that includes a
processor (e.g., one or more processors) which processor can
perform and/or implement one or more instructions (e.g., processes,
routines and/or subroutines) from the range setting module. In some
embodiments, expected ranges and elevations are provided by an
apparatus that includes multiple processors, such as processors
coordinated and working in parallel. In some embodiments, a range
setting module operates with one or more external processors (e.g.,
an internal or external network, server, storage device and/or
storage network (e.g., a cloud)). In some embodiments, expected
ranges are provided by an apparatus comprising a suitable
peripheral or component. A range setting module can receive
normalized data from a normalization module or comparison data from
a comparison module. Data and/or information derived from or
transformed by a range setting module (e.g., set ranges, range
limits, expected elevation ranges, thresholds, and/or threshold
ranges) can be transferred from a range setting module to an
adjustment module, an outcome module, a categorization module,
plotting module or other suitable apparatus and/or module.
[0428] Categorization Module
[0429] A copy number variation (e.g., a maternal and/or fetal copy
number variation, a fetal copy number variation, a duplication,
insertion, deletion) can be categorized by a categorization module
or by an apparatus comprising a categorization module. Sometimes a
copy number variation (e.g., a maternal and/or fetal copy number
variation) is categorized by a categorization module. Sometimes an
elevation (e.g., a first elevation) determined to be significantly
different from another elevation (e.g., a second elevation) is
identified as representative of a copy number variation by a
categorization module. Sometimes the absence of a copy number
variation is determined by a categorization module. In some
embodiments, a determination of a copy number variation can be
determined by an apparatus comprising a categorization module. A
categorization module can be specialized for categorizing a
maternal and/or fetal copy number variation, a fetal copy number
variation, a duplication, deletion or insertion or lack thereof or
combination of the foregoing. For example, a categorization module
that identifies a maternal deletion can be different than and/or
distinct from a categorization module that identifies a fetal
duplication. In some embodiments, a categorization module or an
apparatus comprising a categorization module is required to
identify a copy number variation or an outcome determinative of a
copy number variation. An apparatus comprising a categorization
module can comprise at least one processor. In some embodiments, a
copy number variation or an outcome determinative of a copy number
variation is categorized by an apparatus that includes a processor
(e.g., one or more processors) which processor can perform and/or
implement one or more instructions (e.g., processes, routines
and/or subroutines) from the categorization module. In some
embodiments, a copy number variation or an outcome determinative of
a copy number variation is categorized by an apparatus that may
include multiple processors, such as processors coordinated and
working in parallel. In some embodiments, a categorization module
operates with one or more external processors (e.g., an internal or
external network, server, storage device and/or storage network
(e.g., a cloud)). Sometimes a categorization module transfers or
receives and/or gathers data and/or information to or from a
component or peripheral. Often a categorization module receives,
gathers and/or assembles counts, elevations, profiles, normalized
data and/or information, reference elevations, expected elevations,
expected ranges, uncertainty values, adjustments, adjusted
elevations, plots, comparisons and/or constants. Sometimes a
categorization module accepts and gathers input data and/or
information from an operator of an apparatus. For example,
sometimes an operator of an apparatus provides a constant, a
threshold value, a formula or a predetermined value to a module. In
some embodiments, data and/or information are provided by an
apparatus that includes multiple processors, such as processors
coordinated and working in parallel. In some embodiments,
identification or categorization of a copy number variation or an
outcome determinative of a copy number variation is provided by an
apparatus comprising a suitable peripheral or component. Sometimes
a categorization module gathers, assembles and/or receives data
and/or information from another module or apparatus. A
categorization module can receive normalized data from a
normalization module, expected elevations and/or ranges from a
range setting module, comparison data from a comparison module,
plots from a plotting module, and/or adjustment data from an
adjustment module. A categorization module can transform data
and/or information that it receives into a determination of the
presence or absence of a copy number variation. A categorization
module can transform data and/or information that it receives into
a determination that an elevation represents a genomic section
comprising a copy number variation or a specific type of copy
number variation (e.g., a maternal homozygous deletion). Data
and/or information related to a copy number variation or an outcome
determinative of a copy number variation can be transferred from a
categorization module to a suitable apparatus and/or module. A copy
number variation or an outcome determinative of a copy number
variation categorized by methods described herein can be
independently verified by further testing (e.g., by targeted
sequencing of maternal and/or fetal nucleic acid).
[0430] Fetal Fraction Determination Based on Elevation
[0431] In some embodiments, a fetal fraction is determined
according to an elevation categorized as representative of a
maternal and/or fetal copy number variation. For example
determining fetal fraction often comprises assessing an expected
elevation for a maternal and/or fetal copy number variation
utilized for the determination of fetal fraction. Sometimes a fetal
fraction is determined for an elevation (e.g., a first elevation)
categorized as representative of a copy number variation according
to an expected elevation range determined for the same type of copy
number variation. Often a fetal fraction is determined according to
an observed elevation that falls within an expected elevation range
and is thereby categorized as a maternal and/or fetal copy number
variation. Sometimes a fetal fraction is determined when an
observed elevation (e.g., a first elevation) categorized as a
maternal and/or fetal copy number variation is different than the
expected elevation determined for the same maternal and/or fetal
copy number variation.
[0432] In some embodiments an elevation (e.g., a first elevation,
an observed elevation), is significantly different than a second
elevation, the first elevation is categorized as a maternal and/or
fetal copy number variation, and a fetal fraction is determined
according to the first elevation. Sometimes a first elevation is an
observed and/or experimentally obtained elevation that is
significantly different than a second elevation in a profile and a
fetal fraction is determined according to the first elevation.
Sometimes the first elevation is an average, mean or summed
elevation and a fetal fraction is determined according to the first
elevation. In some cases a first elevation and a second elevation
are observed and/or experimentally obtained elevations and a fetal
fraction is determined according to the first elevation. In some
instances a first elevation comprises normalized counts for a first
set of genomic sections and a second elevation comprises normalized
counts for a second set of genomic sections and a fetal fraction is
determined according to the first elevation. Sometimes a first set
of genomic sections of a first elevation includes a copy number
variation (e.g., the first elevation is representative of a copy
number variation) and a fetal fraction is determined according to
the first elevation. Sometimes the first set of genomic sections of
a first elevation includes a homozygous or heterozygous maternal
copy number variation and a fetal fraction is determined according
to the first elevation. Sometimes a profile comprises a first
elevation for a first set of genomic sections and a second
elevation for a second set of genomic sections, the second set of
genomic sections includes substantially no copy number variation
(e.g., a maternal copy number variation, fetal copy number
variation, or a maternal copy number variation and a fetal copy
number variation) and a fetal fraction is determined according to
the first elevation.
[0433] In some embodiments an elevation (e.g., a first elevation,
an observed elevation), is significantly different than a second
elevation, the first elevation is categorized as for a maternal
and/or fetal copy number variation, and a fetal fraction is
determined according to the first elevation and/or an expected
elevation of the copy number variation. Sometimes a first elevation
is categorized as for a copy number variation according to an
expected elevation for a copy number variation and a fetal fraction
is determined according to a difference between the first elevation
and the expected elevation. In some cases an elevation (e.g., a
first elevation, an observed elevation) is categorized as a
maternal and/or fetal copy number variation, and a fetal fraction
is determined as twice the difference between the first elevation
and expected elevation of the copy number variation. Sometimes an
elevation (e.g., a first elevation, an observed elevation) is
categorized as a maternal and/or fetal copy number variation, the
first elevation is subtracted from the expected elevation thereby
providing a difference, and a fetal fraction is determined as twice
the difference. Sometimes an elevation (e.g., a first elevation, an
observed elevation) is categorized as a maternal and/or fetal copy
number variation, an expected elevation is subtracted from a first
elevation thereby providing a difference, and the fetal fraction is
determined as twice the difference.
[0434] Often a fetal fraction is provided as a percent. For
example, a fetal fraction can be divided by 100 thereby providing a
percent value. For example, for a first elevation representative of
a maternal homozygous duplication and having an elevation of 155
and an expected elevation for a maternal homozygous duplication
having an elevation of 150, a fetal fraction can be determined as
10% (e.g., (fetal fraction=2.times.(155-150)).
[0435] In some embodiments a fetal fraction is determined from two
or more elevations within a profile that are categorized as copy
number variations. For example, sometimes two or more
elevations
[0436] (e.g., two or more first elevations) in a profile are
identified as significantly different than a reference elevation
(e.g., a second elevation, an elevation that includes substantially
no copy number variation), the two or more elevations are
categorized as representative of a maternal and/or fetal copy
number variation and a fetal fraction is determined from each of
the two or more elevations. Sometimes a fetal fraction is
determined from about 3 or more, about 4 or more, about 5 or more,
about 6 or more, about 7 or more, about 8 or more, or about 9 or
more fetal fraction determinations within a profile. Sometimes a
fetal fraction is determined from about 10 or more, about 20 or
more, about 30 or more, about 40 or more, about 50 or more, about
60 or more, about 70 or more, about 80 or more, or about 90 or more
fetal fraction determinations within a profile. Sometimes a fetal
fraction is determined from about 100 or more, about 200 or more,
about 300 or more, about 400 or more, about 500 or more, about 600
or more, about 700 or more, about 800 or more, about 900 or more,
or about 1000 or more fetal fraction determinations within a
profile. Sometimes a fetal fraction is determined from about 10 to
about 1000, about 20 to about 900, about 30 to about 700, about 40
to about 600, about 50 to about 500, about 50 to about 400, about
50 to about 300, about 50 to about 200, or about 50 to about 100
fetal fraction determinations within a profile.
[0437] In some embodiments a fetal fraction is determined as the
average or mean of multiple fetal fraction determinations within a
profile. In some cases, a fetal fraction determined from multiple
fetal fraction determinations is a mean (e.g., an average, a mean,
a standard average, a median, or the like) of multiple fetal
fraction determinations. Often a fetal fraction determined from
multiple fetal fraction determinations is a mean value determined
by a suitable method known in the art or described herein.
Sometimes a mean value of a fetal fraction determination is a
weighted mean. Sometimes a mean value of a fetal fraction
determination is an unweighted mean. A mean, median or average
fetal fraction determination (i.e., a mean, median or average fetal
fraction determination value) generated from multiple fetal
fraction determinations is sometimes associated with an uncertainty
value (e.g., a variance, standard deviation, MAD, or the like).
Before determining a mean, median or average fetal fraction value
from multiple determinations, one or more deviant determinations
are removed in some embodiments (described in greater detail
herein).
[0438] Some fetal fraction determinations within a profile
sometimes are not included in the overall determination of a fetal
fraction (e.g., mean or average fetal fraction determination).
Sometimes a fetal fraction determination is derived from a first
elevation (e.g., a first elevation that is significantly different
than a second elevation) in a profile and the first elevation is
not indicative of a genetic variation. For example, some first
elevations (e.g., spikes or dips) in a profile are generated from
anomalies or unknown causes. Such values often generate fetal
fraction determinations that differ significantly from other fetal
fraction determinations obtained from true copy number variations.
Sometimes fetal fraction determinations that differ significantly
from other fetal fraction determinations in a profile are
identified and removed from a fetal fraction determination. For
example, some fetal fraction determinations obtained from anomalous
spikes and dips are identified by comparing them to other fetal
fraction determinations within a profile and are excluded from the
overall determination of fetal fraction.
[0439] Sometimes, an independent fetal fraction determination that
differs significantly from a mean, median or average fetal fraction
determination is an identified, recognized and/or observable
difference. In some cases, the term "differs significantly" can
mean statistically different and/or a statistically significant
difference. An "independent" fetal fraction determination can be a
fetal fraction determined (e.g., in some cases a single
determination) from a specific elevation categorized as a copy
number variation. Any suitable threshold or range can be used to
determine that a fetal fraction determination differs significantly
from a mean, median or average fetal fraction determination. In
some cases a fetal fraction determination differs significantly
from a mean, median or average fetal fraction determination and the
determination can be expressed as a percent deviation from the
average or mean value. In some cases a fetal fraction determination
that differs significantly from a mean, median or average fetal
fraction determination differs by about 10 percent or more.
Sometimes a fetal fraction determination that differs significantly
from a mean, median or average fetal fraction determination differs
by about 15 percent or more. Sometimes a fetal fraction
determination that differs significantly from a mean, median or
average fetal fraction determination differs by about 15% to about
100% or more.
[0440] In some cases a fetal fraction determination differs
significantly from a mean, median or average fetal fraction
determination according to a multiple of an uncertainty value
associated with the mean or average fetal fraction determination.
Often an uncertainty value and constant n (e.g., a confidence
interval) defines a range (e.g., an uncertainty cutoff). For
example, sometimes an uncertainty value is a standard deviation for
fetal fraction determinations (e.g., +/-5) and is multiplied by a
constant n (e.g., a confidence interval) thereby defining a range
or uncertainty cutoff (e.g., 5n to -5n, sometimes referred to as 5
sigma). Sometimes an independent fetal fraction determination falls
outside a range defined by the uncertainty cutoff and is considered
significantly different from a mean, median or average fetal
fraction determination. For example, for a mean value of 10 and an
uncertainty cutoff of 3, an independent fetal fraction greater than
13 or less than 7 is significantly different. Sometimes a fetal
fraction determination that differs significantly from a mean,
median or average fetal fraction determination differs by more than
n times the uncertainty value (e.g., n.times.sigma) where n is
about equal to or greater than 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10.
Sometimes a fetal fraction determination that differs significantly
from a mean, median or average fetal fraction determination differs
by more than n times the uncertainty value (e.g., n.times.sigma)
where n is about equal to or greater than 1.1, 1.2, 1.3, 1.4, 1.5,
1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8,
2.9, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, or 4.0.
[0441] In some embodiments, an elevation is representative of a
fetal and/or maternal microploidy. Sometimes an elevation (e.g., a
first elevation, an observed elevation), is significantly different
than a second elevation, the first elevation is categorized as a
maternal and/or fetal copy number variation, and the first
elevation and/or second elevation is representative of a fetal
microploidy and/or a maternal microploidy. In some cases a first
elevation is representative of a fetal microploidy, Sometimes a
first elevation is representative of a maternal microploidy. Often
a first elevation is representative of a fetal microploidy and a
maternal microploidy. Sometimes an elevation (e.g., a first
elevation, an observed elevation), is significantly different than
a second elevation, the first elevation is categorized as a
maternal and/or fetal copy number variation, the first elevation is
representative of a fetal and/or maternal microploidy and a fetal
fraction is determined according to the fetal and/or maternal
microploidy. In some instances a first elevation is categorized as
a maternal and/or fetal copy number variation, the first elevation
is representative of a fetal microploidy and a fetal fraction is
determined according to the fetal microploidy. Sometimes a first
elevation is categorized as a maternal and/or fetal copy number
variation, the first elevation is representative of a maternal
microploidy and a fetal fraction is determined according to the
maternal microploidy. Sometimes a first elevation is categorized as
a maternal and/or fetal copy number variation, the first elevation
is representative of a maternal and a fetal microploidy and a fetal
fraction is determined according to the maternal and fetal
microploidy.
[0442] In some embodiments, a determination of a fetal fraction
comprises determining a fetal and/or maternal microploidy.
Sometimes an elevation (e.g., a first elevation, an observed
elevation), is significantly different than a second elevation, the
first elevation is categorized as a maternal and/or fetal copy
number variation, a fetal and/or maternal microploidy is determined
according to the first elevation and/or second elevation and a
fetal fraction is determined. Sometimes a first elevation is
categorized as a maternal and/or fetal copy number variation, a
fetal microploidy is determined according to the first elevation
and/or second elevation and a fetal fraction is determined
according to the fetal microploidy. In some cases a first elevation
is categorized as a maternal and/or fetal copy number variation, a
maternal microploidy is determined according to the first elevation
and/or second elevation and a fetal fraction is determined
according to the maternal microploidy. Sometimes a first elevation
is categorized as a maternal and/or fetal copy number variation, a
maternal and fetal microploidy is determined according to the first
elevation and/or second elevation and a fetal fraction is
determined according to the maternal and fetal microploidy.
[0443] A fetal fraction often is determined when the microploidy of
the mother is different from (e.g., not the same as) the
microploidy of the fetus for a given elevation or for an elevation
categorized as a copy number variation. Sometimes a fetal fraction
is determined when the mother is homozygous for a duplication
(e.g., a microploidy of 2) and the fetus is heterozygous for the
same duplication (e.g., a microploidy of 1.5). Sometimes a fetal
fraction is determined when the mother is heterozygous for a
duplication (e.g., a microploidy of 1.5) and the fetus is
homozygous for the same duplication (e.g., a microploidy of 2) or
the duplication is absent in the fetus (e.g., a microploidy of 1).
Sometimes a fetal fraction is determined when the mother is
homozygous for a deletion (e.g., a microploidy of 0) and the fetus
is heterozygous for the same deletion (e.g., a microploidy of 0.5).
Sometimes a fetal fraction is determined when the mother is
heterozygous for a deletion (e.g., a microploidy of 0.5) and the
fetus is homozygous for the same deletion (e.g., a microploidy of
0) or the deletion is absent in the fetus (e.g., a microploidy of
1).
[0444] In some cases, a fetal fraction cannot be determined when
the microploidy of the mother is the same (e.g., identified as the
same) as the microploidy of the fetus for a given elevation
identified as a copy number variation. For example, for a given
elevation where both the mother and fetus carry the same number of
copies of a copy number variation, a fetal fraction is not
determined, in some embodiments. For example, a fetal fraction
cannot be determined for an elevation categorized as a copy number
variation when both the mother and fetus are homozygous for the
same deletion or homozygous for the same duplication. In some
cases, a fetal fraction cannot be determined for an elevation
categorized as a copy number variation when both the mother and
fetus are heterozygous for the same deletion or heterozygous for
the same duplication. In embodiments where multiple fetal fraction
determinations are made for a sample, determinations that
significantly deviate from a mean, median or average value can
result from a copy number variation for which maternal ploidy is
equal to fetal ploidy, and such determinations can be removed from
consideration.
[0445] In some embodiments the microploidy of a maternal copy
number variation and fetal copy number variation is unknown.
Sometimes, in cases when there is no determination of fetal and/or
maternal microploidy for a copy number variation, a fetal fraction
is generated and compared to a mean, median or average fetal
fraction determination. A fetal fraction determination for a copy
number variation that differs significantly from a mean, median or
average fetal fraction determination is sometimes because the
microploidy of the mother and fetus are the same for the copy
number variation. A fetal fraction determination that differs
significantly from a mean, median or average fetal fraction
determination is often excluded from an overall fetal fraction
determination regardless of the source or cause of the difference.
In some embodiments, the microploidy of the mother and/or fetus is
determined and/or verified by a method known in the art (e.g., by
targeted sequencing methods).
[0446] Elevation Adjustments
[0447] In some embodiments, one or more elevations are adjusted. A
process for adjusting an elevation often is referred to as padding.
In some embodiments, multiple elevations in a profile (e.g., a
profile of a genome, a chromosome profile, a profile of a portion
or segment of a chromosome) are adjusted. Sometimes, about 1 to
about 10,000 or more elevations in a profile are adjusted.
Sometimes about 1 to about a 1000, 1 to about 900, 1 to about 800,
1 to about 700, 1 to about 600, 1 to about 500, 1 to about 400, 1
to about 300, 1 to about 200, 1 to about 100, 1 to about 50, 1 to
about 25, 1 to about 20, 1 to about 15, 1 to about 10, or 1 to
about 5 elevations in a profile are adjusted. Sometimes one
elevation is adjusted. In some embodiments, an elevation (e.g., a
first elevation of a normalized count profile) that significantly
differs from a second elevation is adjusted. Sometimes an elevation
categorized as a copy number variation is adjusted. Sometimes an
elevation (e.g., a first elevation of a normalized count profile)
that significantly differs from a second elevation is categorized
as a copy number variation (e.g., a copy number variation, e.g., a
maternal copy number variation) and is adjusted. In some
embodiments, an elevation (e.g., a first elevation) is within an
expected elevation range for a maternal copy number variation,
fetal copy number variation, or a maternal copy number variation
and a fetal copy number variation and the elevation is adjusted.
Sometimes, one or more elevations (e.g., elevations in a profile)
are not adjusted. In some embodiments, an elevation (e.g., a first
elevation) is outside an expected elevation range for a copy number
variation and the elevation is not adjusted. Often, an elevation
within an expected elevation range for the absence of a copy number
variation is not adjusted. Any suitable number of adjustments can
be made to one or more elevations in a profile. In some
embodiments, one or more elevations are adjusted. Sometimes 2 or
more, 3 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or
more and sometimes 10 or more elevations are adjusted.
[0448] In some embodiments, a value of a first elevation is
adjusted according to a value of a second elevation. Sometimes a
first elevation, identified as representative of a copy number
variation, is adjusted to the value of a second elevation, where
the second elevation is often associated with no copy number
variation. In some cases, a value of a first elevation, identified
as representative of a copy number variation, is adjusted so the
value of the first elevation is about equal to a value of a second
elevation.
[0449] An adjustment can comprise a suitable mathematical
operation. Sometimes an adjustment comprises one or more
mathematical operations. Sometimes an elevation is adjusted by
normalizing, filtering, averaging, multiplying, dividing, adding or
subtracting or combination thereof. Sometimes an elevation is
adjusted by a predetermined value or a constant. Sometimes an
elevation is adjusted by modifying the value of the elevation to
the value of another elevation. For example, a first elevation may
be adjusted by modifying its value to the value of a second
elevation. A value in such cases may be a processed value (e.g.,
mean, normalized value and the like).
[0450] Sometimes an elevation is categorized as a copy number
variation (e.g., a maternal copy number variation) and is adjusted
according to a predetermined value referred to herein as a
predetermined adjustment value (PAV). Often a PAV is determined for
a specific copy number variation. Often a PAV determined for a
specific copy number variation (e.g., homozygous duplication,
homozygous deletion, heterozygous duplication, heterozygous
deletion) is used to adjust an elevation categorized as a specific
copy number variation (e.g., homozygous duplication, homozygous
deletion, heterozygous duplication, heterozygous deletion). In some
cases, an elevation is categorized as a copy number variation and
is then adjusted according to a PAV specific to the type of copy
number variation categorized. Sometimes an elevation (e.g., a first
elevation) is categorized as a maternal copy number variation,
fetal copy number variation, or a maternal copy number variation
and a fetal copy number variation and is adjusted by adding or
subtracting a PAV from the elevation. Often an elevation (e.g., a
first elevation) is categorized as a maternal copy number variation
and is adjusted by adding a PAV to the elevation. For example, an
elevation categorized as a duplication (e.g., a maternal, fetal or
maternal and fetal homozygous duplication) can be adjusted by
adding a PAV determined for a specific duplication (e.g., a
homozygous duplication) thereby providing an adjusted elevation.
Often a PAV determined for a copy number duplication is a negative
value. In some embodiments providing an adjustment to an elevation
representative of a duplication by utilizing a PAV determined for a
duplication results in a reduction in the value of the elevation.
In some embodiments, an elevation (e.g., a first elevation) that
significantly differs from a second elevation is categorized as a
copy number deletion (e.g., a homozygous deletion, heterozygous
deletion, homozygous duplication, homozygous duplication) and the
first elevation is adjusted by adding a PAV determined for a copy
number deletion. Often a PAV determined for a copy number deletion
is a positive value. In some embodiments providing an adjustment to
an elevation representative of a deletion by utilizing a PAV
determined for a deletion results in an increase in the value of
the elevation.
[0451] A PAV can be any suitable value. Often a PAV is determined
according to and is specific for a copy number variation (e.g., a
categorized copy number variation). In some cases a PAV is
determined according to an expected elevation for a copy number
variation (e.g., a categorized copy number variation) and/or a PAV
factor. A PAV sometimes is determined by multiplying an expected
elevation by a PAV factor. For example, a PAV for a copy number
variation can be determined by multiplying an expected elevation
determined for a copy number variation (e.g., a heterozygous
deletion) by a PAV factor determined for the same copy number
variation (e.g., a heterozygous deletion). For example, PAV can be
determined by the formula below:
PAV.sub.k=(Expected Elevation).sub.k.times.(PAV factor).sub.k
for the copy number variation k (e.g., k=a heterozygous
deletion)
[0452] A PAV factor can be any suitable value. Sometimes a PAV
factor for a homozygous duplication is between about -0.6 and about
-0.4. Sometimes a PAV factor for a homozygous duplication is about
-0.60, -0.59, -0.58, -0.57, -0.56, -0.55, -0.54, -0.53, -0.52,
-0.51, -0.50, -0.49, -0.48, -0.47, -0.46, -0.45, -0.44, -0.43,
-0.42, -0.41 and -0.40. Often a PAV factor for a homozygous
duplication is about -0.5.
[0453] For example, for an NRV of about 1 and an expected elevation
of a homozygous duplication equal to about 2, the PAV for the
homozygous duplication is determined as about -1 according to the
formula above. In this case, a first elevation categorized as a
homozygous duplication is adjusted by adding about -1 to the value
of the first elevation, for example.
[0454] Sometimes a PAV factor for a heterozygous duplication is
between about -0.4 and about -0.2. Sometimes a PAV factor for a
heterozygous duplication is about -0.40, -0.39, -0.38, -0.37,
-0.36, -0.35, -0.34, -0.33, -0.32, -0.31, -0.30, -0.29, -0.28,
-0.27, -0.26, -0.25, -0.24, -0.23, -0.22, -0.21 and -0.20. Often a
PAV factor for a heterozygous duplication is about -0.33.
[0455] For example, for an NRV of about 1 and an expected elevation
of a heterozygous duplication equal to about 1.5, the PAV for the
homozygous duplication is determined as about -0.495 according to
the formula above. In this case, a first elevation categorized as a
heterozygous duplication is adjusted by adding about -0.495 to the
value of the first elevation, for example.
[0456] Sometimes a PAV factor for a heterozygous deletion is
between about 0.4 and about 0.2. Sometimes a PAV factor for a
heterozygous deletion is about 0.40, 0.39, 0.38, 0.37, 0.36, 0.35,
0.34, 0.33, 0.32, 0.31, 0.30, 0.29, 0.28, 0.27, 0.26, 0.25, 0.24,
0.23, 0.22, 0.21 and 0.20. Often a PAV factor for a heterozygous
deletion is about 0.33.
[0457] For example, for an NRV of about 1 and an expected elevation
of a heterozygous deletion equal to about 0.5, the PAV for the
heterozygous deletion is determined as about 0.495 according to the
formula above. In this case, a first elevation categorized as a
heterozygous deletion is adjusted by adding about 0.495 to the
value of the first elevation, for example.
[0458] Sometimes a PAV factor for a homozygous deletion is between
about 0.6 and about 0.4. Sometimes a PAV factor for a homozygous
deletion is about 0.60, 0.59, 0.58, 0.57, 0.56, 0.55, 0.54, 0.53,
0.52, 0.51, 0.50, 0.49, 0.48, 0.47, 0.46, 0.45, 0.44, 0.43, 0.42,
0.41 and 0.40. Often a PAV factor for a homozygous deletion is
about 0.5.
[0459] For example, for an NRV of about 1 and an expected elevation
of a homozygous deletion equal to about 0, the PAV for the
homozygous deletion is determined as about 1 according to the
formula above. In this case, a first elevation categorized as a
homozygous deletion is adjusted by adding about 1 to the value of
the first elevation, for example.
[0460] In some cases, a PAV is about equal to or equal to an
expected elevation for a copy number variation (e.g., the expected
elevation of a copy number variation).
[0461] In some embodiments, counts of an elevation are normalized
prior to making an adjustment. In some cases, counts of some or all
elevations in a profile are normalized prior to making an
adjustment. For example, counts of an elevation can be normalized
according to counts of a reference elevation or an NRV. In some
cases, counts of an elevation (e.g., a second elevation) are
normalized according to counts of a reference elevation or an NRV
and the counts of all other elevations (e.g., a first elevation) in
a profile are normalized relative to the counts of the same
reference elevation or NRV prior to making an adjustment.
[0462] In some embodiments, an elevation of a profile results from
one or more adjustments. In some cases, an elevation of a profile
is determined after one or more elevations in the profile are
adjusted. In some embodiments, an elevation of a profile is
re-calculated after one or more adjustments are made.
[0463] In some embodiments, a copy number variation (e.g., a
maternal copy number variation, fetal copy number variation, or a
maternal copy number variation and a fetal copy number variation)
is determined (e.g., determined directly or indirectly) from an
adjustment. For example, an elevation in a profile that was
adjusted (e.g., an adjusted first elevation) can be identified as a
maternal copy number variation. In some embodiments, the magnitude
of the adjustment indicates the type of copy number variation
(e.g., heterozygous deletion, homozygous duplication, and the
like). In some cases, an adjusted elevation in a profile can be
identified as representative of a copy number variation according
to the value of a PAV for the copy number variation. For example,
for a given profile, PAV is about -1 for a homozygous duplication,
about -0.5 for a heterozygous duplication, about 0.5 for a
heterozygous deletion and about 1 for a homozygous deletion. In the
preceding example, an elevation adjusted by about -1 can be
identified as a homozygous duplication, for example. In some
embodiments, one or more copy number variations can be determined
from a profile or an elevation comprising one or more
adjustments.
[0464] In some cases, adjusted elevations within a profile are
compared. Sometimes anomalies and errors are identified by
comparing adjusted elevations. For example, often one or more
adjusted elevations in a profile are compared and a particular
elevation may be identified as an anomaly or error. Sometimes an
anomaly or error is identified within one or more genomic sections
making up an elevation. An anomaly or error may be identified
within the same elevation (e.g., in a profile) or in one or more
elevations that represent genomic sections that are adjacent,
contiguous, adjoining or abutting. Sometimes one or more adjusted
elevations are elevations of genomic sections that are adjacent,
contiguous, adjoining or abutting where the one or more adjusted
elevations are compared and an anomaly or error is identified. An
anomaly or error can be a peak or dip in a profile or elevation
where a cause of the peak or dip is known or unknown. In some cases
adjusted elevations are compared and an anomaly or error is
identified where the anomaly or error is due to a stochastic,
systematic, random or user error. Sometimes adjusted elevations are
compared and an anomaly or error is removed from a profile. In some
cases, adjusted elevations are compared and an anomaly or error is
adjusted.
[0465] Adjustment Module
[0466] In some embodiments, adjustments (e.g., adjustments to
elevations or profiles) are made by an adjustment module or by an
apparatus comprising an adjustment module. In some embodiments, an
adjustment module or an apparatus comprising an adjustment module
is required to adjust an elevation. An apparatus comprising an
adjustment module can comprise at least one processor. In some
embodiments, an adjusted elevation is provided by an apparatus that
includes a processor (e.g., one or more processors) which processor
can perform and/or implement one or more instructions (e.g.,
processes, routines and/or subroutines) from the adjustment module.
In some embodiments, an elevation is adjusted by an apparatus that
may include multiple processors, such as processors coordinated and
working in parallel. In some embodiments, an adjustment module
operates with one or more external processors (e.g., an internal or
external network, server, storage device and/or storage network
(e.g., a cloud)). Sometimes an apparatus comprising an adjustment
module gathers, assembles and/or receives data and/or information
from another module or apparatus. Sometimes an apparatus comprising
an adjustment module provides and/or transfers data and/or
information to another module or apparatus.
[0467] Sometimes an adjustment module receives and gathers data
and/or information from a component or peripheral. Often an
adjustment module receives, gathers and/or assembles counts,
elevations, profiles, reference elevations, expected elevations,
expected elevation ranges, uncertainty values, adjustments and/or
constants. Often an adjustment module receives gathers and/or
assembles elevations (e.g., first elevations) that are categorized
or determined to be copy number variations
[0468] (e.g., a maternal copy number variation, fetal copy number
variation, or a maternal copy number variation and a fetal copy
number variation). Sometimes an adjustment module accepts and
gathers input data and/or information from an operator of an
apparatus. For example, sometimes an operator of an apparatus
provides a constant, a threshold value, a formula or a
predetermined value to a module. In some embodiments, data and/or
information are provided by an apparatus that includes multiple
processors, such as processors coordinated and working in parallel.
In some embodiments, an elevation is adjusted by an apparatus
comprising a suitable peripheral or component. An apparatus
comprising an adjustment module can receive normalized data from a
normalization module, ranges from a range setting module,
comparison data from a comparison module, elevations identified
(e.g., identified as a copy number variation) from a categorization
module, and/or adjustment data from another adjustment module. An
adjustment module can receive data and/or information, transform
the received data and/or information and provide adjustments. Data
and/or information derived from, or transformed by, an adjustment
module can be transferred from an adjustment module to a
categorization module or to a suitable apparatus and/or module. An
elevation adjusted by methods described herein can be independently
verified and/or adjusted by further testing (e.g., by targeted
sequencing of maternal and or fetal nucleic acid).
[0469] Plotting Module
[0470] In some embodiments a count, an elevation, and/or a profile
is plotted (e.g., graphed). Sometimes a plot (e.g., a graph)
comprises an adjustment. Sometimes a plot comprises an adjustment
of a count, an elevation, and/or a profile. Sometimes a count, an
elevation, and/or a profile is plotted and a count, elevation,
and/or a profile comprises an adjustment. Often a count, an
elevation, and/or a profile is plotted and a count, elevation,
and/or a profile are compared. Sometimes a copy number variation
(e.g., an aneuploidy, copy number variation) is identified and/or
categorized from a plot of a count, an elevation, and/or a profile.
Sometimes an outcome is determined from a plot of a count, an
elevation, and/or a profile. In some embodiments, a plot (e.g., a
graph) is made (e.g., generated) by a plotting module or an
apparatus comprising a plotting module. In some embodiments, a
plotting module or an apparatus comprising a plotting module is
required to plot a count, an elevation or a profile. A plotting
module may display a plot or send a plot to a display (e.g., a
display module). An apparatus comprising a plotting module can
comprise at least one processor. In some embodiments, a plot is
provided by an apparatus that includes a processor (e.g., one or
more processors) which processor can perform and/or implement one
or more instructions (e.g., processes, routines and/or subroutines)
from the plotting module. In some embodiments, a plot is made by an
apparatus that may include multiple processors, such as processors
coordinated and working in parallel. In some embodiments, a
plotting module operates with one or more external processors
(e.g., an internal or external network, server, storage device
and/or storage network (e.g., a cloud)). Sometimes an apparatus
comprising a plotting module gathers, assembles and/or receives
data and/or information from another module or apparatus. Sometimes
a plotting module receives and gathers data and/or information from
a component or peripheral. Often a plotting module receives,
gathers, assembles and/or plots sequence reads, genomic sections,
mapped reads, counts, elevations, profiles, reference elevations,
expected elevations, expected elevation ranges, uncertainty values,
comparisons, categorized elevations (e.g., elevations identified as
copy number variations) and/or outcomes, adjustments and/or
constants. Sometimes a plotting module accepts and gathers input
data and/or information from an operator of an apparatus. For
example, sometimes an operator of an apparatus provides a constant,
a threshold value, a formula or a predetermined value to a plotting
module. In some embodiments, data and/or information are provided
by an apparatus that includes multiple processors, such as
processors coordinated and working in parallel. In some
embodiments, a count, an elevation and/or a profile is plotted by
an apparatus comprising a suitable peripheral or component. An
apparatus comprising a plotting module can receive normalized data
from a normalization module, ranges from a range setting module,
comparison data from a comparison module, categorization data from
a categorization module, and/or adjustment data from an adjustment
module. A plotting module can receive data and/or information,
transform the data and/or information and provided plotted data.
Sometimes an apparatus comprising a plotting module provides and/or
transfers data and/or information to another module or apparatus.
An apparatus comprising a plotting module can plot a count, an
elevation and/or a profile and provide or transfer data and/or
information related to the plotting to a suitable apparatus and/or
module. Often a plotting module receives, gathers, assembles and/or
plots elevations (e.g., profiles, first elevations) and transfers
plotted data and/or information to and from an adjustment module
and/or comparison module. Plotted data and/or information is
sometimes transferred from a plotting module to a categorization
module and/or a peripheral (e.g., a display or printer). In some
embodiments, plots are categorized and/or determined to comprise a
genetic variation (e.g., an aneuploidy) or a copy number variation
(e.g., a maternal and/or fetal copy number variation). A count, an
elevation and/or a profile plotted by methods described herein can
be independently verified and/or adjusted by further testing (e.g.,
by targeted sequencing of maternal and or fetal nucleic acid).
[0471] Sometimes an outcome is determined according to one or more
elevations. In some embodiments, a determination of the presence or
absence of a genetic variation (e.g., a chromosome aneuploidy) is
determined according to one or more adjusted elevations. Sometimes,
a determination of the presence or absence of a genetic variation
(e.g., a chromosome aneuploidy) is determined according to a
profile comprising 1 to about 10,000 adjusted elevations. Often a
determination of the presence or absence of a genetic variation
(e.g., a chromosome aneuploidy) is determined according to a
profile comprising about 1 to about a 1000, 1 to about 900, 1 to
about 800, 1 to about 700, 1 to about 600, 1 to about 500, 1 to
about 400, 1 to about 300, 1 to about 200, 1 to about 100, 1 to
about 50, 1 to about 25, 1 to about 20, 1 to about 15, 1 to about
10, or 1 to about 5 adjustments. Sometimes a determination of the
presence or absence of a genetic variation (e.g., a chromosome
aneuploidy) is determined according to a profile comprising about 1
adjustment (e.g., one adjusted elevation). Sometimes an outcome is
determined according to one or more profiles (e.g., a profile of a
chromosome or segment thereof) comprising one or more, 2 or more, 3
or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more or
sometimes 10 or more adjustments. Sometimes, a determination of the
presence or absence of a genetic variation (e.g., a chromosome
aneuploidy) is determined according to a profile where some
elevations in a profile are not adjusted. Sometimes, a
determination of the presence or absence of a genetic variation
(e.g., a chromosome aneuploidy) is determined according to a
profile where adjustments are not made.
[0472] In some embodiments, an adjustment of an elevation (e.g., a
first elevation) in a profile reduces a false determination or
false outcome. In some embodiments, an adjustment of an elevation
(e.g., a first elevation) in a profile reduces the frequency and/or
probability (e.g., statistical probability, likelihood) of a false
determination or false outcome. A false determination or outcome
can be a determination or outcome that is not accurate. A false
determination or outcome can be a determination or outcome that is
not reflective of the actual or true genetic make-up or the actual
or true genetic disposition (e.g., the presence or absence of a
genetic variation) of a subject (e.g., a pregnant female, a fetus
and/or a combination thereof). Sometimes a false determination or
outcome is a false negative determination. In some embodiments a
negative determination or negative outcome is the absence of a
genetic variation (e.g., aneuploidy, copy number variation).
Sometimes a false determination or false outcome is a false
positive determination or false positive outcome. In some
embodiments a positive determination or positive outcome is the
presence of a genetic variation (e.g., aneuploidy, copy number
variation). In some embodiments, a determination or outcome is
utilized in a diagnosis. In some embodiments, a determination or
outcome is for a fetus.
[0473] Outcome
[0474] Methods described herein can provide a determination of the
presence or absence of a genetic variation (e.g., fetal aneuploidy)
for a sample, thereby providing an outcome (e.g., thereby providing
an outcome determinative of the presence or absence of a genetic
variation (e.g., fetal aneuploidy)). A genetic variation often
includes a gain, a loss and/or alteration (e.g., duplication,
deletion, fusion, insertion, mutation, reorganization, substitution
or aberrant methylation) of genetic information (e.g., chromosomes,
segments of chromosomes, polymorphic regions, translocated regions,
altered nucleotide sequence, the like or combinations of the
foregoing) that results in a detectable change in the genome or
genetic information of a test subject with respect to a reference.
Presence or absence of a genetic variation can be determined by
transforming, analyzing and/or manipulating sequence reads that
have been mapped to genomic sections (e.g., genomic bins).
[0475] Methods described herein sometimes determine presence or
absence of a fetal aneuploidy (e.g., full chromosome aneuploidy,
partial chromosome aneuploidy or segmental chromosomal aberration
(e.g., mosaicism, deletion and/or insertion)) for a test sample
from a pregnant female bearing a fetus. Sometimes methods described
herein detect euploidy or lack of euploidy (non-euploidy) for a
sample from a pregnant female bearing a fetus. Methods described
herein sometimes detect trisomy for one or more chromosomes (e.g.,
chromosome 13, chromosome 18, chromosome 21 or combination thereof)
or segment thereof.
[0476] In some embodiments, presence or absence of a genetic
variation (e.g., a fetal aneuploidy) is determined by a method
described herein, by a method known in the art or by a combination
thereof. Presence or absence of a genetic variation generally is
determined from counts of sequence reads mapped to genomic sections
of a reference genome. Counts of sequence reads utilized to
determine presence or absence of a genetic variation sometimes are
raw counts and/or filtered counts, and often are normalized counts.
A suitable normalization process or processes can be used to
generate normalized counts, non-limiting examples of which include
bin-wise normalization, normalization by GC content, linear and
nonlinear least squares regression, LOESS, GC LOESS, LOWESS, PERUN,
RM, GCRM and combinations thereof. Normalized counts sometimes are
expressed as one or more levels or elevations in a profile for a
particular set or sets of genomic sections. Normalized counts
sometimes are adjusted or padded prior to determining presence or
absence of a genetic variation.
[0477] Presence or absence of a genetic variation (e.g., fetal
aneuploidy) sometimes is determined without comparing counts for a
set of genomic sections to a reference. Counts measured for a test
sample and are in a test region (e.g., a set of genomic sections of
interest) are referred to as "test counts" herein. Test counts
sometimes are processed counts, averaged or summed counts, a
representation, normalized counts, or one or more levels or
elevations, as described herein. Sometimes test counts are averaged
or summed (e.g., an average, mean, median, mode or sum is
calculated) for a set of genomic sections, and the averaged or
summed counts are compared to a threshold or range. Test counts
sometimes are expressed as a representation, which can be expressed
as a ratio or percentage of counts for a first set of genomic
sections to counts for a second set of genomic sections. Sometimes
the first set of genomic sections is for one or more test
chromosomes (e.g., chromosome 13, chromosome 18, chromosome 21, or
combination thereof) and sometimes the second set of genomic
sections is for the genome or a part of the genome (e.g., autosomes
or autosomes and sex chromosomes). Sometimes a representation is
compared to a threshold or range. Sometimes test counts are
expressed as one or more levels or elevations for normalized counts
over a set of genomic sections, and the one or more levels or
elevations are compared to a threshold or range. Test counts (e.g.,
averaged or summed counts, representation, normalized counts, one
or more levels or elevations) above or below a particular
threshold, in a particular range or outside a particular range
sometimes are determinative of the presence of a genetic variation
or lack of euploidy (e.g., not euploidy). Test counts (e.g.,
averaged or summed counts, representation, normalized counts, one
or more levels or elevations) below or above a particular
threshold, in a particular range or outside a particular range
sometimes are determinative of the absence of a genetic variation
or euploidy.
[0478] Presence or absence of a genetic variation (e.g., fetal
aneuploidy) sometimes is determined by comparing test counts (e.g.,
raw counts, filtered counts, averaged or summed counts,
representation, normalized counts, one or more levels or
elevations, for a set of genomic sections) to a reference. A
reference can be a suitable determination of counts. Counts for a
reference sometimes are raw counts, filtered counts, averaged or
summed counts, representation, normalized counts, one or more
levels or elevations, for a set of genomic sections. Reference
counts often are counts for a euploid test region.
[0479] In certain embodiments, test counts sometimes are for a
first set of genomic sections and a reference includes counts for a
second set of genomic sections different than the first set of
genomic sections. Reference counts sometimes are for a nucleic acid
sample from the same pregnant female from which the test sample is
obtained. Sometimes reference counts are for a nucleic acid sample
from one or more pregnant females different than the female from
which the test sample was obtained. In some embodiments, a first
set of genomic sections is in chromosome 13, chromosome 18,
chromosome 21, segment thereof or combination of the foregoing, and
the second set of genomic sections is in another chromosome or
chromosomes or segment thereof. In a non-limiting example, where a
first set of genomic sections is in chromosome 21 or segment
thereof, a second set of genomic sections often is in another
chromosome (e.g., chromosome 1, chromosome 13, chromosome 14,
chromosome 18, chromosome 19, segment thereof or combination of the
foregoing). A reference often is located in a chromosome or segment
thereof that is typically euploid. For example, chromosome 1 and
chromosome 19 often are euploid in fetuses owing to a high rate of
early fetal mortality associated with chromosome 1 and chromosome
19 aneuploidies. A measure of deviation between the test counts and
the reference counts can be generated.
[0480] Sometimes a reference comprises counts for the same set of
genomic sections as for the test counts, where the counts for the
reference are from one or more reference samples (e.g., often
multiple reference samples from multiple reference subjects). A
reference sample often is from one or more pregnant females
different than the female from which a test sample is obtained. A
measure of deviation between the test counts and the reference
counts can be generated.
[0481] A suitable measure of deviation between test counts and
reference counts can be selected, non-limiting examples of which
include standard deviation, average absolute deviation, median
absolute deviation, maximum absolute deviation, standard score
(e.g., z-value, z-score, normal score, standardized variable) and
the like. In some embodiments, reference samples are euploid for a
test region and deviation between the test counts and the reference
counts is assessed. A deviation of less than three between test
counts and reference counts (e.g., 3-sigma for standard deviation)
often is indicative of a euploid test region (e.g., absence of a
genetic variation). A deviation of greater than three between test
counts and reference counts often is indicative of a non-euploid
test region (e.g., presence of a genetic variation). Test counts
significantly below reference counts, which reference counts are
indicative of euploidy, sometimes are determinative of a monosomy.
Test counts significantly above reference counts, which reference
counts are indicative of euploidy, sometimes are determinative of a
trisomy. A measure of deviation between test counts for a test
sample and reference counts for multiple reference subjects can be
plotted and visualized (e.g., z-score plot).
[0482] Any other suitable reference can be factored with test
counts for determining presence or absence of a genetic variation
(or determination of euploid or non-euploid) for a test region of a
test sample.
[0483] For example, a fetal fraction determination can be factored
with test counts to determine the presence or absence of a genetic
variation. A suitable process for quantifying fetal fraction can be
utilized, non-limiting examples of which include a mass
spectrometric process, sequencing process or combination
thereof.
[0484] Laboratory personnel (e.g., a laboratory manager) can
analyze values (e.g., test counts, reference counts, level of
deviation) underlying a determination of the presence or absence of
a genetic variation (or determination of euploid or non-euploid for
a test region). For calls pertaining to presence or absence of a
genetic variation that are close or questionable, laboratory
personnel can re-order the same test, and/or order a different test
(e.g., karyotyping and/or amniocentesis in the case of fetal
aneuploidy determinations), that makes use of the same or different
sample nucleic acid from a test subject.
[0485] A genetic variation sometimes is associated with medical
condition. An outcome determinative of a genetic variation is
sometimes an outcome determinative of the presence or absence of a
condition (e.g., a medical condition), disease, syndrome or
abnormality, or includes, detection of a condition, disease,
syndrome or abnormality (e.g., non-limiting examples listed in
Table 1). In some cases a diagnosis comprises assessment of an
outcome. An outcome determinative of the presence or absence of a
condition (e.g., a medical condition), disease, syndrome or
abnormality by methods described herein can sometimes be
independently verified by further testing (e.g., by karyotyping
and/or amniocentesis).
[0486] Analysis and processing of data can provide one or more
outcomes. The term "outcome" as used herein can refer to a result
of data processing that facilitates determining the presence or
absence of a genetic variation (e.g., an aneuploidy, a copy number
variation). Sometimes the term "outcome" as used herein refers to a
conclusion that predicts and/or determines the presence or absence
of a genetic variation (e.g., an aneuploidy, a copy number
variation). Sometimes the term "outcome" as used herein refers to a
conclusion that predicts and/or determines a risk or probability of
the presence or absence of a genetic variation (e.g., an
aneuploidy, a copy number variation) in a subject (e.g., a fetus).
A diagnosis sometimes comprises use of an outcome. For example, a
health practitioner may analyze an outcome and provide a diagnosis
bases on, or based in part on, the outcome. In some embodiments,
determination, detection or diagnosis of a condition, syndrome or
abnormality (e.g., listed in Table 1) comprises use of an outcome
determinative of the presence or absence of a genetic variation. In
some embodiments, an outcome based on counted mapped sequence reads
or transformations thereof is determinative of the presence or
absence of a genetic variation. In certain embodiments, an outcome
generated utilizing one or more methods (e.g., data processing
methods) described herein is determinative of the presence or
absence of one or more conditions, syndromes or abnormalities
listed in Table 1. Sometimes a diagnosis comprises a determination
of a presence or absence of a condition, syndrome or abnormality.
Often a diagnosis comprises a determination of a genetic variation
as the nature and/or cause of a condition, syndrome or abnormality.
Sometimes an outcome is not a diagnosis. An outcome often comprises
one or more numerical values generated using a processing method
described herein in the context of one or more considerations of
probability. A consideration of risk or probability can include,
but is not limited to: an uncertainty value, a measure of
variability, confidence level, sensitivity, specificity, standard
deviation, coefficient of variation (CV) and/or confidence level,
Z-scores, Chi values, Phi values, ploidy values, fitted fetal
fraction, area ratios, median elevation, the like or combinations
thereof. A consideration of probability can facilitate determining
whether a subject is at risk of having, or has, a genetic
variation, and an outcome determinative of a presence or absence of
a genetic disorder often includes such a consideration.
[0487] An outcome sometimes is a phenotype. An outcome sometimes is
a phenotype with an associated level of confidence (e.g., an
uncertainty value, e.g., a fetus is positive for trisomy 21 with a
confidence level of 99%, a test subject is negative for a cancer
associated with a genetic variation at a confidence level of 95%).
Different methods of generating outcome values sometimes can
produce different types of results. Generally, there are four types
of possible scores or calls that can be made based on outcome
values generated using methods described herein: true positive,
false positive, true negative and false negative. The terms
"score", "scores", "call" and "calls" as used herein refer to
calculating the probability that a particular genetic variation is
present or absent in a subject/sample. The value of a score may be
used to determine, for example, a variation, difference, or ratio
of mapped sequence reads that may correspond to a genetic
variation. For example, calculating a positive score for a selected
genetic variation or genomic section from a data set, with respect
to a reference genome can lead to an identification of the presence
or absence of a genetic variation, which genetic variation
sometimes is associated with a medical condition (e.g., cancer,
preeclampsia, trisomy, monosomy, and the like). In some
embodiments, an outcome comprises an elevation, a profile and/or a
plot (e.g., a profile plot). In those embodiments in which an
outcome comprises a profile, a suitable profile or combination of
profiles can be used for an outcome. Non-limiting examples of
profiles that can be used for an outcome include z-score profiles,
p-value profiles, chi value profiles, phi value profiles, the like,
and combinations thereof.
[0488] An outcome generated for determining the presence or absence
of a genetic variation sometimes includes a null result (e.g., a
data point between two clusters, a numerical value with a standard
deviation that encompasses values for both the presence and absence
of a genetic variation, a data set with a profile plot that is not
similar to profile plots for subjects having or free from the
genetic variation being investigated). In some embodiments, an
outcome indicative of a null result still is a determinative
result, and the determination can include the need for additional
information and/or a repeat of the data generation and/or analysis
for determining the presence or absence of a genetic variation.
[0489] An outcome can be generated after performing one or more
processing steps described herein, in some embodiments. In certain
embodiments, an outcome is generated as a result of one of the
processing steps described herein, and in some embodiments, an
outcome can be generated after each statistical and/or mathematical
manipulation of a data set is performed. An outcome pertaining to
the determination of the presence or absence of a genetic variation
can be expressed in a suitable form, which form comprises without
limitation, a probability (e.g., odds ratio, p-value), likelihood,
value in or out of a cluster, value over or under a threshold
value, value within a range (e.g., a threshold range), value with a
measure of variance or confidence, or risk factor, associated with
the presence or absence of a genetic variation for a subject or
sample. In certain embodiments, comparison between samples allows
confirmation of sample identity (e.g., allows identification of
repeated samples and/or samples that have been mixed up (e.g.,
mislabeled, combined, and the like)).
[0490] In some embodiments, an outcome comprises a value above or
below a predetermined threshold or cutoff value (e.g., greater than
1, less than 1), and an uncertainty or confidence level associated
with the value. Sometimes a predetermined threshold or cutoff value
is an expected elevation or an expected elevation range. An outcome
also can describe an assumption used in data processing. In certain
embodiments, an outcome comprises a value that falls within or
outside a predetermined range of values (e.g., a threshold range)
and the associated uncertainty or confidence level for that value
being inside or outside the range. In some embodiments, an outcome
comprises a value that is equal to a predetermined value (e.g.,
equal to 1, equal to zero), or is equal to a value within a
predetermined value range, and its associated uncertainty or
confidence level for that value being equal or within or outside a
range. An outcome sometimes is graphically represented as a plot
(e.g., profile plot).
[0491] As noted above, an outcome can be characterized as a true
positive, true negative, false positive or false negative. The term
"true positive" as used herein refers to a subject correctly
diagnosed as having a genetic variation. The term "false positive"
as used herein refers to a subject wrongly identified as having a
genetic variation. The term "true negative" as used herein refers
to a subject correctly identified as not having a genetic
variation. The term "false negative" as used herein refers to a
subject wrongly identified as not having a genetic variation. Two
measures of performance for any given method can be calculated
based on the ratios of these occurrences: (i) a sensitivity value,
which generally is the fraction of predicted positives that are
correctly identified as being positives; and (ii) a specificity
value, which generally is the fraction of predicted negatives
correctly identified as being negative. The term "sensitivity" as
used herein refers to the number of true positives divided by the
number of true positives plus the number of false negatives, where
sensitivity (sens) may be within the range of
0.ltoreq.sens.ltoreq.1. Ideally, the number of false negatives
equal zero or close to zero, so that no subject is wrongly
identified as not having at least one genetic variation when they
indeed have at least one genetic variation. Conversely, an
assessment often is made of the ability of a prediction algorithm
to classify negatives correctly, a complementary measurement to
sensitivity. The term "specificity" as used herein refers to the
number of true negatives divided by the number of true negatives
plus the number of false positives, where sensitivity (spec) may be
within the range of 0 spec 1. Ideally, the number of false
positives equal zero or close to zero, so that no subject is
wrongly identified as having at least one genetic variation when
they do not have the genetic variation being assessed.
[0492] In certain embodiments, one or more of sensitivity,
specificity and/or confidence level are expressed as a percentage.
In some embodiments, the percentage, independently for each
variable, is greater than about 90% (e.g., about 90, 91, 92, 93,
94, 95, 96, 97, 98 or 99%, or greater than 99% (e.g., about 99.5%,
or greater, about 99.9% or greater, about 99.95% or greater, about
99.99% or greater)). Coefficient of variation (CV) in some
embodiments is expressed as a percentage, and sometimes the
percentage is about 10% or less (e.g., about 10, 9, 8, 7, 6, 5, 4,
3, 2 or 1%, or less than 1% (e.g., about 0.5% or less, about 0.1%
or less, about 0.05% or less, about 0.01% or less)). A probability
(e.g., that a particular outcome is not due to chance) in certain
embodiments is expressed as a Z-score, a p-value, or the results of
a t-test. In some embodiments, a measured variance, confidence
interval, sensitivity, specificity and the like (e.g., referred to
collectively as confidence parameters) for an outcome can be
generated using one or more data processing manipulations described
herein. Specific examples of generating outcomes and associated
confidence levels are described in the Example section.
[0493] A method that has sensitivity and specificity equaling one,
or 100%, or near one (e.g., between about 90% to about 99%)
sometimes is selected. In some embodiments, a method having a
sensitivity equaling 1, or 100% is selected, and in certain
embodiments, a method having a sensitivity near 1 is selected
(e.g., a sensitivity of about 90%, a sensitivity of about 91%, a
sensitivity of about 92%, a sensitivity of about 93%, a sensitivity
of about 94%, a sensitivity of about 95%, a sensitivity of about
96%, a sensitivity of about 97%, a sensitivity of about 98%, or a
sensitivity of about 99%). In some embodiments, a method having a
specificity equaling 1, or 100% is selected, and in certain
embodiments, a method having a specificity near 1 is selected
(e.g., a specificity of about 90%, a specificity of about 91%, a
specificity of about 92%, a specificity of about 93%, a specificity
of about 94%, a specificity of about 95%, a specificity of about
96%, a specificity of about 97%, a specificity of about 98%, or a
specificity of about 99%).
[0494] Outcome Module
[0495] The presence or absence of a genetic variation (an
aneuploidy, a fetal aneuploidy, a copy number variation) can be
identified by an outcome module or by an apparatus comprising an
outcome module. Sometimes a genetic variation is identified by an
outcome module. Often a determination of the presence or absence of
an aneuploidy is identified by an outcome module. In some
embodiments, an outcome determinative of a genetic variation (an
aneuploidy, a copy number variation) can be identified by an
outcome module or by an apparatus comprising an outcome module. An
outcome module can be specialized for determining a specific
genetic variation (e.g., a trisomy, a trisomy 21, a trisomy 18).
For example, an outcome module that identifies a trisomy 21 can be
different than and/or distinct from an outcome module that
identifies a trisomy 18. In some embodiments, an outcome module or
an apparatus comprising an outcome module is required to identify a
genetic variation or an outcome determinative of a genetic
variation (e.g., an aneuploidy, a copy number variation). An
apparatus comprising an outcome module can comprise at least one
processor. In some embodiments, a genetic variation or an outcome
determinative of a genetic variation is provided by an apparatus
that includes a processor (e.g., one or more processors) which
processor can perform and/or implement one or more instructions
(e.g., processes, routines and/or subroutines) from the outcome
module. In some embodiments, a genetic variation or an outcome
determinative of a genetic variation is identified by an apparatus
that may include multiple processors, such as processors
coordinated and working in parallel. In some embodiments, an
outcome module operates with one or more external processors (e.g.,
an internal or external network, server, storage device and/or
storage network (e.g., a cloud)). Sometimes an apparatus comprising
an outcome module gathers, assembles and/or receives data and/or
information from another module or apparatus. Sometimes an
apparatus comprising an outcome module provides and/or transfers
data and/or information to another module or apparatus. Sometimes
an outcome module transfers, receives or gathers data and/or
information to or from a component or peripheral. Often an outcome
module receives, gathers and/or assembles counts, elevations,
profiles, normalized data and/or information, reference elevations,
expected elevations, expected ranges, uncertainty values,
adjustments, adjusted elevations, plots, categorized elevations,
comparisons and/or constants. Sometimes an outcome module accepts
and gathers input data and/or information from an operator of an
apparatus. For example, sometimes an operator of an apparatus
provides a constant, a threshold value, a formula or a
predetermined value to an outcome module. In some embodiments, data
and/or information are provided by an apparatus that includes
multiple processors, such as processors coordinated and working in
parallel. In some embodiments, identification of a genetic
variation or an outcome determinative of a genetic variation is
provided by an apparatus comprising a suitable peripheral or
component. An apparatus comprising an outcome module can receive
normalized data from a normalization module, expected elevations
and/or ranges from a range setting module, comparison data from a
comparison module, categorized elevations from a categorization
module, plots from a plotting module, and/or adjustment data from
an adjustment module. An outcome module can receive data and/or
information, transform the data and/or information and provide an
outcome. An outcome module can provide or transfer data and/or
information related to a genetic variation or an outcome
determinative of a genetic variation to a suitable apparatus and/or
module. A genetic variation or an outcome determinative of a
genetic variation identified by methods described herein can be
independently verified by further testing (e.g., by targeted
sequencing of maternal and/or fetal nucleic acid).
[0496] After one or more outcomes have been generated, an outcome
often is used to provide a determination of the presence or absence
of a genetic variation and/or associated medical condition. An
outcome typically is provided to a health care professional (e.g.,
laboratory technician or manager; physician or assistant). Often an
outcome is provided by an outcome module. Sometimes an outcome is
provided by a plotting module. Sometimes an outcome is provided on
a peripheral or component of an apparatus. For example, sometimes
an outcome is provided by a printer or display. In some
embodiments, an outcome determinative of the presence or absence of
a genetic variation is provided to a healthcare professional in the
form of a report, and in certain embodiments the report comprises a
display of an outcome value and an associated confidence parameter.
Generally, an outcome can be displayed in a suitable format that
facilitates determination of the presence or absence of a genetic
variation and/or medical condition. Non-limiting examples of
formats suitable for use for reporting and/or displaying data sets
or reporting an outcome include digital data, a graph, a 2D graph,
a 3D graph, and 4D graph, a picture, a pictograph, a chart, a bar
graph, a pie graph, a diagram, a flow chart, a scatter plot, a map,
a histogram, a density chart, a function graph, a circuit diagram,
a block diagram, a bubble map, a constellation diagram, a contour
diagram, a cartogram, spider chart, Venn diagram, nomogram, and the
like, and combination of the foregoing. Various examples of outcome
representations are shown in the drawings and are described in the
Examples.
[0497] Generating an outcome can be viewed as a transformation of
nucleic acid sequence read data, or the like, into a representation
of a subject's cellular nucleic acid, in certain embodiments. For
example, analyzing sequence reads of nucleic acid from a subject
and generating a chromosome profile and/or outcome can be viewed as
a transformation of relatively small sequence read fragments to a
representation of relatively large chromosome structure. In some
embodiments, an outcome results from a transformation of sequence
reads from a subject (e.g., a pregnant female), into a
representation of an existing structure (e.g., a genome, a
chromosome or segment thereof) present in the subject (e.g., a
maternal and/or fetal nucleic acid). In some embodiments, an
outcome comprises a transformation of sequence reads from a first
subject (e.g., a pregnant female), into a composite representation
of structures (e.g., a genome, a chromosome or segment thereof),
and a second transformation of the composite representation that
yields a representation of a structure present in a first subject
(e.g., a pregnant female) and/or a second subject (e.g., a
fetus).
[0498] Use of Outcomes
[0499] A health care professional, or other qualified individual,
receiving a report comprising one or more outcomes determinative of
the presence or absence of a genetic variation can use the
displayed data in the report to make a call regarding the status of
the test subject or patient. The healthcare professional can make a
recommendation based on the provided outcome, in some
embodiments.
[0500] A health care professional or qualified individual can
provide a test subject or patient with a call or score with regards
to the presence or absence of the genetic variation based on the
outcome value or values and associated confidence parameters
provided in a report, in some embodiments. In certain embodiments,
a score or call is made manually by a healthcare professional or
qualified individual, using visual observation of the provided
report. In certain embodiments, a score or call is made by an
automated routine, sometimes embedded in software, and reviewed by
a healthcare professional or qualified individual for accuracy
prior to providing information to a test subject or patient. The
term "receiving a report" as used herein refers to obtaining, by a
communication means, a written and/or graphical representation
comprising an outcome, which upon review allows a healthcare
professional or other qualified individual to make a determination
as to the presence or absence of a genetic variation in a test
subject or patient. The report may be generated by a computer or by
human data entry, and can be communicated using electronic means
(e.g., over the internet, via computer, via fax, from one network
location to another location at the same or different physical
sites), or by a other method of sending or receiving data (e.g.,
mail service, courier service and the like). In some embodiments
the outcome is transmitted to a health care professional in a
suitable medium, including, without limitation, in verbal,
document, or file form. The file may be, for example, but not
limited to, an auditory file, a computer readable file, a paper
file, a laboratory file or a medical record file.
[0501] The term "providing an outcome" and grammatical equivalents
thereof, as used herein also can refer to a method for obtaining
such information, including, without limitation, obtaining the
information from a laboratory (e.g., a laboratory file). A
laboratory file can be generated by a laboratory that carried out
one or more assays or one or more data processing steps to
determine the presence or absence of the medical condition. The
laboratory may be in the same location or different location (e.g.,
in another country) as the personnel identifying the presence or
absence of the medical condition from the laboratory file. For
example, the laboratory file can be generated in one location and
transmitted to another location in which the information therein
will be transmitted to the pregnant female subject. The laboratory
file may be in tangible form or electronic form (e.g., computer
readable form), in certain embodiments.
[0502] In some embodiments, an outcome can be provided to a health
care professional, physician or qualified individual from a
laboratory and the health care professional, physician or qualified
individual can make a diagnosis based on the outcome. In some
embodiments, an outcome can be provided to a health care
professional, physician or qualified individual from a laboratory
and the health care professional, physician or qualified individual
can make a diagnosis based, in part, on the outcome along with
additional data and/or information and other outcomes
[0503] A healthcare professional or qualified individual, can
provide a suitable recommendation based on the outcome or outcomes
provided in the report. Non-limiting examples of recommendations
that can be provided based on the provided outcome report includes,
surgery, radiation therapy, chemotherapy, genetic counseling, after
birth treatment solutions (e.g., life planning, long term assisted
care, medicaments, symptomatic treatments), pregnancy termination,
organ transplant, blood transfusion, the like or combinations of
the foregoing. In some embodiments the recommendation is dependent
on the outcome based classification provided (e.g., Down's
syndrome, Turner syndrome, medical conditions associated with
genetic variations in T13, medical conditions associated with
genetic variations in T18).
[0504] Software can be used to perform one or more steps in the
processes described herein, including but not limited to; counting,
data processing, generating an outcome, and/or providing one or
more recommendations based on generated outcomes, as described in
greater detail hereafter.
[0505] Transformations
[0506] As noted above, data sometimes is transformed from one form
into another form. The terms "transformed", "transformation", and
grammatical derivations or equivalents thereof, as used herein
refer to an alteration of data from a physical starting material
(e.g., test subject and/or reference subject sample nucleic acid)
into a digital representation of the physical starting material
(e.g., sequence read data), and in some embodiments includes a
further transformation into one or more numerical values or
graphical representations of the digital representation that can be
utilized to provide an outcome. In certain embodiments, the one or
more numerical values and/or graphical representations of digitally
represented data can be utilized to represent the appearance of a
test subject's physical genome (e.g., virtually represent or
visually represent the presence or absence of a genomic insertion,
duplication or deletion; represent the presence or absence of a
variation in the physical amount of a sequence associated with
medical conditions). A virtual representation sometimes is further
transformed into one or more numerical values or graphical
representations of the digital representation of the starting
material. These procedures can transform physical starting material
into a numerical value or graphical representation, or a
representation of the physical appearance of a test subject's
genome.
[0507] In some embodiments, transformation of a data set
facilitates providing an outcome by reducing data complexity and/or
data dimensionality. Data set complexity sometimes is reduced
during the process of transforming a physical starting material
into a virtual representation of the starting material (e.g.,
sequence reads representative of physical starting material). A
suitable feature or variable can be utilized to reduce data set
complexity and/or dimensionality. Non-limiting examples of features
that can be chosen for use as a target feature for data processing
include GC content, fetal gender prediction, identification of
chromosomal aneuploidy, identification of particular genes or
proteins, identification of cancer, diseases, inherited
genes/traits, chromosomal abnormalities, a biological category, a
chemical category, a biochemical category, a category of genes or
proteins, a gene ontology, a protein ontology, co-regulated genes,
cell signaling genes, cell cycle genes, proteins pertaining to the
foregoing genes, gene variants, protein variants, co-regulated
genes, co-regulated proteins, amino acid sequence, nucleotide
sequence, protein structure data and the like, and combinations of
the foregoing. Non-limiting examples of data set complexity and/or
dimensionality reduction include; reduction of a plurality of
sequence reads to profile plots, reduction of a plurality of
sequence reads to numerical values (e.g., normalized values,
Z-scores, p-values); reduction of multiple analysis methods to
probability plots or single points; principle component analysis of
derived quantities; and the like or combinations thereof.
[0508] Genomic Section Normalization Systems, Apparatus and
Computer Program Products
[0509] In certain aspects provided is a system comprising one or
more processors and memory, which memory comprises instructions
executable by the one or more processors and which memory comprises
counts of sequence reads of circulating, cell-free sample nucleic
acid from a test subject mapped to genomic sections of a reference
genome; and which instructions executable by the one or more
processors are configured to: (a) generate a sample normalized
count profile by normalizing counts of the sequence reads for each
of the genomic sections; and (b) determine the presence or absence
of a segmental chromosomal aberration or a fetal aneuploidy or both
from the sample normalized count profile in (a).
[0510] Provided also in certain aspects is an apparatus comprising
one or more processors and memory, which memory comprises
instructions executable by the one or more processors and which
memory comprises counts of sequence reads of circulating, cell-free
sample nucleic acid from a test subject mapped to genomic sections
of a reference genome; and which instructions executable by the one
or more processors are configured to: (a) generate a sample
normalized count profile by normalizing counts of the sequence
reads for each of the genomic sections; and (b) determine the
presence or absence of a segmental chromosomal aberration or a
fetal aneuploidy or both from the sample normalized count profile
in (a).
[0511] Also provided in certain aspects is a computer program
product tangibly embodied on a computer-readable medium, comprising
instructions that when executed by one or more processors are
configured to: (a) access counts of sequence reads of circulating,
cell-free sample nucleic acid from a test subject mapped to genomic
sections of a reference genome; (b) generate a sample normalized
count profile by normalizing counts of the sequence reads for each
of the genomic sections; and (c) determine the presence or absence
of a segmental chromosomal aberration or a fetal aneuploidy or both
from the sample normalized count profile in (b).
[0512] In some embodiments, the counts of the sequence reads for
each of the genomic sections in a segment of the reference genome
(e.g., the segment is a chromosome) individually are normalized
according to the total counts of sequence reads in the genomic
sections in the segment. Certain genomic sections in the segment
sometimes are removed (e.g., filtered) and the remaining genomic
sections in the segment are normalized.
[0513] In certain embodiments, the system, apparatus and/or
computer program product comprises a: (i) a sequencing module
configured to obtain nucleic acid sequence reads; (ii) a mapping
module configured to map nucleic acid sequence reads to portions of
a reference genome; (iii) a weighting module configured to weight
genomic sections, (iv) a filtering module configured to filter
genomic sections or counts mapped to a genomic section, (v) a
counting module configured to provide counts of nucleic acid
sequence reads mapped to portions of a reference genome; (vi) a
normalization module configured to provide normalized counts; (vii)
a comparison module configured to provide an identification of a
first elevation that is significantly different than a second
elevation; (viii) a range setting module configured to provide one
or more expected level ranges; (ix) a categorization module
configured to identify an elevation representative of a copy number
variation; (x) an adjustment module configured to adjust a level
identified as a copy number variation; (xi) a plotting module
configured to graph and display a level and/or a profile; (xii) an
outcome module configured to determine an outcome (e.g., outcome
determinative of the presence or absence of a fetal aneuploidy);
(xiii) a data display organization module configured to indicate
the presence or absence of a segmental chromosomal aberration or a
fetal aneuploidy or both; (xiv) a logic processing module
configured to perform one or more of map sequence reads, count
mapped sequence reads, normalize counts and generate an outcome; or
(xv) combination of two or more of the foregoing.
[0514] In some embodiments the sequencing module and mapping module
are configured to transfer sequence reads from the sequencing
module to the mapping module. The mapping module and counting
module sometimes are configured to transfer mapped sequence reads
from the mapping module to the counting module. The counting module
and filtering module sometimes are configured to transfer counts
from the counting module to the filtering module. The counting
module and weighting module sometimes are configured to transfer
counts from the counting module to the weighting module. The
mapping module and filtering module sometimes are configured to
transfer mapped sequence reads from the mapping module to the
filtering module. The mapping module and weighting module sometimes
are configured to transfer mapped sequence reads from the mapping
module to the weighting module. Sometimes the weighting module,
filtering module and counting module are configured to transfer
filtered and/or weighted genomic sections from the weighting module
and filtering module to the counting module. The weighting module
and normalization module sometimes are configured to transfer
weighted genomic sections from the weighting module to the
normalization module. The filtering module and normalization module
sometimes are configured to transfer filtered genomic sections from
the filtering module to the normalization module. In some
embodiments, the normalization module and/or comparison module are
configured to transfer normalized counts to the comparison module
and/or range setting module. The comparison module, range setting
module and/or categorization module independently are configured to
transfer (i) an identification of a first elevation that is
significantly different than a second elevation and/or (ii) an
expected level range from the comparison module and/or range
setting module to the categorization module, in some embodiments.
In certain embodiments, the categorization module and the
adjustment module are configured to transfer an elevation
categorized as a copy number variation from the categorization
module to the adjustment module. In some embodiments, the
adjustment module, plotting module and the outcome module are
configured to transfer one or more adjusted levels from the
adjustment module to the plotting module or outcome module. The
normalization module sometimes is configured to transfer mapped
normalized sequence read counts to one or more of the comparison
module, range setting module, categorization module, adjustment
module, outcome module or plotting module.
[0515] Parameterized Error Removal and Unbiased Normalization
Systems, Apparatus and Computer Program Products
[0516] Provided in certain aspects is a system comprising one or
more processors and memory, which memory comprises instructions
executable by the one or more processors and which memory comprises
counts of sequence reads mapped to portions of a reference genome,
which sequence reads are reads of circulating cell-free nucleic
acid from a test sample; and which instructions executable by the
one or more processors are configured to: (a) determine a guanine
and cytosine (GC) bias for each of the portions of the reference
genome for multiple samples from a fitted relation for each sample
between (i) the counts of the sequence reads mapped to each of the
portions of the reference genome, and (ii) GC content for each of
the portions; and (b) calculate a genomic section level for each of
the portions of the reference genome from a fitted relation between
(i) the GC bias and (ii) the counts of the sequence reads mapped to
each of the portions of the reference genome, thereby providing
calculated genomic section levels, whereby bias in the counts of
the sequence reads mapped to each of the portions of the reference
genome is reduced in the calculated genomic section levels.
[0517] Also provided in some aspects is an apparatus comprising one
or more processors and memory, which memory comprises instructions
executable by the one or more processors and which memory comprises
counts of sequence reads mapped to portions of a reference genome,
which sequence reads are reads of circulating cell-free nucleic
acid from a test sample; and which instructions executable by the
one or more processors are configured to: (a) determine a guanine
and cytosine (GC) bias for each of the portions of the reference
genome for multiple samples from a fitted relation for each sample
between (i) the counts of the sequence reads mapped to each of the
portions of the reference genome, and (ii) GC content for each of
the portions; and (b) calculate a genomic section level for each of
the portions of the reference genome from a fitted relation between
(i) the GC bias and (ii) the counts of the sequence reads mapped to
each of the portions of the reference genome, thereby providing
calculated genomic section levels, whereby bias in the counts of
the sequence reads mapped to each of the portions of the reference
genome is reduced in the calculated genomic section levels.
[0518] Also provided in certain aspects is a computer program
product tangibly embodied on a computer-readable medium, comprising
instructions that when executed by one or more processors are
configured to: (a) access counts of sequence reads mapped to
portions of a reference genome, which sequence reads are reads of
circulating cell-free nucleic acid from a test sample; (b)
determine a guanine and cytosine (GC) bias for each of the portions
of the reference genome for multiple samples from a fitted relation
for each sample between (i) the counts of the sequence reads mapped
to each of the portions of the reference genome, and (ii) GC
content for each of the portions; and (c) calculate a genomic
section level for each of the portions of the reference genome from
a fitted relation between (i) the GC bias and (ii) the counts of
the sequence reads mapped to each of the portions of the reference
genome, thereby providing calculated genomic section levels,
whereby bias in the counts of the sequence reads mapped to each of
the portions of the reference genome is reduced in the calculated
genomic section levels.
[0519] Provided in certain aspects is a system comprising one or
more processors and memory, which memory comprises instructions
executable by the one or more processors and which memory comprises
counts of sequence reads mapped to portions of a reference genome,
which sequence reads are reads of circulating cell-free nucleic
acid from a pregnant female bearing a fetus; and which instructions
executable by the one or more processors are configured to: (a)
determine a guanine and cytosine (GC) bias for each of the portions
of the reference genome for multiple samples from a fitted relation
for each sample between (i) the counts of the sequence reads mapped
to each of the portions of the reference genome, and (ii) GC
content for each of the portions; (b) calculate a genomic section
level for each of the portions of the reference genome from a
fitted relation between the GC bias and the counts of the sequence
reads mapped to each of the portions of the reference genome,
thereby providing calculated genomic section levels; and (c)
identify the presence or absence of an aneuploidy for the fetus
according to the calculated genomic section levels with a
sensitivity of 95% or greater and a specificity of 95% or
greater.
[0520] Also provided in certain aspects is an apparatus comprising
one or more processors and memory, which memory comprises
instructions executable by the one or more processors and which
memory comprises counts of sequence reads mapped to portions of a
reference genome, which sequence reads are reads of circulating
cell-free nucleic acid from a pregnant female bearing a fetus; and
which instructions executable by the one or more processors are
configured to: (a) determine a guanine and cytosine (GC) bias for
each of the portions of the reference genome for multiple samples
from a fitted relation for each sample between (i) the counts of
the sequence reads mapped to each of the portions of the reference
genome, and (ii) GC content for each of the portions; (b) calculate
a genomic section level for each of the portions of the reference
genome from a fitted relation between the GC bias and the counts of
the sequence reads mapped to each of the portions of the reference
genome, thereby providing calculated genomic section levels; and
(c) identify the presence or absence of an aneuploidy for the fetus
according to the calculated genomic section levels with a
sensitivity of 95% or greater and a specificity of 95% or
greater.
[0521] Provided also in certain aspects is a computer program
product tangibly embodied on a computer-readable medium, comprising
instructions that when executed by one or more processors are
configured to: (a) access counts of sequence reads mapped to
portions of a reference genome, which sequence reads are reads of
circulating cell-free nucleic acid from a pregnant female bearing a
fetus; (b) determine a guanine and cytosine (GC) bias for each of
the portions of the reference genome for multiple samples from a
fitted relation for each sample between (i) the counts of the
sequence reads mapped to each of the portions of the reference
genome, and (ii) GC content for each of the portions; (c) calculate
a genomic section level for each of the portions of the reference
genome from a fitted relation between the GC bias and the counts of
the sequence reads mapped to each of the portions of the reference
genome, thereby providing calculated genomic section levels; and
(d) identify the presence or absence of an aneuploidy for the fetus
according to the calculated genomic section levels with a
sensitivity of 95% or greater and a specificity of 95% or
greater.
[0522] Also provided in certain aspects is a system comprising one
or more processors and memory, which memory comprises instructions
executable by the one or more processors and which memory comprises
counts of sequence reads mapped to portions of a reference genome,
which sequence reads are reads of circulating cell-free nucleic
acid from a pregnant female bearing a fetus; and which instructions
executable by the one or more processors are configured to: (a)
determine experimental bias for each of the portions of the
reference genome for multiple samples from a fitted relation
between (i) the counts of the sequence reads mapped to each of the
portions of the reference genome, and (ii) a mapping feature for
each of the portions; and (b) calculate a genomic section level for
each of the portions of the reference genome from a fitted relation
between the experimental bias and the counts of the sequence reads
mapped to each of the portions of the reference genome, thereby
providing calculated genomic section levels, whereby bias in the
counts of the sequence reads mapped to each of the portions of the
reference genome is reduced in the calculated genomic section
levels.
[0523] Provided also in certain aspects is an apparatus comprising
one or more processors and memory, which memory comprises
instructions executable by the one or more processors and which
memory comprises counts of sequence reads mapped to portions of a
reference genome, which sequence reads are reads of circulating
cell-free nucleic acid from a pregnant female bearing a fetus; and
which instructions executable by the one or more processors are
configured to: (a) determine experimental bias for each of the
portions of the reference genome for multiple samples from a fitted
relation between (i) the counts of the sequence reads mapped to
each of the portions of the reference genome, and (ii) a mapping
feature for each of the portions; and (b) calculate a genomic
section level for each of the portions of the reference genome from
a fitted relation between the experimental bias and the counts of
the sequence reads mapped to each of the portions of the reference
genome, thereby providing calculated genomic section levels,
whereby bias in the counts of the sequence reads mapped to each of
the portions of the reference genome is reduced in the calculated
genomic section levels.
[0524] Also provided in certain aspects is a computer program
product tangibly embodied on a computer-readable medium, comprising
instructions that when executed by one or more processors are
configured to: (a) access counts of sequence reads mapped to
portions of a reference genome, which sequence reads are reads of
circulating cell-free nucleic acid from a test sample; (b)
determine experimental bias for each of the portions of the
reference genome for multiple samples from a fitted relation
between (i) the counts of the sequence reads mapped to each of the
portions of the reference genome, and (ii) a mapping feature for
each of the portions; and (c) calculate a genomic section level for
each of the portions of the reference genome from a fitted relation
between the experimental bias and the counts of the sequence reads
mapped to each of the portions of the reference genome, thereby
providing calculated genomic section levels, whereby bias in the
counts of the sequence reads mapped to each of the portions of the
reference genome is reduced in the calculated genomic section
levels.
[0525] In certain embodiments, the system, apparatus and/or
computer program product comprises a: (i) a sequencing module
configured to obtain nucleic acid sequence reads; (ii) a mapping
module configured to map nucleic acid sequence reads to portions of
a reference genome; (iii) a weighting module configured to weight
genomic sections; (iv) a filtering module configured to filter
genomic sections or counts mapped to a genomic section; (v) a
counting module configured to provide counts of nucleic acid
sequence reads mapped to portions of a reference genome; (vi) a
normalization module configured to provide normalized counts; (vii)
a comparison module configured to provide an identification of a
first elevation that is significantly different than a second
elevation; (viii) a range setting module configured to provide one
or more expected level ranges; (ix) a categorization module
configured to identify an elevation representative of a copy number
variation; (x) an adjustment module configured to adjust a level
identified as a copy number variation; (xi) a plotting module
configured to graph and display a level and/or a profile; (xii) an
outcome module configured to determine an outcome (e.g., outcome
determinative of the presence or absence of a fetal aneuploidy);
(xiii) a data display organization module configured to indicate
the presence or absence of a segmental chromosomal aberration or a
fetal aneuploidy or both; (xiv) a logic processing module
configured to perform one or more of map sequence reads, count
mapped sequence reads, normalize counts and generate an outcome; or
(xv) combination of two or more of the foregoing.
[0526] In some embodiments the sequencing module and mapping module
are configured to transfer sequence reads from the sequencing
module to the mapping module. The mapping module and counting
module sometimes are configured to transfer mapped sequence reads
from the mapping module to the counting module. The counting module
and filtering module sometimes are configured to transfer counts
from the counting module to the filtering module. The counting
module and weighting module sometimes are configured to transfer
counts from the counting module to the weighting module. The
mapping module and filtering module sometimes are configured to
transfer mapped sequence reads from the mapping module to the
filtering module. The mapping module and weighting module sometimes
are configured to transfer mapped sequence reads from the mapping
module to the weighting module. Sometimes the weighting module,
filtering module and counting module are configured to transfer
filtered and/or weighted genomic sections from the weighting module
and filtering module to the counting module. The weighting module
and normalization module sometimes are configured to transfer
weighted genomic sections from the weighting module to the
normalization module. The filtering module and normalization module
sometimes are configured to transfer filtered genomic sections from
the filtering module to the normalization module. In some
embodiments, the normalization module and/or comparison module are
configured to transfer normalized counts to the comparison module
and/or range setting module. The comparison module, range setting
module and/or categorization module independently are configured to
transfer (i) an identification of a first elevation that is
significantly different than a second elevation and/or (ii) an
expected level range from the comparison module and/or range
setting module to the categorization module, in some embodiments.
In certain embodiments, the categorization module and the
adjustment module are configured to transfer an elevation
categorized as a copy number variation from the categorization
module to the adjustment module. In some embodiments, the
adjustment module, plotting module and the outcome module are
configured to transfer one or more adjusted levels from the
adjustment module to the plotting module or outcome module. The
normalization module sometimes is configured to transfer mapped
normalized sequence read counts to one or more of the comparison
module, range setting module, categorization module, adjustment
module, outcome module or plotting module.
[0527] Adjustment Systems, Apparatus and Computer Program
Products
[0528] Provided in certain aspects is a system comprising one or
more processors and memory, which memory comprises instructions
executable by the one or more processors and which memory comprises
counts of nucleic acid sequence reads mapped to genomic sections of
a reference genome, which sequence reads are reads of circulating
cell-free nucleic acid from a pregnant female; and which
instructions executable by the one or more processors are
configured to: (a) normalize the counts mapped to the genomic
sections of the reference genome, thereby providing a profile of
normalized counts for the genomic sections; (b) identify a first
elevation of the normalized counts significantly different than a
second elevation of the normalized counts in the profile, which
first elevation is for a first set of genomic sections, and which
second elevation is for a second set of genomic sections; (c)
determine an expected elevation range for a homozygous and
heterozygous copy number variation according to an uncertainty
value for a segment of the genome; (d) adjust the first elevation
by a predetermined value when the first elevation is within one of
the expected elevation ranges, thereby providing an adjustment of
the first elevation; and (e) determine the presence or absence of a
chromosome aneuploidy in the fetus according to the elevations of
genomic sections comprising the adjustment of (d), whereby the
outcome determinative of the presence or absence of the chromosome
aneuploidy is generated from the nucleic acid sequence reads.
[0529] Also provided in some aspects is an apparatus comprising one
or more processors and memory, which memory comprises instructions
executable by the one or more processors and which memory comprises
counts of nucleic acid sequence reads mapped to genomic sections of
a reference genome, which sequence reads are reads of circulating
cell-free nucleic acid from a pregnant female; and which
instructions executable by the one or more processors are
configured to: (a) normalize the counts mapped to the genomic
sections of the reference genome, thereby providing a profile of
normalized counts for the genomic sections; (b) identify a first
elevation of the normalized counts significantly different than a
second elevation of the normalized counts in the profile, which
first elevation is for a first set of genomic sections, and which
second elevation is for a second set of genomic sections; (c)
determine an expected elevation range for a homozygous and
heterozygous copy number variation according to an uncertainty
value for a segment of the genome; (d) adjust the first elevation
by a predetermined value when the first elevation is within one of
the expected elevation ranges, thereby providing an adjustment of
the first elevation; and (e) determine the presence or absence of a
chromosome aneuploidy in the fetus according to the elevations of
genomic sections comprising the adjustment of (d), whereby the
outcome determinative of the presence or absence of the chromosome
aneuploidy is generated from the nucleic acid sequence reads.
[0530] Provided also in certain aspects is a computer program
product tangibly embodied on a computer-readable medium, comprising
instructions that when executed by one or more processors are
configured to: (a) access counts of nucleic acid sequence reads
mapped to genomic sections of a reference genome, which sequence
reads are reads of circulating cell-free nucleic acid from a
pregnant female; (b) normalize the counts mapped to the genomic
sections of the reference genome, thereby providing a profile of
normalized counts for the genomic sections; (c) identify a first
elevation of the normalized counts significantly different than a
second elevation of the normalized counts in the profile, which
first elevation is for a first set of genomic sections, and which
second elevation is for a second set of genomic sections; (d)
determine an expected elevation range for a homozygous and
heterozygous copy number variation according to an uncertainty
value for a segment of the genome; (e) adjust the first elevation
by a predetermined value when the first elevation is within one of
the expected elevation ranges, thereby providing an adjustment of
the first elevation; and (f) determine the presence or absence of a
chromosome aneuploidy in the fetus according to the elevations of
genomic sections comprising the adjustment of (e), whereby the
outcome determinative of the presence or absence of the chromosome
aneuploidy is generated from the nucleic acid sequence reads.
[0531] Also provided in certain aspects is a system comprising one
or more processors and memory, which memory comprises instructions
executable by the one or more processors and which memory comprises
counts of nucleic acid sequence reads mapped to genomic sections of
a reference genome, which sequence reads are reads of circulating
cell-free nucleic acid from a pregnant female; and which
instructions executable by the one or more processors are
configured to: (a) normalize the counts mapped to the genomic
sections of the reference genome, thereby providing a profile of
normalized counts for the genomic sections; (b) identify a first
elevation of the normalized counts significantly different than a
second elevation of the normalized counts in the profile, which
first elevation is for a first set of genomic sections, and which
second elevation is for a second set of genomic sections; (c)
determine an expected elevation range for a homozygous and
heterozygous copy number variation according to an uncertainty
value for a segment of the genome; and (d) identify a maternal
and/or fetal copy number variation within the genomic section based
on one of the expected elevation ranges, whereby the maternal
and/or fetal copy number variation is identified from the nucleic
acid sequence reads.
[0532] Provided also in some aspects is an apparatus comprising one
or more processors and memory, which memory comprises instructions
executable by the one or more processors and which memory comprises
counts of nucleic acid sequence reads mapped to genomic sections of
a reference genome, which sequence reads are reads of circulating
cell-free nucleic acid from a pregnant female; and which
instructions executable by the one or more processors are
configured to: (a) normalize the counts mapped to the genomic
sections of the reference genome, thereby providing a profile of
normalized counts for the genomic sections; (b) identify a first
elevation of the normalized counts significantly different than a
second elevation of the normalized counts in the profile, which
first elevation is for a first set of genomic sections, and which
second elevation is for a second set of genomic sections; (c)
determine an expected elevation range for a homozygous and
heterozygous copy number variation according to an uncertainty
value for a segment of the genome; and (d) identify a maternal
and/or fetal copy number variation within the genomic section based
on one of the expected elevation ranges, whereby the maternal
and/or fetal copy number variation is identified from the nucleic
acid sequence reads.
[0533] Also provided in certain aspects is a computer program
product tangibly embodied on a computer-readable medium, comprising
instructions that when executed by one or more processors are
configured to: (a) access counts of nucleic acid sequence reads
mapped to genomic sections of a reference genome, which sequence
reads are reads of circulating cell-free nucleic acid from a
pregnant female; (b) normalize the counts mapped to the genomic
sections of the reference genome, thereby providing a profile of
normalized counts for the genomic sections; (c) identify a first
elevation of the normalized counts significantly different than a
second elevation of the normalized counts in the profile, which
first elevation is for a first set of genomic sections, and which
second elevation is for a second set of genomic sections; (d)
determine an expected elevation range for a homozygous and
heterozygous copy number variation according to an uncertainty
value for a segment of the genome; and (e) identify a maternal
and/or fetal copy number variation within the genomic section based
on one of the expected elevation ranges, whereby the maternal
and/or fetal copy number variation is identified from the nucleic
acid sequence reads.
[0534] Provided also in some aspects is a system comprising one or
more processors and memory, which memory comprises instructions
executable by the one or more processors and which memory comprises
counts of nucleic acid sequence reads mapped to genomic sections of
a reference genome, which sequence reads are reads of circulating
cell-free nucleic acid from a pregnant female; and which
instructions executable by the one or more processors are
configured to: (a) normalize the counts mapped to the genomic
sections of the reference genome, thereby providing a profile of
normalized counts for the genomic sections; (b) identify a first
elevation of the normalized counts significantly different than a
second elevation of the normalized counts in the profile, which
first elevation is for a first set of genomic sections, and which
second elevation is for a second set of genomic sections; (c)
determine an expected elevation range for a homozygous and
heterozygous copy number variation according to an uncertainty
value for a segment of the genome; (d) adjust the first elevation
according to the second elevation, thereby providing an adjustment
of the first elevation; and (e) determine the presence or absence
of a chromosome aneuploidy in the fetus according to the elevations
of genomic sections comprising the adjustment of (d), whereby the
outcome determinative of the presence or absence of the chromosome
aneuploidy is generated from the nucleic acid sequence reads.
[0535] In certain aspects provided is an apparatus comprising one
or more processors and memory, which memory comprises instructions
executable by the one or more processors and which memory comprises
counts of nucleic acid sequence reads mapped to genomic sections of
a reference genome, which sequence reads are reads of circulating
cell-free nucleic acid from a pregnant female; and which
instructions executable by the one or more processors are
configured to: (a) normalize the counts mapped to the genomic
sections of the reference genome, thereby providing a profile of
normalized counts for the genomic sections; (b) identify a first
elevation of the normalized counts significantly different than a
second elevation of the normalized counts in the profile, which
first elevation is for a first set of genomic sections, and which
second elevation is for a second set of genomic sections; (c)
determine an expected elevation range for a homozygous and
heterozygous copy number variation according to an uncertainty
value for a segment of the genome; (d) adjust the first elevation
according to the second elevation, thereby providing an adjustment
of the first elevation; and (e) determine the presence or absence
of a chromosome aneuploidy in the fetus according to the elevations
of genomic sections comprising the adjustment of (d), whereby the
outcome determinative of the presence or absence of the chromosome
aneuploidy is generated from the nucleic acid sequence reads.
[0536] Provided in some aspects is a computer program product
tangibly embodied on a computer-readable medium, comprising
instructions that when executed by one or more processors are
configured to: (a) access counts of nucleic acid sequence reads
mapped to genomic sections of a reference genome, which sequence
reads are reads of circulating cell-free nucleic acid from a
pregnant female; (b) normalize the counts mapped to the genomic
sections of the reference genome, thereby providing a profile of
normalized counts for the genomic sections; (c) identify a first
elevation of the normalized counts significantly different than a
second elevation of the normalized counts in the profile, which
first elevation is for a first set of genomic sections, and which
second elevation is for a second set of genomic sections; (d)
determine an expected elevation range for a homozygous and
heterozygous copy number variation according to an uncertainty
value for a segment of the genome; (e) adjust the first elevation
according to the second elevation, thereby providing an adjustment
of the first elevation; and (f) determine the presence or absence
of a chromosome aneuploidy in the fetus according to the elevations
of genomic sections comprising the adjustment of (e), whereby the
outcome determinative of the presence or absence of the chromosome
aneuploidy is generated from the nucleic acid sequence reads.
[0537] In certain embodiments, the system, apparatus and/or
computer program product comprises a: (i) a sequencing module
configured to obtain nucleic acid sequence reads; (ii) a mapping
module configured to map nucleic acid sequence reads to portions of
a reference genome; (iii) a weighting module configured to weight
genomic sections; (iv) a filtering module configured to filter
genomic sections or counts mapped to a genomic section; (v) a
counting module configured to provide counts of nucleic acid
sequence reads mapped to portions of a reference genome; (vi) a
normalization module configured to provide normalized counts; (vii)
a comparison module configured to provide an identification of a
first elevation that is significantly different than a second
elevation; (viii) a range setting module configured to provide one
or more expected level ranges; (ix) a categorization module
configured to identify an elevation representative of a copy number
variation; (x) an adjustment module configured to adjust a level
identified as a copy number variation; (xi) a plotting module
configured to graph and display a level and/or a profile; (xii) an
outcome module configured to determine an outcome (e.g., outcome
determinative of the presence or absence of a fetal aneuploidy);
(xiii) a data display organization module configured to indicate
the presence or absence of a segmental chromosomal aberration or a
fetal aneuploidy or both; (xiv) a logic processing module
configured to perform one or more of map sequence reads, count
mapped sequence reads, normalize counts and generate an outcome; or
(xv) combination of two or more of the foregoing.
[0538] In some embodiments the sequencing module and mapping module
are configured to transfer sequence reads from the sequencing
module to the mapping module. The mapping module and counting
module sometimes are configured to transfer mapped sequence reads
from the mapping module to the counting module. The counting module
and filtering module sometimes are configured to transfer counts
from the counting module to the filtering module. The counting
module and weighting module sometimes are configured to transfer
counts from the counting module to the weighting module. The
mapping module and filtering module sometimes are configured to
transfer mapped sequence reads from the mapping module to the
filtering module. The mapping module and weighting module sometimes
are configured to transfer mapped sequence reads from the mapping
module to the weighting module. Sometimes the weighting module,
filtering module and counting module are configured to transfer
filtered and/or weighted genomic sections from the weighting module
and filtering module to the counting module. The weighting module
and normalization module sometimes are configured to transfer
weighted genomic sections from the weighting module to the
normalization module. The filtering module and normalization module
sometimes are configured to transfer filtered genomic sections from
the filtering module to the normalization module. In some
embodiments, the normalization module and/or comparison module are
configured to transfer normalized counts to the comparison module
and/or range setting module. The comparison module, range setting
module and/or categorization module independently are configured to
transfer (i) an identification of a first elevation that is
significantly different than a second elevation and/or (ii) an
expected level range from the comparison module and/or range
setting module to the categorization module, in some embodiments.
In certain embodiments, the categorization module and the
adjustment module are configured to transfer an elevation
categorized as a copy number variation from the categorization
module to the adjustment module. In some embodiments, the
adjustment module, plotting module and the outcome module are
configured to transfer one or more adjusted levels from the
adjustment module to the plotting module or outcome module. The
normalization module sometimes is configured to transfer mapped
normalized sequence read counts to one or more of the comparison
module, range setting module, categorization module, adjustment
module, outcome module or plotting module.
[0539] Machines, Software and Interfaces
[0540] Certain processes and methods described herein (e.g.,
quantifying, mapping, normalizing, range setting, adjusting,
categorizing, counting and/or determining sequence reads, counts,
elevations (e.g., elevations) and/or profiles) often cannot be
performed without a computer, processor, software, module or other
apparatus. Methods described herein typically are
computer-implemented methods, and one or more portions of a method
sometimes are performed by one or more processors. Embodiments
pertaining to methods described in this document generally are
applicable to the same or related processes implemented by
instructions in systems, apparatus and computer program products
described herein. In some embodiments, processes and methods
described herein (e.g., quantifying, counting and/or determining
sequence reads, counts, elevations and/or profiles) are performed
by automated methods. In some embodiments, an automated method is
embodied in software, modules, processors, peripherals and/or an
apparatus comprising the like, that determine sequence reads,
counts, mapping, mapped sequence tags, elevations, profiles,
normalizations, comparisons, range setting, categorization,
adjustments, plotting, outcomes, transformations and
identifications. As used herein, software refers to computer
readable program instructions that, when executed by a processor,
perform computer operations, as described herein.
[0541] Sequence reads, counts, elevations, and profiles derived
from a test subject (e.g., a patient, a pregnant female) and/or
from a reference subject can be further analyzed and processed to
determine the presence or absence of a genetic variation. Sequence
reads, counts, elevations and/or profiles sometimes are referred to
as "data" or "data sets". In some embodiments, data or data sets
can be characterized by one or more features or variables (e.g.,
sequence based [e.g., GC content, specific nucleotide sequence, the
like], function specific [e.g., expressed genes, cancer genes, the
like], location based [genome specific, chromosome specific,
genomic section or bin specific], the like and combinations
thereof). In certain embodiments, data or data sets can be
organized into a matrix having two or more dimensions based on one
or more features or variables. Data organized into matrices can be
organized using any suitable features or variables. A non-limiting
example of data in a matrix includes data that is organized by
maternal age, maternal ploidy, and fetal contribution. In certain
embodiments, data sets characterized by one or more features or
variables sometimes are processed after counting.
[0542] Apparatuses, software and interfaces may be used to conduct
methods described herein. Using apparatuses, software and
interfaces, a user may enter, request, query or determine options
for using particular information, programs or processes (e.g.,
mapping sequence reads, processing mapped data and/or providing an
outcome), which can involve implementing statistical analysis
algorithms, statistical significance algorithms, statistical
algorithms, iterative steps, validation algorithms, and graphical
representations, for example. In some embodiments, a data set may
be entered by a user as input information, a user may download one
or more data sets by a suitable hardware media (e.g., flash drive),
and/or a user may send a data set from one system to another for
subsequent processing and/or providing an outcome (e.g., send
sequence read data from a sequencer to a computer system for
sequence read mapping; send mapped sequence data to a computer
system for processing and yielding an outcome and/or report).
[0543] A system typically comprises one or more apparatus. Each
apparatus comprises one or more of memory, one or more processors,
and instructions. Where a system includes two or more apparatus,
some or all of the apparatus may be located at the same location,
some or all of the apparatus may be located at different locations,
all of the apparatus may be located at one location and/or all of
the apparatus may be located at different locations. Where a system
includes two or more apparatus, some or all of the apparatus may be
located at the same location as a user, some or all of the
apparatus may be located at a location different than a user, all
of the apparatus may be located at the same location as the user,
and/or all of the apparatus may be located at one or more locations
different than the user.
[0544] A system sometimes comprises a computing apparatus and a
sequencing apparatus, where the sequencing apparatus is configured
to receive physical nucleic acid and generate sequence reads, and
the computing apparatus is configured to process the reads from the
sequencing apparatus. The computing apparatus sometimes is
configured to determine the presence or absence of a genetic
variation (e.g., copy number variation; fetal chromosome
aneuploidy) from the sequence reads.
[0545] A user may, for example, place a query to software which
then may acquire a data set via internet access, and in certain
embodiments, a programmable processor may be prompted to acquire a
suitable data set based on given parameters. A programmable
processor also may prompt a user to select one or more data set
options selected by the processor based on given parameters. A
programmable processor may prompt a user to select one or more data
set options selected by the processor based on information found
via the internet, other internal or external information, or the
like. Options may be chosen for selecting one or more data feature
selections, one or more statistical algorithms, one or more
statistical analysis algorithms, one or more statistical
significance algorithms, iterative steps, one or more validation
algorithms, and one or more graphical representations of methods,
apparatuses, or computer programs.
[0546] Systems addressed herein may comprise general components of
computer systems, such as, for example, network servers, laptop
systems, desktop systems, handheld systems, personal digital
assistants, computing kiosks, and the like. A computer system may
comprise one or more input means such as a keyboard, touch screen,
mouse, voice recognition or other means to allow the user to enter
data into the system. A system may further comprise one or more
outputs, including, but not limited to, a display screen (e.g., CRT
or LCD), speaker, FAX machine, printer (e.g., laser, ink jet,
impact, black and white or color printer), or other output useful
for providing visual, auditory and/or hardcopy output of
information (e.g., outcome and/or report).
[0547] In a system, input and output means may be connected to a
central processing unit which may comprise among other components,
a microprocessor for executing program instructions and memory for
storing program code and data. In some embodiments, processes may
be implemented as a single user system located in a single
geographical site. In certain embodiments, processes may be
implemented as a multi-user system. In the case of a multi-user
implementation, multiple central processing units may be connected
by means of a network. The network may be local, encompassing a
single department in one portion of a building, an entire building,
span multiple buildings, span a region, span an entire country or
be worldwide. The network may be private, being owned and
controlled by a provider, or it may be implemented as an internet
based service where the user accesses a web page to enter and
retrieve information. Accordingly, in certain embodiments, a system
includes one or more machines, which may be local or remote with
respect to a user. More than one machine in one location or
multiple locations may be accessed by a user, and data may be
mapped and/or processed in series and/or in parallel. Thus, a
suitable configuration and control may be utilized for mapping
and/or processing data using multiple machines, such as in local
network, remote network and/or "cloud" computing platforms.
[0548] A system can include a communications interface in some
embodiments. A communications interface allows for transfer of
software and data between a computer system and one or more
external devices. Non-limiting examples of communications
interfaces include a modem, a network interface (such as an
Ethernet card), a communications port, a PCMCIA slot and card, and
the like. Software and data transferred via a communications
interface generally are in the form of signals, which can be
electronic, electromagnetic, optical and/or other signals capable
of being received by a communications interface. Signals often are
provided to a communications interface via a channel. A channel
often carries signals and can be implemented using wire or cable,
fiber optics, a phone line, a cellular phone link, an RF link
and/or other communications channels. Thus, in an example, a
communications interface may be used to receive signal information
that can be detected by a signal detection module.
[0549] Data may be input by a suitable device and/or method,
including, but not limited to, manual input devices or direct data
entry devices (DDEs). Non-limiting examples of manual devices
include keyboards, concept keyboards, touch sensitive screens,
light pens, mouse, tracker balls, joysticks, graphic tablets,
scanners, digital cameras, video digitizers and voice recognition
devices. Non-limiting examples of DDEs include bar code readers,
magnetic strip codes, smart cards, magnetic ink character
recognition, optical character recognition, optical mark
recognition, and turnaround documents.
[0550] In some embodiments, output from a sequencing apparatus may
serve as data that can be input via an input device. In certain
embodiments, mapped sequence reads may serve as data that can be
input via an input device. In certain embodiments, simulated data
is generated by an in silico process and the simulated data serves
as data that can be input via an input device. The term "in silico"
refers to research and experiments performed using a computer. In
silico processes include, but are not limited to, mapping sequence
reads and processing mapped sequence reads according to processes
described herein.
[0551] A system may include software useful for performing a
process described herein, and software can include one or more
modules for performing such processes (e.g., sequencing module,
logic processing module, data display organization module). The
term "software" refers to computer readable program instructions
that, when executed by a computer, perform computer operations.
Instructions executable by the one or more processors sometimes are
provided as executable code, that when executed, can cause one or
more processors to implement a method described herein. A module
described herein can exist as software, and instructions (e.g.,
processes, routines, subroutines) embodied in the software can be
implemented or performed by a processor.
[0552] For example, a module (e.g., a software module) can be a
part of a program that performs a particular process or task. The
term "module" refers to a self-contained functional unit that can
be used in a larger apparatus or software system. A module can
comprise a set of instructions for carrying out a function of the
module. A module can transform data and/or information. Data and/or
information can be in a suitable form. For example, data and/or
information can be digital or analogue. In some cases, data and/or
information can be packets, bytes, characters, or bits. In some
embodiments, data and/or information can be any gathered, assembled
or usable data or information. Non-limiting examples of data and/or
information include a suitable media, pictures, video, sound (e.g.
frequencies, audible or non-audible), numbers, constants, a value,
objects, time, functions, instructions, maps, references,
sequences, reads, mapped reads, elevations, ranges, thresholds,
signals, displays, representations, or transformations thereof. A
module can accept or receive data and/or information, transform the
data and/or information into a second form, and provide or transfer
the second form to an apparatus, peripheral, component or another
module. A module can perform one or more of the following
non-limiting functions: mapping sequence reads, providing counts,
assembling genomic sections, providing or determining an elevation,
providing a count profile, normalizing (e.g., normalizing reads,
normalizing counts, and the like), providing a normalized count
profile or elevations of normalized counts, comparing two or more
elevations, providing uncertainty values, providing or determining
expected elevations and expected ranges (e.g., expected elevation
ranges, threshold ranges and threshold elevations), providing
adjustments to elevations (e.g., adjusting a first elevation,
adjusting a second elevation, adjusting a profile of a chromosome
or a segment thereof, and/or padding), providing identification
(e.g., identifying a copy number variation, genetic variation or
aneuploidy), categorizing, plotting, and/or determining an outcome,
for example. A processor can, in some cases, carry out the
instructions in a module. In some embodiments, one or more
processors are required to carry out instructions in a module or
group of modules. A module can provide data and/or information to
another module, apparatus or source and can receive data and/or
information from another module, apparatus or source.
[0553] A computer program product sometimes is embodied on a
tangible computer-readable medium, and sometimes is tangibly
embodied on a non-transitory computer-readable medium. A module
sometimes is stored on a computer readable medium (e.g., disk,
drive) or in memory (e.g., random access memory). A module and
processor capable of implementing instructions from a module can be
located in an apparatus or in different apparatus. A module and/or
processor capable of implementing an instruction for a module can
be located in the same location as a user (e.g., local network) or
in a different location from a user (e.g., remote network, cloud
system). In embodiments in which a method is carried out in
conjunction with two or more modules, the modules can be located in
the same apparatus, one or more modules can be located in different
apparatus in the same physical location, and one or more modules
may be located in different apparatus in different physical
locations.
[0554] An apparatus, in some embodiments, comprises at least one
processor for carrying out the instructions in a module. Counts of
sequence reads mapped to genomic sections of a reference genome
sometimes are accessed by a processor that executes instructions
configured to carry out a method described herein. Counts that are
accessed by a processor can be within memory of a system, and the
counts can be accessed and placed into the memory of the system
after they are obtained. In some embodiments, an apparatus includes
a processor (e.g., one or more processors) which processor can
perform and/or implement one or more instructions (e.g., processes,
routines and/or subroutines) from a module. In some embodiments, an
apparatus includes multiple processors, such as processors
coordinated and working in parallel. In some embodiments, an
apparatus operates with one or more external processors (e.g., an
internal or external network, server, storage device and/or storage
network (e.g., a cloud)). In some embodiments, an apparatus
comprises a module. Sometimes an apparatus comprises one or more
modules. An apparatus comprising a module often can receive and
transfer one or more of data and/or information to and from other
modules. In some cases, an apparatus comprises peripherals and/or
components. Sometimes an apparatus can comprise one or more
peripherals or components that can transfer data and/or information
to and from other modules, peripherals and/or components. Sometimes
an apparatus interacts with a peripheral and/or component that
provides data and/or information. Sometimes peripherals and
components assist an apparatus in carrying out a function or
interact directly with a module. Non-limiting examples of
peripherals and/or components include a suitable computer
peripheral, I/O or storage method or device including but not
limited to scanners, printers, displays (e.g., monitors, LED, LCT
or CRTs), cameras, microphones, pads (e.g., ipads, tablets), touch
screens, smart phones, mobile phones, USB I/O devices, USB mass
storage devices, keyboards, a computer mouse, digital pens, modems,
hard drives, jump drives, flash drives, a processor, a server, CDs,
DVDs, graphic cards, specialized I/O devices (e.g., sequencers,
photo cells, photo multiplier tubes, optical readers, sensors,
etc.), one or more flow cells, fluid handling components, network
interface controllers, ROM, RAM, wireless transfer methods and
devices (Bluetooth, WiFi, and the like), the world wide web (www),
the internet, a computer and/or another module.
[0555] One or more of a sequencing module, logic processing module
and data display organization module can be utilized in a method
described herein. Sometimes a logic processing module, sequencing
module or data display organization module, or an apparatus
comprising one or more such modules, gather, assemble, receive,
provide and/or transfer data and/or information to or from another
module, apparatus, component, peripheral or operator of an
apparatus. For example, sometimes an operator of an apparatus
provides a constant, a threshold value, a formula or a
predetermined value to a logic processing module, sequencing module
or data display organization module. A logic processing module,
sequencing module or data display organization module can receive
data and/or information from another module, non-limiting examples
of which include a logic processing module, sequencing module, data
display organization module, sequencing module, sequencing module,
mapping module, counting module, normalization module, comparison
module, range setting module, categorization module, adjustment
module, plotting module, outcome module, data display organization
module and/or logic processing module, the like or combination
thereof. Data and/or information derived from or transformed by a
logic processing module, sequencing module or data display
organization module can be transferred from a logic processing
module, sequencing module or data display organization module to a
sequencing module, sequencing module, mapping module, counting
module, normalization module, comparison module, range setting
module, categorization module, adjustment module, plotting module,
outcome module, data display organization module, logic processing
module or other suitable apparatus and/or module. A sequencing
module can receive data and/or information form a logic processing
module and/or sequencing module and transfer data and/or
information to a logic processing module and/or a mapping module,
for example. Sometimes a logic processing module orchestrates,
controls, limits, organizes, orders, distributes, partitions,
transforms and/or regulates data and/or information or the transfer
of data and/or information to and from one or more other modules,
peripherals or devices. A data display organization module can
receive data and/or information form a logic processing module
and/or plotting module and transfer data and/or information to a
logic processing module, plotting module, display, peripheral or
device. An apparatus comprising a logic processing module,
sequencing module or data display organization module can comprise
at least one processor. In some embodiments, data and/or
information are provided by an apparatus that includes a processor
(e.g., one or more processors) which processor can perform and/or
implement one or more instructions (e.g., processes, routines
and/or subroutines) from the logic processing module, sequencing
module and/or data display organization module. In some
embodiments, a logic processing module, sequencing module or data
display organization module operates with one or more external
processors (e.g., an internal or external network, server, storage
device and/or storage network (e.g., a cloud)).
[0556] Software often is provided on a program product containing
program instructions recorded on a computer readable medium,
including, but not limited to, magnetic media including floppy
disks, hard disks, and magnetic tape; and optical media including
CD-ROM discs, DVD discs, magneto-optical discs, flash drives, RAM,
floppy discs, the like, and other such media on which the program
instructions can be recorded. In online implementation, a server
and web site maintained by an organization can be configured to
provide software downloads to remote users, or remote users may
access a remote system maintained by an organization to remotely
access software. Software may obtain or receive input information.
Software may include a module that specifically obtains or receives
data (e.g., a data receiving module that receives sequence read
data and/or mapped read data) and may include a module that
specifically processes the data (e.g., a processing module that
processes received data (e.g., filters, normalizes, provides an
outcome and/or report). The terms "obtaining" and "receiving" input
information refers to receiving data (e.g., sequence reads, mapped
reads) by computer communication means from a local, or remote
site, human data entry, or any other method of receiving data. The
input information may be generated in the same location at which it
is received, or it may be generated in a different location and
transmitted to the receiving location. In some embodiments, input
information is modified before it is processed (e.g., placed into a
format amenable to processing (e.g., tabulated)). In some
embodiments, provided are computer program products, such as, for
example, a computer program product comprising a computer usable
medium having a computer readable program code embodied therein,
the computer readable program code adapted to be executed to
implement a method comprising: (a) obtaining sequence reads of
sample nucleic acid from a test subject; (b) mapping the sequence
reads obtained in (a) to a known genome, which known genome has
been divided into genomic sections; (c) counting the mapped
sequence reads within the genomic sections; (d) generating a sample
normalized count profile by normalizing the counts for the genomic
sections obtained in (c); and (e) determining the presence or
absence of a genetic variation from the sample normalized count
profile in (d).
[0557] Software can include one or more algorithms in certain
embodiments. An algorithm may be used for processing data and/or
providing an outcome or report according to a finite sequence of
instructions. An algorithm often is a list of defined instructions
for completing a task. Starting from an initial state, the
instructions may describe a computation that proceeds through a
defined series of successive states, eventually terminating in a
final ending state. The transition from one state to the next is
not necessarily deterministic (e.g., some algorithms incorporate
randomness). By way of example, and without limitation, an
algorithm can be a search algorithm, sorting algorithm, merge
algorithm, numerical algorithm, graph algorithm, string algorithm,
modeling algorithm, computational genometric algorithm,
combinatorial algorithm, machine learning algorithm, cryptography
algorithm, data compression algorithm, parsing algorithm and the
like. An algorithm can include one algorithm or two or more
algorithms working in combination. An algorithm can be of any
suitable complexity class and/or parameterized complexity. An
algorithm can be used for calculation and/or data processing, and
in some embodiments, can be used in a deterministic or
probabilistic/predictive approach. An algorithm can be implemented
in a computing environment by use of a suitable programming
language, non-limiting examples of which are C, C++, Java, Perl,
Python, Fortran, and the like. In some embodiments, an algorithm
can be configured or modified to include margin of errors,
statistical analysis, statistical significance, and/or comparison
to other information or data sets (e.g., applicable when using a
neural net or clustering algorithm).
[0558] In certain embodiments, several algorithms may be
implemented for use in software. These algorithms can be trained
with raw data in some embodiments. For each new raw data sample,
the trained algorithms may produce a representative processed data
set or outcome. A processed data set sometimes is of reduced
complexity compared to the parent data set that was processed.
Based on a processed set, the performance of a trained algorithm
may be assessed based on sensitivity and specificity, in some
embodiments. An algorithm with the highest sensitivity and/or
specificity may be identified and utilized, in certain
embodiments.
[0559] In certain embodiments, simulated (or simulation) data can
aid data processing, for example, by training an algorithm or
testing an algorithm. In some embodiments, simulated data includes
hypothetical various samplings of different groupings of sequence
reads. Simulated data may be based on what might be expected from a
real population or may be skewed to test an algorithm and/or to
assign a correct classification. Simulated data also is referred to
herein as "virtual" data. Simulations can be performed by a
computer program in certain embodiments. One possible step in using
a simulated data set is to evaluate the confidence of an identified
result, e.g., how well a random sampling matches or best represents
the original data. One approach is to calculate a probability value
(p-value), which estimates the probability of a random sample
having better score than the selected samples. In some embodiments,
an empirical model may be assessed, in which it is assumed that at
least one sample matches a reference sample (with or without
resolved variations). In some embodiments, another distribution,
such as a Poisson distribution for example, can be used to define
the probability distribution.
[0560] A system may include one or more processors in certain
embodiments. A processor can be connected to a communication bus. A
computer system may include a main memory, often random access
memory (RAM), and can also include a secondary memory. Memory in
some embodiments comprises a non-transitory computer-readable
storage medium. Secondary memory can include, for example, a hard
disk drive and/or a removable storage drive, representing a floppy
disk drive, a magnetic tape drive, an optical disk drive, memory
card and the like. A removable storage drive often reads from
and/or writes to a removable storage unit. Non-limiting examples of
removable storage units include a floppy disk, magnetic tape,
optical disk, and the like, which can be read by and written to by,
for example, a removable storage drive. A removable storage unit
can include a computer-usable storage medium having stored therein
computer software and/or data.
[0561] A processor may implement software in a system. In some
embodiments, a processor may be programmed to automatically perform
a task described herein that a user could perform. Accordingly, a
processor, or algorithm conducted by such a processor, can require
little to no supervision or input from a user (e.g., software may
be programmed to implement a function automatically). In some
embodiments, the complexity of a process is so large that a single
person or group of persons could not perform the process in a
timeframe short enough for determining the presence or absence of a
genetic variation.
[0562] In some embodiments, secondary memory may include other
similar means for allowing computer programs or other instructions
to be loaded into a computer system. For example, a system can
include a removable storage unit and an interface device.
Non-limiting examples of such systems include a program cartridge
and cartridge interface (such as that found in video game devices),
a removable memory chip (such as an EPROM, or PROM) and associated
socket, and other removable storage units and interfaces that allow
software and data to be transferred from the removable storage unit
to a computer system.
[0563] One entity can generate counts of sequence reads, map the
sequence reads to genomic sections, count the mapped reads, and
utilize the counted mapped reads in a method, system, apparatus or
computer program product described herein, in some embodiments.
Counts of sequence reads mapped to genomic sections sometimes are
transferred by one entity to a second entity for use by the second
entity in a method, system, apparatus or computer program product
described herein, in certain embodiments.
[0564] In some embodiments, one entity generates sequence reads and
a second entity maps those sequence reads to genomic sections in a
reference genome in some embodiments. The second entity sometimes
counts the mapped reads and utilizes the counted mapped reads in a
method, system, apparatus or computer program product described
herein. Sometimes the second entity transfers the mapped reads to a
third entity, and the third entity counts the mapped reads and
utilizes the mapped reads in a method, system, apparatus or
computer program product described herein. Sometimes the second
entity counts the mapped reads and transfers the counted mapped
reads to a third entity, and the third entity utilizes the counted
mapped reads in a method, system, apparatus or computer program
product described herein. In embodiments involving a third entity,
the third entity sometimes is the same as the first entity. That
is, the first entity sometimes transfers sequence reads to a second
entity, which second entity can map sequence reads to genomic
sections in a reference genome and/or count the mapped reads, and
the second entity can transfer the mapped and/or counted reads to a
third entity. A third entity sometimes can utilize the mapped
and/or counted reads in a method, system, apparatus or computer
program product described herein, wherein the third entity
sometimes is the same as the first entity, and sometimes the third
entity is different from the first or second entity.
[0565] In some embodiments, one entity obtains blood from a
pregnant female, optionally isolates nucleic acid from the blood
(e.g., from the plasma or serum), and transfers the blood or
nucleic acid to a second entity that generates sequence reads from
the nucleic acid.
[0566] Certain System, Apparatus and Computer Program Product
Embodiments
[0567] Multiple system, apparatus and computer program product
embodiments are described herein. In certain aspects, provided is a
system comprising one or more processors and memory, which memory
comprises instructions executable by the one or more processors and
which memory comprises counts of partial nucleotide sequence reads
mapped to genomic sections of a reference genome, which partial
nucleotide sequence reads are reads of circulating cell-free
nucleic acid from a test sample, wherein at least some of the
partial nucleotide sequence reads comprise: (i) multiple nucleobase
gaps between identified nucleobases, or (ii) one or more nucleobase
classes, wherein each nucleobase class comprises a subset of
nucleobases present in the sample nucleic acid, or a combination of
(i) and (ii); and which instructions executable by the one or more
processors are configured to: (a) normalize the counts of the
partial nucleotide sequence reads, thereby providing normalized
counts, and (b) detect the presence or absence of a genetic
variation based on the normalized counts.
[0568] Provided also in certain aspects is an apparatus comprising
one or more processors and memory, which memory comprises
instructions executable by the one or more processors and which
memory comprises counts of partial nucleotide sequence reads mapped
to genomic sections of a reference genome, which partial nucleotide
sequence reads are reads of circulating cell-free nucleic acid from
a test sample, wherein at least some of the partial nucleotide
sequence reads comprise: (i) multiple nucleobase gaps between
identified nucleobases, or (ii) one or more nucleobase classes,
wherein each nucleobase class comprises a subset of nucleobases
present in the sample nucleic acid, or a combination of (i) and
(ii); and which instructions executable by the one or more
processors are configured to: (a) normalize the counts of the
partial nucleotide sequence reads, thereby providing normalized
counts, and (b) detect the presence or absence of a genetic
variation based on the normalized counts.
[0569] Also provide in certain aspects is a computer program
product tangibly embodied on a computer-readable medium, comprising
instructions that when executed by one or more processors are
configured to: (a) access counts of partial nucleotide sequence
reads mapped to genomic sections of a reference genome, which
partial nucleotide sequence reads are reads of circulating
cell-free nucleic acid from a test sample, wherein at least some of
the partial nucleotide sequence reads comprise: (i) multiple
nucleobase gaps between identified nucleobases, or (ii) one or more
nucleobase classes, wherein each nucleobase class comprises a
subset of nucleobases present in the sample nucleic acid, or a
combination of (i) and (ii); (b) normalize the counts of the
partial nucleotide sequence reads, thereby providing normalized
counts; and (c) detect the presence or absence of a genetic
variation based on the normalized counts.
[0570] In some embodiments, the system, apparatus and/or computer
program product comprises a reference comparison module. A
reference comparison module often is configured to compare the
number of counts of partial nucleotide sequence reads mapped to
genomic sections of a sample, or compare normalized counts, or
derivative of the foregoing, to mapped counts of a reference, or
portion thereof, thereby making a reference comparison. Counts for
a reference segment and counts for a test segment to which the
reference counts are compared may be raw, filtered or normalized
counts, or a combination of the foregoing. A reference sometimes is
counts of sequence reads or partial nucleotide sequence reads
mapped to a reference chromosome or segment thereof. Counts mapped
for the reference sometimes are from the same sample or from a
different sample with respect to the counts of partial nucleotide
sequence reads or normalized counts to which the reference is
compared. Sometimes, the reference is for a reference chromosome
(e.g., chromosome 1, 14 and/or 19) or segment thereof, and counts
for the reference are compared to counts of partial nucleotide
sequence reads for a test chromosome (e.g., chromosome 13, 18
and/or 21) or segment thereof (a "test genomic segment"). A
reference comparison module often is configured to access, receive,
utilize, store, search for and/or align counts of partial
nucleotide sequence reads (e.g., from a mapping module or
normalization module). A reference comparison module often is
configured to provide a suitable comparison between counts of
partial nucleotide sequence reads for the test genomic segment and
a reference, non-limiting examples of which comparison include a
simple comparison (e.g., match or no match between counts of
partial nucleotide sequence reads for the test genomic segment and
counts for the reference), mathematical comparison (e.g., ratio,
percentage), statistical comparison (e.g., multiple comparisons,
multiple testing, standardization (e.g., Z-score analyses)), the
like and combinations thereof. A suitable reference comparison
value can be provided by a reference comparison module,
non-limiting examples of which include presence or absence of a
match between counts of partial nucleotide sequence reads for a
test genomic segment and counts for a reference, a ratio,
percentage, Z-score, a value coupled with a measure of variance or
uncertainty (e.g., standard deviation, median absolute deviation,
confidence interval), the like and combinations thereof. A
reference comparison module sometimes is configured to transmit a
comparison value to another module or apparatus, such as a an
outcome module, display apparatus or printer apparatus, for
example.
[0571] An apparatus or system comprising a module described herein
(e.g., a reference comparison module) can comprise one or more
processors. In some embodiments, an apparatus or system can include
multiple processors, such as processors coordinated and working in
parallel. A processor (e.g., one or more processors) in a system or
apparatus can perform and/or implement one or more instructions
(e.g., processes, routines and/or subroutines) in a module
described herein. A module described herein sometimes is located in
memory or associated with an apparatus or system. In some
embodiments, a module described herein operates with one or more
external processors (e.g., an internal or external network, server,
storage device and/or storage network (e.g., a cloud)). Sometimes a
module described herein is configured to access, gather, assemble
and/or receive data and/or information from another module,
apparatus or system (e.g., component, peripheral). Sometimes a
module described herein is configured to provide and/or transfer
data and/or information to another module, apparatus or system
(e.g., component, peripheral). Sometimes a module described herein
is configured to access, accept, receive and/or gather input data
and/or information from an operator of an apparatus or system
(i.e., user). For example, sometimes a user provides a constant, a
threshold value, a formula and/or a predetermined value to a
module. A module described herein sometimes is configured to
transform data and/or information it accesses, receives, gathers
and/or assembles.
[0572] Modules described herein that are configured to generate,
access, transfer and/or manipulate nucleotide sequence reads often
are configured to generate, access, transfer and/or manipulate
partial nucleotide sequence reads. In some embodiments, a
sequencing apparatus is configured only to generate partial
nucleotide sequence reads, and sometimes is configured to generate
partial nucleotide sequence reads and full sequence reads (i.e.,
positions of each of four bases are known for reads).
[0573] In certain embodiments, a system, apparatus and/or computer
program product comprises a: (i) a sequencing module configured to
obtain nucleic acid sequence reads and/or partial nucleotide
sequence reads; (ii) a mapping module configured to map nucleic
acid sequence reads to portions of a reference genome; (iii) a
counting module configured to provide counts of nucleic acid
sequence reads mapped to portions of a reference genome; (iv) a
normalization module configured to provide normalized counts; (v) a
comparison module configured to provide an identification of a
first elevation that is significantly different than a second
elevation; (vi) a range setting module configured to provide one or
more expected level ranges; (vii) a categorization module
configured to identify an elevation representative of a copy number
variation; (viii) an adjustment module configured to adjust a level
identified as a copy number variation; (ix) a plotting module
configured to graph and display a level and/or a profile; (x) an
outcome module configured to determine the presence or absence of a
genetic variation, or determine an outcome (e.g., outcome
determinative of the presence or absence of a fetal aneuploidy);
(xi) a data display organization module configured to indicate the
presence or absence of a segmental chromosomal aberration or a
fetal aneuploidy or both; (xii) a logic processing module
configured to perform one or more of map sequence reads, count
mapped sequence reads, normalize counts and generate an outcome;
(xiii) a reference comparison module, or (xiv) combination of two
or more of the foregoing.
[0574] In some embodiments a sequencing module and mapping module
are configured to transfer sequence reads from the sequencing
module to the mapping module. The mapping module and counting
module sometimes are configured to transfer mapped sequence reads
from the mapping module to the counting module. In some
embodiments, the normalization module and/or comparison module are
configured to transfer normalized counts to the comparison module
and/or range setting module. The comparison module, range setting
module and/or categorization module independently are configured to
transfer (i) an identification of a first elevation that is
significantly different than a second elevation and/or (ii) an
expected level range from the comparison module and/or range
setting module to the categorization module, in some embodiments.
In certain embodiments, the categorization module and the
adjustment module are configured to transfer an elevation
categorized as a copy number variation from the categorization
module to the adjustment module. In some embodiments, the
adjustment module, plotting module and the outcome module are
configured to transfer one or more adjusted levels from the
adjustment module to the plotting module or outcome module. The
normalization module sometimes is configured to transfer mapped
normalized sequence read counts to one or more of the comparison
module, range setting module, categorization module, adjustment
module, outcome module or plotting module.
[0575] Genetic Variations and Medical Conditions
[0576] The presence or absence of a genetic variance can be
determined using a method or apparatus described herein. In certain
embodiments, the presence or absence of one or more genetic
variations is determined according to an outcome provided by
methods and apparatuses described herein. A genetic variation
generally is a particular genetic phenotype present in certain
individuals, and often a genetic variation is present in a
statistically significant sub-population of individuals. In some
embodiments, a genetic variation is a chromosome abnormality (e.g.,
aneuploidy), partial chromosome abnormality or mosaicism, each of
which is described in greater detail herein. Non-limiting examples
of genetic variations include one or more deletions (e.g.,
micro-deletions), duplications (e.g., micro-duplications),
insertions, mutations, polymorphisms (e.g., single-nucleotide
polymorphisms), fusions, repeats (e.g., short tandem repeats),
distinct methylation sites, distinct methylation patterns, the like
and combinations thereof. An insertion, repeat, deletion,
duplication, mutation or polymorphism can be of any length, and in
some embodiments, is about 1 base or base pair (bp) to about 250
megabases (Mb) in length. In some embodiments, an insertion,
repeat, deletion, duplication, mutation or polymorphism is about 1
base or base pair (bp) to about 1,000 kilobases (kb) in length
(e.g., about 10 bp, 50 bp, 100 bp, 500 bp, 1 kb, 5 kb, 10 kb, 50
kb, 100 kb, 500 kb, or 1000 kb in length).
[0577] A genetic variation is sometime a deletion. Sometimes a
deletion is a mutation (e.g., a genetic aberration) in which a part
of a chromosome or a sequence of DNA is missing. A deletion is
often the loss of genetic material. Any number of nucleotides can
be deleted. A deletion can comprise the deletion of one or more
entire chromosomes, a segment of a chromosome, an allele, a gene,
an intron, an exon, any non-coding region, any coding region, a
segment thereof or combination thereof. A deletion can comprise a
microdeletion. A deletion can comprise the deletion of a single
base.
[0578] A genetic variation is sometimes a genetic duplication.
Sometimes a duplication is a mutation (e.g., a genetic aberration)
in which a part of a chromosome or a sequence of DNA is copied and
inserted back into the genome. Sometimes a genetic duplication
(i.e. duplication) is any duplication of a region of DNA. In some
embodiments, a duplication is a nucleic acid sequence that is
repeated, often in tandem, within a genome or chromosome. In some
embodiments, a duplication can comprise a copy of one or more
entire chromosomes, a segment of a chromosome, an allele, a gene,
an intron, an exon, any non-coding region, any coding region,
segment thereof or combination thereof. A duplication can comprise
a microduplication. A duplication sometimes comprises one or more
copies of a duplicated nucleic acid. A duplication sometimes is
characterized as a genetic region repeated one or more times (e.g.,
repeated 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 times). Duplications can
range from small regions (thousands of base pairs) to whole
chromosomes in some instances. Duplications frequently occur as the
result of an error in homologous recombination or due to a
retrotransposon event. Duplications have been associated with
certain types of proliferative diseases. Duplications can be
characterized using genomic microarrays or comparative genetic
hybridization (CGH).
[0579] A genetic variation is sometimes an insertion. An insertion
is sometimes the addition of one or more nucleotide base pairs into
a nucleic acid sequence. An insertion is sometimes a
microinsertion. Sometimes an insertion comprises the addition of a
segment of a chromosome into a genome, chromosome, or segment
thereof. Sometimes an insertion comprises the addition of an
allele, a gene, an intron, an exon, any non-coding region, any
coding region, segment thereof or combination thereof into a genome
or segment thereof. Sometimes an insertion comprises the addition
(i.e., insertion) of nucleic acid of unknown origin into a genome,
chromosome, or segment thereof. Sometimes an insertion comprises
the addition (i.e. insertion) of a single base.
[0580] As used herein a "copy number variation" generally is a
class or type of genetic variation or chromosomal aberration. A
copy number variation can be a deletion (e.g. micro-deletion),
duplication (e.g., a micro-duplication) or insertion (e.g., a
micro-insertion). Often, the prefix "micro" as used herein
sometimes is a segment of nucleic acid less than 5 Mb in length. A
copy number variation can include one or more deletions (e.g.
micro-deletion), duplications and/or insertions (e.g., a
micro-duplication, micro-insertion) of a segment of a chromosome.
In some cases a duplication comprises an insertion. Sometimes an
insertion is a duplication. Sometimes an insertion is not a
duplication. For example, often a duplication of a sequence in a
genomic section increases the counts for a genomic section in which
the duplication is found. Often a duplication of a sequence in a
genomic section increases the elevation. Sometimes, a duplication
present in genomic sections making up a first elevation increases
the elevation relative to a second elevation where a duplication is
absent. Sometimes an insertion increases the counts of a genomic
section and a sequence representing the insertion is present (i.e.,
duplicated) at another location within the same genomic section.
Sometimes an insertion does not significantly increase the counts
of a genomic section or elevation and the sequence that is inserted
is not a duplication of a sequence within the same genomic section.
Sometimes an insertion is not detected or represented as a
duplication and a duplicate sequence representing the insertion is
not present in the same genomic section.
[0581] In some embodiments a copy number variation is a fetal copy
number variation. Often, a fetal copy number variation is a copy
number variation in the genome of a fetus. In some embodiments a
copy number variation is a maternal copy number variation.
Sometimes a maternal and/or fetal copy number variation is a copy
number variation within the genome of a pregnant female (e.g., a
female subject bearing a fetus), a female subject that gave birth
or a female capable of bearing a fetus. A copy number variation can
be a heterozygous copy number variation where the variation (e.g.,
a duplication or deletion) is present on one allele of a genome. A
copy number variation can be a homozygous copy number variation
where the variation is present on both alleles of a genome. In some
embodiments a copy number variation is a heterozygous or homozygous
fetal copy number variation. In some embodiments a copy number
variation is a heterozygous or homozygous maternal and/or fetal
copy number variation. A copy number variation sometimes is present
in a maternal genome and a fetal genome, a maternal genome and not
a fetal genome, or a fetal genome and not a maternal genome.
[0582] "Ploidy" refers to the number of chromosomes present in a
fetus or mother. Sometimes "Ploidy" is the same as "chromosome
ploidy". In humans, for example, autosomal chromosomes are often
present in pairs. For example, in the absence of a genetic
variation, most humans have two of each autosomal chromosome (e.g.,
chromosomes 1-22). The presence of the normal complement of 2
autosomal chromosomes in a human is often referred to as euploid.
"Microploidy" is similar in meaning to ploidy. "Microploidy" often
refers to the ploidy of a segment of a chromosome. The term
"microploidy" sometimes refers to the presence or absence of a copy
number variation (e.g., a deletion, duplication and/or an
insertion) within a chromosome (e.g., a homozygous or heterozygous
deletion, duplication, or insertion, the like or absence thereof).
"Ploidy" and "microploidy" sometimes are determined after
normalization of counts of an elevation in a profile (e.g., after
normalizing counts of an elevation to an NRV of 1). Thus, an
elevation representing an autosomal chromosome pair (e.g., a
euploid) is often normalized to an NRV of 1 and is referred to as a
ploidy of 1. Similarly, an elevation within a segment of a
chromosome representing the absence of a duplication, deletion or
insertion is often normalized to an NRV of 1 and is referred to as
a microploidy of 1. Ploidy and microploidy are often bin-specific
(e.g., genomic section specific) and sample-specific. Ploidy is
often defined as integral multiples of 1/2, with the values of 1,
1/2, 0, 3/2, and 2 representing euploidy (e.g., 2 chromosomes), 1
chromosome present (e.g., a chromosome deletion), no chromosome
present, 3 chromosomes (e.g., a trisomy) and 4 chromosomes,
respectively. Likewise, microploidy is often defined as integral
multiples of 1/2, with the values of 1, 1/2, 0, 3/2, and 2
representing euploidy (e.g., no copy number variation), a
heterozygous deletion, homozygous deletion, heterozygous
duplication and homozygous duplication, respectively. Some examples
of ploidy values for a fetus are provided in Table 2 for an NRV of
1.
[0583] Sometimes the microploidy of a fetus matches the microploidy
of the mother of the fetus (i.e., the pregnant female subject).
Sometimes the microploidy of a fetus matches the microploidy of the
mother of the fetus and both the mother and fetus carry the same
heterozygous copy number variation, homozygous copy number
variation or both are euploid. Sometimes the microploidy of a fetus
is different than the microploidy of the mother of the fetus. For
example, sometimes the microploidy of a fetus is heterozygous for a
copy number variation, the mother is homozygous for a copy number
variation and the microploidy of the fetus does not match (e.g.,
does not equal) the microploidy of the mother for the specified
copy number variation.
[0584] A microploidy is often associated with an expected
elevation. For example, sometimes an elevation (e.g., an elevation
in a profile, sometimes an elevation that includes substantially no
copy number variation) is normalized to an NRV of 1 and the
microploidy of a homozygous duplication is 2, a heterozygous
duplication is 1.5, a heterozygous deletion is 0.5 and a homozygous
deletion is zero.
[0585] A genetic variation for which the presence or absence is
identified for a subject is associated with a medical condition in
certain embodiments. Thus, technology described herein can be used
to identify the presence or absence of one or more genetic
variations that are associated with a medical condition or medical
state. Non-limiting examples of medical conditions include those
associated with intellectual disability (e.g., Down Syndrome),
aberrant cell-proliferation (e.g., cancer), presence of a
micro-organism nucleic acid (e.g., virus, bacterium, fungus,
yeast), and preeclampsia.
[0586] Non-limiting examples of genetic variations, medical
conditions and states are described hereafter.
[0587] Fetal Gender
[0588] In some embodiments, the prediction of a fetal gender or
gender related disorder (e.g., sex chromosome aneuploidy) can be
determined by a method or apparatus described herein. Gender
determination generally is based on a sex chromosome. In humans,
there are two sex chromosomes, the X and Y chromosomes. The Y
chromosome contains a gene, SRY, which triggers embryonic
development as a male. The Y chromosomes of humans and other
mammals also contain other genes needed for normal sperm
production. Individuals with XX are female and XY are male and
non-limiting variations, often referred to as sex chromosome
aneuploidies, include X0, XYY, XXX and XXY. In some cases, males
have two X chromosomes and one Y chromosome (XXY; Klinefelter's
Syndrome), or one X chromosome and two Y chromosomes (XYY syndrome;
Jacobs Syndrome), and some females have three X chromosomes (XXX;
Triple X Syndrome) or a single X chromosome instead of two (X0;
Turner Syndrome). In some cases, only a portion of cells in an
individual are affected by a sex chromosome aneuploidy which may be
referred to as a mosaicism (e.g., Turner mosaicism). Other cases
include those where SRY is damaged (leading to an XY female), or
copied to the X (leading to an XX male).
[0589] In certain cases, it can be beneficial to determine the
gender of a fetus in utero. For example, a patient (e.g., pregnant
female) with a family history of one or more sex-linked disorders
may wish to determine the gender of the fetus she is carrying to
help assess the risk of the fetus inheriting such a disorder.
Sex-linked disorders include, without limitation, X-linked and
Y-linked disorders. X-linked disorders include X-linked recessive
and X-linked dominant disorders. Examples of X-linked recessive
disorders include, without limitation, immune disorders (e.g.,
chronic granulomatous disease (CYBB), Wiskott-Aldrich syndrome,
X-linked severe combined immunodeficiency, X-linked
agammaglobulinemia, hyper-IgM syndrome type 1, IPEX, X-linked
lymphoproliferative disease, Properdin deficiency), hematologic
disorders (e.g., Hemophilia A, Hemophilia B, X-linked sideroblastic
anemia), endocrine disorders (e.g., androgen insensitivity
syndrome/Kennedy disease, KAL1 Kallmann syndrome, X-linked adrenal
hypoplasia congenital), metabolic disorders (e.g., ornithine
transcarbamylase deficiency, oculocerebrorenal syndrome,
adrenoleukodystrophy, glucose-6-phosphate dehydrogenase deficiency,
pyruvate dehydrogenase deficiency, Danon disease/glycogen storage
disease Type IIb, Fabry's disease, Hunter syndrome, Lesch-Nyhan
syndrome, Menkes disease/occipital horn syndrome), nervous system
disorders (e.g., Coffin-Lowry syndrome, MASA syndrome, X-linked
alpha thalassemia mental retardation syndrome, Siderius X-linked
mental retardation syndrome, color blindness, ocular albinism,
Norrie disease, choroideremia, Charcot-Marie-Tooth disease
(CMTX2-3), Pelizaeus-Merzbacher disease, SMAX2), skin and related
tissue disorders (e.g., dyskeratosis congenital, hypohidrotic
ectodermal dysplasia (EDA), X-linked ichthyosis, X-linked
endothelial corneal dystrophy), neuromuscular disorders (e.g.,
Becker's muscular dystrophy/Duchenne, centronuclear myopathy
(MTM1), Conradi-Hunermann syndrome, Emery-Dreifuss muscular
dystrophy 1), urologic disorders (e.g., Alport syndrome, Dent's
disease, X-linked nephrogenic diabetes insipidus), bone/tooth
disorders (e.g., AMELX Amelogenesis imperfecta), and other
disorders (e.g., Barth syndrome, McLeod syndrome,
Smith-Fineman-Myers syndrome, Simpson-Golabi-Behmel syndrome,
Mohr-Tranebj.ae butted.rg syndrome, Nasodigitoacoustic syndrome).
Examples of X-linked dominant disorders include, without
limitation, X-linked hypophosphatemia, Focal dermal hypoplasia,
Fragile X syndrome, Aicardi syndrome, Incontinentia pigmenti, Rett
syndrome, CHILD syndrome, Lujan-Fryns syndrome, and Orofaciodigital
syndrome 1. Examples of Y-linked disorders include, without
limitation, male infertility, retinits pigmentosa, and
azoospermia.
[0590] Chromosome Abnormalities
[0591] In some embodiments, the presence or absence of a fetal
chromosome abnormality can be determined by using a method or
apparatus described herein. Chromosome abnormalities include,
without limitation, a gain or loss of an entire chromosome or a
region of a chromosome comprising one or more genes. Chromosome
abnormalities include monosomies, trisomies, polysomies, loss of
heterozygosity, deletions and/or duplications of one or more
nucleotide sequences (e.g., one or more genes), including deletions
and duplications caused by unbalanced translocations. The terms
"aneuploidy" and "aneuploid" as used herein refer to an abnormal
number of chromosomes in cells of an organism. As different
organisms have widely varying chromosome complements, the term
"aneuploidy" does not refer to a particular number of chromosomes,
but rather to the situation in which the chromosome content within
a given cell or cells of an organism is abnormal. In some
embodiments, the term "aneuploidy" herein refers to an imbalance of
genetic material caused by a loss or gain of a whole chromosome, or
part of a chromosome. An "aneuploidy" can refer to one or more
deletions and/or insertions of a segment of a chromosome.
[0592] The term "monosomy" as used herein refers to lack of one
chromosome of the normal complement. Partial monosomy can occur in
unbalanced translocations or deletions, in which only a segment of
the chromosome is present in a single copy. Monosomy of sex
chromosomes (45, X) causes Turner syndrome, for example.
[0593] The term "disomy" refers to the presence of two copies of a
chromosome. For organisms such as humans that have two copies of
each chromosome (those that are diploid or "euploid"), disomy is
the normal condition. For organisms that normally have three or
more copies of each chromosome (those that are triploid or above),
disomy is an aneuploid chromosome state. In uniparental disomy,
both copies of a chromosome come from the same parent (with no
contribution from the other parent).
[0594] The term "euploid", in some embodiments, refers a normal
complement of chromosomes.
[0595] The term "trisomy" as used herein refers to the presence of
three copies, instead of two copies, of a particular chromosome.
The presence of an extra chromosome 21, which is found in human
Down syndrome, is referred to as "Trisomy 21." Trisomy 18 and
Trisomy 13 are two other human autosomal trisomies. Trisomy of sex
chromosomes can be seen in females (e.g., 47, XXX in Triple X
Syndrome) or males (e.g., 47, XXY in Klinefelter's Syndrome; or 47,
XYY in Jacobs Syndrome).
[0596] The terms "tetrasomy" and "pentasomy" as used herein refer
to the presence of four or five copies of a chromosome,
respectively. Although rarely seen with autosomes, sex chromosome
tetrasomy and pentasomy have been reported in humans, including
XXXX, XXXY, XXYY, XYYY, XXXXX, XXXXY, XXXYY, XXYYY and XYYYY.
[0597] Chromosome abnormalities can be caused by a variety of
mechanisms. Mechanisms include, but are not limited to (i)
nondisjunction occurring as the result of a weakened mitotic
checkpoint, (ii) inactive mitotic checkpoints causing
non-disjunction at multiple chromosomes, (iii) merotelic attachment
occurring when one kinetochore is attached to both mitotic spindle
poles, (iv) a multipolar spindle forming when more than two spindle
poles form, (v) a monopolar spindle forming when only a single
spindle pole forms, and (vi) a tetraploid intermediate occurring as
an end result of the monopolar spindle mechanism.
[0598] The terms "partial monosomy" and "partial trisomy" as used
herein refer to an imbalance of genetic material caused by loss or
gain of part of a chromosome. A partial monosomy or partial trisomy
can result from an unbalanced translocation, where an individual
carries a derivative chromosome formed through the breakage and
fusion of two different chromosomes. In this situation, the
individual would have three copies of part of one chromosome (two
normal copies and the segment that exists on the derivative
chromosome) and only one copy of part of the other chromosome
involved in the derivative chromosome.
[0599] The term "mosaicism" as used herein refers to aneuploidy in
some cells, but not all cells, of an organism. Certain chromosome
abnormalities can exist as mosaic and non-mosaic chromosome
abnormalities. For example, certain trisomy 21 individuals have
mosaic Down syndrome and some have non-mosaic Down syndrome.
Different mechanisms can lead to mosaicism. For example, (i) an
initial zygote may have three 21st chromosomes, which normally
would result in simple trisomy 21, but during the course of cell
division one or more cell lines lost one of the 21st chromosomes;
and (ii) an initial zygote may have two 21st chromosomes, but
during the course of cell division one of the 21st chromosomes were
duplicated. Somatic mosaicism likely occurs through mechanisms
distinct from those typically associated with genetic syndromes
involving complete or mosaic aneuploidy. Somatic mosaicism has been
identified in certain types of cancers and in neurons, for example.
In certain instances, trisomy 12 has been identified in chronic
lymphocytic leukemia (CLL) and trisomy 8 has been identified in
acute myeloid leukemia (AML). Also, genetic syndromes in which an
individual is predisposed to breakage of chromosomes (chromosome
instability syndromes) are frequently associated with increased
risk for various types of cancer, thus highlighting the role of
somatic aneuploidy in carcinogenesis. Methods and protocols
described herein can identify presence or absence of non-mosaic and
mosaic chromosome abnormalities.
[0600] Tables 1A and 1B present a non-limiting list of chromosome
conditions, syndromes and/or abnormalities that can be potentially
identified by methods and apparatus described herein. Table 1B is
from the DECIPHER database as of Oct. 6, 2011 (e.g., version 5.1,
based on positions mapped to GRCh37; available at uniform resource
locator (URL) dechipher.sanger.ac.uk).
TABLE-US-00001 TABLE 1A Chromo- some Abnormality Disease
Association X XO Turner's Syndrome Y XXY Klinefelter syndrome Y XYY
Double Y syndrome Y XXX Trisomy X syndrome Y XXXX Four X syndrome Y
Xp21 deletion Duchenne's/Becker syndrome, congenital adrenal
hypoplasia, chronic granulomatus disease Y Xp22 deletion steroid
sulfatase deficiency Y Xq26 deletion X-linked lymphproliferative
disease 1 1p (somatic) neuroblastoma monosomy trisomy 2 monosomy
growth retardation, developmental trisomy 2q and mental delay, and
minor physical abnormalities 3 monosomy Non-Hodgkin's lymphoma
trisomy (somatic) 4 monosomy Acute non lymphocytic leukemia (ANLL)
trisomy (somatic) 5 5p Cri du chat; Lejeune syndrome 5 5q
myelodysplastic syndrome (somatic) monosomy trisomy 6 monosomy
clear-cell sarcoma trisomy (somatic) 7 7q11.23 deletion William's
syndrome 7 monosomy monosomy 7 syndrome of childhood; trisomy
somatic: renal cortical adenomas; myelodysplastic syndrome 8 8q24.1
deletion Langer-Giedon syndrome 8 monosomy myelodysplastic
syndrome; trisomy Warkany syndrome; somatic: chronic myelogenous
leukemia 9 monosomy 9p Alfi's syndrome 9 monosomy 9p Rethore
syndrome partial trisomy 9 trisomy complete trisomy 9 syndrome;
mosaic trisomy 9 syndrome 10 Monosomy ALL or ANLL trisomy (somatic)
11 11p- Aniridia; Wilms tumor 11 11q- Jacobson Syndrome 11 monosomy
myeloid lineages affected (somatic) trisomy (ANLL, MDS) 12 monosomy
CLL, Juvenile granulosa trisomy (somatic) cell tumor (JGCT) 13 13q-
13q-syndrome; Orbeli syndrome 13 13q14 deletion retinoblastoma 13
monosomy Patau's syndrome trisomy 14 monosomy myeloid disorders
(MDS, ANLL, trisomy (somatic) atypical CML) 15 15q11-q13
Prader-Willi, Angelman's deletion syndrome monosomy 15 trisomy
(somatic) myeloid and lymphoid lineages affected, e.g., MDS, ANLL,
ALL, CLL) 16 16q13.3 deletion Rubenstein-Taybi 3 monosomy papillary
renal cell carcinomas trisomy (somatic) (malignant) 17
17p-(somatic) 17p syndrome in myeloid malignancies 17 17811.2
deletion Smith-Magenis 17 17q13.3 Miller-Dieker 17 monosomy renal
cortical adenomas trisomy (somatic) 17 17p11.2-12 Charcot-Marie
Tooth Syndrome trisomy type 1; HNPP 18 18p- 18p partial monosomy
syndrome or Grouchy Lamy Thieffry syndrome 18 18q- Grouchy Lamy
Salmon Landry Syndrome 18 monosomy Edwards Syndrome trisomy 19
monosomy trisomy 20 20p- trisomy 20p syndrome 20 20p11.2-12
Alagille deletion 20 20q- somatic: MDS, ANLL, polycythemia vera,
chronic neutrophilic leukemia 20 monosomy papillary renal cell
carcinomas (malignant) trisomy (somatic) 21 monosomy Down's
syndrome trisomy 22 22q11.2 deletion DiGeorge's syndrome,
velocardiofacial syndrome, conotruncal anomaly face syndrome,
autosomal dominant Opitz G/BBB syndrome, Caylor cardiofacial
syndrome 22 monosomy complete trisomy 22 syndrome trisomy
TABLE-US-00002 TABLE 1B Chromo- Interval Syndrome some Start End
(Mb) Grade 12q14 12 65,071,919 68,645,525 3.57 microdeletion
syndrome 15q13.3 15 30,769,995 32,701,482 1.93 microdeletion
syndrome 15q24 recurrent 15 74,377,174 76,162,277 1.79
microdeletion syndrome 15q26 15 99,357,970 102,521,392 3.16
overgrowth syndrome 16p11.2 16 29,501,198 30,202,572 0.70 micro-
duplication syndrome 16p11.2-p12.2 16 21,613,956 29,042,192 7.43
microdeletion syndrome 16p13.11 recurrent 16 15,504,454 16,284,248
0.78 microdeletion (neurocognitive disorder susceptibility locus)
16p13.11 16 15,504,454 16,284,248 0.78 recurrent micro- duplication
(neurocognitive disorder susceptibility locus) 17q21.3 17
43,632,466 44,210,205 0.58 1 recurrent microdeletion syndrome 1p36
micro- 1 10,001 5,408,761 5.40 1 deletion syndrome 1q21.1 1
146,512,930 147,737,500 1.22 3 recurrent microdeletion
(susceptibility locus for neuro- developmental disorders) 1q21.1 1
146,512,930 147,737,500 1.22 3 recurrent micro- duplication
(possible susceptibility locus for neuro- developmental disorders)
1q21.1 1 145,401,253 145,928,123 0.53 3 susceptibility locus for
Thrombo- cytopenia- Absent Radius (TAR) syndrome 22q11 deletion 22
18,546,349 22,336,469 3.79 1 syndrome (Velocardio- facial/ DiGeorge
syndrome) 22q11 22 18,546,349 22,336,469 3.79 3 duplication
syndrome 22q11.2 distal 22 22,115,848 23,696,229 1.58 deletion
syndrome 22813 deletion 22 51,045,516 51,187,844 0.14 1 syndrome
(Phelan- Mcdermid syndrome) 2p15-16.1 2 57,741,796 61,738,334 4.00
microdeletion syndrome 2q33.1 deletion 2 196,925,089 205,206,940
8.28 1 syndrome 2q37 2 239,954,693 243,102,476 3.15 1 monosomy 3q29
micro- 3 195,672,229 197,497,869 1.83 deletion syndrome 3q29 3
195,672,229 197,497,869 1.83 micro- duplication syndrome 7q11.23 7
72,332,743 74,616,901 2.28 duplication syndrome 8p23.1 deletion 8
8,119,295 11,765,719 3.65 syndrome 9q subtelomeric 9 140,403,363
141,153,431 0.75 1 deletion syndrome Adult-onset 5 126,063,045
126,204,952 0.14 autosomal dominant leukodystrophy (ADLD) Angelman
15 22,876,632 28,557,186 5.68 1 syndrome (Type 1) Angelman 15
23,758,390 28,557,186 4.80 1 syndrome (Type 2) ATR-16 16 60,001
834,372 0.77 1 syndrome AZFa Y 14,352,761 15,154,862 0.80 AZFb Y
20,118,045 26,065,197 5.95 AZFb + AZFc Y 19,964,826 27,793,830 7.83
AZFc Y 24,977,425 28,033,929 3.06 Cat-Eye 22 1 16,971,860 16.97
Syndrome (Type I) Charcot-Marie- 17 13,968,607 15,434,038 1.47 1
Tooth syndrome type 1A (CMT1A) Cri du Chat 5 10,001 11,723,854
11.71 1 Syndrome (5p deletion) Early-onset 21 27,037,956 27,548,479
0.51 Alzheimer disease with cerebral amyloid angiopathy Familial 5
112,101,596 112,221,377 0.12 Adenomatous Polyposis Hereditary 17
13,968,607 15,434,038 1.47 1 Liability to Pressure Palsies (HNPP)
Leri-Weill X 751,878 867,875 0.12 dyschondro- stosis (LWD)-SHOX
deletion Leri-Weill X 460,558 753,877 0.29 dyschondro- stosis
(LWD)-SHOX deletion Miller-Dieker 17 1 2,545,429 2.55 1 syndrome
(MDS) NF1-micro- 17 29,162,822 30,218,667 1.06 1 deletion syndrome
Pelizaeus- X 102,642,051 103,131,767 0.49 Merzbacher disease
Potocki-Lupski 17 16,706,021 20,482,061 3.78 syndrome (17p11.2
duplication syndrome) Potocki-Shaffer 11 43,985,277 46,064,560 2.08
1 syndrome Prader-Willi 15 22,876,632 28,557,186 5.68 1 syndrome
(Type 1) Prader-Willi 15 23,758,390 28,557,186 4.80 1 Syndrome
(Type 2) RCAD 17 34,907,366 36,076,803 1.17 (renal cysts and
diabetes) Rubinstein-Taybi 16 3,781,464 3,861,246 0.08 1 Syndrome
Smith-Magenis 17 16,706,021 20,482,061 3.78 1 Syndrome Sotos
syndrome 5 175,130,402 177,456,545 2.33 1 Split hand/foot 7
95,533,860 96,779,486 1.25 malformation 1 (SHFM1) Steroid
sulphatase X 6,441,957 8,167,697 1.73 deficiency (STS) WAGR 11p13
11 31,803,509 32,510,988 0.71 deletion syndrome Williams-Beuren 7
72,332,743 74,616,901 2.28 1 Syndrome (WBS) Wolf-Hirschhorn 4
10,001 2,073,670 2.06 1 Syndrome Xq28 (MECP2) X 152,749,900
153,390,999 0.64 duplication
[0601] Grade 1 conditions often have one or more of the following
characteristics; pathogenic anomaly; strong agreement amongst
geneticists; highly penetrant; may still have variable phenotype
but some common features; all cases in the literature have a
clinical phenotype; no cases of healthy individuals with the
anomaly; not reported on DVG databases or found in healthy
population; functional data confirming single gene or multi-gene
dosage effect; confirmed or strong candidate genes; clinical
management implications defined; known cancer risk with implication
for surveillance; multiple sources of information (OMIM,
GeneReviews, Orphanet, Unique, Wikipedia); and/or available for
diagnostic use (reproductive counseling).
[0602] Grade 2 conditions often have one or more of the following
characteristics; likely pathogenic anomaly; highly penetrant;
variable phenotype with no consistent features other than DD; small
number of cases/reports in the literature; all reported cases have
a clinical phenotype; no functional data or confirmed pathogenic
genes; multiple sources of information (OMIM, Genereviews,
Orphanet, Unique, Wikipedia); and/or may be used for diagnostic
purposes and reproductive counseling.
[0603] Grade 3 conditions often have one or more of the following
characteristics; susceptibility locus; healthy individuals or
unaffected parents of a proband described; present in control
populations; non penetrant; phenotype mild and not specific;
features less consistent; no functional data or confirmed
pathogenic genes; more limited sources of data; possibility of
second diagnosis remains a possibility for cases deviating from the
majority or if novel clinical finding present; and/or caution when
using for diagnostic purposes and guarded advice for reproductive
counseling.
[0604] Preeclampsia
[0605] In some embodiments, the presence or absence of preeclampsia
is determined by using a method or apparatus described herein.
Preeclampsia is a condition in which hypertension arises in
pregnancy (i.e. pregnancy-induced hypertension) and is associated
with significant amounts of protein in the urine. In some cases,
preeclampsia also is associated with elevated levels of
extracellular nucleic acid and/or alterations in methylation
patterns. For example, a positive correlation between extracellular
fetal-derived hypermethylated RASSF1A levels and the severity of
pre-eclampsia has been observed. In certain examples, increased DNA
methylation is observed for the H19 gene in preeclamptic placentas
compared to normal controls.
[0606] Preeclampsia is one of the leading causes of maternal and
fetal/neonatal mortality and morbidity worldwide. Circulating
cell-free nucleic acids in plasma and serum are novel biomarkers
with promising clinical applications in different medical fields,
including prenatal diagnosis. Quantitative changes of cell-free
fetal (cff) DNA in maternal plasma as an indicator for impending
preeclampsia have been reported in different studies, for example,
using real-time quantitative PCR for the male-specific SRY or DYS
14 loci. In cases of early onset preeclampsia, elevated levels may
be seen in the first trimester. The increased levels of cffDNA
before the onset of symptoms may be due to hypoxia/reoxygenation
within the intervillous space leading to tissue oxidative stress
and increased placental apoptosis and necrosis. In addition to the
evidence for increased shedding of cffDNA into the maternal
circulation, there is also evidence for reduced renal clearance of
cffDNA in preeclampsia. As the amount of fetal DNA is currently
determined by quantifying Y-chromosome specific sequences,
alternative approaches such as measurement of total cell-free DNA
or the use of gender-independent fetal epigenetic markers, such as
DNA methylation, offer an alternative. Cell-free RNA of placental
origin is another alternative biomarker that may be used for
screening and diagnosing preeclampsia in clinical practice. Fetal
RNA is associated with subcellular placental particles that protect
it from degradation. Fetal RNA levels sometimes are ten-fold higher
in pregnant females with preeclampsia compared to controls, and
therefore is an alternative biomarker that may be used for
screening and diagnosing preeclampsia in clinical practice.
[0607] Pathogens
[0608] In some embodiments, the presence or absence of a pathogenic
condition is determined by a method or apparatus described herein.
A pathogenic condition can be caused by infection of a host by a
pathogen including, but not limited to, a bacterium, virus or
fungus. Since pathogens typically possess nucleic acid (e.g.,
genomic DNA, genomic RNA, mRNA) that can be distinguishable from
host nucleic acid, methods and apparatus provided herein can be
used to determine the presence or absence of a pathogen. Often,
pathogens possess nucleic acid with characteristics unique to a
particular pathogen such as, for example, epigenetic state and/or
one or more sequence variations, duplications and/or deletions.
Thus, methods provided herein may be used to identify a particular
pathogen or pathogen variant (e.g. strain).
[0609] Cancers
[0610] In some embodiments, the presence or absence of a cell
proliferation disorder (e.g., a cancer) is determined by using a
method or apparatus described herein. For example, levels of
cell-free nucleic acid in serum can be elevated in patients with
various types of cancer compared with healthy patients. Patients
with metastatic diseases, for example, can sometimes have serum DNA
levels approximately twice as high as non-metastatic patients.
Patients with metastatic diseases may also be identified by
cancer-specific markers and/or certain single nucleotide
polymorphisms or short tandem repeats, for example. Non-limiting
examples of cancer types that may be positively correlated with
elevated levels of circulating DNA include breast cancer,
colorectal cancer, gastrointestinal cancer, hepatocellular cancer,
lung cancer, melanoma, non-Hodgkin lymphoma, leukemia, multiple
myeloma, bladder cancer, hepatoma, cervical cancer, esophageal
cancer, pancreatic cancer, and prostate cancer. Various cancers can
possess, and can sometimes release into the bloodstream, nucleic
acids with characteristics that are distinguishable from nucleic
acids from non-cancerous healthy cells, such as, for example,
epigenetic state and/or sequence variations, duplications and/or
deletions. Such characteristics can, for example, be specific to a
particular type of cancer. Thus, it is further contemplated that a
method provided herein can be used to identify a particular type of
cancer.
EXAMPLES
[0611] The examples set forth below illustrate certain embodiments
and do not limit the technology.
Example 1
General Methods for Detecting Conditions Associated with Genetic
Variations
[0612] The methods and underlying theory described herein can be
utilized to detect various conditions associated with genetic
variation and determine the presence or absence of a genetic
variation. Non-limiting examples of genetic variations that can be
detected with the methods described herein include, segmental
chromosomal aberrations (e.g., deletions, duplications),
aneuploidy, gender, sample identification, disease conditions
associated with genetic variation, the like or combinations of the
foregoing.
[0613] Bin Filtering
[0614] The information content of a genomic region in a target
chromosome can be visualized by plotting the result of the average
separation between euploid and trisomy counts normalized by
combined uncertainties, as a function of chromosome position.
Increased uncertainty (see FIG. 1) or reduced gap between triploids
and euploids (e.g. triploid pregnancies and euploid
pregnancies)(see FIG. 2) both result in decreased Z-values for
affected cases, sometimes reducing the predictive power of
Z-scores.
[0615] FIG. 3 graphically illustrates a p-value profile, based on
t-distribution, plotted as a function of chromosome position along
chromosome 21. Analysis of the data presented in FIG. 3 identifies
36 uninformative chromosome 21 bins, each about 50 kilo-base pairs
(kbp) in length. The uninformative region is located in the p-arm,
close to centromere (21p11.2-21p11.1). Removing all 36 bins from
the calculation of Z-scores, as schematically outlined in FIG. 4,
sometimes can significantly increase the Z-values for all trisomy
cases, while introducing only random variations into euploid
Z-values.
[0616] The improvement in predictive power afforded by removal of
the 36 uninformative bins can be explained by examining the count
profile for chromosome 21 (see FIG. 5). In FIG. 5, two arbitrarily
chosen samples demonstrate the general tendency of count versus
(vs) bin profiles to follow substantially similar trends, apart
from short-range noise. The profiles shown in FIG. 5 are
substantially parallel. The highlighted region of the profile plot
presented in FIG. 5 (e.g., the region in the ellipse), while still
exhibiting parallelism, also exhibit large fluctuations relative to
the rest of chromosome. Removal of the fluctuating bins (e.g., the
36 uninformative bins) can improve precision and consistency of Z
statistics, in some embodiments.
[0617] Bin Normalization
[0618] Filtering out uninformative bins, as described in Example 1,
sometimes does not provide the desired improvement to the
predictive power of Z-values. When chromosome 18 data is filtered
to remove uninformative bins, as described in Example 1, the
z-values did not substantially improve (see FIG. 6). As seen with
the chromosome 21 count profiles presented in Example 1, the
chromosome 18 count profiles also are substantially parallel,
disregarding short range noise. However, two chromosome 18 samples
used to evaluate binwise count uncertainties (see the bottom of
FIG. 6) significantly deviate from the general parallelism of count
profiles. The dips in the middle of the two traces, highlighted by
the ellipse, represent large deletions. Other samples examined
during the course of the experiment did not exhibit this deletion.
The deletion coincides with the location of a dip in p-value
profiles for chromosome 18, illustrated in by the ellipse shown in
FIG. 7. That is, the dip observed in the p-value profiles for
chromosome 18 are explained by the presence of the deletion in the
chromosome 18 samples, which cause an increase in the variance of
counts in the affected region. The variance in counts is not
random, but represents a rare event (e.g., the deletion of a
segment of chromosome 18), which, if included with other, random
fluctuations from other samples, decreases the predictive power bin
filtering procedure.
[0619] Two questions arise from this example; (1) how are p-value
signals determined to be meaningful and/or useful, and (2) can the
p-value approach described herein be generalized for use with any
bin data (e.g., from within any chromosome, not only bins from
within chromosomes 13, 18 or 21). A generalized procedure could be
used to remove variability in the total counts for the entire
genome, which can often be used as the normalization factor when
evaluating Z-scores. The data presented in FIG. 8 can be used to
investigate the answers to the questions above by reconstructing
the general contour of the data by assigning the median reference
count to each bin, and normalizing each bin count in the test
sample with respect to the assigned median reference count.
[0620] The medians are extracted from a set of known euploid
references. Prior to computing the reference median counts,
uninformative bins throughout the genome are filtered out. The
remaining bin counts are normalized with respect to the total
residual number of counts. The test sample is also normalized with
respect to the sum of counts observed for bins that are not
filtered out. The resulting test profile often centers around a
value of 1, except in areas of maternal deletions or duplication,
and areas in which the fetus is triploid (see FIG. 9). The bin-wise
normalized profile illustrated in FIG. 10 confirms the validity of
the normalization procedure, and clearly reveals the heterozygous
maternal deletion (e.g., central dip in the gray segment of the
profile tracing) in chromosome 18 and the elevated chromosomal
representation of chromosome 18 of the tested sample (see the gray
area of profile tracing in FIG. 10). As can be seen from FIG. 10,
the median value for the gray segment of the tracing centers around
about 1.1, where the median value for the black segment of the
tracing centers around 1.0.
[0621] Peak Elevation
[0622] FIG. 11 graphically illustrates the results of analyzing
multiple samples using bin-wise normalization, from a patient with
a discernible feature or trait (e.g., maternal duplication,
maternal deletion, the like or combinations thereof). The
identities of the samples often can be determined by comparing
their respective normalized count profiles. In the example
illustrated in FIG. 11, the location of the dip in the normalized
profile and its elevation, as well as its rarity, indicate that
both samples originate from the same patient. Forensic panel data
often can be used to substantiate these findings.
[0623] FIGS. 12 and 13 graphically illustrate the results of the
use of normalized bin profiles for identifying patient identity, or
sample identity. The samples analyzed in FIGS. 12 and 13 carry wide
maternal aberrations in chromosomes 4 and 22, which are absent in
the other samples in the profile tracings, confirming the shared
origin of the top and bottom traces. Results such as this can lead
to the determination that a particular sample belongs to a specific
patient, and also can be used to determine if a particular sample
has already been analyzed.
[0624] Bin-wise normalization facilitates the detection of
aberrations, however, comparison of peaks from different samples
often is further facilitated by analyzing quantitative measures of
peak elevations and locations (e.g., peak edges). The most
prominent descriptor of a peak often is its elevation, followed by
the locations of its edges. Features from different count profiles
often can be compared using the following non-limiting analysis.
[0625] (a) Determine the confidence in a features detected peaks in
a single test sample. If the feature is distinguishable from
background noise or processing artifacts, the feature can be
further analyzed against the general population. [0626] (b)
Determine the prevalence of the detected feature in the general
population. If the feature is rare, it can be used as a marker for
rare aberrations. Features that are found frequently in the general
population are less useful for analysis. Ethnic origins can play a
role in determining the relevance of a detected features peak
elevation. Thus, some features provide useful information for
samples from certain ethnic origins. [0627] (c) Derive the
confidence in the comparison between features observed in different
samples.
[0628] Illustrated in FIG. 14 are the normalized bin counts in
chromosome 5, from a euploid subject. The average elevation
generally is the reference baseline from which the elevations of
aberrations are measured, in some embodiments. Small and/or narrow
deviations are less reliable predictors than wide, pronounced
aberrations. Thus, the background noise or variance from low fetal
contribution and/or processing artifacts is an important
consideration when aberrations are not large or do not have a
significant peak elevation above the background. An example of this
is presented in FIG. 15, where a peak that would be significant in
the upper trace, can be masked in the background noise observed in
the bottom profile trace. The confidence in the peak elevation (see
FIG. 16) can be determined by the average deviation from the
reference (shown as the delta symbol), relative to the width of the
euploid distribution (e.g., combined with the variance (shown as
the sigma symbol) in the average deviation). The error in the
average stretch elevation can be derived from the known formula for
the error of the mean. If a stretch longer than one bin is treated
as a random (non-contiguous) sample of all bins within a
chromosome, the error in the average elevation decreases with the
square root of the number of bins within the aberration. This
reasoning neglects the correlation between neighboring bins, an
assumption confirmed by the correlation function shown in FIG. 17
(e.g., the equation for G(n)). Non-normalized profiles sometimes
exhibit strong medium-range correlations (e.g., the wavelike
variation of the baseline), however, the normalized profiles smooth
out the correlation, leaving only random noise. The close match
between the standard error of the mean, the correction for
autocorrelation, and the actual sample estimates of the standard
deviation of the mean elevation in chromosome 5 (see FIG. 18)
confirms the validity of the assumed lack of correlation. Z-scores
(see FIG. 19) and p-values calculated from Z-scores associated with
deviations from the expected elevation of 1 (see FIG. 20) can then
be evaluated in light of the estimate for uncertainty in the
average elevation. The p-values are based on a t-distribution whose
order is determined by the number of bins in a peak. Depending on
the desired level of confidence, a cutoff can suppress noise and
allow unequivocal detection of the actual signal.
Z = .DELTA. 1 - .DELTA. 2 .sigma. 1 2 ( 1 N 1 + 1 n 1 ) + .sigma. 2
2 ( 1 N 2 + 1 n 2 ) ( 1 ) ##EQU00002##
[0629] Equation 1 can be used to directly compare peak elevation
from two different samples, where N and n refer to the numbers of
bins in the entire chromosome and within the aberration,
respectively. The order of the t-test that will yield a p-value
measuring the similarity between two samples is determined by the
number of bins in the shorter of the two deviant stretches.
[0630] Peak Edge
[0631] In addition to comparing average elevations of aberrations
in a sample, the beginning and end of the compared stretches also
can provide useful information for statistical analysis. The upper
limit of resolution for comparisons of peak edges often is
determined by the bin size (e.g., 50 kbps in the examples described
herein). FIG. 21 illustrates 3 possible peak edge scenarios; (a) a
peak from one sample can be completely contained within the
matching peak from another sample, (b) the edges from one sample
can partially overlap the edges of another sample, or (c) the
leading edge from one sample can just marginally touch or overlap
the trailing edge of another sample.
[0632] FIG. 22 illustrates and example of the scenario described in
(c) (e.g., see the middle trace, where the trailing edge of the
middle trace marginally touches the leading edge of the upper
trace). The lateral tolerance associated with an edge often can be
used to distinguish random variations from true, aberration edges.
The position and the width of an edge can be quantified by
numerically evaluating the first derivative of the aberrant count
profile, as shown in FIG. 23. If the aberration is represented as a
composite of two Heaviside functions, its derivative will be the
sum of two Dirac's delta functions. The starting edge corresponds
to an upward absorption-shaped peak, while the ending edge is a
downward, 180 degree-shifted absorption peak. If the aberration is
narrow, the two spikes are close to one another, forming a
dispersion-like contour. The locations of the edges can be
approximated by the extrema of the first derivative spikes, while
the edge tolerance is determined by their widths.
[0633] Comparison between different samples often can be reduced to
determining the difference between two matching edge locations,
divided by the combined edge uncertainties. However, the
derivatives sometimes are lost in background noise, as illustrated
in FIG. 24. While the aberration itself benefits from the
collective information contributed from all its bins, the first
derivative only can afford information from the few points at the
edge of the aberration, which can be insufficient to overcome the
noise. Sliding window averaging, used to create FIG. 24, is of
limited value in this situation. Noise can be suppressed by
combining the first derivative (e.g., akin to a point estimate)
with the peak elevation (e.g., comparable to an integral estimate).
In some embodiments the first derivative and the peak elevation can
be combined by multiplying them together, which is equivalent to
taking the first derivative of a power of the peak elevation, as
shown in FIG. 25. The results presented in FIG. 25 successfully
suppress noise outside of the aberration, however, noise within the
aberration is enhanced by the manipulation. The first derivative
peaks are still clearly discernible, allowing them to be used to
extract edge locations and lateral tolerances, thereby allowing the
aberration to be clearly identified in the lower profile
tracing.
[0634] Median Chromosomal Elevation
[0635] The median normalized elevation within the target chromosome
in a euploid patient is expected to remain close to 1 regardless of
the fetal fraction. However, as shown in FIGS. 9 and 10, median
elevations in trisomy patients increase with the fetal fraction.
The increase generally is substantially linear with a slope of 0.5.
Experimental measurements confirm these expectations. FIG. 26
illustrates a histogram of median elevations for 86 euploid samples
(shown in dotted bars in FIG. 26). The median values are tightly
clustered around 1 (median=1.0000, median absolute deviation
(MAD)=0.0042, mean=0.9996, standard deviation (SD)=0.0046). None of
the euploid median elevations exceeds 1.012, as shown in the
histogram presented in FIG. 26. In contrast, out of 35 trisomy
samples shown (hatched bars) in FIG. 26, all but one have median
elevations exceeding 1.02, significantly above the euploid range.
The gap between the two groups of patients in this example is large
enough to allow classification as euploid or aneuploid.
[0636] Fetal Fraction as the Limiting Factor in Classification
Accuracy
[0637] The ratio between the fetal fraction and the width of the
distribution of median normalized counts in euploids (e.g. euploid
pregnancies) can be used to determine the reliability of
classification using median normalized elevations, in some
embodiments. Since median normalized counts, as well as other
descriptors such as Z-values, linearly increase with the fetal
fraction with the proportionality constant of 0.5, the fetal
fraction must exceed four standard deviations of the distribution
of median normalized counts to achieve 95% confidence in
classification, or six standard deviations to achieve 99%
confidence in classification. Increasing the number of aligned
sequences tags can serve to decrease the error in measured profiles
and sharpen the distribution of median normalized elevations, in
certain embodiments. Thus, the effect of increasingly precise
measurements is to improve the ratio between fetal fraction and the
width of the distribution of euploid median normalized
elevations.
[0638] Area Ratio
[0639] The median of the distribution of normalized counts
generally is a point estimate and, as such, often is a less
reliable estimate than integral estimates, such as areas under the
distribution (e.g., area under the curve. Samples containing high
fetal level fractions are not as affected by using a point
estimate, however at low fetal fraction values, it becomes
difficult to distinguish a truly elevated normalized profile from a
euploid sample that has a slightly increased median count due to
random errors. A histogram illustrating the median distribution of
normalized counts from a trisomy case with a relatively low fetal
fraction (e.g., F=about 7%; F(7%)) is shown in FIG. 27. The median
of the distribution is 1.021, not far from 1+F/2=1.035. However,
the width of the distribution (MAD=0.054, SD=0.082) far exceeds the
deviation of the median from the euploid value of 1, precluding any
claims that the sample is abnormal. Visual inspection of the
distribution suggests an alternative analysis: although the shift
of the peak to the right is relatively small, it significantly
perturbs the balance between the areas to the left (backward
slashed) and to the right (forward slashed) from the euploid
expectation of 1. Thus the ratio between the two areas, being an
integral estimate, can be advantageous in cases where
classification is difficult due to low fetal fraction values.
Calculation of the integral estimate for the forward slashed and
backward slashed areas under the curve is explained in more detail
below.
[0640] If a Gaussian distribution of normalized counts is assumed,
then
P ( q ) = 1 .sigma. 2 .pi. exp [ - ( q - q 0 ) / ( 2 .sigma. ' ) ]
. ( 2 ) ##EQU00003##
[0641] In euploid cases, the expectation for the normalized counts
is 1. For trisomy patients, the expectation is
q.sub.0=1+F/2 (3).
[0642] Since the reference point for calculating the area ratio is
1, the argument to the exponential function is z.sup.2, where
z=-F/(2.sigma. {square root over (2)}) (4).
[0643] The area to the left of the reference point is
B = .intg. - .infin. 1 P ( q ) q = 1 2 [ 1 + erf ( z ) ] . ( 5 )
##EQU00004##
[0644] The error function erf(z) can be evaluated using its Taylor
expansion:
erf ( z ) = 2 .pi. n = 0 .infin. ( - 1 ) n Z zn + 1 n ! ( 2 n + 1 )
. ( 6 ) ##EQU00005##
[0645] The area to the right from the reference point is 1-B. The
ratio between two areas is therefore
R = 1 - B B = 1 - erf ( z ) 1 + erf ( z ) = 1 - erf [ - F / ( 2
.sigma. 2 ) ] 1 + erf [ - F / ( 2 .sigma. 2 ) ] . ( 7 )
##EQU00006##
[0646] Error propagation from measured fetal fractions into area
ratios R can be estimated by simply replacing F in equation 7 with
F-.DELTA.F and F+.DELTA.F. FIG. 28 shows the frequencies of euploid
and trisomy area ratios in a set of 480 samples. The overlap
between two groups involves trisomy samples with low fetal
fractions.
[0647] Combined Classification Criteria
[0648] FIG. 29 illustrates the interrelation and interdependence of
median elevations and area ratios, both of which described
substantially similar phenomena. Similar relationships connect
median elevations and area ratios with other classification
criteria, such as Z-scores, fitted fetal fractions, various sums of
squared residuals, and Bayesian p-values (see FIG. 30). Individual
classification criteria can suffer from ambiguity stemming from
partial overlap between euploid and trisomy distributions in gap
regions, however, a combination of multiple criteria can reduce or
eliminate any ambiguities. Spreading the signal along multiple
dimensions can have the same effect as measuring NMR frequencies of
different nuclei, in some embodiments, resolving overlapping peaks
into well-defined, readily identifiable entities. Since no attempt
is made to quantitatively predict any theoretical parameter using
mutually correlated descriptors, the cross-correlations observed
between different classification criteria do not interfere.
Defining a region in multidimensional space that is exclusively
populated by euploids, allows classification of any sample that is
located outside of the limiting surface of that region. Thus the
classification scheme is reduced to a consensus vote for
euploidy.
[0649] In some embodiments utilizing a combined classification
criteria approach, classification criteria described herein can be
combined with additional classification criteria known in the art.
Certain embodiments can use a subset of the classification criteria
listed here. Certain embodiments can mathematically combine (e.g.,
add, subtract, divide, multiply, and the like) one or more
classification criteria among themselves and/or with fetal fraction
to derive new classification criteria. Some embodiments can apply
principal components analysis to reduce the dimensionality of the
multidimensional classification space. Some embodiments can use one
or more classification criteria to define the gap between affected
and unaffected patients and to classify new data sets. Any
combination of classification criteria can be used to define the
gap between affected and unaffected patients and to classify new
data sets. Non-limiting examples of classification criteria that
can be used in combination with other classification criteria to
define the gap between affected and unaffected patients and to
classify new data sets include: linear discriminant analysis,
quadratic discriminant analysis, flexible discriminant analysis,
mixture discriminant analysis, k Nearest Neighbors, classification
tree, bagging, boosting, neural networks, support vector machines,
and/or random forest.
Example 2
Methods for Detection of Genetic Variations Associated with Fetal
Aneuploidy Using Measured Fetal Fractions and Bin-Weighted Sums of
Squared Residuals
[0650] Z-value statistics and other statistical analysis of
sequence read data frequently are suitable for determining or
providing an outcome determinative of the presence or absence of a
genetic variation with respect to fetal aneuploidy, however, in
some instances it can be useful to include additional analysis
based on fetal fraction contribution and ploidy assumptions. When
including fetal fraction contribution in a classification scheme, a
reference median count profile from a set of known euploids (e.g.
euploid pregnancies) generally is utilized for comparison. A
reference median count profile can be generated by dividing the
entire genome into N bins, where N is the number of bins. Each bin
i is assigned two numbers: (i) a reference count F, and (ii) the
uncertainty (e.g., standard deviation or a) for the bin reference
counts.
[0651] The following relationship can be utilized to incorporate
fetal fraction, maternal ploidy, and median reference counts into a
classification scheme for determining the presence or absence of a
genetic variation with respect to fetal aneuploidy,
y.sub.i=(1-F)M.sub.if.sub.i+FXf.sub.i (8)
[0652] where Y.sub.i represents the measured counts for a bin in
the test sample corresponding to the bin in the median count
profile, F represents the fetal fraction, X represents the fetal
ploidy, and M.sub.i represents maternal ploidy assigned to each
bin. Possible values used for X in equation (8) are: 1 if the fetus
is euploid; 3/2, if the fetus is triploid; and, 5/4, if there are
twin fetuses and one is affected and one is not. 5/4 is used in the
case of twins where one fetus is affected and the other not,
because the term Fin equation (8) represents total fetal DNA,
therefore all fetal DNA must be taken into account. In some
embodiments, large deletions and/or duplications in the maternal
genome can be accounted for by assigning maternal ploidy, M.sub.i,
to each bin or genomic section. Maternal ploidy often is assigned
as a multiple of 1/2, and can be estimated using bin-wise
normalization, in some embodiments. Because maternal ploidy often
is a multiple of 1/2, maternal ploidy can be readily accounted for,
and therefore will not be included in further equations to simplify
derivations.
[0653] Fetal ploidy can be assessed using any suitable approach. In
some embodiments, fetal ploidy can be assessed using equation (8),
or derivations thereof. In certain embodiments, fetal ploidy can be
classified using one of the following, equation (8) based,
non-limiting approaches: [0654] 1) Measure fetal fraction F and use
the value to form two sums of squared residuals. To calculate the
sum of squared residuals, subtract the right hand side (RHS) of
equation (8) from its left hand side (LHS), square the difference,
and sum over selected genomic bins, or in those embodiments using
all bins, sum over all bins. This process is performed to calculate
each of the two sums of squared residuals. One sum of square
residuals is evaluated with fetal ploidy set to 1 (e.g., X=1) and
the other sum of squared residuals is evaluated with fetal ploidy
set to 3/2 (e.g., X=3/2). If the fetal test subject is euploid, the
difference between the two sums of squared residuals is negative,
otherwise the difference is positive. [0655] 2) Fix fetal fraction
at its measured value and optimize ploidy value. Fetal ploidy
generally can take on only 1 of two discrete values, 1 or 3/2,
however, the ploidy sometimes can be treated as a continuous
function. Linear regression can be used to generate an estimate for
ploidy. If the estimate resulting from linear regression analysis
is close to 1, the fetal test sample can be classified as euploid.
If the estimate is close to 3/2, the fetus can be classified as
triploid. [0656] 3) Fix fetal ploidy and optimize fetal fraction
using linear regression analysis. The fetal fraction can be
measured and a restraint term can be included to keep the fitted
fetal fraction close to the measured fetal fraction value, with a
weighting function that is reciprocally proportional to the
estimated error in the measure fetal fraction. Equation (8) is
solved twice, once with ploidy set at 3/2, and once for fetal
ploidy set to 1. When solving equation (8) with ploidy set to 1,
the fetal fraction need not be fitted. A sum of square residuals is
formed for each result and the sum of squared residuals subtracted.
If the difference is negative, the fetal test subject is euploid.
If the difference is positive, the fetal test subject is
triploid.
[0657] The generalized approaches described in 1), 2) and 3) are
described in further detail herein.
[0658] Fixed Ploidy, Fixed Fetal Fraction: Sums of Squared
Residuals
[0659] In some embodiments, fetal aneuploidy can be determined
using a model which analyzes two variables, fetal ploidy (e.g., X)
and fetal nucleic acid fraction (e.g., fetal fraction; F). In
certain embodiments, fetal ploidy can take on discrete values, and
in some embodiments, fetal fraction can be a continuum of values.
Fetal fraction can be measured, and the measured valued used to
generate a result for equation (8), for each possible value for
fetal ploidy. Fetal ploidy values that can be used to generate a
result for equation (8) include 1 and 3/2 for a single fetus
pregnancy, and in the case of a twin fetus pregnancy where one
fetus is affected and the other fetus unaffected, 5/4 can be used.
The sum of squared residuals obtained for each fetal ploidy value
measures the success with which the method reproduces the
measurements, in some embodiments. When evaluating equation (8) at
X=1, (e.g., euploid assumption), the fetal fraction is canceled out
and the following equation results for the sum of squared
residuals:
.PHI. E = i = 1 N 1 .sigma. i 2 ( y i - f i ) 2 = i = 1 N y i 2
.sigma. i 2 - 2 i = 1 N y i f i .sigma. i 2 + i = 1 N f i 2 .sigma.
i 2 = .XI. yy - 2 .XI. fy + .XI. ff ( 9 ) ##EQU00007##
[0660] To simplify equation (9) and subsequent calculations, the
following notion is utilized:
.XI. yy = i = 1 N y i 2 .sigma. i 2 ( 10 ) .XI. ff = i = 1 N f i 2
.sigma. i 2 ( 11 ) .XI. fy = i = 1 N y i f i .sigma. i 2 ( 12 )
##EQU00008##
[0661] When evaluating equation (8) at X=3/2 (e.g., triploid
assumption), the following equation results for the sum of the
squared residuals:
.PHI. T = i = 1 N 1 .sigma. i 2 ( y i - f i - 1 2 Ff i ) 2 = .XI.
yy - 2 .XI. fy + .XI. ff + F ( .XI. ff - .XI. fy ) + 1 4 F 2 .XI.
ff ( 13 ) ##EQU00009##
[0662] The difference between equations (9) and (13) forms the
functional result (e.g., phi) that can be used to test the null
hypothesis (e.g., euploid, X=1) against the alternative hypothesis
(e.g., trisomy singleton, X=3/2):
.PHI. = .PHI. E - .PHI. T = F ( .XI. fy - .XI. ff ) - 1 4 F 2 .XI.
ff ( 14 ) ##EQU00010##
[0663] The profile of phi with respect to F is a parabola defined
to the right of the ordinate (since F is greater than or equal to
0). Phi converges to the origin as F approaches zero, regardless of
experimental errors and uncertainties in the model parameters.
[0664] In some embodiments, the functional Phi is dependent on the
measured fetal fraction F with a negative second-order quadratic
coefficient (see equation (14)). Phi's dependence on the measured
fetal fraction would seem to imply a convex shape for both euploid
and triploid cases. If this analysis were correct, trisomy cases
would reverse the sign at high F values, however equation (12)
depends on F. Combining equations (8) and (14), disregarding
maternal ploidy, setting X=3/2 and neglecting experimental errors,
the equation for trisomy cases becomes:
.XI. fy = i = 1 N y i f i .sigma. i 2 = i = 1 N f i .sigma. i 2 [ (
1 - F ) f i + FXf i ] = ( 1 + 1 2 F ) i = 1 N f i 2 .sigma. i 2 ( 1
+ 1 2 F ) .XI. ff ( 15 ) ##EQU00011##
[0665] The relationship between equations (11) and (12) for
triploids holds under ideal circumstances, in the absence of any
measurement errors. Combining equations (14) and (15) results in
the following expression, which often yields a concave parabola in
triploid cases:
.PHI. = F ( .XI. fy - .XI. ff ) - 1 4 F 2 .XI. ff = F [ ( 1 + 1 2 F
) .XI. ff - .XI. ff ] - 1 4 F 2 .XI. ff = 1 4 F 2 .XI. ff ( Trisomy
) ( 16 ) ##EQU00012##
[0666] For euploids, equations (11) and (12) should have the same
value, with the exception of measurement errors, which sometimes
yields a convex parabola:
.PHI. = F ( .XI. fy - .XI. ff ) - 1 4 F 2 .XI. ff = - 1 4 F 2 .XI.
ff ( 17 ) ##EQU00013##
[0667] Simulated functional phi profiles for typical model
parameter values are shown in FIG. 31, for trisomy (dashed line)
and euploid (solid line, bottom) cases. FIG. 32 shows an example
using actual data. In FIGS. 31 and 32, data points below the
abscissa generally represent cases classified as euploids. Data
points above the abscissa generally represent cases classified as
trisomy 21 (T21) cases. In FIG. 32, the solitary data point in the
fourth quadrant (e.g., middle lower quadrant) is a twin pregnancy
with one affected fetus. The data set utilized to generate FIG. 32
includes other affected twin samples as well, explaining the spread
of T21 data points toward the abscissa.
[0668] Equations (9) and (10) often can be interpreted as follows:
For triploids, the euploid model sometimes generates larger errors,
implying that phi.sub.E (see equation (9)) is greater than
phi.sub.T (see equation (13)). As a result, functional phi (see
equation (7)) occupies the first quadrant (e.g., upper left
quadrant). For euploids, the trisomy model sometimes generates
larger errors, the rank of equations (2) and (6) reverses and
functional phi (equation (7)) occupies in the fourth quadrant.
Thus, in principle, classification of a sample as euploid or
triploid sometimes reduces to evaluating the sign of phi.
[0669] In some embodiments, the curvature of the data points shown
in FIGS. 31 and 32 can be reduced or eliminated by replacing
functional phi (equation (7)) with the square root of functional
phi's absolute value, multiplied by its sign. The linear
relationship generated with respect to F sometimes can improve
separation between triploids and euploids at low fetal fraction
values, as shown in FIG. 33. Linearizing the relationship with
respect to F sometimes results in increase uncertainty intervals at
low fetal fraction (e.g., F) values, therefore, the gains realized
from this process are related to making visual inspection of the
differences substantially easier; the gray area remains unchanged.
Extension of the process to analysis of twin pregnancies is
relatively straightforward. The reason used to generate equation
(9) implies that in a twin pregnancy with one affected and one
normal fetus, functional phi should reduce to zero, plus or minus
experimental error, regardless of F. Twin pregnancies generally
produce more fetal DNA than single pregnancies.
[0670] Optimized Ploidy, Fixed Fetal Fraction: Linear
Regression
[0671] In certain embodiments, fetal aneuploidy can be determined
using a model in which the fetal fraction is fixed at its measured
value and ploidy is varied to optimize the sum of squared
residuals. In some embodiments, the resulting fitted fetal fraction
value can be used to classify a case as trisomy or euploid,
depending on whether the value is close to 1, 3/2, or 5/4 in the
case of twins.
[0672] Starting from equation (8), the sum of squared residuals can
be formed as follows:
.PHI. = i = 1 N 1 .sigma. i 2 [ y i - ( 1 - F ) M i f i = FXf i ] 2
= i = 1 N 1 .sigma. i 2 [ y i 2 - 2 ( 1 - F ) M i f i y i - 2 FXf i
y i + ( 1 - F ) 2 M i 2 f i 2 + 2 F ( 1 - F ) XM i f i 2 + F 2 X 2
f i 2 ] ( 18 ) ##EQU00014##
[0673] To minimize phi as a function of X, the first derivative of
phi with respect to X is generated, set equal to zero, and the
resulting equation solved for X. The resulting expression is
presented in equation (19).
1 2 ( .PHI. X ) = 0 = XF 2 i = 1 N f i 2 .sigma. i 2 - F i = 1 N f
i y i .sigma. i 2 + F ( 1 - F ) i = 1 N M i f i 2 .sigma. i 2 ( 19
) ##EQU00015##
[0674] The optimal ploidy value sometimes is given by the following
expression:
X = i = 1 N f i y i .sigma. i 2 - ( 1 - F ) i = 1 N M i f i 2
.sigma. i 2 F i = 1 N f i 2 .sigma. i 2 ( 20 ) ##EQU00016##
[0675] As noted previously, the term for maternal ploidy, M.sub.i,
can be omitted from further mathematical derivations. The resulting
expression for X corresponds to the relatively simple, and often
most frequently occurring, special case of when the mother has no
deletions or duplications in the chromosome or chromosomes being
evaluated. The resulting expression is presented in FIG. 21.
X = .XI. fy - ( 1 - F ) .XI. ff F .XI. ff = .XI. fy F .XI. ff - 1 -
F F = 1 + 1 F ( .XI. fy .XI. ff - 1 ) ( 21 ) ##EQU00017##
[0676] Xi.sub.ff and Xi.sub.fy are given by equations (11) and
(12), respectively. In embodiments where all experimental errors
are negligible, solving equation (21) results in a value of 1 for
euploids where Xi.sub.ff=Xi.sub.fy. In certain embodiments where
all experimental errors are negligible, solving equation (21)
results in a value of 3/2 for triploids (see equation (15) for
triploid relationship between Xi.sub.ff and Xi.sub.fy.
[0677] Optimized Ploidy, Fixed Fetal Fraction: Error
Propagation
[0678] Optimized ploidy often is inexact due to various sources of
error. Three, non-limiting examples of error sources include:
reference bin counts f.sub.i, measured bin counts y.sub.i, and
fetal fraction F. The contribution of the non-limiting examples of
error will be examined separately.
[0679] Errors in Measured Fetal Fractions: Quality of Fitted Fetal
Fraction
[0680] Fetal fraction estimates based on the number of sequence
tags mapped to the Y chromosome (e.g., Y-counts) sometimes show
relatively large deviations with respect to FQA fetal fraction
values (see FIG. 34). Z-values for triploid often also exhibit a
relatively wide spread around the diagonal shown in FIG. 35. The
diagonal line in FIG. 35 represents a theoretically expected
increase of the chromosomal representation for chromosome 21 with
increasing fetal fraction in trisomy 21 cases. Fetal fraction can
be estimated using a suitable method. A non-limiting example of a
method that can be utilized to estimate fetal fraction is the fetal
quantifier assay (e.g., FQA). Other methods for estimating fetal
fraction are known in the art. Various methods utilized to estimate
fetal fraction sometimes also show a substantially similar spread
around the central diagonal, as shown in FIG. 36-39. In FIG. 36,
the deviations are substantially similar (e.g., negative at high
F.sub.0) to those observed in fitted fetal fraction (see equation
(33)). In some embodiments, the slope of the linear approximation
to the average chromosome Y (e.g., chromosome Y) fetal fraction
(see the middle histogram line in FIG. 36) in the range between 0%
and 20% is about 3/4. In certain embodiments, the linear
approximation for standard deviation (see FIG. 36, upper and lower
histogram lines) is about 2/3+F.sub.0/6. In some embodiments, fetal
fraction estimates based on chromosome 21 (e.g., chromosome 21) are
substantially similar to those obtained by fitting fetal fractions
(see FIG. 37). Another qualitatively similar set of gender-based
fetal fraction estimates is shown in FIG. 38. FIG. 39 illustrates
the medians of normalized bin counts for T21 cases, which are
expected to have a slope whose linear approximation is
substantially similar to 1+F.sub.0/2 (see gray line from origin to
the midpoint of the top of the graph in FIG. 39).
[0681] FIG. 36-39 share the following common features: [0682] a)
slope not equal to 1 (either greater or less than 1, depending on
the method, with the exception of Z-values), [0683] b) large spread
fetal fraction estimation, and [0684] c) the extent of spread
increases with fetal fraction.
[0685] To account for these observations, errors in measured fetal
fraction will be modeled using the formula .DELTA.F=2/3+F.sub.0/6,
in some embodiments.
[0686] Errors in Measured Fetal Fractions: Error Propagation from
Measured Fetal Fractions to Fitted Ploidy
[0687] If the assumption is made that f.sub.i and y.sub.i are
errorless, to simplify analysis, the measured fetal fraction F is
composed of F.sub.v (e.g., the true fetal fraction) and .DELTA.F
(e.g., the error in measured fetal fraction):
F=F.sub.V+.DELTA.F (22).
[0688] In some instances, uncertainties in fitted X values
originate from errors in measured fetal fraction, F. Optimized
values for X are given by equation (21), however the true ploidy
value is given by X.sub.V, where X.sub.V=1 or 3/2. X.sub.V varies
discretely, whereas X varies continuously and only accumulates
around X.sub.V under favorable conditions (e.g., relatively low
error).
[0689] Assuming again that f.sub.i and y.sub.i are errorless,
equation (8) becomes:
y.sub.i=(1-F.sub.V)M.sub.if.sub.i+F.sub.VXf.sub.i (23).
[0690] Combining equations (21) to (23) generates the following
relationship between true ploidy X.sub.V and the ploidy estimate X
that includes the error .DELTA.F. The relationship also includes
the assumption that maternal ploidy equals 1 (e.g., euploid), and
the term for maternal ploidy, M.sub.i is replaced by 1.
X = 1 + 1 F V + .DELTA. F { i = 1 N f i .sigma. i 2 [ ( 1 - F V ) f
i + F V X V f i ] i = 1 N f i 2 .sigma. i 2 - 1 } = 1 + F V ( X V -
1 ) F V + .DELTA. F ( 24 ) ##EQU00018##
[0691] In some instances, the term X.sub.V-1 is substantially
identical to zero in euploids, and .DELTA.F does not contribute to
errors in X. In triploid cases, the error term does not reduce to
zero (e.g., is not substantially identical to zero). Thus, in some
embodiments, ploidy estimates can be viewed as a function of the
error .DELTA.F:
g(.DELTA.F) (25)
[0692] Simulated profiles of fitted triploid X as a function of
F.sub.0 with fixed errors .DELTA.F=plus or minus 0.2% are shown in
FIG. 40. Results obtained using actual data are shown in FIG. 41.
The data points generally conform to the asymmetric trumpet-shaped
contour predicted by equation (24). Smaller fetal fractions often
are qualitatively associated with larger ploidy errors.
Underestimated fetal fraction sometimes is compensated by ploidy
overestimates; overestimated fetal fraction often is linked to
underestimates in ploidy. The effect frequently is stronger when
fetal fraction is underestimated. This is consistent with the
asymmetry seen in the graphs presented in FIGS. 40 and 41, (e.g.,
as F decreases, the growth of the upper branch is substantially
faster than the decay of the lower branch). Simulations with
different levels of error in F follow the same pattern, with the
extent of the deviations from X.sub.V increasing with .DELTA.F.
[0693] A probability distribution for X can be used to quantify
these observations. In some embodiments, the distribution of
.DELTA.F can be used to derive the density function for X using the
following expression:
f Y ( y ) = 1 g ' ( g - 1 ( y ) ) f X ( g - 1 ( y ) ) ( 26 )
##EQU00019##
where, f.sub.Y(y) is the unknown density function for y=g(x)
f.sub.x(x) is the given density function for x g' (x) is the first
derivative of the given function y=g(x) g.sup.-1(y) is the inverse
of the given function g:x=g.sup.-1(y) g'(g.sup.-1(y)) is the value
of the derivative at the point g.sup.-1(y)
[0694] In equation 26.times. is .DELTA.F, y is X (e.g., ploidy
estimate), and g(x) is given by equation (24). The derivative is
evaluated according to the following expression:
g .DELTA. F = - F V ( X V - 1 ) ( F V + .DELTA. F ) 2 ( 27 )
##EQU00020##
[0695] The inverse g.sup.-1(y) can be obtained from equation (24),
in some embodiments:
.DELTA. F = F V ( X V - X ) X - 1 ( 28 ) ##EQU00021##
[0696] If the error in F conforms to a Gaussian distribution,
f.sub.x(x) in equation (26) can be replaced with the following
expression:
P ( .DELTA. F ) = exp [ - ( .DELTA. F ) 2 / ( 2 .sigma. 2 ) ]
.sigma. 2 .pi. ( 29 ) ##EQU00022##
[0697] In certain embodiments, combining equations (26) to (29)
results in a probability distribution for X at different levels of
.DELTA.F, as shown in FIG. 42.
[0698] In some instances, a bias towards higher ploidy values,
which sometimes are prominent at high levels of errors in F, often
is reflected in the asymmetric shape of the density function: a
relatively long, slowly decaying tail to the right of the right
vertical line, vertically in line with X, along the X axis, as
shown in FIG. 42, panels A-C. In some embodiments, for any value of
.DELTA.F, the area under the probability density function to the
left of the right vertical line (X.sub.V=3/2) equals the area to
the right of the right vertical line. That is, one half of all
fitted ploidy values often are overestimates, while the other half
of all fitted ploidy values sometimes are underestimates. In some
instances, the bias generally only concerns the extent of errors in
X, not the prevalence of one or the other direction. The median of
the distribution remains equal to X.sub.V, in some embodiments.
FIG. 43 illustrates euploid and trisomy distributions obtained for
actual data. Uncertainties in measured fetal fraction values
sometimes explain part of the variance seen in the fitted ploidy
values for triploids, however errors in estimated X values for
euploids often require examining error propagation from bin
counts.
[0699] Fixed Ploidy, Optimized Fetal Fraction: Linear
Regression
[0700] A continuously varying fetal fraction often can be optimized
while keeping ploidy fixed at one of its possible values (e.g., 1
for euploids, 3/2 for singleton triploids, 5/4 for twin triploids),
as opposed to fitting ploidy that often can take on a limited
number of known discrete values. In embodiments in which the
measured fetal fraction (F.sub.0) is known, optimization of the
fetal fraction can be restrained such that the fitted F remains
close to F.sub.0, within experimental error (e.g., .DELTA.F). In
some instances, the observed (e.g., measured) fetal fraction
F.sub.0, sometimes differs from fetal fraction, F.sub.V, described
in equations (22) to (28). A robust error propagation analysis
should be able to distinguish between F.sub.0 and F.sub.V. To
simplify the following derivations, difference between the observed
fetal fraction and the true fetal fraction will be ignored.
[0701] Equation (8) is presented below in a rearranged format that
also omits the maternal ploidy term (e.g., M.sub.i).
y.sub.i=F(X-1)f.sub.i+f.sub.i (30)
[0702] A functional term that needs to be minimized is defined as
follows, in some embodiments:
.PHI. ( F ) = ( F - F 0 ) 2 ( .DELTA. F ) 2 + i = 1 N 1 .sigma. i 2
[ y i - F ( X - 1 ) f i - f i ] 2 = ( F - F 0 ) 2 ( .DELTA. F ) 2 +
i = 1 N 1 .sigma. i 2 [ y i 2 + F 2 ( X - 1 ) 2 f i 2 + f i 2 - 2 F
( X - 1 ) f i y i - 2 f i y i + 2 F ( X - 1 ) f i 2 ] = ( F - F 0 )
2 ( .DELTA. F ) 2 + F 2 ( X - 1 ) 2 i = 1 N f i 2 .sigma. i 2 + 2 F
( X - 1 ) i = 1 N f i 2 - f i y i .sigma. i 2 + i = 1 N ( y i - f i
) 2 .sigma. i 2 ( 31 ) ##EQU00023##
[0703] When equation (31) is evaluated for euploids (e.g., X=1),
the term
( F - F 0 ) 2 ( .DELTA. F ) 2 ##EQU00024##
often depends on F, thus fitted F frequently equals F.sub.0. In
some instances, when equation (24) is evaluated for euploids, the
equation sometimes reduces to
i = 1 N ( y i - f i ) 2 .sigma. i 2 . ##EQU00025##
[0704] When equation (24) is evaluated for singleton trisomy cases
(e.g., X=3/2), the coefficients that multiply F contain both fetal
fraction measurements and bin counts, therefore the optimized value
for F often depends on both parameters. The first derivative of
equation (24) with respect to F reduces to zero in some
instances:
1 2 ( .PHI. F ) = 0 = ( F - F 0 ) ( .DELTA. F ) 2 + F ( X - 1 ) 2 i
= 1 N f i 2 .sigma. i 2 + ( X - 1 ) i = 1 N f i 2 - f i y i .sigma.
i 2 ( 32 ) ##EQU00026##
[0705] In some embodiments, replacing X=3/2 and solving equation
(32) for F yields an optimized value for F:
F = F 0 + ( .DELTA. F ) 2 2 i = 1 N 1 .sigma. i 2 ( f i y i - f i 2
) 1 + ( .DELTA. F ) 2 4 i = 1 N f i 2 / .sigma. i 2 . ( 33 )
##EQU00027##
[0706] To simplify further calculations and/or derivations, the
following auxiliary variables will be utilized:
S 0 = ( .DELTA. F ) 2 4 i = 1 N 1 .sigma. i 2 ( 34 ) S f = (
.DELTA. F ) 2 4 i = 1 N f i .sigma. i 2 ( 35 ) S y = ( .DELTA. F )
2 4 i = 1 N y i .sigma. i 2 ( 36 ) S yy = ( .DELTA. F ) 2 4 i = 1 N
y i 2 .sigma. i 2 ( 37 ) S ff = ( .DELTA. F ) 2 4 i = 1 N f i 2
.sigma. i 2 ( 38 ) S fy = ( .DELTA. F ) 2 4 i = 1 N y i f i .sigma.
i 2 ( 39 ) ##EQU00028##
[0707] Utilizing the auxiliary variables, the optimized fetal
fraction for X=3/2 for equation (33) then reduces to:
F = F 0 + 2 S fy - 2 S ff 1 + S ff ( 40 ) ##EQU00029##
[0708] Fitted F often is linearly proportional to the measured
value F.sub.0, but sometimes is not necessarily equal to F.sub.0.
The ratio between errors in fetal fraction measurements and
uncertainties in bin counts determines the relative weight given to
the measured F.sub.0 versus individual bins, in some embodiments.
In some instances, the larger the error .DELTA.F, the stronger the
influence that bin counts will exert on the fitted F.
Alternatively, small .DELTA.F generally implies that the fitted
value F will be dominated by F.sub.0. In some embodiments, if a
data set comes from a trisomy sample, and all errors are
negligible, equation (40) reduces to identity between F and
F.sub.0. By way of mathematic proof, using fetal ploidy set to
X=3/2, and assuming that F.sub.0 (observed) and F.sub.V(true) have
the same value, equation (30) becomes:
y i = 1 2 F 0 f i + f i ( 41 ) ##EQU00030##
[0709] The assumption that F.sub.0 and F.sub.V generally is an
acceptable assumption for the sake of the qualitative analysis
presented herein. Combing equations (39) and (41) yields
S fy = ( .DELTA. F ) 2 4 i = 1 N y i f i .sigma. i 2 = ( .DELTA. F
) 2 4 i = 1 N ( 1 2 F 0 f i + f i ) f i .sigma. i 2 = ( 1 2 F 0 + 1
) S ff ( 42 ) ##EQU00031##
[0710] Combining equations (40) and (42) results in identity
between F.sub.0 and F.sub.V:
F = F 0 + 2 S fy - 2 S ff 1 + S ff = F 0 + 2 ( 1 2 F 0 + 1 ) S ff -
2 S ff 1 + S ff = F 0 ( 1 + S ff ) 1 + S ff .ident. F 0 QED ( 43 )
##EQU00032##
[0711] To further illustrate the theoretical model, if the true
ploidy is 1 (e.g., euploid) but the ploidy value use in equation
(40) is set to X=3/2 (e.g., triploid singleton), the resulting
fitted F does not equal F.sub.0, nor does it reduce to zero, and
the following expression generally is true:
y i = f i S fy = ( .DELTA. F ) 2 4 i = 1 N y i f i .sigma. i 2 = (
.DELTA. F ) 2 4 i = 1 N f i 2 .sigma. i 2 = S ff F = F 0 + 2 S fy -
2 S ff 1 + S ff = F 0 1 + S ff . ( 44 ) ##EQU00033##
[0712] Thus, application of triploid equations when testing a
euploid case generally results in a non-zero fitted F that is
proportional to F.sub.0 with a coefficient of proportionality
between 0 and 1 (exclusive), depending on reference bin counts and
associated uncertainties (cf. equation (38)), in certain
embodiments. A similar analysis is shown in FIG. 44, using actual
data from 86 know euploids as reference. The slope of the straight
line from equation (44) is close to 20 degrees, as shown in FIG.
44.
[0713] The solitary data point between euploid and T21 cases (e.g.,
measured fetal fraction approximately 40%, fitted fraction
approximately 20%) represents a T21 twin. When a constant .DELTA.F
is assumed the euploid branch of the graph shown in FIG. 44
generally is sloped, however when .DELTA.F=2/3+F.sub.0/6 is used
the euploid branch of the graph often becomes substantially
horizontal, as described herein in the section entitled "Fixed
ploidy, optimized fetal fraction, error propagation: fitted fetal
fractions".
[0714] Fixed Ploidy, Optimized Fetal Fraction: Sums of Squared
Residuals
[0715] In some instances for euploid cases, were fitted F for
equation (32) equals F.sub.0 and X=1, the sum of square residuals
for a euploid model follows from equation (31):
.PHI. E = i = 1 N 1 .sigma. i 2 ( y i - f i ) 2 = .XI. yy - 2 .XI.
fy + .XI. ff ( 45 ) ##EQU00034##
which is substantially the same result as equation (9). In certain
instances for euploid cases, equation (40) can be combined into
equation (31). The resulting mathematical expression quadratically
depends on F.sub.0, in some embodiments. In certain embodiments,
classification of a genetic variation is performed by subtracting
the triploid sum of squared residuals from the euploid sum of
squared residuals. The result of the classification obtained by
subtracting the triploid sum of squared residuals from the euploid
sum of squared residuals also frequently depends on F.sub.0:
.PHI. E - .PHI. T = - 1 ( .DELTA. F ) 2 [ ( F 0 + 2 S fy - 2 S ff 1
+ S ff - F 0 ) 2 + ( F 0 + 2 S fy - 2 S ff 1 + S ff ) 2 ( .DELTA. F
) 2 4 i = 1 N f i 2 .sigma. i 2 + ( F 0 + 2 S fy - 2 S ff 1 + S ff
) ( .DELTA. F ) 2 i = 1 N f i 2 - f i y i .sigma. i 2 ] = - 1 (
.DELTA. F ) 2 [ ( F 0 + 2 S fy - 2 S ff 1 + S ff - F 0 ) 2 + ( F 0
+ 2 S fy - 2 S ff 1 + S ff ) 2 S ff + 4 ( F 0 + 2 S fy - 2 S ff 1 +
S ff ) ( S ff - S fy ) ] = - [ ( 2 S fy - 2 S ff - F 0 S ff ) 2 + (
F 0 + 2 S fy - 2 S ff ) 2 S ff + 4 ( F 0 + 2 S fy - 2 S ff ) ( 1 +
S ff ) ( S ff - S fy ) ] ( .DELTA. F ) 2 ( 1 + S ff ) 2 = - 1 (
.DELTA. F ) 2 ( 1 + S ff ) 2 [ ( 4 S fy 2 + 4 S ff 2 + F 0 2 S ff 2
- 8 S fy S ff - 4 F 0 S fy S ff + 4 F 0 S ff 2 ) + ( F 0 2 S ff + 4
S fy 2 S ff + 4 S ff 3 + 4 F 0 S fy S ff - 4 F 0 S ff 2 - 8 S fy S
ff 2 ) + ( 4 F 0 S ff + 8 S fy S ff - 8 S ff 2 - 4 F 0 S fy - 8 F 0
S fy + 8 S fy S ff ) + 4 F 0 S ff 2 + 8 S fy S ff 2 - 8 S ff 3 - 4
F 0 S fy S ff - 8 S fy 2 S ff + 8 S fy S ff 2 ) ] = - 1 ( .DELTA. F
) 2 ( 1 + S ff ) [ F 0 2 S ff + 4 F 0 ( S ff - S fy ) - 4 ( S ff -
S fy ) 2 ] ( 46 ) ##EQU00035##
[0716] The term S.sub.fy generally depends on fetal fraction, as
also seen for equation (14). The dependence of
.phi..sub.E-.phi..sub.T on the measured fetal fraction can be
analyzed by accounting for the fetal fraction, in some embodiments.
The fetal fraction often can be accounted for by assuming that
measured fetal fraction F.sub.0 equals true fetal fraction F.sub.V.
In some embodiments, if the sample's karyotype is euploid, S.sub.fy
and S.sub.ff have the same values (e.g., with the exception of
experimental errors). As a result, the difference between the two
sums of squared residuals often reduces to:
.PHI. E - .PHI. T = - F 0 2 S ff ( .DELTA. F ) 2 ( 1 + S ff ) (
Euploids ) ( 47 ) ##EQU00036##
[0717] In certain embodiments, if the sample's karyotype is
triploid, equations (41) and (42) can be combined with equation
(46), yielding:
.PHI. E - .PHI. T = F 0 2 S ff ( .DELTA. F ) 2 ( Triploids ) ( 48 )
##EQU00037##
[0718] Thus, if the difference of .phi..sub.E-.phi..sub.T, is
positive, the fetus is triploid, in some embodiments, and in
certain embodiments, if the difference is negative, the fetus is
unaffected. The graphical representation for the positive or
negative result frequently is a parabola; concave for triploids and
convex for euploids. Both branches tend towards zero as F.sub.0
decreases, with experimental error having little effect on the
shape of the graph. Neither branch has a substantially linear or
free term, but the second order coefficients differ in size in
addition to having different signs, in many instances. With
.DELTA.F approximately 2%, the value of the term S.sub.ff is close
to 3.7, using the reference counts and uncertainties extracted from
the 86 euploid set (see FIG. 45).
[0719] In the example shown in FIG. 45, the two branches often are
asymmetric due to the different coefficients multiplying the square
of the measured fetal fraction in equations (47) and (48). The
triploid (e.g., positive) branch increases relatively quickly,
becoming distinguishable from zero substantially earlier than the
euploid branch. FIG. 46, obtained using a real data set, confirms
the qualitative results shown in FIG. 45. In FIG. 46 the solitary
dark gray point in the fourth quadrant (e.g., lower middle
quadrant) is an affected twin. In the data set used to generate
FIG. 46, both the euploid and T21 branches of the graph show
curvature because both show quadratic dependence on F.sub.0 from
the trisomy version of equation (31)
[0720] In some embodiments, both branches of the graph can be
linearized to facilitate visual inspection. The value of the
linearization often is conditioned on the error propagation
analysis. The results presented in FIGS. 45 and 46 were based on
the assumption that the error in measured fetal fractions is
uniform the entire range of fetal fractions. However, the
assumption is not always the case. In some instances, the more
realistic assumption, based on a linear relationship between error
.DELTA.F and measured fetal fraction
F.sub.0(.DELTA.F=2/3+F.sub.0/6), produces the results presented in
FIG. 47. In FIG. 47, the euploid branch is substantially flat,
almost constant (e.g., the parabolic character is substantially
lost), however, the trisomy branch remains parabolic. The three
light gray points interspersed in the dark gray points of the
trisomy branch represent data from twins. Twin data sometimes are
elevated relative to the fixed error model.
[0721] Classification of whether or not a sample is affected by a
genetic variation often is carried out using one of three
processes: (1) classification based on parabolic differences of
summed squares of residuals, (see FIGS. 45 and 46), (2)
classification based on linear differences of summed squares of
residuals, (see FIGS. 47 and 48), and (3) classification based on
fitted fetal fraction (see equation (33)). In some embodiments, the
chosen approach takes error propagation into account.
[0722] Fixed Ploidy, Optimized Fetal Fraction: Systematic
Error--Reference Offset
[0723] Ideally, reference and measured bin counts should contain
zero systematic error (e.g., offset), however, in practice,
reference and measured bin counts sometimes are shifted with
respect to one another. In some instances, the effect of the shift
with respect to one another can be analyzed using equation (33),
assuming the shift d is constant across the chromosome of interest.
For euploid cases, if random errors are neglected, the following
relationships hold, in some embodiments:
f.sub.i=f.sub.i.sup.0=.DELTA. (49)
y.sub.i=f.sub.i.sup.0=f.sub.i-.DELTA. (50)
f.sub.i.sup.0 represents the true reference bin count i, and
f.sub.i represents the reference bin counts used, including any
systematic error .DELTA.. In certain embodiments, replacing
equations (49) and (50) into equation (33) generates the following
expression for the euploid branch of the fitted fetal fraction
graph:
F E = F 0 + ( .DELTA. F ) 2 2 i = 1 N 1 .sigma. i 2 ( f i y i - f i
2 ) 1 + ( .DELTA. F ) 2 4 i = 1 N f i 2 / .sigma. i 2 = F 0 + (
.DELTA. F ) 2 2 i = 1 N 1 .sigma. i 2 [ ( f i 0 + .DELTA. ) f i 0 -
( f i 0 + .DELTA. ) 2 ] 1 + ( .DELTA. F ) 2 4 i = 1 N ( f i 0 +
.DELTA. ) 2 / .sigma. i 2 = F 0 - ( .DELTA. F ) 2 2 ( .DELTA. i = 1
N f i 0 .sigma. i 2 + .DELTA. 2 i = 1 N 1 .sigma. i 2 ) 1 + (
.DELTA. F ) 2 4 ( i = 1 N 1 .sigma. i 2 ( f i 0 ) 2 + 2 .DELTA. i =
1 N f i 0 .sigma. i 2 + .DELTA. 2 i = 1 N 1 .sigma. i 2 ) = F 0 - 2
S f 0 .DELTA. - 2 S 0 0 .DELTA. 2 1 + S ff 0 + 2 S f 0 .DELTA. + S
0 0 .DELTA. 2 ( 51 ) ##EQU00038##
[0724] The coefficients S.sub.0.sup.0, S.sub.f.sup.0 and
S.sub.f.sup.0.sub.f, are generated from equations (33) to (39) by
replacing f.sub.i with f.sub.i.sup.0, in some embodiments. In
certain embodiments, the reciprocal slope of the linear functional
relationship between fitted euploid value F.sub.E and measured
F.sub.0 equals
1+S.sub.f.sup.0.sub.f+2S.sub.f.sup.0.DELTA.+S.sub.0.sup.0.DELTA..sup.2,
which often allows estimation of the systematic error d by solving
a relatively simple quadratic equation. For triploids, assuming
F.sub.0 equals F.sub.V, measured bin counts sometimes become:
y i = f i 0 + 1 2 F 0 f i 0 ( 52 ) ##EQU00039##
[0725] Combining equations (52), (49) and (33) generates the
following expression for the triploid branch of the fitted fetal
fraction graph:
F E = F 0 + ( .DELTA. F ) 2 2 i = 1 N 1 .sigma. i 2 ( f i y i - f i
2 ) 1 + ( .DELTA. F ) 2 4 i = 1 N f i 2 / .sigma. i 2 = F 0 + (
.DELTA. F ) 2 2 i = 1 N 1 .sigma. i 2 [ ( f i 0 + .DELTA. ) ( f i 0
+ 1 2 F 0 f i 0 ) - ( f i 0 + .DELTA. ) 2 ] 1 + ( .DELTA. F ) 2 4 i
= 1 N ( f i 0 + .DELTA. ) 2 / .sigma. i 2 = F 0 + ( .DELTA. F ) 2 2
( 1 2 F 0 i = 1 N 1 .sigma. i 2 ( f i 0 ) 2 + 1 2 F 0 .DELTA. i = 1
N f i 0 .sigma. i 2 - .DELTA. i = 1 N f i 0 .sigma. i 2 - .DELTA. 2
i = 1 N 1 .sigma. i 2 ) 1 + ( .DELTA. F ) 2 4 ( i = 1 N 1 .sigma. i
2 ( f i 0 ) 2 + 2 .DELTA. i = 1 N f i 0 .sigma. i 2 + .DELTA. 2 i =
1 N 1 .sigma. i 2 ) = F 0 ( 1 + S ff 0 + S f 0 .DELTA. ) - S f 0
.DELTA. - S 0 0 .DELTA. 2 1 + S ff 0 + 2 S f 0 .DELTA. + S 0 0
.DELTA. 2 ( 53 ) ##EQU00040##
[0726] In some embodiments, equations (51) and (53) predict that
fitted triploid and euploid fetal fractions will behave as shown in
FIG. 48. In FIG. 48 black lines (e.g., upper lines in each set of 3
lines) correspond to negative offset .DELTA., dark gray lines
(e.g., bottom lines in each set of 3 lines) correspond to positive
offset .DELTA., and light gray lines (e.g., middle lines in each
set of 3 lines), correspond to the absence of offset. FIG. 49
illustrates the effects of simulated systematic errors d
artificially imposed on actual data.
[0727] FIG. 50 illustrates the dependence of fitted fetal fraction
on systematic error offset for euploid and triploid data sets. For
both euploid and triploid cases, the theoretical expressions of
equations (51) and (53) often capture the qualitative dependence of
fitted fetal fraction on measured fetal fraction and on systematic
error offset. Coefficients used for the graphs in FIGS. 49 and 50
were obtained from raw reference bin counts, without removing any
potential systematic bias.
[0728] Fixed Ploidy, Optimized Fetal Fraction, Error Propagation:
Fitted Fetal Fraction
[0729] Contributions to errors in fitted fetal fractions often fall
into one of two types of errors: 1) from measured fetal fractions,
and 2) from measured and reference bin counts. The two types of
errors will be analyzed separately, using different approaches, and
later combined to generate final error ranges. Errors propagated
from measure fetal fractions can be evaluated by replacing F.sub.0
in equation (40) first with F.sub.0-2.DELTA.F (e.g., for the lower
error boundary) and then with F.sub.0+2.DELTA.F (e.g., for the
upper error boundary). This relatively simple approach produces
correct qualitative behavior at 95% confidence intervals, in
certain embodiments. For a different desired level of confidence, a
more general pair of bounds, F.sub.0-n.DELTA.F and
F.sub.0+n.DELTA.F, can be utilized. The terms used to generate
upper and lower error boundaries sometimes underestimates the total
error because the contributions from errors in measure and
reference bin counts often are neglected.
[0730] To better assess the contribution from measured and
reference bin counts on error in fitted fetal fraction, equations
(38) to (40) can be utilized, in some embodiments. In certain
embodiments, equation (33) can be expanded for fitted fetal
fraction into a Taylor series with respect to f.sub.i and y.sub.i,
truncated to the first order, square and average. In some
instances, it can be assumed that uncertainties in y.sub.i often
are the same as uncertainties in f.sub.i. To simply analysis,
cross-terms and higher-order terms are assumed to reduce to zero
upon averaging. Taylor expansion coefficients often are obtained
utilizing the chain rule. The mean squared variation in the fitted
fetal fraction is then given by equation (54) shown below. The
model represented by equation ignores contributions from estimates
for .DELTA.F, in some embodiments. Partial derivatives can be
evaluated using the expressions presented below equation (54).
( .delta. F ) 2 = i = 1 N ( .differential. F .differential. f i ) 2
.sigma. i 2 + i = 1 N ( .differential. F .differential. y i ) 2
.sigma. i 2 = i = 1 N [ ( .differential. F .differential. S ff ) (
.differential. S ff .differential. f i ) + ( .differential. F
.differential. S fy ) ( .differential. S fy .differential. f i ) ]
2 .sigma. i 2 + i = 1 N [ ( .differential. F .differential. S fy )
( .differential. S fy .differential. y i ) ] 2 .sigma. i 2 ( 54 ) (
.differential. F .differential. S ff ) = - F 0 + 2 S fy + 2 ( 1 + S
ff ) 2 ( 55 ) ( .differential. F .differential. S fy ) = 2 1 + S ff
( 56 ) ( .differential. S ff .differential. f i ) = ( .DELTA. F ) 2
2 ( f i .sigma. i 2 ) ( 57 ) ( .differential. S fy .differential. f
i ) = ( .DELTA. F ) 2 4 ( y i .sigma. i 2 ) ( 58 ) ( .differential.
S fy .differential. y i ) = ( .DELTA. F ) 2 4 ( f i .sigma. i 2 ) (
59 ) ##EQU00041##
[0731] Combining equations (54) to (59) generates the following
expression:
( .delta. F ) 2 = [ ( .DELTA. F ) 2 4 ] 2 { i = 1 N 1 .sigma. i 2 [
2 y i 1 + S ff - 2 f i F 0 + 2 S fy + 2 ( 1 + S ff ) 2 ] 2 + i = 1
N 1 .sigma. i 2 ( 2 f i 1 + S ff ) } = [ ( .DELTA. F ) 2 4 ] 2 i =
1 N 1 .sigma. i 2 [ ( 2 y i 1 + S ff ) 2 - 8 f i y i F 0 + 2 S fy +
2 ( 1 + S ff ) 2 + 4 f i 2 ( F 0 + 2 S fy + 2 ) 2 ( 1 + S ff ) 4 +
( 2 f i 1 + S ff ) 2 ] = [ ( .DELTA. F ) 2 4 ] 2 { 4 ( 1 + S ff ) 2
i = 1 N y i 2 .sigma. i 2 - 8 F 0 + 2 S fy + 2 ( 1 + S ff ) 2 i = 1
N f i y i .sigma. i 2 + 4 [ ( F 0 + 2 S fy + 2 ) 2 ( 1 + S ff ) 4 +
1 ( 1 + S ff ) 2 ] i = 1 N f i 2 .sigma. i 2 } = ( .DELTA. F ) 2 {
S yy ( 1 + S ff ) 2 - 2 S fy F 0 + 2 S fy + 2 ( 1 + S ff ) 2 + S ff
[ ( F 0 + 2 S fy + 2 ) 2 ( 1 + S ff ) 4 + 1 ( 1 + S ff ) 2 ] } ( 60
) ##EQU00042##
[0732] To evaluate equation (60) at a 95% confidence interval, the
following upper and lower bounds can be used, in some
embodiments:
[ F Lower F Upper ] = F 0 + 2 S fy - 2 S ff 1 + S ff + [ - 2 2 ]
.DELTA. F { 1 1 + S ff + S yy ( 1 + S ff ) 2 - 2 S fy F 0 + 2 S fy
+ 2 ( 1 + S ff ) 2 + S ff [ ( F 0 + 2 S fy + 2 ) 2 ( 1 + S ff ) 4 +
1 ( 1 + S ff ) 2 ] } ( 61 ) ##EQU00043##
[0733] In embodiments in which substantially all possible sources
of error (e.g., F.sub.0, f.sub.i, y.sub.i) are included in the
Taylor expansion series, the same equation often is obtained. In
some instances, dependence of F on F.sub.o, can be accounted for
through S.sub.fy. In some embodiments, power series terms
corresponding to F.sub.0 often take the form;
[ ( .differential. F .differential. F 0 ) + ( .differential. F
.differential. S fy ) ( .differential. S fy .differential. F 0 ) ]
2 ( .DELTA. F ) 2 , but [ ( .differential. F .differential. F 0 ) +
( .differential. F .differential. S fy ) ( .differential. S fy
.differential. F 0 ) ] 2 ##EQU00044##
equals 1 for triploids. Thus, relatively simple subtraction and
addition of .DELTA.F to F.sub.0 often is justified, even though
.DELTA.F often increases with F.sub.0 and becomes large at high
F.sub.0. The outcome is due to both F and S.sub.fy depending
linearly on F.sub.0, in some embodiments. Simulations based on
equation (61) are shown in FIG. 51, along with fitted fetal
fractions obtained from test subject derived data. In the
simulations presented in FIG. 51, .DELTA.F=2/3+F.sub.0/6, as
described herein.
Example 3
Sliding Window Analysis and Cumulative Sums as a Function of
Genomic Position
[0734] Identification of recognizable features (e.g., regions of
genetic variation, regions of copy number variation) in a
normalized count profile sometimes is a relatively time consuming
and/or relatively expensive process. The process of identifying
recognizable features often is complicated by data sets containing
noisy data and/or low fetal nucleic acid contribution.
Identification of recognizable features that represent true genetic
variations or copy number variations can help avoid searching
large, featureless regions of a genome. Identification of
recognizable features can be achieved by removing highly variable
genomic sections from a data set being searched and obtaining, from
the remaining genomic sections, data points that deviate from the
mean profile elevation by a predetermined multiple of the profile
variance.
[0735] In some embodiments, obtaining data points that deviate from
the mean profile elevation by a predetermined multiple of the
profile variance can be used to reduce the number of candidate
genomic sections from greater than 50,000 or 100,000 genomic
sections to in the range of about 100 to about 1000 candidate
genomic sections that represent true signals or solitary noise
spikes (e.g., about 100 genomic sections, about 200 genomic
sections, about 300 genomic sections, about 400 genomic sections,
about 500 genomic sections, about 600 genomic sections, about 700
genomic sections, about 800 genomic sections, about 900 genomic
sections, or about 1000 genomic sections). The reduction in the
number of candidate genomic sections can be achieved relatively
quickly and easily and often speeds up the search for and/or
identification of genetic aberrations by two or more orders of
magnitude. Reduction in the number of genomic sections searched for
the presence or absence of candidate regions of genomic variation
often reduces the complexity and/or dimensionality of a data
set.
[0736] After a reduced data set containing data points that deviate
from the mean profile elevation by a predetermined multiple of the
profile variance is generated, the reduced data set is filtered to
eliminate solitary noise spikes, in some embodiments. Filtering a
reduced data set to remove solitary noise spikes often generates a
filtered, reduced data set. In some embodiments, a filtered,
reduced data set retains contiguous clusters of data points, and in
certain embodiments, a filtered, reduced data set retains clusters
of data points that are largely contiguous with allowance for a
predetermined number and/or size of gaps. Data points from the
filtered, reduced data set that deviate from the average profile
elevation in substantially the same direction are grouped together,
in some embodiments.
[0737] Due to the background noise often present in nucleic acid
samples (e.g., ratio of regions of interest compared to the total
nucleic acid in a sample), distinguishing regions of genetic
variation or genetic aberration from background noise often is
challenging. Methods that improve the signal-to-noise ratio often
are useful for facilitating the identification of candidate regions
representative of regions of true genetic variation and/or genetic
aberration. Any method that improves the signal-to-noise ratio of
regions of true genetic variation with respect to the genomic
background noise can be used. A non-limiting example of a method
suitable for use in improving the signal-to-noise ratio of regions
of true genetic variation with respect to the genomic background
noise is the use of integrals over the suspected aberration and its
immediate surroundings. In some embodiments, the use of integrals
over the suspected aberration and its immediate surroundings is
beneficial, because summation cancel out random noise. After noise
has been reduced or eliminated, even relatively minor signals can
become readily detectable using a cumulative sum of the candidate
peak and its surroundings, in some embodiments. A cumulative sum
sometimes is defined with respect to an arbitrarily chosen origin
outside (e.g., on one side or the other) of the peak. A cumulative
sum often is a numerical estimate of the integral of the normalized
count profile over the selected genetic section or sections.
[0738] In the absence of aberrations, the cumulative sum as a
function of the genomic position often behaves as a straight line
with unit slope (e.g., slope equal to 1). If deletions or
duplications are present, the cumulative sum profile often consists
of two or more line segments. In some embodiments, areas outside of
aberrations map to line segments with unit slopes. For areas within
aberrations, the line segments are connected by other line segments
whose slopes equal the count profile elevation or depression within
the aberration, in certain embodiments.
[0739] In those samples having maternal aberrations, the slopes
(e.g., equivalent to the count profile elevation) are relatively
easily determined: 0 for homozygous maternal deletions, 0.5 for
heterozygous maternal deletions, 1.5 for heterozygous duplications,
2.0 for homozygous duplications. In those samples having fetal
aberrations, the actual slopes depend both on the type of the
aberration (e.g., homozygous deletion, heterozygous deletion,
homozygous duplication or heterozygous duplication) and on the
fetal fraction. In some embodiments, inheritance of a maternal
aberration by the fetus also is taken into account when evaluating
fetal samples for genetic variations.
[0740] In some embodiments, line segments with unit slopes,
corresponding to normal genomic areas to the left and to the right
of an aberration, are vertically shifted with respect to one
another. The difference (e.g., subtractive result) between their
intercepts equals the product between the width of the aberration
(number of affected genomic sections) and the aberration level
(e.g., -1 for homozygous maternal deletion, -0.5 for heterozygous
maternal deletion, +0.5 for heterozygous duplication, +1 for
homozygous duplication, and the like). Refer to FIGS. 52-61F for
examples of data sets processed using cumulative sums as a function
of genomic position (e.g., sliding window analysis).
Example 4
Parameterized Error Removal and Unbiased Normalization (PERUN)
[0741] Variability of Measured Counts
[0742] Ideally, the measured chromosomal elevation is a straight
horizontal line with the elevation of 1 for euploids, as in FIG.
62. For trisomy pregnancies, the desired behavior of the measured
chromosomal elevation is a step-function, with the deviation from 1
proportional to the fetal fraction, as simulated in FIG. 63 for
fetal fraction equal to 15%. Exceptions arise out of maternal
deletions/duplications, which are readily recognized and
distinguished from fetal abnormalities based on their magnitudes,
which are multiples of one-half.
[0743] What was actually measured was not ideal. FIG. 64 shows
overlaid raw counts for chromosomes 20, 21, and 22 collected from
1093 euploid pregnancies and FIG. 65 shows overlaid raw counts for
chromosomes 20, 21, and 22 collected from 134 trisomy 21
pregnancies. Visual inspection of the two sets of profiles failed
to confirm that chromosome 21 traces in trisomy cases were
elevated. Stochastic noise and systematic bias both made the
elevation of chromosome 21 difficult to visualize. Furthermore, the
far right segment of chromosome 21 incorrectly suggested that
euploid chromosome 21 traces were elevated, rather than the trisomy
profiles. A large part of the systematic bias originated from the
GC content associated with a particular genomic region.
[0744] Attempts to remove the systematic bias due to GC content
included multiplicative LOESS GC smoothing, Repeat Masking (RM),
combination of LOESS and RM (GCRM), and others, such as cQN. FIG.
66 shows the results of a GCRM procedure as applied to 1093 euploid
traces and FIG. 67 shows the GCRM profiles for 134 trisomy cases.
GCRM successfully flattened the elevated, GC-rich, rightmost
segment of chromosome 21 in euploids. However, the procedure
evidently increased the overall stochastic noise. Moreover, it
created a new systematic bias, absent from the raw measurements
(leftmost region of chromosome 20 (Chr20)). The improvements that
were due to GCRM were offset by increased noise and bias, rendering
the usefulness of the procedure questionable. The tiny elevation
from chromosome 21 as observed in FIG. 63 was lost in the high
noise as shown in FIG. 66 and FIG. 67.
[0745] PERUN (Parameterized Error Removal and Unbiased
Normalization) was developed as a viable alternative to previously
described GC normalization methods. FIG. 68 and FIG. 69 contrast
the PERUN method results against those presented in FIG. 64 through
67. PERUN results were obtained on the same two subpopulations of
data that was analyzed in FIG. 64 through 67. Most of the
systematic bias was absent from PERUN traces, only leaving
stochastic noise and biological variation, such as the prominent
deletion in chromosome 20 of one of the euploid samples (FIG. 68).
The chromosome 20 deletion was also observable in raw count
profiles (FIG. 64), but completely masked in the GCRM traces. The
inability of GCRM to reveal this huge deviation clearly
disqualifies it for the purposes of measuring the miniscule fetal
T21 elevations. PERUN traces contain fewer bins than raw or GCRM
profiles. As shown in FIG. 62-63, the PERUN results look at least
as good as the measurement errors permit.
[0746] Normalization with Respect to Reference Median Count
Profile
[0747] Conventional GC normalization procedures can perform
suboptimally. A part of the reason has been that GC bias is not the
only source of variation. A stack plot of many individual raw count
profiles revealed parallelism between different samples. While some
genomic regions were consistently over-represented, others were
consistently under-represented, as illustrated by the traces from a
480v2 study (FIG. 6). While GC bias varied from one sample to
another, the systematic, bin-specific bias observed in these
profiles followed the same pattern for all samples.
[0748] All the profiles in FIG. 6 zigzagged in a coordinated
fashion. The only exceptions were the middle portions of the bottom
two samples, which turned out to originate from maternal deletions.
To correct for this bin-specific bias, a median reference profile
was used. The median reference profile was constructed from a set
of known euploids (e.g. euploid pregnancies) or from all the
samples in a flow cell. The procedure generated the reference
profile by evaluating median counts per bin for a set of reference
samples. The MAD associated with a bin measured the reliability of
a bin. Highly variable bins and bins that consistently have
vanishing representations were removed from further analysis (FIG.
4). The measured counts in a test data set were then normalized
with respect to the median reference profile, as illustrated in
FIG. 8. The highly variable bins are removed from the normalized
profile, leaving a trace that is approximately 1 in the diploid
sections, 1.5 in the regions of heterozygous duplication, 0.5 in
the areas of heterozygous deletion, and so on (FIG. 9). The
resulting normalized profiles reasonably reduced the variability,
enabling detection of maternal deletions and duplications and
tracing of sample identities (FIG. 12, 22, 13, 11). Normalization
based on median count profile can clarify outcomes, but GC bias
still has a negative effect on such methods. PERUN methods
described here can be used to address GC bias and provide outcomes
with higher sensitivity and specificity.
[0749] Detrimental Effects of Multiplicative LOESS Correction
[0750] FIG. 11. illustrated why binwise counts fluctuate more after
application of GC-LOESS or GCRM (FIG. 66-67) than before (FIG.
64-65). LOESS GC correction removed the trend from the raw counts
(FIG. 70, upper panel) by dividing the raw counts with the
regression line (straight line, FIG. 70, upper panel). The point
defined by the median counts and the median genome GC content was
kept immobile. On average, counts below the median count were
divided by small numbers, while counts exceeding the median count
were divided by large numbers. In either case, on average, counts
were scaled up or down to match 1 (FIG. 70, lower panel). The
scaling of small counts, in addition to inflating the counts, also
inflated their variability. The end result (FIG. 70, lower panel)
to the left from the median GC genome content displayed a larger
spread than the corresponding raw counts (FIG. 70, upper panel),
forming the typical triangular shape (FIG. 70, lower panel,
triangle). To detrend the counts, GC LOESS/GCRM sacrificed
precision as such corrective processes generally are multiplicative
and not additive. Normalization provided by PERUN generally is
additive in nature and enhances precision over multiplicative
techniques.
[0751] Inadequacy of a Genome-Wide Pivot for GC-Bias Scaling
[0752] An alternative approach applied the LOESS correction
separately to individual chromosomes instead of subjecting the
entire genome to a collective GC-Bias scaling. The scaling of
individual chromosomes was impractical for purposes of classifying
samples as euploid or trisomy because it canceled out the signal
from over-represented chromosomes. However, the conclusions from
this study were eventually useful as catalyzers for developing the
PERUN algorithm. FIG. 71 illustrates the fact that LOESS curves
obtained for the same chromosome from multiple samples share a
common intersection (pivot).
[0753] FIG. 72 demonstrated that tilting chromosome-specific LOESS
curves around the pivot by an angle proportional to the GC bias
coefficients measured in those samples caused all the curves to
coalesce. The tilting of the chromosome-specific LOESS curves by
the sample-specific GC bias coefficients significantly reduced the
spread of the family of LOESS curves obtained for multiple samples,
as shown in FIG. 73 (filled circles (before tilting) and open
circles (after tilting)). The point where the filled circles and
open circles touch coincided with the pivot. In addition, it became
evident that the location on the GC content axis of the
chromosome-specific pivot coincided with the median GC content of
the given chromosome (FIG. 74, left vertical line: median, right
vertical line: mean). Similar results were obtained for all
chromosomes, as shown in FIG. 75A through FIG. 75F (left vertical
line: median, right vertical line: mean). All autosomes and
chromosome X were ordered according to their median GC content.
[0754] The genome-wide GC LOESS scaling pivoted the transformation
on the median GC content of the entire genome, as shown in FIG. 76.
That pivot was acceptable for chromosomes that have median GC
content similar to the GC content of the entire genome, but became
suboptimal for chromosomes with extreme GC contents, such as
chromosomes 19, 20, 17, and 16 (extremely high GC content). The
pivoting of those chromosomes centered on the median GC content of
the entire genome maintained the spread observed within the left
box in FIG. 76, missing the low-variability region enclosed by the
right box in FIG. 76 (the chromosome-specific pivot).
[0755] Pivoting on the chromosome-specific median GC content,
however, significantly reduced the variability (FIG. 75). The
following observations were made: [0756] 1) GC correction should be
done on small genomic sections or segments, rather than on the
entire genome, to reduce the variability. The smaller the section
or segment, the more focused GC correction becomes, minimizing the
residual error. [0757] 2) In this particular instance, those small
genomic sections or segments are identical to chromosomes. In
principle, the concept is more general: the sections or segments
could be any genomic regions, including 50 kbp bins. [0758] 3) The
GC bias within individual genomic regions can be rectified using
the sample-specific, genome-wide GC coefficient evaluated for the
entire genome. This concept is important: while some descriptors of
the genomic sections (such as the location of the pivot point, GC
content distribution, median GC content, shape of the LOESS curve,
and so on) are specific to each section and independent of the
sample, the GC coefficient value used to rectify the bias is the
same for all the sections and different for each sample.
[0759] These general conclusions guided the development of PERUN,
as will become apparent from the detailed description of its
processes.
[0760] Separability of Sources of Systematic Bias
[0761] Careful inspection of a multitude of raw count profiles
measured using different library preparation chemistries,
clustering environments, sequencing technologies, and sample
cohorts consistently confirmed the existence of at least two
independent sources of systematic variability: [0762] 1)
sample-specific bias based on GC-content, affecting all bins within
a given sample in the same manner, varying from sample to sample,
and [0763] 2) bin-specific attenuation pattern common to all
samples.
[0764] The two sources of variability are intermingled in the data.
Thorough removal of both required their deconvolution. The
deficiencies of the error-removal procedures predating PERUN stem
from the fact that they only correct for one of the two sources of
systematic bias, while neglecting the other.
[0765] For example, the GCRM (or GC LOESS) method treated
identically all the bins with GC content values falling within a
narrow GC content range. The bins belonging to that subset may be
characterized by a wide range of different intrinsic elevations, as
reflected by the reference median count profile. However, GCRM was
blind to their inherent properties other than their GC content.
GCRM therefore maintains (or even enlarges) the spread already
present in the bin subset. On the other hand, the binwise reference
median count disregarded the modulation of the bin-specific
attenuation pattern by the GC bias, maintaining the spread caused
by the varying GC content.
[0766] The sequential application of the methods dealing with the
opposite extremes of the error spectrum unsuccessfully attempts to
resolve the two biases globally (genome-wide), ignoring the need to
dissociate the two biases on the bin elevation. Without being
limited by theory, PERUN apparently owes its success to the fact
that it separates the two sources of bias locally, on the bin
elevation.
[0767] Removal of Uninformative Bins
[0768] Multiple attempts to remove uninformative bins have
indicated that bin selection has the potential to improve
classification. The first such approach evaluated the mean
chromosome 21, chromosome 18, and chromosome 13 counts per bin for
all 480v2 trisomy cases and compared it with the mean counts per
bin for all 480v2 euploids. The gap between affected and unaffected
cases was scaled with the combined binwise uncertainty derived from
bin counts measured in both groups. The resulting t-statistic was
used to evaluate binwise p-value profile, shown in FIG. 77. In the
case of chromosome 21, the procedure identified 36 uninformative
bins (center panel, labeled with ellipse on FIG. 77). Elimination
of those bins from calculation of Z scores noticeably increased the
Z-values for affected cases, while randomly perturbing the
unaffected Z-scores (FIG. 78), thereby increasing the gap between
euploids and trisomy 21 cases.
[0769] In chromosome 18, the procedure only improved Z scores for
two affected cases (FIG. 79).
[0770] A post-hoc analysis showed that the improvement of the
Z-scores in those two samples resulted from removal of the large
maternal deletion in chromosome 18 (FIG. 11) and that the two
samples actually come from the same patient. These improvements
were sample-specific, with no generalizing power. In chromosome 13,
the procedure did not lead to any improvements of Z-scores.
[0771] An alternative bin filtering scheme removes bins with
extremely low or extremely high GC content. This approach yielded
mixed results, with noticeably reduced variance in chromosomes 9,
15, 16, 19, and 22 (depending on the cutoffs), but adverse effects
on chromosomes 13 and 18.
[0772] Yet another simple bin selection scheme eliminates bins with
consistently low counts. The procedure corrected two LDTv2CE
chromosome 18 false negatives (FIG. 80) and two chromosome 21 false
negatives (FIG. 81). It also corrected at least three chromosome 18
false positives, but created at least one new chromosome 18 false
positive (FIG. 80):
[0773] In conclusion, the different criteria used to filter out
uninformative bins made it clear that data processing will benefit
from bin selection based on how much useful information the bins
contribute to the classification.
[0774] Separation of GC Bias from Systematic Binwise Bias
[0775] To resolve and eliminate the different systematic biases
found in the measured counts, the data processing workflow needed
to optimally combine the partial procedures described from the
previous section entitled "Normalization with Respect to Reference
Median Count Profile" to the section entitled "Removal of
Uninformative Bias". The first step is to order different samples
according to their GC bias coefficient values and then stack their
plots of counts-vs.-GC content. The result is a three-dimensional
surface that twists like a propeller, schematically shown on FIG.
82.
[0776] Thus arranged, the measurements suggest that a set of
sample-specific GC bias coefficient can be applied to rectify
errors within an individual genomic section or segment. In FIG. 82,
the sections or segments are defined by their GC content. An
alternative partition of the genome gives contiguous,
non-overlapping bins. The successive starting locations of the bins
uniformly cover the genome. For one such 50 kbp long bin, FIG. 83
explores the behavior of the count values measured within that bin
for a set of samples. The counts are plotted against the GC bias
coefficients observed in those samples. The counts within the bin
evidently increase linearly with the sample-specific GC bias. The
same pattern in observed in an overwhelming majority of bins. The
observations can be modeled using the simple linear
relationship:
M=LI+GS (A)
[0777] The various terms in Eq. A have the following meanings:
[0778] M: measured counts, representing the primary information
polluted by unwanted variation. [0779] L: chromosomal
elevation--this is the desired output from the data processing
procedure. L indicates fetal and/or maternal aberrations from
euploidy. This is the quantity that is masked both by stochastic
errors and by the systematic biases. The chromosomal elevation L is
both sample specific and bin-specific. [0780] G: GC bias
coefficient measured using linear model, LOESS, or any equivalent
approach. G represents secondary information, extracted from M and
from a set of bin-specific GC content values, usually derived from
the reference genome (but may be derived from actually observed GC
contents as well). G is sample specific and does not vary along the
genomic position. It encapsulates a portion of the unwanted
variation. [0781] I: Intercept of the linear model (diagonal line
in FIG. 83). This model parameter is fixed for a given experimental
setup, independent on the sample, and bin-specific. [0782] S: Slope
of the linear model (diagonal line in FIG. 83). This model
parameter is fixed for a given experimental setup, independent on
the sample, and bin specific.
[0783] The quantities M and G are measured. Initially, the
bin-specific values I and S are unknown. To evaluate unknown I and
S, we must assume that L=1 for all bins in euploid samples. The
assumption is not always true, but one can reasonably expect that
any samples with deletions/duplications will be overwhelmed by
samples with normal chromosomal elevations. A linear model applied
to the euploid samples extracts the I and S parameter values
specific for the selected bin (assuming L=1). The same procedure is
applied to all the bins in the human genome, yielding a set of
intercepts I and slopes S for every genomic location.
Cross-validation randomly selects a work set containing 90% of all
LDTv2CE euploids and uses that subset to train the model. The
random selection is repeated 100 times, yielding a set of 100
slopes and 100 intercepts for every bin. The previous section
entitled "Cross-Validation of PERUN Parameters" describes the
cross-validation procedure in more detail.
[0784] FIG. 84-85 show 100 intercept values and 100 slope values,
respectively, evaluated for bin #2404 in chromosome 2. The two
distributions correspond to 100 different 90% subsets of 1093
LDTv2CE euploids shown in FIG. 83. Both distributions are
relatively narrow and irregularly shaped. Their spreads are similar
to the errors in the coefficient as reported by the linear model.
As a rule, the slope is less reliable than the intercept because
fewer samples populate the extreme sections of the GC-bias
range.
[0785] Interpretation of PERUN Parameters I and S
[0786] The meaning of the intercept I is illustrated by FIG. 86.
The graph correlates the estimated bin intercepts with the data
extracted from a set of technical replicates, obtained when one
LDTv2CE flow cell was subjected to three separate sequencing runs.
The y-axis contains median values of binwise counts from those
three measurements. These median values are related conceptually to
the median reference profile, previously used to normalize profiles
as described in the section entitled "Normalization with Respect to
Reference Median Count Profile". The binwise intercepts are plotted
along the x-axis. The striking correlation between the two
quantities reveals the true meaning of the intercepts as the
expected counts per bin in the absence of GC bias. The problem with
the median reference count profile is that it fails to account for
the GC bias (see section entitled "Normalization with Respect to
Reference Median Count Profile"). In PERUN, without being limited
by theory, the task of an intercept/is to deal with the
bin-specific attenuation, while the GC bias is relegated to the
other model parameter, the slope S.
[0787] FIG. 86 excludes chromosome Y from the correlation because
the set of technical replicates does not reflect the general
population of male pregnancies.
[0788] The distribution of the slope S (FIG. 87) illustrates the
meaning of that model parameter.
[0789] The marked semblance between the distribution from FIG. 87
and the distribution of the genome-wide GC content (FIG. 88)
indicates that the slope S approximates the GC content of a bin,
shifted by the median GC content of the containing chromosome. The
thin vertical line in FIG. 88 marks the median GC content of the
entire genome.
[0790] FIG. 89 reaffirms the close relationship between the slope S
and the GC content per bin. While slightly bent, the observed trend
is extremely tight and consistent, with only a handful of notable
outlier bins.
[0791] Extraction of Chromosomal Elevation from Measured Counts
[0792] Assuming that the model parameter values I and S are
available for every bin, measurements M collected on a new test
sample are used to evaluate the chromosomal elevation according to
the following expression:
L=(M-GS)/I (B)
[0793] As in Eq. A, the GC bias coefficient G is evaluated as the
slope of the regression between the binwise measured raw counts M
and the GC content of the reference genome. The chromosomal
elevation L is then used for further analyses (Z-values, maternal
deletions/duplications, fetal microdeletions/microduplications,
fetal gender, sex aneuploidies, and so on). The procedure
encapsulated by Eq. B is named Parameterized Error Removal and
Unbiased Normalization (PERUN).
[0794] Cross-Validation of PERUN Parameters
[0795] As inferred in the section entitled "Separation of GC Bias
from Systematic Binwise Bias", the evaluation of I and S randomly
selects 10% of known euploids (a set of 1093 LDTv2 in FIG. 83) and
sets them aside for cross-validation. Linear model applied to the
remaining 90% of euploids extracts the I and S parameter values
specific for the selected bin (assuming L=1). Cross validation then
uses the I and S estimates for a given bin to reproduce measured M
values from measured G values both in the work set and in the
remaining 10% euploids (again assuming L=1). The random selection
of the cross-validation subset is repeated many times (100 times in
FIG. 83, although 10 repetitions would suffice). 100 diagonal
straight lines in FIG. 83 represent the linear models for 100
different 90% work subset selections. The same procedure is applied
to all the bins in the human genome, yielding a set of intercepts I
and slopes S for every genomic location.
[0796] To quantify the success of the model and avoid biasing the
results, we use the R-factor, defined as follows:
R = i = 1 N M i - P i i = 1 N M i ( C ) ##EQU00045##
[0797] The numerator in Eq. B sums up the absolute deviations of
the predicted count values (P, Eq. B) from the actual measurements
(M). The numerator simply sums up the measurements. The R factor
may be interpreted as the residual error in the model, or the
unexplained variation. The R factor is directly borrowed from the
crystallographic model refinement practice, which is vulnerable to
bias. In crystallography, the bias is detected and measured by the
R-factor evaluated within the cross-validation subset of
observables. The same concepts are applied in the context of
genome-wide count bias removal.
[0798] FIG. 90 shows the R-factors evaluated for the
cross-validation subset (y-axis) plotted against R-factors
evaluated for the work (training) set for bin #2404 from chromosome
2. There are 100 data points since the random selection of the
cross-validation subset was repeated 100 times.
[0799] Typical linear relationship is observed, with the increasing
R.sub.cv values (measuring bias) accompanying the decreasing
R.sub.work.
[0800] FIG. 90 may be interpreted in terms of the percentage error
(or relative error) of the model for this particular bin. R.sub.cv
always exceeds R.sub.work, usually by .about.1%. Here, both
R.sub.cv and R.sub.work remain below 6%, meaning that one can
expect .about.6% error in the predicted M values using the measured
GC bias coefficient G and the model parameters I and S from the
procedure described above.
[0801] Cross-Validation Error Values
[0802] FIG. 90-91 show cross-validation errors for bins
chr2.sub.--2404 and chr2.sub.--2345, respectively. For those and
many other bins, the errors never exceed 6%. Some bins, such as
chr1.sub.--31 (FIG. 92) have cross-validation errors approaching
8%. Still others (FIG. 93-95) have much larger cross-validation
errors, at times exceeding 100% (40% for chr1.sub.--10 in FIG. 93,
350% for chr1.sub.--9 in FIG. 94, and 800% for chr1.sub.--8 in FIG.
95).
[0803] FIG. 96 shows the distribution of max(R.sub.cv, R.sub.work)
1 for all bins. Only a handful of bins have errors below 5%. Most
bins have errors below 7% (48956 autosomes out of 61927 total
including X and Y). A few bins have errors between 7% and 10%. The
tail consists of bins with errors exceeding 10%.
[0804] FIG. 97 correlates the cross-validation errors with the
relative errors per bin estimated from the set of technical
replicates. Data points in the center region (i.e., data points
located between the two vertical lines) correspond to
cross-validation errors between 7% and 10%. Data points in the
region to the right of the two vertical lines denote bins with
cross-validation error exceeding 10%. Data points in the region to
the left of the two vertical lines (error <7%) represent the
bulk of bins.
[0805] In FIG. 91-95, the number in parentheses following the bin
name above the top right inset indicates the ratio between the
intercept found for that particular bin and the genome-wise median
count per bin. The cross-validation errors evidently increase with
the decreasing value of that ratio. For example, the bin
chr1.sub.--8 never gets more than 3 counts and its relative error
approaches 800%. The smaller the expected number of counts for a
given bin, the less reliable that bin becomes.
[0806] Bin Selection Based on Cross-Validation
[0807] Based on the observations described in the previous section
entitled "Removal of Uninformative Bins" (FIG. 78 and FIG. 80-81),
cross-validation errors were used as a criterion for bin filtering.
The selection procedure throws away all bins with cross-validation
errors exceeding 7%. The filtering also eliminates all bins that
consistently contain zero counts. The remaining subset contains
48956 autosomal bins. Those are the bins used to evaluate
chromosomal representations and to classify samples as affected or
euploid. The cutoff of 7% is justified by the fact that the gap
separating euploid Z-scores from trisomy Z-scores plateaus at the
7% cross-validation error (FIG. 98).
[0808] FIG. 99A (all bins) and 99B (cross-validated bins)
demonstrate that the bin selection described above mostly removes
bins with low mappability.
[0809] As expected, most removed bins have intercepts far smaller
than the genome-wide median bin count. Not surprisingly, the bin
selection largely overlaps with the selection described in the
previous section entitled "Removal of Uninformative Bins" (FIGS. 25
and 27-28).
[0810] Errors in Model Parameters
[0811] FIG. 100-101 show the 95% confidence intervals (curved
lines) of the fitted linear model (thin straight line) for two bins
(chr18.sub.--6 and chr18.sub.--8). The thick grey straight lines
are obtained by replacing the S parameter with the difference
between the GC contents of these two bins and the median GC content
of chromosome 18. The error range is evaluated based on errors in
the model parameters I and S for those two bins, as reported by the
linear model. In addition, larger GC bias coefficients also contain
larger errors. The large uncertainty corresponding to extremely
large GC bias coefficients suggests that the range of applicability
of the unmodified PERUN is limited to modest GC bias coefficients.
Beyond that range, additional measures need to be taken to remove
the residual GC bias. Fortunately, only very few samples are
affected (roughly 10% of the LDTv2CE population).
[0812] FIG. 102-104 show the errors in the model parameters I and S
and the correlation between the error in S and the value of the
intercept.
[0813] Secondary Normalization
[0814] High values of GC bias coefficients exceed the linear range
assumed by the PERUN model and are remedied by an additional LOESS
GC normalization step after PERUN normalization. The multiplicative
nature of the LOESS procedure does not significantly inflate the
variability since the normalized counts are already very close to
1. Alternatively, LOESS can be replaced with an additive procedure
that subtracts residuals. The optional secondary normalization
often is utilized only required for a minority of samples (roughly
10%).
[0815] Hole Padding (Padding)
[0816] FIG. 68-69 confirm the presence of a large number of
maternal deletions and duplications that have the potential to
create false positives or false negatives, depending on their sizes
and locations. An optional procedure called hole-padding has been
devised to eliminate the interferences from these maternal
aberrations. The procedure simply pads the normalized profile to
remain close to 1 when it deviates above 1.3 or below 0.7. In
LDTv2CE, hole padding (i.e., padding) did not significantly affect
the classification. However, FIG. 105 shows a WI profile that
contains a large deletion in chromosome 4. Hole padding converts
that profile from chromosome 13 false positive to chromosome 13
true negative.
[0817] Results
[0818] This section discusses PERUN results for trisomy 13, trisomy
18 and trisomy 21 (T13, T18 and T21, respectively), gender
determination, and sex aneuploidy.
[0819] Reduced Variability
[0820] FIG. 106 compares the distribution of standard deviations of
the binwise count profiles before and after PERUN normalization.
The resulting distributions of chromosome representations for
euploids and trisomy cases are shown in FIG. 107.
[0821] Improved T13, T18, and T21 Classification
[0822] FIG. 108-111 compare LDTv2CE PERUN classification results
with those obtained using GCRM counts. In addition to removing two
chromosome 18 false positives, two chromosome 18 false negatives,
and two chromosome 21 false negatives, PERUN almost doubles the gap
between the euploids and the affected cases, in spite of the fact
that the higher plexing elevation decreased the number of counts
per sample (ELAND data). Similar results are obtained when PERUN
parameters trained on LDTv2CE Eland data are applied to WI
measurements. Bowtie alignments require a different set of
parameters and additional bin filtering, accounting for low
mappability in some bins, but its results approach those seen with
ELAND alignments.
Example 5
Additional Description of PERUN
[0823] Examples of parameterized Error Removal and Unbiased
Normalization (PERUN) methods are described in Example 4, and an
additional description of such methods is provided in this Example
5.
[0824] Massive parallel sequencing of cell-free circulating DNA
(e.g. from maternal plasma) can, under ideal conditions, quantify
chromosomal elevations by counting sequenced reads if unambiguously
aligned to a reference human genome. Such methods that incorporate
massive amounts of replicate data can, in some cases, show
statistically significant deviations between the measured and
expected chromosomal elevations that can imply aneuploidy [Chiu et
al., Noninvasive prenatal diagnosis of fetal chromosomal aneuploidy
by massively parallel genomic sequencing of DNA in maternal plasma.
Proc Natl Acad Sci USA. 2008; 105:20458-20463; Fan et al.,
Noninvasive diagnosis of fetal aneuploidy by shotgun sequencing DNA
from maternal blood. Proc Natl Acad Sci USA. 2008; 105:16266-16271;
Ehrich et al., Noninvasive detection of fetal trisomy 21 bp
sequencing of DNA in maternal blood: a study in a clinical setting,
American Journal of Obstetrics and Gynecology--AMER J OBSTET
GYNECOL, vol. 204, no. 3, pp. 205.e1-205.e11, 2011 DOI:
10.1016/j.ajog.2010.12.060]. Ideally, the distribution of aligned
reads should cover euploid sections of the genome at a constant
level (FIG. 62 and FIG. 63). In practice, uniformity can be
difficult to attain because multiplexed Next Generation Sequencing
(NGS) measurements typically yield low coverage (about 0.1) with
sparsely scattered read start positions. In some embodiments, this
problem is partially overcome by partitioning the genome into
non-overlapping sections (bins) of equal lengths and assigning to
each bin the number of the reads that align within it. In some
embodiments, residual unevenness stemming from GC bias [Dohm J C,
Lottaz C, Borodina T, Himmelbauer H. Substantial biases in
ultra-short read data sets from high-throughput DNA sequencing.
Nucleic Acids Res. 2008 September; 36(16):e105. Epub 2008 Jul. 26.]
is largely suppressed using multiplicative detrending with respect
to the binwise GC content (Fan H C, Quake S R (2010) Sensitivity of
Noninvasive Prenatal Detection of Fetal Aneuploidy from Maternal
Plasma Using Shotgun Sequencing Is Limited Only by Counting
Statistics. PLoS ONE 5(5): e10439.
doi:10.1371/journal.pone.0010439). In some embodiments, the
resulting flattening of the count profile allows for successful
classification of fetal trisomies in a clinical setting using
quadruplex barcoding [Palomaki et al., DNA sequencing of maternal
plasma to detect Down syndrome: an international clinical
validation study. Genet Med., 2011 November; 13(11):913-20].
[0825] The transition from a quadruplex (i.e. 4 simultaneous sample
reads) to higher sample plexing levels (e.g., dodecaplex (i.e. 12
simultaneous sample reads)) pushes the limits of NGS-based
detection of genetic variations (e.g. aneuploidy, trisomy, and the
like) in a test subject (e.g. a pregnant female), reducing both the
number of reads per sample and the gap separating genetic
variations (e.g. euploid from trisomy samples). The downsampling
driven by increased multiplexing can impose new, more stringent
requirements on data processing algorithms (FIG. 64, FIG. 65 and
Example 4). In some embodiments, GC detrending, even when coupled
with repeat masking, requires some improvement (FIG. 66, FIG. 67
and Example 4). In some embodiments, to maintain the sensitivity
achieved with quadruplex barcoding (e.g., quadruplex indexing),
methods and algorithms are presented that are capable of extracting
a minute signal of interest from an overwhelming background noise
as illustrated and described below and in FIG. 7, FIG. 8 and
Example 4. In some embodiments, a novel method termed "PERUN"
(Parameterized Error Removal and Unbiased Normalization) is
described.
[0826] Conventional GC detrending can be multiplicative in nature
(FIG. 17 and Example 4) and may not address additional sources of
systematic bias, illustrated in FIG. 6. In some cases, a reference
median count profile constructed from a set of known euploid
samples can eliminate additional bias and lead to qualitative
improvements. In some cases, a reference median count profile
constructed from a set of known euploid samples can inherit a
mixture of residual GC biases from the reference samples. In some
embodiments, a normalization removes one or more orthogonal types
of bias by separating them from one another at the bin elevation,
rather than tackling them in bulk. In some embodiments GC bias is
removed and binwise separation of the GC bias from the
position-dependent attenuation is achieved (FIG. 68. FIG. 69 and
Example 4). In some embodiments, substantially increased gaps
between euploid and trisomy Z-scores are obtained relative to both
quadruplex and dodecaplex GCRM results. In some embodiments,
maternal and fetal microdeletions and duplications are detected. In
some embodiments fetal fractions are accurately measured. In some
embodiments gender is determined reliably. In some embodiments sex
aneuploidy (e.g. fetal sex aneuploidy) is identified.
[0827] PERUN Method and Definitions
[0828] In some embodiments the entire reference genome is
partitioned into an ordered set B of J bins:
B={b.sub.j|j=1, . . . , J} (D)
[0829] Bin lengths can be constrained to accommodate genomic
stretches of relatively uniform GC content. In some embodiments
adjacent bins can overlap. In some embodiments adjacent bins do not
overlap. In some embodiments the bin edges can be equidistant or
can vary to offset systematic biases, such as nucleotide
composition or signal attenuation. In some embodiments a bin
comprises genomic positions within a single chromosome. Each bin
b.sub.j is characterized by the GC content g.sub.j.sup.0 of the
corresponding portion of the reference genome. In some embodiments,
the entire genome is assigned a reference GC content profile:
g.sup.0=[g.sub.1.sup.0g.sub.2.sup.0 . . . g.sub.J.sup.0] (E)
[0830] The same g.sup.0 profile can apply to all samples aligned to
the chosen reference genome.
[0831] A proper or trivial subset of bins b,
b.OR right.B (F)
can be selected to satisfy certain criteria, such as to exclude
bins with g.sub.j.sup.0=0, bins with extreme g.sub.j.sup.0 values,
bins characterized by low complexity or low mappability (Derrien T,
Estelle J, Marco Sola S, Knowles D G, Raineri E, et al. (2012) Fast
Computation and Applications of Genome Mappability. PLoS ONE 7(1):
e30377, doi:10.1371/journal.pone.0030377), highly variable or
otherwise uninformative bins, regions with consistently attenuated
signal, observed maternal aberrations, or entire chromosomes (X, Y,
triploid chromosomes, and/or chromosomes with extreme GC content).
The symbol .parallel.b.parallel. denotes the size of b.
[0832] All sequenced reads from sample i unambiguously aligned
within a bin b.sub.j form a set a.sub.ij whose cardinality M.sub.ij
represents raw measured counts assigned to that bin. In some
embodiments, the vector of measured bin counts for sample i
constitutes the raw count profile for that sample. In some
embodiments this is the primary observation for the purposes of
PERUN:
M.sub.i=[M.sub.i1M.sub.i2 . . . M.sub.ij] (G)
[0833] To enable comparisons among different samples, the scaling
constant N.sub.i is evaluated as the sum of raw bin counts over a
subset of the bins:
N i = b B M ij ( H ) ##EQU00046##
[0834] In some embodiments b in Eq. H is restricted to autosomal
bins. In some embodiments b in Eq. H is not restricted to autosomal
bins. Division of M.sub.i by the total counts N.sub.i yields the
scaled raw bin counts m.sub.ij:
m.sub.i=[m.sub.i1m.sub.i2 . . . m.sub.ij]=M.sub.i/N.sub.i (I)
[0835] The nucleotide composition of the set a.sub.ij is described
by the bin's observed GC content g.sub.ij. The sample-specific
observed GC content profile g.sub.i gathers individual bin-specific
GC contents into a vector:
g.sub.i=[g.sub.i1g.sub.i2 . . . g.sub.iJ] (J)
[0836] In some embodiments, g.sub.i.noteq.g.sup.0 and
g.sub.i.sub.1.noteq.g.sub.i.sub.2.sub..noteq.i.sub.1. The symbol g
denotes the GC content profile regardless of its origin, i.e.
whether it is derived from the reference genome or from the
sample-specific read alignments. In some embodiments model
equations use g. In some embodiments, actual implementations can
substitute g with either g.sup.0 or g.sub.i.
[0837] For a single sample i, a linear relationship between m.sub.i
and g is assumed, with G.sub.i and r.sub.i denoting the
sample-specific slope of the regression line and the array of
residuals, respectively:
m.sub.i=G.sub.ig+r.sub.i (K)
[0838] The regression can extend over the entire set B (Eq. D) or
its proper subset b (Eq. F). The observed slope G.sub.i is also
referred to as the scaled GC bias coefficient. G.sub.i expresses
the bulk of the vulnerability of the sample i to the systematic GC
bias. In some embodiments, to minimize the number of model
parameters, higher-order terms, linked with curvature of the
relationship m.sub.i(g) and encapsulated in the residuals r.sub.i
are not explicitly addressed. In some embodiments, since
sample-specific total counts N.sub.i confound the interactions
among observables recorded on different samples, the unscaled
equivalent of G.sub.i, relating M.sub.i to g, is less useful and
will not be considered.
[0839] The vector of true chromosomal elevations l.sub.ij
corresponding to bins b.sub.j.epsilon.b in sample i form the
sample-specific chromosomal elevation profile:
l.sub.i=[l.sub.i1l.sub.i2 . . . l.sub.iJ] (L)
[0840] In some embodiments, the goal is to derive estimates for
l.sub.i from m.sub.i by removing systematic biases present in
m.sub.i.
[0841] The values l.sub.ij are bin-specific and also
sample-specific. They comprise both maternal and fetal
contributions, proportional to their respective ploidies
P.sub.ij.sup.M and P.sub.ij.sup.F. The bin-specific and
sample-specific ploidy P.sub.ij can be defined as an integral
multiple of one-half, with the values of 1, 1/2, 0, 3/2, and 2
representing euploidy, heterozygous deletion, homozygous deletion,
heterozygous duplication, and homozygous duplication, respectively.
In some instances, trisomy of a given chromosome implies ploidy
values of 3/2 along the entire chromosome or its substantial
portion.
[0842] When both the mother and the fetus are diploid
(P.sub.ij.sup.M=P.sub.ij.sup.F=1), l.sub.ij equals some arbitrarily
chosen euploid elevation E. In some embodiments, a convenient
choice sets E to 1/.parallel.b.parallel., thus ensuring that the
profile l.sub.i is normalized. In the absence of bin selection,
.parallel.b.parallel.=.parallel.B.parallel.=JE=1/J. In some
embodiments, E can be set to 1 for visualization. In some
embodiments, the following relationship is satisfied:
l.sub.ij=E[(1-f.sub.i)P.sub.ij.sup.M+f.sub.iP.sub.ij.sup.F] (M)
[0843] The symbol f.sub.i stands for the fraction of the fetal DNA
present in the cell-free circulating DNA from maternal plasma in
sample i. Any deviations from euploidy, either fetal
(P.sub.ij.sup.F.noteq.1) or maternal (P.sub.ij.sup.M.noteq.1),
cause differences between and l.sub.ij that E can be exploited to
estimate f.sub.i and detect microdeletions/microduplications or
trisomy.
[0844] To achieve the goal of extracting l.sub.i from m.sub.i, a
linear relationship is postulated between the bin-specific scaled
raw counts m.sub.ij measured on a given sample and the
sample-specific scaled GC bias coefficients:
m.sub.i=l.sub.iI+G.sub.iS (N)
[0845] The diagonal matrix I and the vector S gather bin-specific
intercepts and slopes of the set of linear equations summarized by
Eq. N:
I = [ I 1 0 0 0 I 2 0 0 0 I J ] ( O ) S = [ S 1 S 2 S J ] ( P )
##EQU00047##
[0846] Both I and S are sample-independent. The intercepts I.sub.j
can be viewed as expected euploid values for scaled row counts in
the absence of GC bias (i.e. when G.sub.i=0). Their actual values
reflect the convention adopted for E (vide supra). The intercepts
S.sub.j are non-linearly related to the differences
g.sub.j.sup.0-g.sub.k.sup.0, where g.sub.k.sup.0 represents the
median GC content of the chromosome containing the bin j.
[0847] Once the values for the parameters I and S are known, the
true chromosomal elevation profile l.sub.i is estimated from the
scaled raw count profile m.sub.i and the scaled GC bias coefficient
G.sub.i by rearranging Eq. N:
l.sub.i=(m.sub.i-G.sub.iS)I.sup.-1 (Q)
[0848] The diagonal character of the intercept matrix/provides for
the matrix inversion in Eq. Q.
[0849] Parameter Estimation
[0850] Model parameters I and S are evaluated from a set of N
scaled raw count profiles collected on samples karyotyped as
euploid pregnancies. N is of the order of 10.sup.3. Scaled GC bias
coefficients G.sub.i are determined for each sample (i=1, . . . ,
N). All samples are segregated into a small number of classes
according to the sizes and signs of their G.sub.i values. The
stratification balances the opposing needs to include sufficiently
large numbers of representatives and a sufficiently small range of
G.sub.i values within each shell. The compromise of four strata
accommodates negative, near-zero, moderately positive, and extreme
positive GC biases, with the near-zero shell being most densely
populated. A fraction of samples (typically 10%) from each stratum
can be randomly selected and set aside for cross-validation. The
remaining samples make up the work set, used to train the model.
Both the training and the subsequent cross-validation assume that
all samples are free of maternal and fetal deletions or
duplications along the entire genome:
P.sub.ij.sup.M=P.sub.ij.sup.F=1,.A-inverted.i=1, . . .
N,.A-inverted.j=1, . . . , J (R)
[0851] The large number of samples compensates for the occasional
maternal deviations from the assumption R. For each bin j, l.sub.ij
is set to E, allowing evaluation of the intercept I.sub.j and the
slope S.sub.j as the coefficients of the linear regression applied
to the training set according to Eq. N. The uncertainty estimates
for I.sub.j and S.sub.j are recorded as well.
[0852] The random partitioning into the working and the
cross-validation subsets is repeated multiple times (e.g.
10.sup.2), yielding distributions of values for the I.sub.j and
S.sub.j parameters. In some embodiments the random partitioning is
repeated between about 10 and about 10.sup.5 times. In some
embodiments the random partitioning is repeated about 10, about
10.sup.2, about 10.sup.3, about 10.sup.4 or about 10.sup.5
times.
[0853] Cross-Validation
[0854] Once derived from the work set, the model parameters I.sub.j
and S.sub.j are employed to back-calculate scaled raw counts from
the scaled GC bias coefficients using Eq. N and assumption R. The
symbol p.sub.ij denotes the predicted scaled raw counts for the bin
b.sub.j in the sample i. The indices W and CV in further text
designate the work and the cross-validation subsets, respectively.
The back-calculation is applied to all samples, both from W and CV.
R-factors, borrowed from the crystallographic structure refinement
practice (Brunger, Free R value: a novel statistical quantity for
assessing the accuracy of crystal structures, Nature 355, 472-475
(30 Jan. 1992); doi:10.1038/355472a0), are separately defined for
the two subsets of samples:
R j W = i .di-elect cons. W m ij - p ij i .di-elect cons. W m ij (
S ) R j C V = i .di-elect cons. C V m ij - p ij i .di-elect cons. C
V m ij ( T ) ##EQU00048##
[0855] Both R-factors are bin-specific. As in crystallography,
R-factors 16-17 can be interpreted as residual relative errors in
the model. Having been excluded from the parameter estimation, the
cross-validation R-factor R.sub.j.sup.CV provides a true measure of
the error for the given W/CV division, while the difference between
R.sub.j.sup.CV and R.sub.j.sup.W reflects the model bias for the
bin j. A separate pair of R-values is evaluated for each bin and
for each random partitioning of the set of samples into W and CV.
The maximum of all R.sub.j.sup.CV and R.sub.j.sup.W values obtained
for the different random partitionings into W and CV is assigned to
the bin j as its overall model error .epsilon..sub.j.
[0856] Bin Selection
[0857] All the bins with zero GC content g.sub.j.sup.0 are
eliminated from further consideration, as is the set {b.sub.j:
M.sub.ij.ident.0, .A-inverted.i=1, . . . , N} of bins that
consistently receive zero counts across a large number of samples.
In addition, a maximum tolerable cross-validation error value
.epsilon. can be imposed on all bins. In some embodiments the bins
with model errors .epsilon..sub.j exceeding the upper limit
.epsilon. are rejected. In some embodiments, filtering uses bin
mappability scores .mu..sub.j.epsilon.[0,1] and imposes a minimum
acceptable mappability .mu., rejecting bins with .mu..sub.j<.mu.
(Derrien T, Estelle J, Marco Sola S, Knowles D G, Raineri E, et al.
(2012) Fast Computation and Applications of Genome Mappability.
PLoS ONE 7(1): e30377, doi:10.1371/journal.pone.0030377). For the
purposes of determining fetal trisomy of chromosomes 21, 18, and
13, the sex chromosomes can be excluded as well. The subset .beta.
of bins that survive all the phases of the bin selection can
undergo further computations. In some embodiments, the same subset
.beta. is used for all samples.
[0858] Normalization and Standardization
[0859] In some embodiments, for a given sample i, the chromosomal
elevations l.sub.ij corresponding to the bin selection .beta. are
estimated according to Eq. Q. In some embodiments, a secondary
normalization is applied to remove any curvature from the
l.sub.ij-vs.-GC content correlation. In some embodiments l.sub.ij
is already nearly unbiased, the secondary detrending is robust and
is immune to error boosting. In some embodiments, standard textbook
procedures suffice.
[0860] In some embodiments, the results of the normalization are
summed up within each chromosome:
L in = b j .di-elect cons. .beta. Chr n l ij , n = 1 , , 22 ( U )
##EQU00049##
[0861] The total autosomal material in sample i can be evaluated as
the sum of all individual L.sub.in terms:
L i = n = 1 22 L i n ( V ) ##EQU00050##
[0862] The chromosomal representation of each chromosome of
interest can be obtained by dividing L.sub.in with L.sub.i:
.chi..sub.in=L.sub.in/L.sub.i (W)
[0863] The variability .sigma..sub.n of the representation of the
chromosome n can be estimated as an uncensored MAD of .chi..sub.in
values across a selection of samples spanning multiple flow cells.
In some embodiments, the expectation .chi..sub.L is evaluated as
the median of .chi..sub.in values corresponding to a selection of
samples from the same flow cell as the tested sample. Both sample
selections can exclude high positive controls, low positive
controls, high negative controls, blanks, samples that fail QC
criteria, and samples with SD(l.sub.i) exceeding a predefined
cutoff (typically 0.10). Together, the values .sigma..sub.n and
.chi..sub.n can provide the context for standardization and
comparison of chromosomal representations among different samples
using Z-scores:
Z.sub.in=(.chi..sub.in-.chi..sub.n)/.sigma..sub.n (X)
[0864] In some embodiments, aberrations such as trisomies 13, 18,
and 21 are indicated by Z-values exceeding a predefined value,
dictated by the desired confidence level.
Example 6
Examples of Formulas
[0865] Provided below are non-limiting examples of mathematical
and/or statistical formulas that can be used in methods described
herein.
Z = .DELTA. 1 - .DELTA. 2 .sigma. 1 2 ( 1 N 1 + 1 n 1 ) + .sigma. 2
2 ( 1 N 2 + 1 n 2 ) ##EQU00051##
Example 7
Identifying and Adjusting (Padding) Elevations
[0866] Maternal deletions and duplications, often represented as
first elevations in a profile, can be removed from count profiles
normalized with PERUN to reduce variability when detecting T21,
T18, or T13. The removal of deletions and duplication from a
profile can reduce the variability (e.g., biological variability)
found in measured chromosomal representations that originates from
maternal aberrations.
[0867] All bins that significantly deviate from the expected
chromosomal elevation of 1 are first identified. In this example
some isolated bins are removed from the selection. This is
optional. In this example only large enough groups of contiguous
outlier bins are kept. This is also optional. Depending on the
elevation assigned to an outlier bin or a group of contiguous
outlier bins, a correction factor is added to the measured
elevation to adjust it closer to the expected elevation of 1. The
PAV values used in this example are +1 (for homozygous maternal
deletions), +0.5 (for heterozygous maternal deletions), -0.5 (for
heterozygous duplications), -1 (for homozygous duplications), or
more (for large spikes). Large spikes are often not identified as
maternal deletions and duplications.
[0868] This padding procedure corrected the classification (e.g.,
the classification as an aneuploidy, e.g., a trisomy) for samples
that contains large maternal aberrations. Padding converted the WI
sample from false positive T13 to true negative due to removal of a
large maternal deletion in Chr4 (FIGS. 112-115).
[0869] Past simulations with experimental data have shown that
depending on the chromosome, fetal fraction, and the type of
aberration (homozygous or heterozygous, duplication or deletion),
maternal aberrations in 20-40 bins long may push the Z-value over
the classification edge (e.g., threshold) and result in a false
positive or a false negative. Padding (e.g., adjusting) can
circumvent this risk.
[0870] This padding procedure can remove uninteresting maternal
aberrations (a confounding factor), reduce euploid variability,
create tighter sigma-values used to standardize Z-scores and
therefore enlarge the gap between euploids and trisomy cases.
Example 8
Determining Fetal Fractions from Maternal and/or Fetal Copy Number
Variations
[0871] A distinguishing feature of the method described herein is
the use of maternal aberrations (e.g., maternal and/or fetal copy
number variations) as a probe providing insight into the fetal
fraction in the case of a pregnant female bearing a fetus (e.g., a
euploid fetus). The detection and quantitation of maternal
aberrations typically is aided by normalization of raw counts. In
this example raw counts are normalized using PERUN. Alternatively,
normalization with respect to a reference median count profile can
be used in a similar manner and for the same purpose.
[0872] PERUN normalization of raw counts yields sample-specific
binwise chromosomal levels I.sub.ij (i counts samples, j counts
bins). They comprise both maternal and fetal contributions,
proportional to their respective ploidy P.sub.ij.sup.M and
P.sub.ij.sup.F. The bin-specific and sample-specific ploidy
P.sub.ij is defined as an integral multiple of 1/2, with the values
of 1, 1/2, 0, 3/2, and 2 representing euploidy, heterozygous
deletion, homozygous deletion, heterozygous duplication, and
homozygous duplication, respectively. In particular, trisomy of a
given chromosome implies ploidy values of 3/2 along the entire
chromosome or its substantial portion.
[0873] When both the mother and the fetus are diploid
(P.sub.ij.sup.M=P.sub.ij.sup.F=1), equals some arbitrarily chosen
euploid level E. A convenient choice sets E to
1/.parallel.b.parallel., where b denotes a proper or trivial subset
of the set of all bins (B). thus ensuring that the profile l.sub.i
is normalized. In the absence of bin selection,
.parallel.b.parallel.=.parallel.B.parallel.=JE=1/J. Alternatively
and preferentially, E may be set to 1 for visualization. In
general, the following relationship is satisfied:
l.sub.ij=E[(1-f.sub.i)P.sub.ij.sup.M+f.sub.iP.sub.ij.sup.F] (Y)
[0874] The symbol f.sub.i stands for the fraction of the fetal DNA
present in the cell-free circulating DNA from maternal plasma in
sample i. Any deviations from euploidy, either fetal
(P.sub.ij.sup.F.noteq.1) or maternal (P.sub.ij.sup.M.noteq.1),
cause differences between and E that can be exploited to estimate
f.sub.i and detect microdeletions/microduplications or trisomy.
[0875] Four different types of maternal aberrations are considered
separately. All four account for possible fetal genotypes, as the
fetus may (or in homozygous cases must) inherit the maternal
aberration. In addition, the fetus may inherit a matching
aberration from the father as well. In general, fetal fraction can
only be measured when P.sub.ij.sup.M.noteq.P.sub.ij.sup.F. [0876]
A) Homozygous maternal deletion (P.sub.ij.sup.M=0. Two possible
accompanying fetal ploidies include: [0877] a. P.sub.ij.sup.F=0, in
which case l.sub.ij=0 and the fetal fraction cannot be evaluated
from the deletion. [0878] b. P.sub.ij.sup.F=1/2, in which case
l.sub.ij=f.sub.i/2 and the fetal fraction is evaluated as twice the
average elevation within the deletion. [0879] B) Heterozygous
maternal deletion (P.sub.ij.sup.M=1/2). Three possible accompanying
fetal ploidies include: [0880] a. P.sub.ij.sup.F=0, in which case
l.sub.ij=(1-f.sub.i)/2 and the fetal fraction is evaluated as twice
the difference between 1/2 and the average elevation within the
deletion. [0881] b. P.sub.ij.sup.F=1/2, in which case l.sub.ij=1/2
and the fetal fraction cannot be evaluated from the deletion.
[0882] c. P.sub.ij.sup.F=1, in which case l.sub.ij=(1+f.sub.i)/2
and the fetal fraction is evaluated as twice the difference between
1/2 and the average elevation within the deletion. [0883] C)
Heterozygous maternal duplication (P.sub.ij.sup.M=3/2). Three
possible accompanying fetal ploidies include: [0884] a.
P.sub.ij.sup.F=1, in which case l.sub.ij=(3-f.sub.i)/2 and the
fetal fraction is evaluated as twice the difference between 3/2 and
the average elevation within the duplication. [0885] b.
P.sub.ij.sup.F=3/2, in which case l.sub.ij=3/2 and the fetal
fraction cannot be evaluated from the duplication. [0886] c.
P.sub.ij.sup.F=2, in which case l.sub.ij=(3+f.sub.i)/2 and the
fetal fraction is evaluated as twice the difference between 3/2 and
the average elevation within the duplication. [0887] D) Homozygous
maternal duplication (P.sub.ij.sup.M=2). Two possible accompanying
fetal ploidies include: [0888] a. P.sub.ij.sup.F=2, in which case
l.sub.ij=2 and the fetal fraction cannot be evaluated from the
duplication. [0889] b. P.sub.ij.sup.F=3/2, in which case
l.sub.ij=2-f.sub.i/2 and the fetal fraction is evaluated as twice
the difference between 2 and the average elevation within the
duplication.
[0890] The following LDTv2CE samples (FIG. 116-131) illustrate the
application of determining fetal fraction from maternal and/or
fetal copy number variations. The patients were not selected
randomly and any agreement with FQA fetal fraction values should
not be construed as the measure of merit of either technique.
Example 9
Binary Genome
[0891] To generate a binary genome, a human reference genome (hg19)
was converted to a two nucleotide system by converting all guanine
nucleotides to adenine (i.e., all purines were converted to
adenine). Similarly, all cytosine nucleotides were converted to
thymidine (i.e., all pyrimidines were converted to thymidine). The
human reference genome was indexed according to Bowtie 2 version
2.0.0-beta3 default instructions (see World Wide Web URL
bowtie-bio.sourceforge.net/bowtie2/manual.shtml). 36 bp sequence
reads from 87 maternal plasma samples from RDFC010224 were
converted to a binary genome by using a custom script that converts
all purines to adenines and all pyrimidines to thymidines. The
sequence quality for each base was left unchanged. Of the 87
maternal plasma samples, there were 4 samples having a chromosome
13 fetal aneuploidy, 9 samples having a chromosome 18 fetal
aneuploidy, and 15 samples having a chromosome 21 fetal aneuploidy.
The sequence reads were aligned against the converted binary genome
using Bowtie 2 along with the "--very-fast" alignment option.
[0892] Results obtained using the binary genome were compared to
the original four nucleotide data and alignments. Comparative
metrics included the following: GC bias, relative coverage, bin
profile variance, total autosomal counts, and chromosome
representation. GC bias, as described by a GC coefficient, is
measured by the slope of a linear model between the percent GC in
the discreet 50 kb bin of the human reference genome and the
observed number of alignments to that bin. Positive (or negative)
slopes indicate a slight relationship with counts and GC content
per 50 kb bin. GC bias, as described by a GC curve, also is
measured by the second order of the linear model to reflect the
overall concavity or convexity of the relationship between GC
content and counts per 50 kb bin. Relative coverage is the relative
error in PCR duplicate rates (where 2 or more reads begin in the
exact same chromosome and position) from simulated expectation
given total depth of coverage. Bin profile variance is the standard
deviation of counts per bin after PERUN with secondary LOESS
normalization. Total autosomal counts reflects the total alignable
data, and chromosome representation is the ratio of reads mapped to
a specific chromosome normalized by the total autosomal counts.
[0893] FIGS. 132 to 143 illustrate a similarity between the
originally processed four nucleotide genome against the in silico
converted two nucleotide (i.e., binary) genome. GC bias and total
autosomal counts, for example, were highly concordant. Raw
chromosome representation also were correlated. PERUN with
secondary LOESS and padding attenuated underlying GC bias of the
sequencing technology and were thus able to detect fetal aneuploidy
events.
[0894] To evaluate the performance of fetal aneuploidy detection in
the absence of bias (i.e. GC biases, sequencing errors,
non-uniformity of coverage), next-generation sequence reads were
simulated via WGSIM (World Wide Web URL github.com/lh3/wgsim) with
complete uniformity of coverage and no mutations/sequencing errors
(parameters: -d=0 and -e=0). After generating 14 million 36 base
pair reads for 7 euploids and 2 aneuploids (chromosome 21 fetal
aneuploidies) at 8% fetal fraction, the same analyses work flow
described above were implemented after converting all bases into a
binary code. To generate an artificial chromosome 21 fetal
aneuploidy, chromosome 21 reads were increased by doubling sampled
reads proportional to the fetal fraction.
[0895] FIG. 144 illustrates that in the absence of bias (GC bias,
mutations, non-uniformity of coverage), raw (non-normalized)
chromosome 21 representation as generated by simulation can detect
aneuploidy without the need of further normalization. Nevertheless,
if PERUN with secondary LOESS without padding was applied,
chromosome 21 representation remained concordant (FIG. 145).
[0896] The total number of unique sequences of a specific length
was determined by the total number of unique nucleotides raised to
a length of sequence (i.e., number of nucleotides). For example, a
4 nucleotide genome of length 3 has 4.sup.3=64 unique sequences
(FIG. 146). Assuming the human reference genome is approximately
3.1 billion bases, every start position could have a unique
sequence if the read length is 15.76 for a 4 nucleotide genome and
31.53 for a 2 nucleotide (i.e., binary) genome. Thus, for a two
nucleotide system (i.e., binary genome) a 36 bp read length, for
example, is sufficiently long enough to uniquely map anywhere in
the genome.
Example 10
Unary Genome
[0897] To generate a unary genome, a human reference genome (hg19)
was converted to a unary nucleotide system (e.g., A, not A; T, not
T; C, not C; or G, not G) using four separate conversion schemes.
For the first scheme, also referred to as unary genome A (A, not
A), all non-adenine nucleotides were converted to guanine. For the
second scheme, also referred to as unary genome G (G, not G), all
non-guanine nucleotides were converted to adenine. For the third
scheme, also referred to as unary genome C (C, not C), all
non-cytosine nucleotides were converted to thymidine. For the
fourth scheme, also referred to as unary genome T (T, not T), all
non-thymidine nucleotides were converted to cytosine. The human
reference genome was indexed according to Bowtie 2 version
2.0.0-beta3 default instructions (see World Wide Web URL
bowtie-bio.sourceforge.net/bowtie2/manual.shtml). 36 bp sequence
reads from 87 maternal plasma samples from RDFC010224 were
converted to a unary genome using the nucleotide conversion
criteria as outline above. The data was processed as described in
Example 9 for the binary genome. Results obtained using the unary
genome were compared to the original four nucleotide data and
alignments by comparative analyses identical to those described in
Example 9 for the binary genome study.
[0898] FIGS. 147 to 154 illustrate the similarity of GC coefficient
and total autosomal counts between the originally processed four
nucleotide genome against the in silico converted unary genome. Raw
chromosome 21 representations for each unary genome are presented
in FIGS. 155 to 158. As shown in FIGS. 159 to 170, PERUN
normalization reduced systematic biases and allowed for some
distinction between euploids and fetal aneuploids. FIGS. 171 and
172 illustrate the similarity between the four base genome and the
unary genome when comparing counts per bin per GC content. FIGS.
173 to 184 demonstrate that in the absence of bias, reads converted
to any of the four unary genomes retained uniqueness such that
fetal aneuploidy detection is feasible.
Example 11
Examples of Embodiments
[0899] 1. A method for detecting the presence or absence of a fetal
aneuploidy comprising: [0900] (a) obtaining partial nucleotide
sequence reads from a sample comprising circulating, cell-free
nucleic acid from a pregnant female, wherein at least some partial
nucleotide sequence reads comprise: [0901] i) multiple nucleobase
gaps between identified nucleobases, or [0902] ii) one or more
nucleobase classes, wherein each nucleobase class comprises a
subset of nucleobases present in the sample nucleic acid, or
combination of (i) and (ii), [0903] (b) mapping the partial
nucleotide sequence reads to reference genome sections, [0904] (c)
counting the number of partial nucleotide sequence reads mapped to
each reference genome section, [0905] (d) comparing the number of
counts of the partial nucleotide sequence reads mapped in (c), or
derivative thereof, to a reference, thereby making a comparison,
and [0906] (e) determining the presence or absence of a fetal
aneuploidy based on the comparison.
[0907] 1.1 A method for detecting the presence or absence of a
fetal aneuploidy comprising: [0908] (a) obtaining partial
nucleotide sequence reads from a sample comprising circulating,
cell-free nucleic acid from a pregnant female, wherein at least
some partial nucleotide sequence reads comprise: [0909] i) multiple
nucleobase gaps between identified nucleobases, or [0910] ii) one
or more nucleobase classes, wherein each nucleobase class comprises
a subset of nucleobases present in the sample nucleic acid, or
combination of (i) and (ii), [0911] (b) mapping the partial
nucleotide sequence reads to reference genome sections, [0912] (c)
counting the number of partial nucleotide sequence reads mapped to
each reference genome section, [0913] (d) comparing the number of
counts of the partial nucleotide sequence reads mapped in (c), or
derivative thereof, to a reference, or portion thereof, thereby
making a comparison, and [0914] (e) determining the presence or
absence of a fetal aneuploidy based on the comparison.
[0915] 2. A method for detecting the presence or absence of a fetal
aneuploidy comprising: [0916] (a) mapping partial nucleotide
sequence reads that have been obtained from a sample comprising
circulating, cell-free nucleic acid from a pregnant female, to
reference genome sections, wherein at least some partial nucleotide
sequence reads comprise: [0917] i) multiple nucleobase gaps between
identified nucleobases, or [0918] ii) one or more nucleobase
classes, wherein each nucleobase class comprises a subset of
nucleobases present in the sample nucleic acid, or combination of
(i) and (ii), [0919] (b) counting the number of partial nucleotide
sequence reads mapped to each reference genome section, [0920] (c)
comparing the number of counts of the partial nucleotide sequence
reads mapped in (b), or derivative thereof, to a reference, thereby
making a comparison, and [0921] (d) determining the presence or
absence of a fetal aneuploidy based on the comparison.
[0922] 2.1 A method for detecting the presence or absence of a
fetal aneuploidy comprising: [0923] (a) mapping partial nucleotide
sequence reads that have been obtained from a sample comprising
circulating, cell-free nucleic acid from a pregnant female, to
reference genome sections, wherein at least some partial nucleotide
sequence reads comprise: [0924] i) multiple nucleobase gaps between
identified nucleobases, or [0925] ii) one or more nucleobase
classes, wherein each nucleobase class comprises a subset of
nucleobases present in the sample nucleic acid, or combination of
(i) and (ii), [0926] (b) counting the number of partial nucleotide
sequence reads mapped to each reference genome section, [0927] (c)
comparing the number of counts of the partial nucleotide sequence
reads mapped in (b), or derivative thereof, to a reference, or
portion thereof, thereby making a comparison, and [0928] (d)
determining the presence or absence of a fetal aneuploidy based on
the comparison.
[0929] 3. A method for detecting the presence or absence of a fetal
aneuploidy comprising: [0930] (a) obtaining a sample comprising
circulating, cell-free nucleic acid from a pregnant female, [0931]
(b) isolating sample nucleic acid from the sample, [0932] (c)
obtaining partial nucleotide sequence reads from the sample nucleic
acid, wherein at least some partial nucleotide sequence reads
comprise: [0933] i) multiple nucleobase gaps between identified
nucleobases, or [0934] ii) one or more nucleobase classes, wherein
each nucleobase class comprises a subset of nucleobases present in
the sample nucleic acid, or combination of (i) and (ii), [0935] (d)
mapping the partial nucleotide sequence reads to reference genome
sections, [0936] (e) counting the number of partial nucleotide
sequence reads mapped to each reference genome section, [0937] (f)
comparing the number of counts of the partial nucleotide sequence
reads mapped in (e), or derivative thereof, to a reference, thereby
making a comparison, and [0938] (g) determining the presence or
absence of a fetal aneuploidy based on the comparison.
[0939] 3.1 A method for detecting the presence or absence of a
fetal aneuploidy comprising: [0940] (a) obtaining a sample
comprising circulating, cell-free nucleic acid from a pregnant
female, [0941] (b) isolating sample nucleic acid from the sample,
[0942] (c) obtaining partial nucleotide sequence reads from the
sample nucleic acid, wherein at least some partial nucleotide
sequence reads comprise: [0943] i) multiple nucleobase gaps between
identified nucleobases, or [0944] ii) one or more nucleobase
classes, wherein each nucleobase class comprises a subset of
nucleobases present in the sample nucleic acid, or combination of
(i) and (ii), [0945] (d) mapping the partial nucleotide sequence
reads to reference genome sections, [0946] (e) counting the number
of partial nucleotide sequence reads mapped to each reference
genome section, [0947] (f) comparing the number of counts of the
partial nucleotide sequence reads mapped in (e), or derivative
thereof, to a reference, or portion thereof, thereby making a
comparison, and [0948] (g) determining the presence or absence of a
fetal aneuploidy based on the comparison.
[0949] 4. A method for detecting the presence or absence of a
genetic variation comprising: [0950] (a) obtaining partial
nucleotide sequence reads from a sample comprising nucleic acid
from a subject, wherein at least some partial nucleotide sequence
reads comprise: [0951] i) multiple nucleobase gaps between
identified nucleobases, or [0952] ii) one or more nucleobase
classes, wherein each nucleobase class comprises a subset of
nucleobases present in the sample nucleic acid, or combination of
(i) and (ii), [0953] (b) mapping the partial nucleotide sequence
reads to reference genome sections, [0954] (c) comparing the
partial nucleotide sequence reads mapped in (b) to a reference,
thereby making a comparison, and [0955] (d) determining the
presence or absence of a genetic variation based on the
comparison.
[0956] 4.1 A method for detecting the presence or absence of a
genetic variation comprising: [0957] (a) obtaining partial
nucleotide sequence reads from a sample comprising nucleic acid
from a subject, wherein at least some partial nucleotide sequence
reads comprise: [0958] i) multiple nucleobase gaps between
identified nucleobases, or [0959] ii) one or more nucleobase
classes, wherein each nucleobase class comprises a subset of
nucleobases present in the sample nucleic acid, or combination of
(i) and (ii), [0960] (b) mapping the partial nucleotide sequence
reads to reference genome sections, [0961] (c) comparing the
partial nucleotide sequence reads mapped in (b) to a reference, or
portion thereof, thereby making a comparison, and [0962] (d)
determining the presence or absence of a genetic variation based on
the comparison.
[0963] 5. A method for detecting the presence or absence of a
genetic variation comprising: [0964] (a) mapping partial nucleotide
sequence reads that have been obtained from a sample comprising
nucleic acid from a subject, to reference genome sections, wherein
at least some partial nucleotide sequence reads comprise: [0965] i)
multiple nucleobase gaps between identified nucleobases, or [0966]
ii) one or more nucleobase classes, wherein each nucleobase class
comprises a subset of nucleobases present in the sample nucleic
acid, or combination of (i) and (ii), [0967] (b) comparing the
partial nucleotide sequence reads mapped in (a) to a reference,
thereby making a comparison, and [0968] (c) determining the
presence or absence of a genetic variation based on the
comparison.
[0969] 5.1 A method for detecting the presence or absence of a
genetic variation comprising: [0970] (a) mapping partial nucleotide
sequence reads that have been obtained from a sample comprising
nucleic acid from a subject, to reference genome sections, wherein
at least some partial nucleotide sequence reads comprise: [0971] i)
multiple nucleobase gaps between identified nucleobases, or [0972]
ii) one or more nucleobase classes, wherein each nucleobase class
comprises a subset of nucleobases present in the sample nucleic
acid, or combination of (i) and (ii), [0973] (b) comparing the
partial nucleotide sequence reads mapped in (a) to a reference, or
portion thereof, thereby making a comparison, and [0974] (c)
determining the presence or absence of a genetic variation based on
the comparison.
[0975] 6. A method for detecting the presence or absence of a
genetic variation comprising: [0976] (a) obtaining a sample
comprising nucleic acid from a subject, [0977] (b) isolating sample
nucleic acid from the sample, [0978] (c) obtaining partial
nucleotide sequence reads from the sample nucleic acid, wherein at
least some partial nucleotide sequence reads comprise: [0979] i)
multiple nucleobase gaps between identified nucleobases, or [0980]
ii) one or more nucleobase classes, wherein each nucleobase class
comprises a subset of nucleobases present in the sample nucleic
acid, or combination of (i) and (ii), [0981] (d) mapping the
partial nucleotide sequence reads to reference genome sections,
[0982] (e) comparing the partial nucleotide sequence reads mapped
in (d) to a reference, thereby making a comparison, and [0983] (f)
determining the presence or absence of a genetic variation based on
the comparison.
[0984] 6.1 A method for detecting the presence or absence of a
genetic variation comprising: [0985] (a) obtaining a sample
comprising nucleic acid from a subject, [0986] (b) isolating sample
nucleic acid from the sample, [0987] (c) obtaining partial
nucleotide sequence reads from the sample nucleic acid, wherein at
least some partial nucleotide sequence reads comprise: [0988] i)
multiple nucleobase gaps between identified nucleobases, or [0989]
ii) one or more nucleobase classes, wherein each nucleobase class
comprises a subset of nucleobases present in the sample nucleic
acid, or combination of (i) and (ii), [0990] (d) mapping the
partial nucleotide sequence reads to reference genome sections,
[0991] (e) comparing the partial nucleotide sequence reads mapped
in (d) to a reference, or portion thereof, thereby making a
comparison, and [0992] (f) determining the presence or absence of a
genetic variation based on the comparison.
[0993] 7. A method for detecting the presence or absence of a
genetic variation, comprising: [0994] (a) obtaining counts of
partial nucleotide sequence reads mapped to genomic sections of a
reference genome, which partial nucleotide sequence reads are reads
of circulating cell-free nucleic acid from a test sample, wherein
at least some of the partial nucleotide sequence reads comprise:
[0995] i) multiple nucleobase gaps between identified nucleobases,
or [0996] ii) one or more nucleobase classes, wherein each
nucleobase class comprises a subset of nucleobases present in the
sample nucleic acid, or a combination of (i) and (ii), [0997] (b)
normalizing the counts of the partial nucleotide sequence reads,
thereby providing normalized counts, and [0998] (c) detecting the
presence or absence of a genetic variation based on the normalized
counts.
[0999] 7.01 The method of embodiment 7, wherein the genetic
variation is a nucleic acid sequence variation.
[1000] 7.02 The method of embodiment 7 or 7.01, wherein the genetic
variation is a copy number variation.
[1001] 7.03 The method of embodiment 7.02, wherein the test sample
is from a pregnant female and the genetic variation is a fetal
aneuploidy.
[1002] 7.04 The method of any one of embodiments 7 to 7.03,
comprising comparing the normalized counts to a reference, thereby
making a comparison, wherein determining the presence or absence of
the genetic variation in (c) is based on the normalized counts and
the comparison.
[1003] 7.05 The method of embodiment 7.04, wherein the reference is
counts of sequence reads mapped to a reference chromosome or
segment thereof.
[1004] 7.06 The method of embodiment 7.05, wherein the reference
chromosome is chromosome 1, chromosome 14, chromosome 19 or
combination thereof.
[1005] 7.07 The method of any one of embodiments 7.04 to 7.06,
wherein the counts of the partial nucleotide sequence reads
obtained in (a) comprise counts of partial nucleotide sequence
reads mapped to a test chromosome or segment thereof.
[1006] 7.08 The method of embodiment 7.07, wherein the test
chromosome is chromosome 13, chromosome 18, chromosome 21 or
combination thereof.
[1007] 7.09 The method of any one of embodiment 7.04 to 7.08,
wherein the counts are expressed as a ratio of counts for genomic
sections in a test chromosome or segment thereof to counts for
genomic sections in autosomes or segment thereof, thereby providing
a count representation.
[1008] 7.10 The method of any one of embodiments 7 to 7.09, wherein
the normalizing in (b) comprises normalizing according to guanine
and cytosine (GC) content of the genomic sections, and providing
calculated genomic section levels.
[1009] 7.11 The method of any one of embodiments 7 to 7.10, wherein
the normalizing in (b) comprises: [1010] (i) determining a guanine
and cytosine (GC) bias for each of the genomic sections for
multiple samples from a fitted relation for each sample between (1)
the counts of the partial nucleotide sequence reads mapped to each
of the genomic sections, and (2) GC content for each of the genomic
sections; and [1011] (ii) calculating a genomic section level for
each of the genomic sections from a fitted relation between (1) the
GC bias and (2) the counts of the partial nucleotide sequence reads
mapped to each of the genomic sections, thereby providing
calculated genomic section levels, whereby bias in the counts of
the partial nucleotide sequence reads mapped to each of the
portions of the reference genome is reduced in the calculated
genomic section levels, and wherein the normalized counts in (b)
comprise the calculated genomic section levels.
[1012] 7.12 The method of embodiment 7.11, wherein the normalized
counts in (b) are the calculated genomic section levels.
[1013] 7.13 The method of any one of embodiments 7 to 7.12, wherein
the normalized counts are adjusted for a first level for a first
set of genomic sections which first level is significantly
different than a second level for a second set of genomic sections,
thereby providing adjusted normalized counts, wherein determining
the presence or absence of the genetic variation in (c) is based on
the adjusted normalized counts.
[1014] 7.14 The method of embodiment 7.13, comprising: [1015] (i)
identifying a first elevation of the normalized counts
significantly different than a second elevation of the normalized
counts in a normalized counts profile, which first elevation is for
a first set of genomic sections, and which second elevation is for
a second set of genomic sections; [1016] (ii) determining an
expected elevation range for a homozygous and heterozygous copy
number variation according to an uncertainty value for a segment of
the genome; and [1017] (iii) adjusting the first elevation by a
predetermined value, or adjusting the first elevation to the second
elevation, when the first elevation is within one of the expected
elevation ranges, thereby providing an adjustment of the first
elevation.
[1018] 7.15 The method of embodiment 7.14, wherein the segment of
the genome comprises the first elevation or the second elevation,
or the first elevation and the second elevation.
[1019] 7.16 The method of any one of embodiments 7 to 7.15, wherein
the normalizing in (b) comprises performing a local regression on
the counts of the partial nucleotide sequence reads or the
calculated genomic section levels, or the counts of the partial
nucleotide sequence reads and the calculated genomic section
levels.
[1020] 7.17 The method of embodiment 7.16, wherein the local
regression comprises a weighted least squares fit.
[1021] 7.18 The method of embodiment 7.17, wherein the local
regression comprises a LOESS regression.
[1022] 7.19 The method of any one of embodiments 7 to 7.18, wherein
the partial nucleotide sequence reads are unary partial reads, for
which unary partial reads one nucleotide species is known at known
positions and the other positions can be any one of three other
nucleotide species.
[1023] 7.20 The method of embodiment 7.19, wherein the partial
nucleotide sequence reads are about 30 base pairs or more. [1024]
7.21 The method of any one of embodiments 7 to 7.18, wherein the
partial nucleotide sequence reads are binary partial reads, for
which binary partial reads a first nucleotide class consisting of
two possible bases is known at known positions and a second
nucleotide class consisting of two possible bases is known at known
positions, wherein the bases of the first nucleotide class are
different than the bases of the second nucleotide class.
[1025] 7.22 The method of embodiment 7.21, wherein the partial
nucleotide sequence reads are about 30 base pairs or more.
[1026] 7.23 The method of any one of embodiments 7 to 7.18, wherein
the partial nucleotide sequence reads are ternary partial reads,
for which ternary partial reads a first nucleotide species is known
at known positions, a second nucleotide species is known at other
known positions and the other positions are any one of two
nucleotide species other than the first nucleotide species and the
second nucleotide species.
[1027] 7.24 The method of embodiment 7.23, wherein the partial
nucleotide sequence reads are about 20 base pairs or more.
[1028] 7.25 The method of any one of embodiments 7 to 7.24,
comprising determining partial nucleotide sequence reads of the
nucleic acid from the test sample.
[1029] 7.26 The method of embodiment 7.25, which partial nucleotide
sequence reads are determined using a method comprising a massively
parallel sequencing (MPS) process or a nanopore process, or a
massively parallel sequencing (MPS) process and a nanopore
process.
[1030] 7.27 The method of embodiment 7.25 or 7.26, comprising
mapping the partial nucleotide sequence reads to genomic sections
of the reference genome.
[1031] 7.28 The method of any one of embodiments 7 to 7.27,
comprising isolating the nucleic acid from the test sample.
[1032] 7.29 The method of any one of embodiments 7 to 7.28,
comprising obtaining the test sample.
[1033] 7.30 The method of embodiment 7.29, wherein the test sample
is obtained from a pregnant female.
[1034] 7.31 The method of any one of embodiments 7 to 7.30, wherein
the test sample is blood plasma, blood serum or urine.
[1035] 8. The method of any one of embodiments 4 to 7.31, wherein
the genetic variation is a nucleic acid sequence variation.
[1036] 8.1 The method of embodiment 8, wherein the nucleotide
sequences of the partial nucleotide sequence reads are compared to
a reference.
[1037] 9. The method of embodiment 8.1, wherein a sequence match or
mismatch is determined.
[1038] 10. The method of any one of embodiments 4 to 6.1, wherein
the genetic variation is a nucleic acid copy number variation.
[1039] 11. The method of embodiment 10, wherein the method further
comprises after the mapping of partial nucleotide sequence reads,
counting the number of partial nucleotide sequence reads mapped to
each reference genome section.
[1040] 12. The method of embodiment 11, wherein the number of
counts of the partial nucleotide sequence reads, or derivative
thereof, are compared to a reference.
[1041] 13. The method of any one of embodiments 4 to 12, wherein
the subject is a fetus and the sample is from a pregnant female
that bears a fetus.
[1042] 14. The method of embodiment 13, wherein the sample
comprises circulating, cell-free nucleic acid.
[1043] 15. The method of embodiment 14, wherein the sample nucleic
acid comprises maternal and fetal nucleic acid.
[1044] 16. The method of any one of embodiments 1 to 15, wherein
the sample is blood.
[1045] 16.1 The method of any one of embodiments 1 to 15, wherein
the sample is urine.
[1046] 16.2 The method of any one of embodiments 1 to 15, wherein
the sample is saliva.
[1047] 16.3 The method of any one of embodiments 1 to 15, wherein
the sample is a cervical swab.
[1048] 17. The method of any one of embodiments 1 to 15, wherein
the sample is serum.
[1049] 18. The method of any one of embodiments 1 to 15, wherein
the sample is plasma.
[1050] 19. The method of any one of embodiments 1 to 18, wherein
the partial nucleotide sequence reads comprise relative positional
information for one or more nucleobase species.
[1051] 20. The method of embodiment 19, wherein the partial
nucleotide sequence reads contain relative positional information
for adenine.
[1052] 21. The method of embodiment 19 or 20, wherein the partial
nucleotide sequence reads contain relative positional information
for guanine.
[1053] 22. The method of any one of embodiments 19 to 21, wherein
the partial nucleotide sequence reads contain relative positional
information for thymine.
[1054] 23. The method of any one of embodiments 19 to 22, wherein
the partial nucleotide sequence reads contain relative positional
information for cytosine.
[1055] 24. The method of any one of embodiments 19 to 23, wherein
the partial nucleotide sequence reads contain relative positional
information for methyl-cytosine.
[1056] 25. The method of embodiment 19, wherein the partial
nucleotide sequence reads contain relative positional information
for two nucleobase species selected from the group consisting of
adenine, guanine, thymine, cytosine or methyl-cytosine.
[1057] 26. The method of embodiment 19, wherein the partial
nucleotide sequence reads contain relative positional information
for three nucleobase species selected from the group consisting of
adenine, guanine, thymine, cytosine or methyl-cytosine.
[1058] 27. The method of embodiment 19, wherein the one or more
nucleobase species comprise one or more detectable labels.
[1059] 28. The method of embodiment 19, wherein the partial
nucleotide sequence reads contain relative positional information
for sequences complementary to one or more hybridized probe
species.
[1060] 29. The method of embodiment 28, wherein the one or more
hybridized probe species comprise one or more detectable
labels.
[1061] 30. The method of any one of embodiments 1 to 18, wherein
the nucleobase class is purine.
[1062] 31. The method of any one of embodiments 1 to 18, wherein
the nucleobase class is pyrimidine.
[1063] 32. The method of embodiment 30 or 31, wherein purines are
distinguished from pyrimidines in the partial nucleotide sequence
reads.
[1064] 33. The method of any one of embodiments 1 to 18, wherein
the sample nucleic acid comprises single stranded nucleic acid.
[1065] 34. The method of any one of embodiments 1 to 18, wherein
the sample nucleic acid comprises double stranded nucleic acid.
[1066] 35. The method of embodiment 34, wherein the nucleobase
class is a nucleobase pair species in a duplex nucleic acid.
[1067] 36. The method of any one of embodiments 1 to 35, wherein
the obtaining partial nucleotide sequence reads includes subjecting
the sample nucleic acid to a sequencing process using a sequencing
device.
[1068] 37. The method of embodiment 36, wherein the partial
nucleotide sequence reads are obtained by nanopore sequencing.
[1069] 38. The method of embodiment 36, wherein the partial
nucleotide sequence reads are obtained by reversible
terminator-based sequencing.
[1070] 39. The method of embodiment 36, wherein the partial
nucleotide sequence reads are obtained by pyrosequencing.
[1071] 40. The method of embodiment 36, wherein the partial
nucleotide sequence reads are obtained by real time sequencing.
[1072] 41. The method of embodiment 36, wherein the partial
nucleotide sequence reads are obtained by oligonucleotide probe
ligation sequencing.
[1073] 41.1 The method of embodiment 36, wherein the partial
nucleotide sequence reads are obtained by sequencing by
hybridization.
[1074] 42. The method of any one of embodiments 1 to 41, wherein
the partial nucleotide sequence reads comprise a number of discrete
position identities sufficient to map to a reference genome
section.
[1075] 43. The method of any one of embodiments 1 to 41, wherein
the partial nucleotide sequence read is of sufficient length to map
to a reference genome section.
[1076] 44. The method of embodiment 43, wherein the partial
nucleotide sequence read length is at least about 36
nucleobases.
[1077] 44.1 The method of embodiment 43, wherein the partial
nucleotide sequence read length is at least about 72
nucleobases.
[1078] 45. The method of embodiment 43, wherein the partial
nucleotide sequence read length is at least about 108
nucleobases.
[1079] 46. The method of any one of embodiments 1 to 45, wherein
the nucleobase gaps in each partial nucleotide sequence read
independently are about 1 to about 100 sequential nucleobases.
[1080] 47. The method of any one of embodiments 1 to 46, which
comprises obtaining full nucleotide sequence reads, which
nucleotide sequence reads do not contain nucleobase gaps between
identified nucleobases or a nucleobase class comprising a subset of
nucleobases present in the sample nucleic acid.
[1081] 48. The method of any one of embodiments 1 to 3, wherein the
fetal aneuploidy is trisomy 13.
[1082] 49. The method of any one of embodiments 1 to 3, wherein the
fetal aneuploidy is trisomy 18.
[1083] 50. The method of any one of embodiments 1 to 3, wherein the
fetal aneuploidy is trisomy 21.
[1084] 51. The method of any one of embodiments 4 to 6.1, wherein
the genetic variation is associated with a medical condition.
[1085] 52. The method of embodiment 51, wherein the medical
condition is cancer.
[1086] 53. The method of embodiment 51, wherein the medical
condition is an aneuploidy.
[1087] 54. A computer program product, comprising a computer usable
medium having a computer readable program code embodied therein,
the computer readable program code comprising distinct software
modules comprising a sequence receiving module, a logic processing
module, and a data display organization module, the computer
readable program code adapted to be executed to implement a method
for identifying the presence or absence of a fetal aneuploidy, the
method comprising: [1088] (a) obtaining, by the sequence receiving
module, partial nucleotide sequence reads from a sample comprising
circulating, cell-free nucleic acid from a pregnant female, wherein
at least some partial nucleotide sequence reads comprise: [1089] i)
multiple nucleobase gaps between identified nucleobases, or [1090]
ii) one or more nucleobase classes, wherein each nucleobase class
comprises a subset of nucleobases present in the sample nucleic
acid, or combination of (i) and (ii); [1091] (b) receiving, by the
logic processing module, the partial nucleotide sequence reads;
[1092] (c) mapping, by the logic processing module, the partial
nucleotide sequence reads to reference genome sections; [1093] (d)
counting, by the logic processing module, the number of partial
nucleotide sequence reads mapped to each reference genome section;
[1094] (e) comparing, by the logic processing module, the number of
counts of the partial nucleotide sequence reads, or derivative
thereof, to a reference, or portion thereof, thereby making a
comparison; [1095] (f) providing, by the logic processing module,
an outcome determinative of the presence or absence of a fetal
aneuploidy based on the comparison; and [1096] (g) organizing, by
the data display organization module in response to being
determined by the logic processing module, a data display
indicating the presence or absence of a fetal aneuploidy.
[1097] 55. An apparatus, comprising memory in which a computer
program product of embodiment 54 is stored.
[1098] 56. The apparatus of embodiment 55, which comprises a
processor that implements one or more functions of the computer
program product specified in embodiment 54.
[1099] 57. A system comprising a nucleic acid sequencing apparatus
and a processing apparatus, wherein the sequencing apparatus
obtains sequence reads from a sample, and the processing apparatus
obtains the sequence reads from the sequencing apparatus and
carries out a method comprising: [1100] (a) mapping partial
nucleotide sequence reads from the sequencing apparatus that have
been obtained from a sample comprising circulating, cell-free
nucleic acid from a pregnant female, to reference genome sections,
wherein at least some partial nucleotide sequence reads comprise:
[1101] i) multiple nucleobase gaps between identified nucleobases,
or [1102] ii) one or more nucleobase classes, wherein each
nucleobase class comprises a subset of nucleobases present in the
sample nucleic acid, or combination of (i) and (ii), [1103] (b)
counting the number of partial nucleotide sequence reads mapped to
each reference genome section, [1104] (c) comparing the number of
counts of the partial nucleotide sequence reads mapped in (b), or
derivative thereof, to a reference, or portion thereof, thereby
making a comparison, and [1105] (d) determining the presence or
absence of a fetal aneuploidy based on the comparison.
[1106] 58. A system comprising one or more processors and memory,
[1107] which memory comprises instructions executable by the one or
more processors and which memory comprises counts of partial
nucleotide sequence reads mapped to genomic sections of a reference
genome, which partial nucleotide sequence reads are reads of
circulating cell-free nucleic acid from a test sample, wherein at
least some of the partial nucleotide sequence reads comprise:
[1108] i) multiple nucleobase gaps between identified nucleobases,
or [1109] ii) one or more nucleobase classes, wherein each
nucleobase class comprises a subset of nucleobases present in the
sample nucleic acid, or a combination of (i) and (ii); and [1110]
which instructions executable by the one or more processors are
configured to: [1111] (a) normalize the counts of the partial
nucleotide sequence reads, thereby providing normalized counts, and
[1112] (b) detect the presence or absence of a genetic variation
based on the normalized counts.
[1113] 59. An apparatus comprising one or more processors and
memory, [1114] which memory comprises instructions executable by
the one or more processors and which memory comprises counts of
partial nucleotide sequence reads mapped to genomic sections of a
reference genome, which partial nucleotide sequence reads are reads
of circulating cell-free nucleic acid from a test sample, wherein
at least some of the partial nucleotide sequence reads comprise:
[1115] i) multiple nucleobase gaps between identified nucleobases,
or [1116] ii) one or more nucleobase classes, wherein each
nucleobase class comprises a subset of nucleobases present in the
sample nucleic acid, or [1117] a combination of (i) and (ii); and
[1118] which instructions executable by the one or more processors
are configured to: [1119] (a) normalize the counts of the partial
nucleotide sequence reads, thereby providing normalized counts, and
[1120] (b) detect the presence or absence of a genetic variation
based on the normalized counts.
[1121] 60. A computer program product tangibly embodied on a
computer-readable medium, comprising instructions that when
executed by one or more processors are configured to: [1122] (a)
access counts of partial nucleotide sequence reads mapped to
genomic sections of a reference genome, which partial nucleotide
sequence reads are reads of circulating cell-free nucleic acid from
a test sample, wherein at least some of the partial nucleotide
sequence reads comprise: [1123] i) multiple nucleobase gaps between
identified nucleobases, or [1124] ii) one or more nucleobase
classes, wherein each nucleobase class comprises a subset of
nucleobases present in the sample nucleic acid, or [1125] a
combination of (i) and (ii), [1126] (b) normalize the counts of the
partial nucleotide sequence reads, thereby providing normalized
counts, and [1127] (c) detect the presence or absence of a genetic
variation based on the normalized counts.
[1128] The entirety of each patent, patent application, publication
and document referenced herein hereby is incorporated by reference.
Citation of the above patents, patent applications, publications
and documents is not an admission that any of the foregoing is
pertinent prior art, nor does it constitute any admission as to the
contents or date of these publications or documents.
[1129] Modifications may be made to the foregoing without departing
from the basic aspects of the technology. Although the technology
has been described in substantial detail with reference to one or
more specific embodiments, those of ordinary skill in the art will
recognize that changes may be made to the embodiments specifically
disclosed in this application, yet these modifications and
improvements are within the scope and spirit of the technology.
[1130] The technology illustratively described herein suitably may
be practiced in the absence of any element(s) not specifically
disclosed herein. Thus, for example, in each instance herein any of
the terms "comprising," "consisting essentially of," and
"consisting of" may be replaced with either of the other two terms.
The terms and expressions which have been employed are used as
terms of description and not of limitation, and use of such terms
and expressions do not exclude any equivalents of the features
shown and described or portions thereof, and various modifications
are possible within the scope of the technology claimed. The term
"a" or "an" can refer to one of or a plurality of the elements it
modifies (e.g., "a reagent" can mean one or more reagents) unless
it is contextually clear either one of the elements or more than
one of the elements is described. The term "about" as used herein
refers to a value within 10% of the underlying parameter (i.e.,
plus or minus 10%), and use of the term "about" at the beginning of
a string of values modifies each of the values (i.e., "about 1, 2
and 3" refers to about 1, about 2 and about 3). For example, a
weight of "about 100 grams" can include weights between 90 grams
and 110 grams. Further, when a listing of values is described
herein (e.g., about 50%, 60%, 70%, 80%, 85% or 86%) the listing
includes all intermediate and fractional values thereof (e.g., 54%,
85.4%). Thus, it should be understood that although the present
technology has been specifically disclosed by representative
embodiments and optional features, modification and variation of
the concepts herein disclosed may be resorted to by those skilled
in the art, and such modifications and variations are considered
within the scope of this technology.
[1131] Certain embodiments of the technology are set forth in the
claim(s) that follow(s).
* * * * *
References