U.S. patent application number 17/160191 was filed with the patent office on 2021-07-08 for methods and systems for generating a virtual progeny genome.
The applicant listed for this patent is Ancestry.com DNA, LLC. Invention is credited to Lee M. Silver.
Application Number | 20210210161 17/160191 |
Document ID | / |
Family ID | 1000005462432 |
Filed Date | 2021-07-08 |
United States Patent
Application |
20210210161 |
Kind Code |
A1 |
Silver; Lee M. |
July 8, 2021 |
METHODS AND SYSTEMS FOR GENERATING A VIRTUAL PROGENY GENOME
Abstract
Methods and systems for assessing the probabilities of the
expression of one or more traits in progeny are described.
Inventors: |
Silver; Lee M.; (New York,
NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ancestry.com DNA, LLC |
Lehi |
UT |
US |
|
|
Family ID: |
1000005462432 |
Appl. No.: |
17/160191 |
Filed: |
January 27, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14293541 |
Jun 2, 2014 |
10916332 |
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17160191 |
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12908636 |
Oct 20, 2010 |
8805620 |
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14293541 |
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61253108 |
Oct 20, 2009 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 50/30 20180101;
G16B 10/00 20190201; G16B 20/00 20190201; C12Q 1/6827 20130101 |
International
Class: |
G16B 20/00 20060101
G16B020/00; G16H 50/30 20060101 G16H050/30; C12Q 1/6827 20060101
C12Q001/6827 |
Claims
1. A method for recommending a selection of a gamete donor from a
plurality of gamete donors for a target individual, the gamete
donors being either sperm donors or egg donors and the target
individual being an individual of opposite sex to the gamete
donors, the method comprising: (a) obtaining a first genome profile
containing first genotypes in a sequence of genetic loci, the first
genome profile generated by a first DNA sequencer scanning a
biological sample from the target individual; (b) generating a
first haplopath from the first genome profile of the target
individual, wherein the first haplopath represents a first sequence
of haploid alleles corresponding to the sequence of genetic loci;
(c) selecting each allele in the first sequence of haploid alleles,
wherein said selecting comprises: (i) retrieving a first linkage
probability that is determined based on linkage disequilibrium
within a first population to which the target individual belongs,
and (ii) selecting a first particular allele for the first sequence
of haploid alleles at a first particular locus based on a first
preceding allele selected for a first preceding locus along the
sequence of genetic loci and the first linkage probability between
the first preceding allele and the particular allele; (d) selecting
a subset of gamete donors from the plurality of gamete donors; (e)
for each potential gamete donor of the subset of gamete donors:
(e1) obtaining a second genome profile containing second genotypes
in the sequence of genetic loci, the second genome profile
generated by a second DNA sequencer scanning a biological sample
from the potential gamete donor; (e2) generating a second haplopath
from the second genome profile of the potential gamete donor,
wherein the second haplopath represents a second sequence of
haploid alleles correspond to the sequence of genetic loci; (e3)
selecting each allele in the second sequence of haploid alleles,
wherein said selecting comprises: (i) retrieving a second linkage
probability that is determined based on linkage disequilibrium
within a second population to which the potential gamete donor
belongs; and (ii) selecting a second particular allele for the
second sequence of haploid alleles at a second particular locus
based on a second preceding allele selected for a second preceding
locus along the sequence of genetic loci and the second linkage
probability between the second preceding allele and the second
particular allele; (e4) forming a diploid genotype profile by
matching the first sequence of haploid alleles and the second
sequence of haploid alleles in accordance with the sequence of
genetic loci, the diploid genotype profile corresponding to the
sequence of genetic loci and having a diploid genotype at each
locus of the sequence of genetic loci, each diploid genotype at
each locus having a first haploid allele from the first sequence of
haploid alleles and a second haploid allele from the second
sequence of haploid alleles; (e5) comparing one or more of the
diploid genotypes of the diploid genotype profile to one or more
databases of genotype-phenotype associations to determine a
likelihood of expression of a set of phenotypes for the potential
progeny having the diploid genotype profile; (e6) repeating steps
(b), (c), (e2), (e3), (e4), and (e5) to generate a plurality of
diploid genotype profiles and associated likelihoods of expressing
the set of phenotypes; and (e7) based on results of step (e6),
generating probabilities or probability distributions of progenies
of the target individual and the potential gamete donor associating
with expressing the set of phenotypes; (f) selecting a gamete donor
from the subset of gamete donors as a recommendation based on the
generated probabilities of progenies of the target individual and
the subset of gamete donors.
2. The method of claim 1, wherein selecting the gamete donor from
the subset of gamete donors as the recommendation further
comprising: selecting one or more gamete donors including the
gamete donor as recommendation from the subset of gamete donors by
subtracting potential gamete donors that are not compatible with
the target individual.
3. The method of claim 1, further comprising: presenting, to the
target individual, information for one or more gamete donors of the
plurality of gamete donors, the information based on probability of
exhibiting one or more traits.
4. The method of claim 3, wherein the one or more traits are
preselected by the target individual.
5. The method of claim 1, wherein: the first genome profile of the
target individual includes phased DNA sequences and the second
genome profile includes phased DNA sequences; generating the first
haplopath includes simulating recombination between the phased DNA
sequences from the first genome profile; and generating the second
haplopath includes simulating recombination between the phased DNA
sequences from the second genome profile.
6. The method of claim 1, wherein, prior to step (a): variants are
identified, the variants comprising single base substitutions,
insertion/deletions, copy number polymorphisms and combinations
thereof at the sequence of genetic loci from DNA samples obtained
from the target individual and each potential gamete donor of the
subset of gamete donors.
7. The method of claim 6, further comprising: for the target
individual and for each potential gamete donor of the subset of
gamete donors, comparing the identified variants to a plurality of
predetermined genomic sequences of haplotypes to identify
haplotypes present in the DNA samples obtained from the target
individual and each potential gamete donor; and constructing the
first and second genome profiles, wherein each of the genome
profiles comprises the identified haplotypes present and
association frequencies of consecutive haplotypes,
respectively.
8. The method of claim 1, further comprising: repeating steps (b),
(c), (e2), (e3), (e4), and (e5) a sufficient number of times to
generate a stable estimate of the probabilities or probability
distributions of progenies of the target individual and the
potential gamete donor associating with expressing the set of
phenotypes.
9. A non-transitory computer readable medium for storing computer
code comprising instructions for recommending a selection of a
gamete donor from a plurality of gamete donors for a target
individual, the gamete donors being either sperm donors or egg
donors and the target individual being an individual of opposite
sex to the gamete donors, the instructions, when executed by one or
more computer processors, cause the one or more computer processors
to perform steps comprising: (a) obtaining a first genome profile
containing first genotypes in a sequence of genetic loci, the first
genome profile generated by a first DNA sequencer scanning a
biological sample from the target individual; (b) generating a
first haplopath from the first genome profile of the target
individual, wherein the first haplopath represents a first sequence
of haploid alleles corresponding to the sequence of genetic loci;
(c) selecting each allele in the first sequence of haploid alleles,
wherein said selecting comprises: (i) retrieving a first linkage
probability that is determined based on linkage disequilibrium
within a first population to which the target individual belongs,
and (ii) selecting a first particular allele for the first sequence
of haploid alleles at a first particular locus based on a first
preceding allele selected for a first preceding locus along the
sequence of genetic loci and the first linkage probability between
the first preceding allele and the first particular allele; (d)
selecting a subset of gamete donors from the plurality of gamete
donors; (e) for each potential gamete donor of the subset of gamete
donors: (e1) obtaining a second genome profile containing second
genotypes in the sequence of genetic loci, the second genome
profile generated by a second DNA sequencer scanning a biological
sample from the potential gamete donor; (e2) generating a second
haplopath from the second genome profile of the potential gamete
donor, wherein the second haplopath represents a second sequence of
haploid alleles correspond to the sequence of genetic loci; (e3)
selecting each allele in the second sequence of haploid alleles,
wherein said selecting comprises: (i) retrieving a second linkage
probability that is determined based on linkage disequilibrium
within a second population to which the potential gamete donor
belongs; and (ii) selecting a particular allele for the second
sequence of haploid alleles at a second particular locus based on a
second preceding allele selected for a second preceding locus along
the sequence of genetic loci and the second linkage probability
between the second preceding allele and the second particular
allele; (e4) forming a diploid genotype profile by matching the
first sequence of haploid alleles and the second sequence of
haploid alleles in accordance with the sequence of genetic loci,
the diploid genotype profile corresponding to the sequence of
genetic loci and having a diploid genotype at each locus of the
sequence of genetic loci, each diploid genotype at each locus
having a first haploid allele from the first sequence of haploid
alleles and a second haploid allele from the second sequence of
haploid alleles; (e5) comparing one or more of the diploid
genotypes of the diploid genotype profile to one or more databases
of genotype-phenotype associations to determine a likelihood of
expression of a set of phenotypes for the potential progeny having
the diploid genotype profile; (e6) repeating steps (b), (c), (e2),
(e3), (e4), and (e5) to generate a plurality of diploid genotype
profiles and associated likelihoods of expressing the set of
phenotypes; and (e7) based on results of repeating step (e6)
generating probabilities or probability distributions of progenies
of the target individual and the potential gamete donor associating
with expressing the set of phenotypes; (f) selecting a gamete donor
from the subset of gamete donors as a recommendation based on the
generated probabilities of progenies of the target individual and
the subset of gamete donors.
10. The computer readable medium of claim 9, wherein selecting the
gamete donor from the subset of gamete donors as the recommendation
further comprising: selecting one or more of gamete donors
including the gamete donor as recommendation from the subset of
gamete donors by subtracting potential gamete donors that are not
compatible with the target individual.
11. The computer readable medium of claim 9, further comprising:
presenting, to the target individual, information for one or more
gamete donors of the plurality of gamete donors, the information
based on probability of exhibiting one or more traits.
12. The computer readable medium of claim 11, wherein the one or
more traits are preselected by the target individual.
13. The computer readable medium of claim 9, wherein: the first
genome profile of the target individual includes phased DNA
sequences and the second genome profile includes phased DNA
sequences; generating the first haplopath includes simulating
recombination between the phased DNA sequences from the first
genome profile; and generating the second haplopath includes
simulating recombination between the phased DNA sequences from the
second genome profile.
14. The computer readable medium of claim 9, wherein, prior to step
(a): variants are identified, the variants comprising single base
substitutions, insertion/deletions, copy number polymorphisms and
combinations thereof at the sequence of genetic loci from DNA
samples obtained from the target individual and each potential
gamete donor of the subset of gamete donors.
15. The computer readable medium of claim 14, further comprising:
for the target individual and for each potential gamete donor of
the subset of gamete donors, comparing the identified variants to a
plurality of predetermined genomic sequences of haplotypes to
identify haplotypes present in the DNA samples obtained from the
target individual and each potential gamete donor; and using the
processors, constructing the first and second genome profiles,
wherein each of the genome profiles comprises the identified
haplotypes present and association frequencies of consecutive
haplotypes, respectively.
16. The computer readable medium of claim 9, further comprising:
repeating steps (b), (c), (e2), (e3), (e4), and (e5) a sufficient
number of times to generate a stable estimate of the probabilities
or probability distributions of progenies of the target individual
and the potential gamete donor associating with expressing the set
of phenotypes.
17. A system comprising: one or more computer processors; and
memory for storing computer code comprising instructions for
performing genetic screening for recommending a selection of a
gamete donor from a plurality of gamete donors for a target
individual, the gamete donors being either sperm donors or egg
donors and the target individual being an individual of opposite
sex to the gamete donors, the instructions, when executed by the
one or more computer processors, cause the one or more computer
processors to perform steps comprising: (a) obtaining a first
genome profile containing first genotypes in a sequence of genetic
loci, the first genome profile generated by a first DNA sequencer
scanning a biological sample from the target individual; (b)
generating a first haplopath from the first genome profile of the
target individual, wherein the first haplopath represents a first
sequence of haploid alleles corresponding to the sequence of
genetic loci; (c) selecting each allele in the first sequence of
haploid alleles, wherein said selecting comprises: (i) retrieving a
first linkage probability that is determined based on linkage
disequilibrium within a first population to which the target
individual belongs, and (ii) selecting a first particular allele
for the first sequence of haploid alleles at a particular locus
based on a first preceding allele selected for a first preceding
locus along the sequence of genetic loci and the first linkage
probability between the first preceding allele and the first
particular allele; (d) selecting a subset of gamete donors from the
plurality of gamete donors; (e) for each potential gamete donor of
the subset of gamete donors: (e1) obtaining a second genome profile
containing second genotypes in the sequence of genetic loci, the
second genome profile generated by a second DNA sequencer scanning
a biological sample from the potential gamete donor; (e2)
generating a second haplopath from the second genome profile of the
potential gamete donor, wherein the second haplopath represents a
second sequence of haploid alleles correspond to the sequence of
genetic loci; (e3) selecting each allele in the second sequence of
haploid alleles, wherein said selecting comprises: (i) retrieving a
second linkage probability that is determined based on linkage
disequilibrium within a second population to which the potential
gamete donor belongs; and (ii) selecting a second particular allele
for the second sequence of haploid alleles at a second particular
locus based on a second preceding allele selected for a second
preceding locus along the sequence of genetic loci and the second
linkage probability between the second preceding allele and the
second particular allele; (e4) forming a diploid genotype profile
by matching the first sequence of haploid alleles and the second
sequence of haploid alleles in accordance with the sequence of
genetic loci, the diploid genotype profile corresponding to the
sequence of genetic loci and having a diploid genotype at each
locus of the sequence of genetic loci, each diploid genotype at
each locus having a first haploid allele from the first sequence of
haploid alleles and a second haploid allele from the second
sequence of haploid alleles; (e5) comparing one of more of the
diploid genotypes of the diploid genotype profile to one or more
databases of genotype-phenotype associations to determine a
likelihood of expression of a set of phenotypes for the potential
progeny having the diploid genotype profile; (e6) repeating steps
(b), (c), (e2), (e3), (e4), and (e5) to generate a plurality of
diploid genotype profiles and associated likelihoods of expressing
the set of phenotypes; and (e7) based on results of repeating step
(e6), generating probabilities or probability distributions of
progenies of the female individual and the potential gamete donor
associating with expressing the set of phenotypes; (f) selecting a
gamete donor from the subset of gamete donors as a recommendation
based on the generated probabilities of progenies of the target
individual and the subset of gamete donors.
18. The system of claim 17, wherein selecting the gamete donor from
the subset of gamete donors as a recommendation further comprises:
selecting one or more of gamete donors including the gamete donor
as the recommendation from the subset of gamete donors by
subtracting potential gamete donors that are not compatible with
the target individual.
19. The system of claim 17, wherein the steps further comprising:
presenting, to the target individual, information for one or more
gamete donors of the plurality of gamete donors, the information
based on probability of exhibiting one or more traits.
20. The system of claim 17, wherein: the first genome profile of
the target individual includes phased DNA sequences and the second
genome profile includes phased DNA sequences; and, wherein the
steps further comprising: generating the first haplopath includes
simulating recombination between the phased DNA sequences from the
first genome profile; and generating the second haplopath includes
simulating recombination between the phased DNA sequences from the
second genome profile.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 14/293,541, filed on Jun. 2, 2014, which is a
continuation of U.S. patent application Ser. No. 12/908,636 (now
U.S. Pat. No. 8,805,620), filed Oct. 20, 2010, which claims
priority to U.S. Provisional Patent Application No. 61/253,108,
filed Oct. 20, 2009, each of which is hereby incorporated by
reference in its entirety for all purposes.
BACKGROUND OF THE INVENTION
[0002] In 1866, Mendel described the rules of probability that
govern the inheritance of simple genetic traits. He demonstrated
that individuals who show no evidence of a disease can still be
silent "carriers" of a disease mutation. The disease only appears
in a child who inherits two defective copies of a particular gene
from two parents who are carriers. Diseases and other traits
inherited in this manner are referred to as Mendelian. Thousands of
Mendelian diseases of metabolism and other physiological functions
have been described in the medical literature.
[0003] Although each individual Mendelian disease mutation is
relatively rare, they are cumulatively so common that nearly every
human being is a carrier for at least one. Carrier testing can
identify two potential parents who have the same mutation and thus
have a 25% likelihood of transmitting the corresponding disease to
their child.
[0004] The vast majority of heritable attributes that distinguish
one person from another in morphology, physiology, disease
resistance and susceptibility, and mental function result from
complex interactions among multiple genes and non-gene loci.
Therefore, in most cases, the likelihood that two healthy parents
will have a child with genetically-influenced health problems
cannot be determined by simply comparing the carrier status of each
individual since carrier status has no meaning in the context of a
complex genetic trait.
[0005] Over the last decade, a pair of conceptual and technological
breakthroughs has revolutionized the practice and potential of
human genetic analysis. The conceptual breakthrough emerged from
the discovery that most genetic variations in the global human
population are confined to a limited number of chromosomal
positions where one of two letters in the DNA alphabet can occur.
These variant genomic positions are called single nucleotide
polymorphism ("SNP") loci.
[0006] The SNP conceptual breakthrough alone would not have been
enough to transform human genetics if the process of determining
DNA genotypes remained as tedious as it had been just a few years
earlier. But high complexity DNA microarrays allowed the
development of a technology for cheaply screening increasingly
large numbers of SNPs or CNPs at ever-lower costs. A current
state-of-the-art DNA microarray can assay over two million
genotypes in an individual human genome.
[0007] Advances in DNA microarray design have enabled the detection
of many types of characterized genetic variants. Whether simple or
complex, most types of genetic variants can be defined in molecular
terms as unique SNPs or CNPs for the purpose of analysis.
[0008] The ultimate description of a genome is its two 3 billion
base pair long DNA sequences. The cost of obtaining a complete
personal genome sequence is dropping rapidly. Scientists predict
that it will become affordable to average consumers within a few
years.
[0009] High complexity DNA microarray technologies, high throughput
whole genome sequencing, and accompanying information technologies
have revolutionized the field of human genetics, with extraordinary
advances in understanding the genetic basis for complex traits and
an enormous depth of public-access genetic datasets that increase
in size daily.
[0010] Among other recent advances, scientists can now use
cost-effective tools to analyze a patient's genome and predict
susceptibility to thousands of medical conditions, including mental
illness, neurological diseases, cancer, stroke and heart
disease.
[0011] While progress has been made in developing computational
tools that use information from an individual's genome to predict
the likelihood of disease for that person, these tools cannot be
applied to the pre-conception prediction of disease in a person's
child. Thus, there is a need for methods of assessing the
inheritance of such complex attributes prior to, or in place of,
conception.
SUMMARY OF THE INVENTION
[0012] A pre-conception method is provided herein for predicting
the likelihood that a hypothetical child of any two persons, of
opposite or same sex, who may or may not be fertile, will express
any trait or disease that is subject to genetic influences that
have been previously characterized, completely or partially.
[0013] Based on an application of formal rules of genetics to a
pair of personal genome profile or whole genome sequences, the
methods allow for the simulation of the generation of multiple
VirtualGametes, each of which contains a single allele from each
genotype in combinatorial frequencies estimated for naturally
produced sperm and eggs.
[0014] The simplest form of VirtualGamete production uses Mendel's
Law of independent assortment with a random number generator to
choose one copy of each genetic locus independently of others for
incorporation into a haploid genome profile. Accuracy and
resolution is augmented with high complexity personal genome
profiles that can be used to provide phasing information and
further genotype imputation.
[0015] A uniquely derived VirtualGamete from one personal genome is
combined with a uniquely derived VirtualGamete from the second
personal genome to produce a discrete Virtual Progeny ("VP") genome
sampling containing two definitive copies of each locus. This
computational process is repeated a sufficient number of times to
obtain a reproducible Virtual Progeny genome.
[0016] A Virtual Progeny genome is a sufficiently large sampling of
discrete genomes that are cumulatively representative of the
likelihoods of alternative genotype combinations in a hypothetical
child conceived from the two progenitors.
[0017] Each discrete sampling that makes up a Virtual Progeny
genome is evaluated independently for the likelihood of expressing
any trait for which a genetic correlation has been previously
determined. The traits associated with each Virtual Progeny
sampling are normalized and combined to obtain a Virtual Progeny
phenome distribution.
[0018] Virtual Progeny may be evaluated for several thousand rare
Mendelian diseases as well as hundreds of more common complex
diseases such as diabetes, arteriosclerosis, autism, and
schizophrenia. Disease likelihood values for a Virtual Progeny
sampling can be calculated indirectly by protein modeling,
computational analysis, and geographical origin of chromosomal
regions, in addition to direct empirical associations.
[0019] Evaluation of Virtual Progeny will be informed by additional
factors not used on actual individuals, including the likelihood
and length of particular chromosome regions that could be inherited
in an identical, homozygous form from two heterozygous parents,
which correlates with an increased frequency of fetal loss and
children born with "birth defects."
[0020] Individuals and couples using a third party to reproduce can
compare virtual Progeny produced with each potential genetic
partner in a database.
[0021] Sperm donor and egg donor agencies can use Virtual Progeny
to screen out specific client/donor "pairings" that could give rise
to offspring with increased disease risk. Sperm donors who
represent a heightened genetic risk in specific combination with a
particular client will be removed from that client's pool of
potential donors. It is likely that these same donors will show no
evidence of risk in combination with most other clients. Since the
generation of Virtual Progeny does not depend on interpreting
genome profiles of the progenitors, it does not involve carrier
screening, and is not burdened with problems of false
stigmatization.
[0022] Same-sex and infertile couples will be able to simulate the
genomic profile of their "own" purely hypothetical child and match
this profile to the ones created virtually with selected
donors.
[0023] Individuals searching for a reproductive partner through a
matchmaking agency can use Virtual Progeny comparisons to
distinguish strong genetic matches based on desired and undesired
offspring traits.
[0024] Committed couples can use their virtual child's profile to
prevent serious conditions through IVF technology or to prepare for
their child's unique gifts and needs.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] The foregoing and other objects of the methods disclosed
herein, the various features thereof, as well as the methods and
compositions themselves, may be more fully understood from the
following description, when read together with the accompanying
drawings, in which:
[0026] FIG. 1A is a schematic illustration of an exemplary protocol
for creating Virtual Progeny where DNA samples from two individuals
(Jane Doe and John Smith) are processed to generate genome
scans.
[0027] FIG. 1B is a schematic illustration showing genome scans
expanded into genome profiles with additional imputed genotypes and
phasing information for genetic loci 01 through N.
[0028] FIG. 1C is a schematic illustration showing the genome
profile for John Smith, taking into account the profile for loci 01
through N.
[0029] FIG. 1D is a schematic illustration showing a random
VirtualGamete from each of Jane Doe and John Smith, the
VirtualGametes being combined to form a Virtual Progeny genome
sampling.
[0030] FIG. 1E is a schematic illustration showing the haplopaths
in rows 1 and 7 for John Smith.
[0031] FIG. 2 is a representation of Virtual Progeny results for
SRS and four partners in the gene region responsible for blue eye
color.
[0032] FIG. 3 is a schematic illustration of Virtual Progeny genome
profiles containing simulated genotype information at approximately
10,000 SNP loci across 82 million base pairs of chromosome 15. The
bottom row shows the location of every SNP locus in this region
(each index mark associated with a differential trait).
[0033] FIG. 4 is a summary of Virtual Progeny determinations from
four matings of a client ("SRS") mated to four potential partners
in which each SNP locus has two alleles designated, A and D. Six
classes of genotype results are possible for each SNPs or CNPs in a
Virtual Progeny genome. The number of SNPs or CNPs in each class is
shown in each mating.
[0034] FIGS. 5A-5F are a schematic representation showing various
diseases or traits associated with various SNPs or CNPs together
with the genotype results of simulated mating of one woman subject
to four potential partners.
[0035] FIGS. 6A and 6B are a graphical representation of 100
haplopaths (H1-H100) across 9 SNP loci numbered 64-72, generated
from 100 Monte Carlo simulations, where homologue index numbers are
(0,1).
DETAILED DESCRIPTION
[0036] All publications, patent applications, patents, and other
references mentioned herein are incorporated by reference in their
entirety. In addition, the materials, methods, and examples are
illustrative only and not intended to be limiting. Unless otherwise
defined, all technical and scientific terms used herein have the
same meaning as commonly understood by one of ordinary skill in the
art to which this invention belongs. Although methods and materials
similar or equivalent to those described herein can be used in the
practice or testing of the present invention, suitable methods and
materials are described below.
Definitions
[0037] For convenience, certain terms employed in the
specification, examples, and appended claims are collected here.
Unless defined otherwise, all technical and scientific terms used
herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this invention belongs. The
initial definition provided for a group or term herein applies to
that group or term throughout the present specification
individually or as part of another group, unless otherwise
indicated.
[0038] The articles "a" and "an" are used herein to refer to one or
to more than one (i.e., to at least one) of the grammatical object
of the article. By way of example, "an element" means one element
or more than one element.
[0039] The term "or" is used herein to mean, and is used
interchangeably with, the term "and/or," unless context clearly
indicates otherwise.
[0040] The term "about" is used herein to mean a value - or +20% of
a given numerical value. Thus, "about 60%" means a value of between
60-(20% of 60) and 60+(20% of 60) (i.e., between 48 and 70).
[0041] As used herein, "haploid cell" refers to a cell with a
haploid number (n) of chromosomes.
[0042] "Gametes", as used herein, are specialized haploid cells
(e.g., spermatozoa and oocytes) produced through the process of
meiosis and involved in sexual reproduction.
[0043] As used herein, "gametotype" refers to single copies with
one allele of each of one or more loci in the haploid genome of a
single gamete.
[0044] As used herein, an "autosome" is any chromosome exclusive of
the X and Y sex chromosomes.
[0045] As used herein, "diploid cell" has a homologous pair of each
of its autosomal chromosomes, and has two copies (2n) of each
autosomal genetic locus.
[0046] The term "chromosome", as used herein, refers to a molecule
of DNA with a sequence of basepairs that corresponds closely to a
defined chromosome reference sequence of the organism in
question.
[0047] The term "gene", as used herein, refers to a DNA sequence in
a chromosome that codes for a product (either RNA or its
translation product, a polypeptide) or otherwise plays a role in
the expression of said product. A gene contains a DNA sequence with
biological function. The biological function may be contained
within the structure of the RNA product or a coding region for a
polypeptide. The coding region includes a plurality of coding
segments ("exons") and intervening non-coding sequences ("introns")
between individual coding segments and non-coding regions preceding
and following the first and last coding regions respectively.
[0048] As used herein, "locus" refers to any segment of DNA
sequence defined by chromosomal coordinates in a reference genome
known to the art, irrespective of biological function. A DNA locus
can contain multiple genes or no genes; it can be a single base
pair or millions of base pairs.
[0049] As used herein, a "polymorphic locus" is a genomic locus at
which two or more alleles have been identified.
[0050] As used herein, an "allele" is one of two or more existing
genetic variants of a specific polymorphic genomic locus.
[0051] As used herein, a "single nucleotide polymorphism" or "SNP"
is a particular base position in the genome where alternative bases
are known to distinguish one individual from another. Most
categories of more complex genetic variants can be reduced for
analytical purposes to one or a few defining SNPs or CNPs.
[0052] As used herein, a "copy number variant" or "CNV" is a
deletion or duplication of a large block of genetic material that
exists in a population at a frequency less than 1%.
[0053] As used herein, a "copy number polymorphism" or "CNP" is a
deletion or duplication of a large block of genetic material that
exists in a population at a frequency of greater than 1%. Since a
CNV in one population can be a CNP in a second population, the two
terms can be used interchangeably
[0054] As used herein, "genotype" refers to the diploid combination
of alleles at a given genetic locus, or set of related loci, in a
given cell or organism. A homozygous subject carries two copies of
the same allele and a heterozygous subject carries two distinct
alleles. In the simplest case of a locus with two alleles "A" and
"a", three genotypes can be formed: A/A, A/a, and a/a.
[0055] As used herein, "genotyping" refers to any experimental,
computational, or observational protocol for distinguishing an
individual's genotype at one or more well-defined loci.
[0056] As used herein, a "haplotype" is a unique set of alleles at
separate loci that are normally grouped closely together on the
same DNA molecule, and are observed to be inherited as a group. A
haplotype can be defined by a set of specific alleles at each
defined polymorphic locus within a haploblock.
[0057] As used herein, a "haploblock" refers to a genomic region
that maintains genetic integrity over multiple generations and is
recognized by linkage disequilibrium within a population.
Haploblocks are defined empirically for a given population of
individuals.
[0058] As used herein, "linkage disequilibrium" is the non-random
association of alleles at two or more loci within a particular
population. Linkage disequilibrium is measured as a departure from
the null hypothesis of linkage equilibrium, where each allele at
one locus associates randomly with each allele at a second locus in
a population of individual genomes.
[0059] As used herein, a "genome" is the total genetic information
carried by an individual organism or cell, represented by the
complete DNA sequences of its chromosomes.
[0060] As used herein, a "genome profile" is a representative
subset of the total information contained within a genome. A genome
profile contains genotypes at a particular set of polymorphic
loci.
[0061] As used herein, a "personal genome profile", abbreviated
PGP, is the genome profile of a particular individual person.
[0062] As used herein, a genetic "trait" is a distinguishing
attribute of an individual, whose expression is fully or partially
influenced by an individual's genetic constitution.
[0063] As used herein, a "phenotype" is a class of alternative
traits which may be discrete or continuous.
[0064] As used herein, a "haplopath" is a haploid path laid out
along a defined region of a diploid genome by a single iteration of
a Monte Carlo simulation or a single chain generated through a
Markov process. A haplopath can be formed by starting at one end of
a personal chromosome or genome and walking from locus to locus,
choosing a single allele at each step based on available linkage
disequilibrium information, inter-locus allele association
coefficients, and formal rules of genetics that describe the
natural process of gamete production in a sexually reproducing
organism. A "haplopath" is generated through the application of
formal rules of genetics that describe the reduction of the diploid
genome into haploid genomes through the natural process of
meiosis.
[0065] As used herein, a "Virtual Gamete" is a single haplopath
that extends across an entire genome.
[0066] As used herein, a "Virtual Progeny genome sampling" is the
discrete genetic product of two Virtual Gametes.
[0067] As used herein, a "Virtual Progeny genome" is a collection
of discrete Virtual Progeny genome samplings, each generated by
combining two uniquely-derived random Virtual Gametes. In some
instances, a Virtual Progeny genome is represented as a probability
mass function over a sample space of all discrete genome states. In
some instances, a Virtual Progeny is an informed simulation of a
child or children that might result as a consequence of sexual
reproduction between two individuals.
[0068] As used herein, a "Virtual Progeny phenome" is a
multi-dimensional likelihood function representing the likelihood
and/or likely degree of expression of a set of one or more traits
from a complete Virtual Progeny genome. In some instances, a
Virtual Progeny phenome is represented as a probability mass
function over a sample space of discrete or continuous phenotypic
states. In some instances, a Virtual Progeny phenome is an informed
simulation of a child or children that might result as a
consequence of sexual reproduction between two individuals.
[0069] As used herein, "partner" includes a marriage partner,
sexual or reproductive partner, domestic partner, opposite-sex
partner, and same-sex partner.
[0070] The methods and compositions disclosed herein relate to
assessing the genotypes of individuals and the phenotypes
associated with particular genotypes of potential progeny from such
individuals. Generally, genome profiles from two individuals are
used to determine the probabilities that potential progeny from
such individuals will express certain traits, such as an increased
risk of disease. Such methods are referred to herein as "Virtual
Progeny assessment."
Virtual Progeny Assessment
[0071] Disclosed herein are methods of generating a library of
potential haploid gamete genomes from an individual diploid
subject. The methods comprise obtaining a genomic DNA sample from a
diploid subject. In addition, the presence or absence of one or
more nucleotide variants (e.g., SNPs or CNPs) are identified at one
or more loci of at least one pair of chromosomes of the genomic
DNA. In particular embodiments, a haploid gamete genome from a
sampling of the potential gamete pool is generated from the diploid
genome profile. In such instances, a sampling of a potential very
large number of gametes is performed rather than identifying each
gamete from a population (i.e., "pool") of gametes. These
identified nucleotide variants are compared to a plurality of
predetermined genomic sequences of haplotypes having predetermined
frequencies at predetermined loci to identify haplotypes present in
the genomic DNA.
[0072] In certain embodiments, the methods also entail the
construction of a diploid genome profile for the subject. In
certain embodiments, the genome profile comprises the identified
haplotypes and a linkage probability determined by the frequencies
of the haplotypes in the plurality of predetermined genomic
sequences. A haploid gamete genome for each potential gamete can be
generated from the diploid genome profile by generating a
combination of the identified haplotypes using the linkage
probability for each combination of the identified haplotypes.
[0073] Aspects of the methods and systems disclosed herein involve
generating a library of potential haploid gamete genomes from an
individual diploid subject. The methods comprise providing a
database having a plurality of predetermined genomic sequences. In
certain embodiments, a first proportion of the genomic sequences
comprises a first predetermined haplotype adjacent to a second
predetermined haplotype. Also, a second proportion of the genomic
sequences comprises the first predetermined haplotype adjacent to a
third predetermined haplotype. In particular embodiments, genomic
DNA sample from the subject is obtained and the presence or absence
of one or more nucleotide variants at one or more loci of at least
one pair of chromosomes of the genomic DNA is identified. The
identified nucleotide variants are compared to the database to
allow for identification of a plurality of sample haplotypes
present in the at least one pair of chromosomes. In certain
embodiments, the plurality of sample haplotypes comprises the first
haplotype adjacent to a wobble haplotype. In some embodiments, the
wobble haplotype is either the second haplotype or the third
haplotype.
[0074] The methods further entail a diploid genome profile that is
constructed for the at least one pair of chromosomes of the subject
from the identified sample haplotypes. The genome profile comprises
the first haplotype, the wobble haplotype, and a linkage
probability determined by the proportion of the predetermined
genomic sequences. The genomic sequences comprise the first
haplotype adjacent to the wobble haplotype. A haploid gamete genome
is generated for each potential gamete from the diploid genome
profile by linearly combining the first haplotype and the wobble
haplotype using the linkage probability. The diploid profiles
generated from the methods disclosed herein, in some embodiments,
comprise additional identified and additional linkage probabilities
determined by the proportion of the predetermined genomic sequences
that comprise the first predetermined haplotype adjacent to each
additional haplotype.
[0075] Furthermore, a method of selecting a potential sperm or
oocyte donor is further disclosed. Specifically, the donor genomic
DNA samples from potential sperm donors or potential oocyte donors
are obtained as well as a recipient genomic DNA sample from a
potential recipient. The DNA is analyzed to identify the presence
or absence of one or more nucleotide variants at one or more loci
of at least one pair of chromosomes of the donor genomic DNA
samples and the recipient genomic DNA sample. The identified
nucleotide variants are compared to a plurality of predetermined
genomic sequences of haplotypes having predetermined frequencies at
predetermined loci to identify haplotypes present in the donor
genomic DNA samples and the recipient genomic DNA sample.
[0076] Using this information, a donor diploid genome profile is
constructed for each potential donor. Each donor diploid genome
profile comprises the identified haplotypes in each donor genomic
DNA sample and a linkage probability determined by the frequencies
of the identified haplotypes in the plurality of predetermined
genomic sequences. In addition, a recipient diploid genome profile
for the potential recipient is constructed. As for the donor, the
recipient genome profile comprises the identified haplotypes (in
this case, the haplotypes in the recipient genomic DNA sample) and
a linkage probability determined by the frequencies of the
identified haplotypes in the plurality of predetermined genomic
sequences. Libraries are generated for donors and recipients, each
library comprising potential haploid gamete genomes from a diploid
genome profile. Each potential haploid gamete genome is generated
by combining the haplotypes identified in the respective genomic
DNA samples using the linkage probability for each combination of
the identified haplotypes. Each haploid gamete genome is
independently combined from each donor library with a second
haploid gamete genome from the recipient library to form a library
of diploid progeny genomes for each potential donor. The diploid
progeny genomes are compared to a database of disease-associated
genomes to assess the risk of disease of each potential progeny,
wherein a sperm donor or an oocyte donor is eliminated from
consideration if an increased risk of disease of the potential
progeny is determined.
[0077] FIGS. 1A-1F depict an exemplary method of the methods
disclosed herein. The exemplary methods shows how to create a
Virtual Progeny. As illustrated in FIG. 1A-1F, DNA samples from two
individuals are obtained. The samples are then processed by
performing genome scans to identify genotypes at genetic markers,
such as SNPs or CNPs, present in each individual's genomic DNA.
Identified genotypes can be used to expand a personal genome
profile by imputation of genotypes at non-processed genetic markers
and to determine haplotypes present within each individual's
genomic DNA. The likelihoods of association of sequential
haplotypes are retrieved from lookup tables generated by the
international HapMap project.
[0078] Using the identified haplotypes together with association
data, a Monte Carlo simulation is performed to generate haplopaths
that extend across each genome, resulting in a Virtual Gamete
population for each individual. Virtual Gamete from each individual
is combined to produce Virtual Progeny genome sampling, each of
which is evaluated for corresponding trait likelihood values. The
entire process is repeated a large number of times, each time
starting with a new Monte Carlo simulation of Virtual Gametes. An
integrated Virtual Progeny phenome likelihood distribution is
determined to assess the probability that potential progeny express
certain traits, such as increased risk of disease.
Genome Scans
[0079] As disclosed herein, the methods comprise obtaining a
genomic DNA sample from the subject. These methods of assessing
potential Virtual Progeny involves performing genome scans on
individuals. In certain embodiments, the individuals share a common
ancestry. Genome scans can be performed using any of a number of
known procedures. For example, a biologic sample from an individual
can first be obtained. Such biological samples include, but are not
limited to, a bodily fluid (such as urine, saliva, plasma, or
serum) or a tissue sample (such as a buccal tissue sample or buccal
cell). The biologic sample can then be used to perform a genome
scan using known methods. For example, DNA arrays can be used to
analyze at least a portion of the genomic sequence of the
individual. Exemplary DNA arrays include GeneChip Arrays, GenFlex
Tag arrays, and Genome-Wide Human SNP Array 6.0 (available from
Affymetrix, Santa Clara, Calif.).
[0080] In certain embodiments, whole or partial genome sequence
information is used to perform the genome scans. Such sequences can
be determined using standard sequencing methods including
chain-termination (Sanger dideoxynucleotide), dye-terminator
sequencing, and SOLiD.sup.T.sup..LAMBDA. sequencing (Applied
Biosystems). Whole genome sequences can be cut by restriction
enzymes or sheared (mechanically) into shorter fragments for
sequencing. DNA sequences can also be amplified using known methods
such as PCR and vector-based cloning methods (e.g., Escherichia
coli).
[0081] In some embodiments, at least a portion of an individual's
genetic material (e.g., DNA, RNA, mRNA, cDNA, other nucleotide
bases or derivative thereof) is scanned or sequenced using, e.g.,
conventional DNA sequencers or chip-based technologies, to identify
the presence or absence of one or more SNPs or copy number
polymorphisms ("CNPs") and their corresponding alleles.
[0082] A scanning step can involve scanning at least about 1,000
bases, at least about 5,000 bases, at least about 10,000 bases, at
least about 20,000 bases, at least about 50,000 bases, at least
about 100,000 bases, at least about 200,000 bases, at least about
500,000 bases, at least about 1,000,000 bases, at least about
2,000,000 bases, at least about 5,000,000 bases, at least about
10,000,000 bases, at least about 20,000,000 bases, at least about
50,000,000 bases, at least about 100,000,000 bases, at least about
200,000,000 bases, at least about 500,000,000 bases, at least about
1,000,000,000 bases, at least about 2,000,000,000 bases, or at
least 3,000,000,000 bases of an individual's genetic material.
[0083] In certain instances, nucleotide bases are scanned from a
first set of individuals (e.g., at least about 10 individuals, at
least about 20 individuals, at least about 30 individuals, at least
about 40 individuals, at least about 50 individuals, at least about
100 individuals, at least about 250 individuals, at least about 500
individuals, or more), and genetic variations between individuals
are identified. Genetic variation data generated from each
individual can be compared with genetic variation data generated
from other individuals in a first set of individuals to discover
genetic variations among the first group of individuals.
[0084] The variations identified in the first set of individuals
can be used in subsequent studies in which such variations are
analyzed to determine if they are associated with a
phenotype-of-interest. These variations can include, e.g., SNPs or
CNPs, common SNPs or CNPs, informative SNPs or CNPs, rare SNPs or
CNPs, deletions, insertions, or frameshift mutations. Such genetic
variations can be detected in, e.g., genomic DNA, RNA, mRNA, or
derivatives thereof. In some instances, genetic variations scanned
and/or identified are informative SNPs or CNPs.
[0085] In certain instances, instead of scanning and reading all of
the bases from each genome or all common SNPs or CNPs, a limited
number of informative SNPs or CNPs, e.g., about 300 to about
500,000, can be scanned or read. Thus, while in some instances
scanning whole genomes is contemplated, in other instances, only
specific chromosomes, loci, common SNPs or CNPs, or informative
SNPs or CNPs are scanned and/or used. Specific chromosomes, loci,
common SNPs or CNPs, or informative SNPs or CNPs can be selected
based on prior knowledge that such regions are related to a
particular phenotype of interest.
[0086] In some instances, the scanning step is supplemented and/or
substituted by obtaining data on genetic variations from databases.
These genomic sequences have been predetermined. Such databases can
provide, for example, a list of identified genetic variations
(e.g., SNPs or CNPs or haplotypes) or genotyping data on particular
individuals or populations. Examples of publicly available
databases useful in the methods described herein include, but are
not limited to, UCSC's Genome Browser, NCBI's dbSNP, MIT's human
SNP database, University of Geneva's human Chromosome 21 SNP
database, and the University of Tokyo's SNP database. Other
databases known in the art can be used in conjunction with the
methods described herein.
[0087] Because of the haplotype structure of the human genome,
analysis of a relatively small number of SNP loci can be used to
profile an entire human genome. For example, a haplotype,
containing dozens or hundreds of SNP alleles, can be "tagged" with
just a few well-chosen "Tag SNPs or CNPs". A nearly complete whole
genome profile of an individual can thus be obtained, e.g., by
using a DNA microarray that distinguishes genotypes at around
500,000 Tag SNPs or CNPs.
[0088] The methods disclosed herein are described below and are
illustrated by haploblock information obtained from the
International HapMap project (hapmap.org) for a population of
European ancestry. In the exemplary method, the presence or absence
of one or more nucleotide variants (e.g., SNPs or CNPs) are
identified at one or more loci of at least one pair of chromosomes
of the genomic DNA and these identified nucleotide variants are
compared to a plurality of predetermined genomic sequences of
haplotypes having predetermined frequencies at predetermined loci
to identify haplotypes present in the genomic DNA. In addition, a
haplogroup and its haplotype members can be represented in tabular
form for a 14,699 base region of human chromosome 15 from position
25,942,585 to 25,957,284 in the hg18 build of the human reference
genome, which is given an index number of "x=69" for this
particular download. This haplogroup contains .lamda.=18 loci with
unique IDs displayed in row 0, and v=6 haplotype variants
represented in rows 1-6. The European frequency of each haplotype
is indicated in the first column. The six shown add % of total
variation. The block of elements with bolded perimeter represents a
modified stochastic matrix ("A.sup.x") with the observed joint
frequencies of occurrence of the corresponding row haplotype of X
together with row haplotype k of haplogroup X+1. The "L" term in
the following equations is " . . . ." Furthermore, the first "a"
term in the following equation is
A X = [ 1 i , .lamda. + k X ] .alpha. [ a i , k X ] i = 1 , L , v X
; k = 1 , L , v X + 1 ##EQU00001##
[0089] For convenience in describing the genetic algorithms
presented in this embodiment, the same tabular format shown in
Table 1 below and the same formal genetic notation described below
is used to denote all loci and meta-loci with one or more loci,
whether variant or nonvariant. Note that the tables below show
haplotypes and association matrices.
TABLE-US-00001 TABLE 1 (Table 1 discloses SEQ ID Nos: 1-6,
respectively, in order of appearance.) X = 69 0 1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17 18 19 20 21 22 69 1 2 3 4 1 0.425 C A C G C
G T G C G G T G C G G G C 0.425 0.000 0.000 0.000 2 0.267 C A A G C
G T G C G A T G C G G G C 0.267 0.000 0.000 0.000 3 0.117 C A C G C
G T G C G G T A C G G G C 0.117 0.000 0.000 0.000 4 0.067 A C C A T
A T G T G G C G T A T A T 0.000 0.067 0.000 0.000 5 0.058 C C C G C
G T G C G G T G T G G G C 0.000 0.000 0.058 0.000 6 0.025 A C C G C
G C A C T G T G T G T G C 0.000 0.000 0.000 0.025 var: 6 96% of the
population chr 15 from 25,942,585 to 25,957,284 = 14,699 bases G#1:
AM CC CC GG AG GG GG GG CC (1, 2) G#2: LMS AC CC AG CT GG GG GG GT
AG CT (3, 4) L.sup.X: = indexed meta-locus; X: = meta-locus index
number a [.sub.00] .lamda.: = number of loci; locus j data a
[.sub.*j.sup.X]: j (1, L, .lamda.) v: = number of variants or
haplotypes; variant i a [.sub.j*.sup.X]: i (1, L, v) f (i): =
population frequency of a variant i a [.sub.i0.sup.X] f( i .sup.X |
k .sup.X+1) a [I.sub.i,.lamda.+k.sup.X] .ident.
[a.sub.i,k.sup.X]
[0090] In the preceding equation, all "L" terms with the exception
of the first term represents " . . . " In addition, the "a" terms
represent n is .fwdarw.. As shown above, genome scans performed for
two individuals ("AM" and "LMS") identify the presence of
particular bases at loci 1, 5, 10, 11, 13, and 16-18 for "AM," and
particular bases as loci 2-6, 10, 11, 13, and 16-18 for "LMS."
Representations of Genomes and Genotype Phasing
[0091] The methods further comprise comparing the plurality of
predetermined genomic sequences of haplotypes having predetermined
frequencies at predetermined loci to identify haplotypes present in
the genomic DNA. In certain embodiments, the assessment of Virtual
Progeny includes imputing from publicly available information to
further characterize the genotypes of individuals. For example,
through an automated process, the composition of haplogroup 69
described above can be reduced to haplotypes 1 and 2 for the "AM"
genome, and haplotypes 3 and 4 for "LMS" (Table 2). With the
assignment of haplotypes, additional association information can be
obtained from the public database for a personal genome as shown in
the Tables 3 and 4.
TABLE-US-00002 TABLE 2 (Table 2 discloses SEQ ID Nos: 1-4,
respectively, in order of appearance.) X = 69 0 1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17 18 19 20 21 22 18 loc 1 2 3 4 G#1: AM CC CC
GG AG GG GG GG CC 1 0.425 C A C G C G T G C G G T G C G G G C 0.425
0.000 0.000 0.000 2 0.267 C A A G C G T G C G A T G C G G G C 0.267
0.000 0.000 0.000 G#2: LMS AC CC AG CT GG GG AG GT AG CT 3 0.117 C
A C G C G T G C G G T A C G G G C 0.117 0.000 0.000 0.000 4 0.067 A
C C A T A T G T G G C G T A T A T 0.000 0.067 0.000 0.000 chr 15
from 25,942,585 to 25,957,284 = 14,699 bases
[0092] A useful, simplified formulation of the extended genotype
(g) carried by an individual person (W) at a previously defined
haplogroup (X) takes the form of the following 3.times.3 matrix
containing a 2.times.2 stochastic matrix partition derived from the
haplogroup lookup table, where each element contains the
empirically-determined joint frequency of haplotypes i.sub.i.sup.X
with .kappa..sub.j.sup.X+1
g .chi. ( w ) = [ X .kappa. 1 .chi. + 1 .kappa. 2 .chi. + 1 i 1
.chi. .alpha. i 1 .chi. , .kappa. 1 .chi. + 1 .alpha. i 1 .chi. ,
.kappa. 2 .chi. + 1 i 2 .chi. .alpha. i 2 .chi. , .kappa. 1 .chi. +
1 .alpha. i 2 .chi. , .kappa. 2 .chi. + 1 ] , i X .di-elect cons. (
1 , L , v .chi. ) , .kappa. X + 1 .di-elect cons. ( 1 , L , v .chi.
+ 1 ) ##EQU00002##
[0093] From the data shown above for "LMS" and "AM", the following
prenormalization genotype matrices can be obtained (Tables 3 and
4).
TABLE-US-00003 TABLE 3 69 1 1 g.sub.69(AM) = 1 0.425 0.425 2 0.267
0.267
TABLE-US-00004 TABLE 4 69 1 2 g.sub.69(LMS) = 3 0.117 0.000 4 0.000
0.067
[0094] These data represent relationships of adjacent loci in a
population, not an individual diploid genome. Thus, in certain
embodiments, initial association matrices can be parsed with
diploid genome consistency rules, leading to a transformation
of:
[].
[0095] Associations can be determined according to several criteria
including, e.g., population data and inter-locus distance on a
chromosome. When no association information is available for
sequential loci, independent assortment can be assumed:
[.alpha..sub.i,k.sup..chi.].sub.i=1,2;k=1,2=0.5
[0096] The complete formulation of a whole genome profile can be an
indexed set of genotype matrices:
G(W)={g.sub.X(W): X=1, . . . ,N}
[0097] An example of a portion of two whole genome profiles across
a distance of 110,000 bases of chromosome 11 for personal genome
scans for "AM" and "LMS" that was expanded directly (through a
haplogroup lookup table into phased genetic information at over 400
individual SNP loci) is given below (Table 5).
TABLE-US-00005 TABLE 5 1 2 3 4 5 6 7 64 1 3 65 1 1 66 1 2 67 1 3 68
1 2 69 1 1 70 1 1 1 0.625 0.125 1 0.617 0.617 1 0.492 0.283 1 0.242
0.033 1 0.350 0.083 1 0.425 0.425 1 0.825 0.825 1 0.625 0.125 3
0.125 0.125 1 0.492 0.283 2 0.300 0.008 3 0.075 0.008 2 0.267 0.267
1 0.825 0.825 64 1 3 65 1 1 66 1 1 67 1 1 68 3 4 69 1 2 70 1 2 1
0.625 0.125 1 0.617 0.617 1 0.492 0.492 1 0.242 0.242 1 0.117 0.025
3 0.117 0.000 1 0.825 0.000 1 0.625 0.125 3 0.125 0.125 1 0.492
0.492 1 0.242 0.242 1 0.117 0.025 4 0.000 0.067 2 0.000 0.075
25,861,367 25,879,159 25,909,368 25,938,449 25,940,622 25,942,586
25,959,712 8 9 71 1 1 72 1 1 1 0.817 0.817 1 0.683 0.683 1 0.817
0.817 1 0.683 0.683 71 1 2 72 3 3 1 0.817 0.008 1 0.017 0.017 2
0.000 0.075 2 0.033 0.033 25,967,383 25,971,652
Formation of Virtual Gametes Using Haplopaths
[0098] In particular embodiments, a haploid gamete genome for each
potential gamete is generated from the diploid genome profile by
generating a combination of the identified haplotypes using the
linkage probability for each combination of the identified
haplotypes. In some embodiments, the Virtual Gametes are then
generated using a computer simulation of gamete production from a
diploid parental genome profile. The simulated haplopath can be
computed or generated by subjecting the parental genome to formal
rules of genetics that operate naturally during meiosis and gamete
production.
[0099] Formal rules of genetics are known in the art, and are
mathematical formulas or algorithms that serve as abstract
representations of the biological processes of sexual reproduction.
Rules are based on allele segregation, independent assortment,
linkage between genetic loci, recombination suppression,
Hardy-Weinberg equilibria, and other probabilistic genetic
processes. Formal rules can be used to estimate the likelihood of
transmission of particular alleles and combinations of alleles at
multiple loci, from an individual to a gamete.
[0100] The geographic origin of a parental genome, or subregion
thereof, can provide population-specific allele and genotype
frequency information that can be incorporated into computational
models that predict genotypic and phenotypic probabilities of
Virtual Progeny.
[0101] A Virtual Gamete includes one permutation of a haploid
genome path, or haplopath (H), from one end of a computed genome
profile (X=1) to the other (X=N). Each haplopath includes a single
allele (variant or haplotype) from each meta-locus, defined by the
index number of the chromosomal homologue.
[0102] A haplopath can be initiated with a random number generator
(e.g., such as a Monte Carlo method) that chooses a random
allele(i) at a random initializing locus in the set of N such loci.
Each prior and subsequent allele along the haplopath can be
generated according to normalized likelihoods derived from
locus-specific association matrices.
P r ( L .kappa. .chi. + 1 ) = [ .alpha. i , k .chi. ] ( [ .alpha. i
, 1 .chi. ] + [ .alpha. i , 2 .chi. ] ) ; i , k .di-elect cons. ( 1
, 2 ) ##EQU00003##
[0103] For each Monte Carlo haplopath [H.sub.i.uparw.] that is
generated computationally, a reciprocal haplopath [H.sub.i.dwnarw.]
can be created with alleles present on the opposite homologue at
every locus. This converse haplopath represents the sister/brother
gamete of a simulated haplopath:
H.sub.i.uparw.={h.sub.1,h.sub.2, . . .
,h.sub.N},h.sub..chi..di-elect cons.(1,2)
H.sub.i.dwnarw.={h.sub.1,h.sub.2, . . .
,h.sub.N},h.sub..chi..di-elect
cons.(1,2);h.sub..chi.,.dwnarw.(h.sub..chi.,i.uparw.)
[0104] The inclusion of converse haplopaths in a Virtual Gamete
pool can increase consistency of the simulated data with Mendel's
first law of equal segregation at every locus. FIG. 6 shows an
example of 100 iterations of a Monte Carlo run on the genomic
region shown above, where homologue index numbers have been
converted from (1,2) to (0,1).
Formation of Virtual Progeny
[0105] In further embodiments, the Virtual Progeny genome is a
collection of permutations of diploid genomes that can each be
formed by combining a random Virtual Gamete from one parent (the
paternal line, pat) with a random Virtual Gamete from a second
parent (the maternal line, mat). Each permutation of a Virtual
Progeny genome comprises a discrete set of defined integer
genotypes. In one embodiment, a single permutation (i) of a Virtual
Progeny genome can take the following form:
G i V P = f ( G p a t , G m a t ) = { [ h .chi. p a t h .chi. m a t
] : X = 1 , , N } .fwdarw. { g .chi. ( i ) : X = 1 , , N }
##EQU00004##
[0106] In exemplary methods, a Virtual Progeny genome can comprise
a set of individual permutations of Virtual Progeny genomes:
G.sup.VP={G.sub.1.sup.VP,G.sub.2.sup.VP, . . . }
[0107] A phenome can comprise a measure of phenotypes and traits
expressed by a diploid organism over the time period of its life.
Numerous discrete or continuous phenotypes associated with discrete
genotypes are listed on databases maintained at the National
Institutes of Health division of Bioinformatic Information and
other public databases. Additional sources of discrete or
continuous phenotypes associated with discrete genotypes are
located in PubMed, the UCSC browser, NCBI (fancy output genomic
browser), Online Mendelian Inheritance in Man ("OMIM"), SNPedia,
GeneTests, Entrez Gene, HuGENavigator,
HuGENavigator/Genopedia/Search, HuGENavigator/Phenopedia/Search,
NextBio Database, and Genetic Association Database. In addition,
databases for SNP and variant datasets include SNP Cluster Report,
Genome-Wide Association Studies (National Human Genome Research
Institute), Autism Chromosome Rearrangement Database (hosted by The
Centre for Applied Genomics), and the Database of Genomic Variants
(hosted by The Centre for Applied Genomics).
[0108] The Virtual Progeny phenome (ph.sup.VP) can comprise a
single probability density function defined by the summation of the
weighted set of phenomes that are individually associated with each
permutation of a Virtual Progeny genome.
[0109] In some methods, prior population genetic data can be used
to predict the population or populations of origin for a
trait-affecting locus, which can be incorporated into models that
can be used to predict trait likelihoods of virtual or actual
progeny. Phenotypes associated with all genotypes in the Virtual
Progeny sample space can be integrated to produce an overall
assessment of phenotypic likelihoods (in terms of penetrance and
expressivity) for each individual trait, alone or in
combination.
[0110] The following is an example showing the application of the
methods described herein. The actual methods can cover many more
genes with a larger number of loci.
[0111] The method entails a genotyping panel with the disease
impacted by each locus, the official name of the locus, the alleles
at each locus that are probed, the frequency of these alleles in
the population being analyzed, standard reference names for probes
that detect each allele, (an abbreviated name for each allele for
the purposes of this illustration), and the impact of the allele on
disease risk or disease expression within each genotypic context.
Such a description is shown in Table 1, which shows information for
a two-locus genotyping panel.
TABLE-US-00006 TABLE 6 Disease genome locus allele freq. probe abb.
effect of allele in genotype Cystic fibrosis CFTR: c.1408A>G
CFTR: p.469V 0.50 rs213950G G GG: no effect CFTR: p.469M 0.50
rs213950A A AA: 0.002 risk; GA: 0.05 risk CFTR: c.1521_1523del
CFTR: p.508F 0.98 rs332TTT T TT, Td: no direct effect, but 3 risk
of other CF mutations remains CFTR: p.508del 0.02 rs332del d dd:
cystic fibrosis, severe
[0112] The findings on genotype-phenotype associations and
population frequencies of alleles, including alleles not determined
in this analysis, will be used to create a risk table that covers
all possible genotype combinations of alleles in the genotyping
panel (Table 7).
TABLE-US-00007 TABLE 7 2-locus Risk of disease in Disease genotype
virtual child Cystic fibrosis GG, TT 1/25,000 GA, TT 1/192,000 AA,
TT 1/3,600,000 GG, dT 1/75 GA, dT 1/1,500 AA, dt 1/30,000 GG, dd
1/1 GA, dd 1/1 AA, dd 1/1
[0113] Genotype data captured from each individual who is a
component of the analysis is assembled (Table 8).
TABLE-US-00008 TABLE 8 Individual 2-locus genotype Donor B GA, TT
Donor F AA, dT Client #1 GA, TT Client #2 AA, TT
[0114] Next, a Monte Carlo algorithm is applied to each individual
genotype profile to generate a pool of "Virtual Gametes" containing
single alleles from each locus. A virtual gamete from each
designated genetic parent is chosen randomly and combined to
produce one permutation of a potential child's genotype profile.
The process of virtual gamete choice and combination to produce a
diploid genome is iterated a sufficient number of times so that the
sum of permutations provides a stable estimate of a child's genome
likelihood distribution, as illustrated in columns 1 and 2 of Table
9.
TABLE-US-00009 TABLE 9 Pairing: Client #1 X Donor B Monte
Carlo-generated Virtual progeny genotypes genotype Likelihood
distribution CF risk from table GG, TT 0.25 1/25,000 GA, TT 0.50
1/192,000 AA, TT 0.25 1/3,600,000
TABLE-US-00010 Normalized disease risk for composite virtual
progeny cystic fibrosis 1/79,000
[0115] The risk of disease associated with each discrete Virtual
Progeny genotype is determined from the previously established risk
table (from Table 7 into column 3 of Table 9). Disease risks
associated with each genotype are weighted according to their
appearance in the set of permutations to assign a normalized
disease risk to the particular pairing under analysis (Table
10).
TABLE-US-00011 TABLE 10 Genotypes Locus #1 Locus #2 rs213950
delF508 DONORS No testing RISK A B C D E F --/-- --/-- 2,500 24%
48% 24% 1% 2% 1% Client 1 G/A, F/F X G/G, F/F G/A, F/F A/A, F/F
G/G, del/F G/A, del/F A/A, del/F G/G F/F 25,000 0.500 0.250 0.000
0.250 0.125 0.000 G/A F/F 191,755 0.500 0.500 0.500 0.250 0.250
0.250 A/A F/F 3,634,742 0.000 0.250 0.500 0.000 0.125 0.250 G/G
del/F 75 0.000 0.000 0.000 0.250 0.125 0.000 G/A del/F 1,500 0.000
0.000 0.000 0.250 0.250 0.250 A/A del/F 30,000 0.000 0.000 0.000
0.000 0.125 0.250 n/n del/del 1 0.000 0.000 0.000 0.000 0.000 0.000
11,638 44,233 78,888 364,291 285 542 5,670 Client 2 A/A, F/F X G/G,
F/F G/A, F/F A/A, F/F G/G, del/F G/A, del/F A/A, del/F G/G F/F
25,000 0.00 0.00 0.00 0.00 0.00 0.00 G/A F/F 191,755 1.00 0.50 0.00
0.50 0.25 0.00 A/A F/F 3,634,742 0.00 0.50 1.00 0.00 0.25 0.50 G/G
del/F 75 0.00 0.00 0.00 0.00 0.00 0.00 G/A del/F 1,500 0.00 0.00
0.00 0.50 0.25 0.00 A/A del/F 30,000 0.00 0.00 0.00 0.00 0.25 0.50
n/n del/del 1 0.00 0.00 0.00 0.00 0.00 0.00 103,198 191,755 364,291
3,634,742 2,977 5,670 59,509 Client 3 G/G, F/F X G/G, F/F G/A, F/F
A/A, F/F G/G, del/F G/A, del/F A/A, del/F G/G F/F 25,000 1.00 0.50
0.00 0.50 0.25 0.00 G/A F/F 191,755 0.00 0.50 1.00 0.00 0.25 0.50
A/A F/F 3,634,742 0.00 0.00 0.00 0.00 0.00 0.00 G/G del/F 75 0.00
0.00 0.00 0.50 0.25 0.00 G/A del/F 1,500 0.00 0.00 0.00 0.00 0.25
0.50 A/A del/F 30,000 0.00 0.00 0.00 0.00 0.00 0.00 n/n del/del 1
0.00 0.00 0.00 0.00 0.00 0.00 6,167 25,000 44,233 191,755 150 285
2,977
[0116] The final result obtained above is used in conjunction with
a chosen risk tolerance cutoff to determine whether the donor
should be retained or removed from the client pool. If risk
tolerance had been set previously at 1/50,000, the result obtained
in this example would lead to no action being taken. Donor B would
remain in client #1's pool. However, the other donors would be
eliminated as potential parents of offspring. In the case of
disease or particular traits, an increased likelihood or risk of
such disease would have been determined, thereby rendering certain
donors eliminated from the pool of potential donors. In certain
instances, the potential parent is a sperm donor. In other
instances, the potential parent is an oocyte donor.
TABLE-US-00012 TABLE 11 Categories of donors (in relation to 2-SBP
genotype) Locus #1 Locus #2 A B C D E F rs213950 delF508 Clients
24% 48% 24% 1% 2% 1% --/-- --/-- 2,500 G/G, F/F G/A, F/F A/A, F/F
G/G, del/F G/A, del/F A/A, del/F G/A F/F 11,638 X 44,233 78,888
364,291 285 542 5,670 A/A F/F 103,198 X 191,755 364,291 3,634,742
2,977 5,670 59,509 G/G F/F 6,167 X 25,000 44,233 191,755 150 285
2,977
[0117] The analysis presented in this illustration can be readily
scaled to any number of diseases and loci. The most time-consuming
components are genome scans and assessing Virtual Progeny, which
only need be performed once. The actual application of the rest of
the methodology is entirely automated, and a test run with a panel
of 1,000 loci (2,000 probes) was performed in less than one minute
on a laptop computer.
Assessing Probability of Trait Expression
[0118] In the particular embodiment, the Virtual Progeny genome,
which actually comprises a collection of likely genome
permutations, or distribution of genome states generated through a
Markov process, can be interrogated for associated trait
expression. Each discrete genome in the Virtual Progeny collection
or distribution can be evaluated independently for expected trait
expression, based on published genotype-phenotype associations. The
weighted summation of traits expressed by individual genome
permutations can yield a Virtual Progeny phenome probability
distribution.
[0119] The disclosed methods and systems can be used to provide
visual representations of differential virtual progeny results for
virtual mating of a client with potential donors (FIG. 2). Further,
each of the virtual matings are compared. The methods disclosed
herein are used to obtain samples, which are processed by
performing genome scans to identify SNPs or CNPs present in each
individual's genomic DNA. The identified genetic markers are used
to expand personal genome data and to determine haplotypes present
within each individual's genomic DNA. The likelihoods of
association of sequential haplotypes are retrieved from lookup
tables generated by the international HapMap project. Using the
identified haplotypes together with association data, reiteration
of a Monte Carlo simulation is performed to generate a haplopath,
resulting in a Virtual Gamete population for each individual.
Virtual Gametes from each individual are combined to produce
Virtual Progeny genome samplings, each of which is evaluated for
corresponding trait likelihood values. Finally, an integrated
Virtual Progeny phenome likelihood distribution is determined to
assess the probability that potential progeny express certain
traits, such as increased risk of disease.
[0120] This methodology was used to identify probable phenotypes of
Virtual Progeny from the simulated matings of four donors with
client SRS (FIG. 2). Virtual Progeny are generated analyzing the
potential phenotypes that would be generated from the matings of
SRS with four different donors. For VP-SRSxTJ (i.e., a mating of
SRS to TJ), the progeny phenotypes will all be blue eyes, while for
VP-SRSxAFl (i.e., a mating SRS to AFl), the phenotypes will all be
brown eyes. For VP-SRSxLMS (i.e., a mating SRS to LMS) and
VP-SRSxNEA (i.e., a mating SRS to NEA), the Virtual Progeny
likelihood phenotype is 50% blue and 50% brown.
[0121] Such divergence increases radically when more loci are added
to the analysis. For example, when 100,000 loci are analyzed, the
number of potential genotypes increases to 2.sup.100,000. The
disclosed methods and systems allow for the analysis of large
numbers of loci without the incredible amount of time or
uncertainty associated with prior methodologies. FIG. 3 shows an
example of a visual representation of Virtual Progeny genome
profiles for the matings of the SRS to the four donors, all of
which are shown across 82 million base pairs of Chromosome 15. The
bottom row shows the loci associated with various traits.
[0122] As described above, the probability that Virtual Progeny
described herein will have a particular trait can be assessed by
comparing discrete Virtual Progeny genome samplings to publicly
available databases. FIG. 4 shows an analysis of Virtual Progeny
("VP") genotypes. Each SNP locus has two alleles (A and D), which
can produce three distinct genotypes (AA, AD, DD). "A" is used to
signify the original "ancestral" allele and "D" is the allele
"derived" by mutation. At any one locus, "A" and "D" each refer to
empirically determined nucleotide bases A, C, G, or T. Utilizing
reference information from databases known in the art and disclosed
herein, "A" is used as a reference allele. Using this analytical
technique, a SNP genotype is completely described by the number of
ancestral alleles it contains (0, 1, 2). For instance, an integer
is used to indicate determination of a single definitive genotype.
If a progeny is described using a "0" for a particular allele, then
the progeny definitively has zero ancestral alleles at the
particular locus. If "1" describes the progeny, the progeny
definitively has one ancestral allele at that locus. If "2"
describes the progeny, then the progeny definitively has both
ancestral alleles. In other words, integers occur when both mates
are homozygous for a particular allele. Non-integers (0.5,1.5)
indicate restriction to the two genotypes that are .+-.0.5 removed.
In other words, non-integers indicate that one mate was
heterozygous for a particular allele. Finally, an out-of-bounds
integer (5) is used when data from the two virtual parents is
non-informative (i.e., both parents are heterozygous at that
locus). In the example provided in FIG. 4, the number of loci
falling into the categories 0, 0.5, 1, 1.5, 2, and 5 are shown. As
is apparent, each partner provides a different profile for SNPs or
CNPs found in the reference databases.
[0123] FIG. 5 provides a more detailed view of actual SNPs or CNPs
that are known and can be analyzed using the methods and systems
disclosed herein. Various SNPs or CNPs and the results of simulated
mating of one woman subject to four potential partners is again
shown as either an integer (0, 1, or 2), a non-integer (0.5 or
1.5), or as non-informative (5) (FIG. 5). As in FIG. 4, the profile
of each VP of each mating is provided for the various traits and
diseases. The data comparison identified virtual progeny genotypes
at over 1,300 SNP loci that influence over 100 disease traits shown
in the table.
Traits
[0124] Nonlimiting examples of traits that may assessed using the
methods described herein include or relate to ability to roll the
tongue, ability to taste PTC, acute inflammation, adaptive
immunity, addiction(s), adipose tissue, adrenal gland, age,
aggression, amino acid level, amyloidosis, anogenital distance,
antigen presenting cells, auditory system, autonomic nervous
system, avoidance learning, axial defects or lack thereof, B cell
deficiency, B cells, B lymphocytes (e.g., antigen presentation),
basophils, bladder size/shape, blinking, blood chemistry, blood
circulation, blood glucose level, blood physiology, blood pressure,
body mass index, body weight, bone density, bone marrow
formation/structure, bone strength, bone/skeletal physiology,
breast size/shape, bursae, cancellous bone, cardiac arrest, cardiac
muscle contractility, cardiac output, cardiac stroke volume,
cardiomyopathy, cardiovascular system/disease, carpal bone,
catalepsy, cell abnormalities, cell death, cell differentiation,
cell morphology, cell number, cell-mediated immunity, central
nervous system, central nervous system physiology, chemotactic
factors, chondrodystrophy, chromosomal instability, chronic
inflammation, circadian rhythm, circulatory system, cleft chin,
clonal anergy, clonal deletion, T and B cell deficiencies,
conditioned emotional response, congenital skeletal deformities,
contextual conditioning, cortical bone thickness, craniofacial
bones, craniofacial defects, crypts of Lieberkuhn, cued
conditioning, cytokines, delayed bone ossification, dendritic cells
(e.g., antigen presentation), Di George syndrome, digestive
function, digestive system, digit dysmorphology, dimples,
discrimination learning, drinking behavior, drug abuse, drug
response, ear size/shape including ear lobe attachment, eating
behavior, ejaculation function, embryogenesis, embryonic death,
embryonic growth/weight/body size, emotional affect,
enzyme/coenzyme level, eosinophils, epilepsy, epiphysis, esophagus,
excretion physiology, extremities, eye blink conditioning, eye
color/shape, eye physiology, eyebrows shape, eyelash length, face
shape, facial cleft, femur, fertility/fecundity, fibula, finger
length/shape, fluid regulation, fontanels, foregut, fragile
skeleton, freckles, gall bladder, gametogenesis, gastrointestinal
hemorrhage, germ cells (e.g., morphology, depletion), gland
dysmorphology, gland function, glucagon level, glucose homeostasis,
glucose tolerance, glycogen catabolism, granulocytes, granulocytes
(e.g., bactericidal activity, chemotaxis), grip strength, grooming
behavior, hair color, hair follicle structure/orientation, hair
growth, hair on mid joints, hair texture, handedness, harderian
glands, head, hearing function, heart, heart rate, heartbeat (e.g.,
rate, irregularity), height, hemarthrosis, hemolymphoid system,
hepatic system, hitchhiker's thumb, homeostasis, humerus, humoral
immune response, hypoplastic axial skeleton, hypothalamus, immune
cell, immune system (e.g., hypersensitivity), immune system
response/function, immune tolerance, immunodeficiency, inability to
urinate, increased sensitivity to gamma-irradiation, inflammatory
mediators, inflammatory response, innate immunity, inner ear,
innervation, insulin level, insulin resistance, intestinal
bleeding, intestine, ion homeostasis, jaw, kidney hemorrhage,
kidney stones, kidney/renal system, kyphoscoliosis, kyphosis,
lacrimal glands, larynx, learning/memory, leukocyte, ligaments,
limb dysmorphology, limb grasping, lipid chemistry, lipid
homeostasis, lips size/shape, liver (e.g., development/function),
liver/hepatic system, locomotor activity, lordosis, lung, lung
development, lymph organ development, macrophages (e.g., antigen
presentation), mammary glands, maternal/paternal behavior, mating
patterns, meiosis, mental acuity, mental stability, mental state,
metabolism of xenobiotics, metaphysis, middle ear, middle ear bone,
morbidity and mortality, motor coordination/balance, motor
learning, mouth, movement, muscle, muscle contractility, muscle
degeneration, muscle development, muscle physiology, muscle
regeneration, muscle spasms, muscle twitching, musculature,
myelination, myogenesis, nervous system, neurocranium,
neuroendocrine glands, neutrophils, NK cells, nociception, nose,
nutrients/absorption, object recognition memory, ocular reflex,
odor preference, olfactory system, oogenesis, operant or "target
response", orbit, osteogenesis, osteogenesis/developmental,
osteomyelitis, osteoporosis, outer ear, oxygen consumption, palate,
pancreas, paralysis, parathyroid glands, pelvis girdle, penile
erection function, perinatal death, peripheral nervous system,
phalanxes, pharynx, photosensitivity, piloerection, pinna reflex,
pituitary gland, PNS glia, postnatal death, postnatal
growth/weight/body size, posture, premature death, preneoplasia,
propensity to cross the right arm over the left of vice versa,
propensity to cross the right thumb over the left thumb when
clasping hands or vice versa, pulmonary circulation, pupillary
reflex, radius, reflexes, reproductive condition, reproductive
system, resistance to fatty liver development, resistance to
hyperlipidemia, respiration (e.g., rate, shallowness), respiratory
distress or failure, respiratory mucosa, respiratory muscle,
respiratory system, response to infection, response to injury,
response to new environment (transfer arousal), ribs, salivary
glands, scoliosis, sebaceous glands, secondary bone resorption,
seizures, self tolerance, senility, sensory capabilities, sensory
system physiology/response, sex, sex glands, shoulder, skin, skin
color, skin texture/condition, skull, skull abnormalities, sleep
pattern, social intelligence, somatic nervous system, spatial
learning, sperm count, sperm motility, spermatogenesis, startle
reflex, sternum defect, stomach, suture closure, sweat glands, T
cell deficiency, T cells (e.g., count), tarsus, taste response,
teeth, temperature regulation, temporal memory, tendons, thyroid
glands, tibia, touch/nociception, trachea, tremors, trunk curl,
tumor incidence, tumorigenesis, ulna, urinary system, urination
pattern, urine chemistry, urogenital condition, urogenital system,
vasculature, vasoactive mediators, vertebrae, vesicoureteral
reflux, vibrissae, vibrissae reflex, viscerocranium, visual system,
weakness, widows peak or lack thereof, etc.
[0125] Other nonlimiting traits include cognitive ability (Ruano et
al., Am. J. Hum. Genet. 86:113 (2010)); Familial Osteochondritis
Dissecans (Stattin et al., Am. J. Hum. Genet. 86:126 (2010));
hearing impairment (Schraders et al., Am. J. Hum. Genet. 86:138
(2010)); mental retardation associated with autism, epilepsy, or
macrocephaly (Giannandrea et al., Am. J. Hum. Genet. 86:185
(2010)); muscular dystrophies (Bolduc et al., Am. J. Hum. Genet.
86:213 (2010)); Diamond-Blackfan anemia (Doherty et al., Am. J.
Hum. Genet. 86:222 (2010)); osteoporotic fractures (Kung et al.,
Am. J. Hum. Genet. 86:229 (2010)); familial exudative
vitreoretinopathy (Poulter et al., Am. J. Hum. Genet. 86:248
(2010)); skeletal dysplasia, eye, and cardiac abnormalities (Iqbal
et al., Am. J. Hum. Genet. 86:254 (2010)); Warsaw breakage syndrome
(van der Lilij et al., Am. J. Hum. Genet. 86:262 (2010)); arterial
calcification of infancy (Lorenz-Depiereux et al., Am. J. Hum.
Genet. 86:267 (2010)); hypophosphatemic rickets (Lorenz-Depiereux
et al., Am. J. Hum. Genet. 86:267 (2010); Levy-Litan et al., Am. J.
Hum. Genet. 86:273 (2010)); rhabdoid tumor predisposition syndrome
(Schneppenheim et al., Am. J. Hum. Genet. 86:279 (2010)); and
multiple sclerosis (Jakkula et al., Am. J. Hum. Genet. 86:285
(2010)).
[0126] Yet other nonlimiting traits include 21-Hydroxylase
Deficiency, ABCC8-Related Hyperinsulinism, ARSACS, Achondroplasia,
Achromatopsia, Adenosine Monophosphate Deaminase 1, Agenesis of
Corpus Callosum with Neuronopathy, Alkaptonuria,
Alpha-1-Antitrypsin Deficiency, Alpha-Mannosidosis,
Alpha-Sarcoglycanopathy, Alpha-Thalassemia, Alzheimers, Angiotensin
II Receptor, Type I, Apolipoprotein E Genotyping,
Argininosuccinicaciduria, Aspartylglycosaminuria, Ataxia with
Vitamin E Deficiency, AtaxiaTelangiectasia, Autoimmune
Polyendocrinopathy Syndrome Type 1, BRCA1 Hereditary Breast/Ovarian
Cancer, BRCA2 Hereditary Breast/Ovarian Cancer, Bardet-Biedl
Syndrome, Best Vitelliform Macular Dystrophy,
Beta-Sarcoglycanopathy, Beta-Thalassemia, Biotinidase Deficiency,
Blau Syndrome, Bloom Syndrome, CFTR-Related Disorders, CLN3-Related
Neuronal Ceroid-Lipofuscinosis, CLN5-Related Neuronal
Ceroid-Lipofuscinosis, CLN8-Related Neuronal Ceroid-Lipofuscinosis,
Canavan Disease, Carnitine Palmitoyltransferase IA Deficiency,
Carnitine Palmitoyltransferase II Deficiency, Cartilage-Hair
Hypoplasia, Cerebral Cavernous Malformation, Choroideremia, Cohen
Syndrome, Congenital Cataracts, Facial Dysmorphism, and Neuropathy,
Congenital Disorder of Glycosylationla, Congenital Disorder of
Glycosylation 1b, Congenital Finnish Nephrosis, Crohn Disease,
Cystinosis, DFNA 9 (COCH), Diabetes and Hearing Loss, Early-Onset
Primary Dystonia (DYTI), Epidermolysis Bullosa Junctional,
Herlitz-Pearson Type, FANCC-Related Fanconi Anemia, FGFR1-Related
Craniosynostosis, FGFR2-Related Craniosynostosis, FGFR3-Related
Craniosynostosis, Factor V Leiden Thrombophilia, Factor V R2
Mutation Thrombophilia, Factor XI Deficiency, Factor XIII
Deficiency, Familial Adenomatous Polyposis, Familial Dysautonomia,
Familial Hypercholesterolemia Type B, Familial Mediterranean Fever,
Free Sialic Acid Storage Disorders, Frontotemporal Dementia with
Parkinsonism-17, Fumarase deficiency, GJB2-Related DFNA 3
Nonsyndromic Hearing Loss and Deafness, GJB2-Related DFNB 1
Nonsyndromic Hearing Loss and Deafness, GNE-Related Myopathies,
Galactosemia, Gaucher Disease, Glucose-6-Phosphate Dehydrogenase
Deficiency, Glutaricacidemia Type 1, Glycogen Storage Disease Type
1a, Glycogen Storage Disease Type 1b, Glycogen Storage Disease Type
II, Glycogen Storage Disease Type III, Glycogen Storage Disease
Type V, Gracile Syndrome, HFE Associated Hereditary
Hemochromatosis, Halder AIMS, Hemoglobin S Beta-Thalassemia,
Hereditary Fructose Intolerance, Hereditary Pancreatitis,
Hereditary Thymine-Uraciluria, Hexosaminidase A Deficiency,
Hidrotic Ectodermal Dysplasia 2, Homocystinuria Caused by
Cystathionine Beta-Synthase Deficiency, Hyperkalemic Periodic
Paralysis Type 1,
Hyperornithinemia-Hyperammonemia-Homocitrullinuria Syndrome,
Hyperoxaluria, Primary, Type 1, Hyperoxaluria, Primary, Type 2,
Hypochondroplasia, Hypokalemic Periodic Paralysis Type 1,
Hypokalemic Periodic Paralysis Type 2, Hypophosphatasia, Infantile
Myopathy and Lactic Acidosis (Fatal and Non-Fatal Forms),
Isovaleric Acidemias, Krabbe Disease, LGMD2I, Leber Hereditary
Optic Neuropathy, Leigh Syndrome, French-Canadian Type, Long Chain
3-Hydroxyacyl-CoA Dehydrogenase Deficiency, MELAS, MERRF, MTHFR
Deficiency, MTHFR Thermolabile Variant, MTRNR1-Related Hearing Loss
and Deafness, MTTS1-Related Hearing Loss and Deafness,
MYH-Associated Polyposis, Maple Syrup Urine Disease Type IA, Maple
Syrup Urine Disease Type 1B, McCune-Albright Syndrome, Medium Chain
Acyl-Coenzyme A Dehydrogenase Deficiency, Megalencephalic
Leukoencephalopathy with Subcortical Cysts, Metachromatic
Leukodystrophy, Mitochondrial Cardiomyopathy, Mitochondrial
DNA-Associated Leigh Syndrome and NARP, Mucolipidosis IV,
Mucopolysaccharidosis Type I, Mucopolysaccharidosis Type IIIA,
Mucopolysaccharidosis Type VII, Multiple Endocrine Neoplasia Type
2, Muscle-Eye-Brain Disease, Nemaline Myopathy, Neurological
phenotype, Niemann-Pick Disease Due to Sphingomyelinase Deficiency,
Niemann-Pick Disease Type Cl, Nijmegen Breakage Syndrome,
PPT1-Related Neuronal Ceroid-Lipofuscinosis, PROP1-related
pituitary hormome deficiency, Pallister-Hall Syndrome, Paramyotonia
Congenita, Pendred Syndrome, Peroxisomal Bifunctional Enzyme
Deficiency, Pervasive Developmental Disorders, Phenylalanine
Hydroxylase Deficiency, Plasminogen Activator Inhibitor I,
Polycystic Kidney Disease, Autosomal Recessive, Prothrombin G20210A
Thrombophilia, Pseudovitamin D Deficiency Rickets, Pycnodysostosis,
Retinitis Pigmentosa, Autosomal Recessive, Bothnia Type, Rett
Syndrome, Rhizomelic Chondrodysplasia Punctata Type 1, Short Chain
Acyl-CoA Dehydrogenase Deficiency, Shwachman-Diamond Syndrome,
Sjogren-Larsson Syndrome, Smith-Lemli-Opitz Syndrome, Spastic
Paraplegia 13, Sulfate Transporter-Related Osteochondrodysplasia,
TFR2-Related Hereditary Hemochromatosis, TPP1-Related Neuronal
Ceroid-Lipofuscinosis, Thanatophoric Dysplasia, Transthyretin
Amyloidosis, Trifunctional Protein Deficiency, Tyrosine
Hydroxylase-Deficient DRD, Tyrosinemia Type I, Wilson Disease,
X-Linked Juvenile Retinoschisis, and Zellweger Syndrome
Spectrum.
[0127] The methods of assessing the probability that progeny will
express certain traits, as described herein, can be implemented
into systems, programs, and/or services, which can be authorized
by, referred by, and/or performed by, e.g., agencies, public or
private companies, genetic counseling centers, dating or
match-making services, sperm banks, egg providers, reproductive
service providers, fertility clinics, or specialty laboratories
Virtual Progeny Assessment and Genetic Counseling Referral
System
[0128] In one example, the methods described herein are integrated
into a testing service that can provide information to a couple on
the probability that the couple's offspring will express one or
more traits described herein, such as risk of a disease. In
addition to the results of the Virtual Progeny assessment,
referrals to genetic counselors and/or other relevant medical
professionals can be provided in order to provide for follow up
testing and consultation.
[0129] In certain embodiments, a Virtual Progeny assessment begins
with a customer order, and the customer can pay a service provider
a fee in exchange for the assessment. A customer can be a two
potential parents, e.g., partners. Alternatively, a customer can be
a physician, a genetic counselor, a medical center, an insurance
company, a website, a dating service, a matchmaking service, a
pharmaceutical company, or a laboratory testing service provider,
who places an order on behalf of two potential parents. For
example, a customer can be two prospective parents who seek to
learn whether their offspring will be at risk for developing
disease. After a customer places an order, DNA collection kits can
be sent to the prospective parents, who can deposit a biological
sample described herein into the collection kits. The collection
kits can then be returned to the company for sending to a specialty
lab or can be returned directly to the specialty lab for performing
the assessment. A specialty lab, either internal within the
company, contracted to work with the company, or external from the
company, can isolate the potential parents' DNA from the provided
samples for genome scanning from which Virtual Progeny can be
generated, as described herein. After analysis of the Virtual
Progeny, the results can be provided to the potential parents. The
results can inform the potential parents of the chances that their
future offspring will express one or more traits, such as traits
described herein. In certain instances, the potential parents can
also receive, for example, direct phone consultation with a genetic
counselor employed by the company, or contact information for
genetic counselors and/or other medical professionals who can
provide the potential parents with follow up testing and
consultation.
Virtual Progeny Assessment and Dating/Marriage Services
[0130] In other instances, the methods described herein can be used
to allow for the evaluation of potential partners in connection
with a matchmaking service. In one example, a Virtual Progeny
assessment can be offered to a customer in connection with a
matchmaking service, for example, through a single company or a
co-marketing or partnership relationship. A user of a matchmaking
service can order an assessment of Virtual Progeny described herein
to determine the probability that an offspring resulting from the
potential match between the user and a candidate partner will
express one or more traits described herein. The user can then use
this information to aid in evaluating the candidate partner for a
potential match. The matchmaking service can be an on-line service,
such as Shaadi.com, eHarmony.com and Match.com.
[0131] In a particular application, assessment of Virtual Progeny
begins with a customer order, where the customer pays a fee in
exchange for the assessment. For example, a customer can be a user
of a matchmaking service who is interested in evaluating another
user for a suitable match. Such a customer can use an assessment of
Virtual Progeny described herein to learn whether the potential
offspring of a match between such customer and a candidate partner
will express one or more traits, such as risk of disease. After
selecting a candidate partner to evaluate, a customer can pay for
both the customer's and the candidate partner's initial genomic
scans with the candidate partner's consent. In other instances, the
customer and the candidate partner can also pay separately for the
initial genomic scans. After a customer places an order, DNA
collection kits can be sent to the customer and the candidate
partner, and the customer and the candidate partner can each
deposit a biological sample into the collection kit. The collection
kits can then be returned to the company for sending to a specialty
lab or can be returned directly to the specialty lab for processing
according to the methods described herein. A specialty lab, either
internal within the company, contracted to work with the company,
or external from the company, can perform genomic scans on the
customer's and candidate partner's DNA from the provided sample and
perform an assessment of Virtual Progeny using the methods
described herein. The results of the assessment can then be
provided to the customer and/or the candidate partner, and the
customer and/or the candidate partner can use the results of the
assessment in determining whether the other party is a suitable
match.
Virtual Progeny Assessment and Sperm Donors/Egg Donors
[0132] In other applications, a female client seeking to have a
child can have a Virtual Progeny assessment performed with one or
more sperm donors to aid in selecting a donor. In one exemplary
method, potential sperm donors are first recruited by a sperm bank
Donors who complete the screening process and are considered
qualified by the sperm bank then provide a biological sample (such
as a buccal swab) that can be processed to obtain whole DNA
sequence, SNP genotypes, CNV genotypes or any other digital genetic
information.
[0133] A female client also provides a biological sample, such as a
buccal swab, which is used to generate a genome profile for the
female client. The female client genome is then recombined
computationally with each donor genome to generate a series of
independent Virtual Progeny genomes, as described herein,
representing each potential donor-client combination. Each Virtual
Progeny genome can then be assessed for the probability of
exhibiting one or more traits, such as increased risk of disease.
In certain instances, incompatible donor-client combinations are
subtracted from the total donor pool to obtain a client-specific
filtered donor pool, which can be used, e.g., as a starting point
for further selection by the client. In other instances, a client
can be given information on the probability of the incidence of one
or more traits from donor-client combinations, such as traits
preselected by the client, for further sperm donor selection by the
client.
[0134] In other applications, a male client seeking to have a child
can have a Virtual Progeny assessment performed with one or more
egg donors to aid in selecting a donor. Egg donors can provide a
biological sample (such as a buccal swab) to generate a genome
profile for the egg donor, as described herein. The male client
also provides a biological sample, such as a buccal swab, which is
used to generate a genome profile for the male client. The male
client genome is then recombined computationally with each egg
donor genome to generate a series of independent Virtual Progeny
genomes, as described herein, representing each potential donor
client combination. Each Virtual Progeny genome can then be
assessed for the probability of exhibiting one or more traits, such
as increased risk of disease. In certain instances, incompatible
donor-client combinations are subtracted from the total donor pool
to obtain a client-specific filtered donor pool, which can be used,
e.g., as a starting point for further selection by the client. In
other instances, a client can be given information on the
probability of the incidence of one or more traits from
donor-client combinations, such as traits preselected by the
client, for further egg donor selection by the client.
[0135] In yet other applications, a heterosexual couple seeking to
use a sperm or egg donor to have a child can use Virtual Progeny
assessments to screen potential donors. For example, the couple may
seek a sperm donor, and the female partner will be the genetic
parent of offspring with the sperm donor. Alternatively, the couple
may seek an egg donor, and the male partner will be the genetic
parent of offspring with the egg donor. In such instances, two
rounds of Virtual Progeny assessments can be performed. A first
round of Virtual Progeny assessment is performed using biological
samples from the heterosexual couple. A second round of Virtual
Progeny assessment is performed between the genetic parent and one
or more potential donors. The results of the first round of Virtual
Progeny assessment can then be compared with the results of the
second round, and a donor can be chosen whose Virtual Progeny
exhibits an acceptable amount of matching in one or more traits
with the Virtual Progeny from the heterosexual couple.
[0136] In still other applications, a female homosexual couple
seeking to use a sperm donor to have a child can use Virtual
Progeny assessments to screen potential sperm donors. Only one of
the female partners will be the genetic parent of offspring with
the sperm donor. A first round of Virtual Progeny assessment is
performed using biological samples from the homosexual couple. A
second round of Virtual Progeny assessment is performed between the
genetic female parent and one or more potential sperm donors. The
results of the first round of Virtual Progeny assessment can then
be compared with the results of the second round, and a sperm donor
can be chosen whose Virtual Progeny exhibits an acceptable amount
of matching in one or more traits with the Virtual Progeny from the
homosexual couple. In some situations, a Virtual Progeny assessment
is also performed with the second female partner and one or more
potential sperm donors, and a donor is selected whose Virtual
Progeny exhibits an acceptable amount of matching in one or more
traits with the Virtual Progeny from the homosexual couple.
[0137] In yet other applications, a male homosexual couple seeking
to use an egg donor to have a child can use Virtual Progeny
assessments to screen potential egg donors. Only one of the male
partners will be the genetic parent of offspring with the egg
donor. A first round of Virtual Progeny assessment is performed
using biological samples from the homosexual couple. A second round
of Virtual Progeny assessment is performed between the genetic male
parent and one or more potential egg donors. The results of the
first round of Virtual Progeny assessment can then be compared with
the results of the second round, and an egg donor can be chosen
whose Virtual Progeny exhibits an acceptable amount of matching in
one or more traits with the Virtual Progeny from the homosexual
couple. In some situations, a Virtual Progeny assessment is also
performed with the second male partner and one or more potential
egg donors, and a donor is selected whose Virtual Progeny exhibits
an acceptable amount of matching in one or more traits with the
Virtual Progeny from the homosexual couple.
[0138] In further applications of the methods disclosed herein, the
risk of disease in a potential progeny can be assessed, as well as
the likelihood of expressing a genetically influenced trait. As
with the other methods disclosed, a DNA sample from a first genomic
DNA are obtained from a first potential parent and a second genomic
DNA sample from a second potential parent. The presence or absence
of one or more nucleotide variants are identified at one or more
loci of at least one pair of chromosomes of the first and the
second genomic DNA samples and these identified nucleotide variants
for the first and second genomic DNA samples are compared to a
plurality of predetermined genomic sequences of haplotypes having
predetermined frequencies at predetermined loci to identify
haplotypes present in the first and second genomic DNA samples. A
first diploid genome profile for the first potential parent is
constructed. The first genome profile comprises the identified
haplotypes in the first genomic DNA sample and a linkage
probability determined by the frequencies of the identified
haplotypes in the plurality of predetermined genomic sequences. A
second diploid genome profile for the second potential parent is
constructed. The second genome profile comprises the identified
haplotypes in the second genomic DNA sample and a linkage
probability determined by the frequencies of the identified
haplotypes in the plurality of predetermined genomic sequences. A
first library is constructed that comprises potential haploid
gamete genomes from the first diploid genome profile by generating
a combination of the haplotypes identified in the first genomic DNA
sample using the linkage probability for each combination of the
identified haplotypes, while a second library is constructed that
comprises potential haploid gamete genomes from the second diploid
genome profile by generating a combination of the haplotypes
identified in the second genomic DNA sample using the linkage
probability for each combination of the identified haplotypes. The
method also entails combining a first haploid gamete genome from
the first library with a second haploid gamete genome from the
second library to form a diploid progeny genome. The diploid
progeny genome is compared to a database of genomes relating to
disease-associated or genetically influenced traits, thereby
assessing the risk of disease or the likelihood of expressing a
genetically influenced trait of the potential progeny.
Computer Systems/Processors
[0139] The methods and systems described herein can be used in
combination with one or more processors, having either single or
multiple cores. The processor can be operatively connected to a
memory. For instance, the memory can be solid state, flash, or
nanoparticle based. The processor and/or memory can be operatively
connected to a network via a network adapter. The network can be
digital, analog, or a combination of the two. The processor can be
operatively connected to the memory to execute computer program
instructions to perform one or more steps described herein. Any
computer language known to those skilled in the art can be
used.
[0140] Input/output circuitry can be included to provide the
capability to input data to, or output data from, the processor
and/or memory. For example, input/output circuitry can include
input devices, such as keyboards, mice, touch pads, trackballs,
scanners, and the like, output devices, such as video adapters,
monitors, printers, and the like, and input/output devices, such
as, modems and the like.
[0141] The memory can store program instructions that are executed
by, and data that are used and processed by, CPUs to perform
various functions. The memory can include electronic memory
devices, such as random-access memory (RAM), read-only memory
(ROM), programmable read-only memory (PROM), electrically erasable
programmable read-only memory (EEPROM), and flash memory, and
electro-mechanical memory, such as magnetic disk drives, tape
drives, and optical disk drives, which can be used as an integrated
drive electronics (IDE) interface, or a variation or enhancement
thereof, such as enhanced IDE (EIDE) or ultra direct memory access
(UDMA), or a small computer system interface (SCSI) based
interface, or a variation or enhancement thereof, such as
fast-SCSI, wide-SCSI, fast and wide-SCSI, etc, or a fiber
channel-arbitrated loop (FC-AL) interface.
[0142] The systems described herein can also include an operating
system that runs on the processor, including UNIX.RTM., OS/2.RTM.,
and Windows.RTM., each of which can be configured to run many tasks
at the same time, e.g., a multitasking operating systems. In one
aspect, the methods are utilized with a wireless communication
and/or computation device, such as a mobile phone, personal digital
assistant, personal computer, and the like. Moreover, the computing
system can be operable to wirelessly transmit data to wireless or
wired communication devices using a data network, such as the
Internet, or a local area network (LAN), wide-area network (WAN),
cellular network, or other wireless networks known to those skilled
in the art.
[0143] In one embodiment, a graphical user interface can be
included to allow human interaction with the computing system. The
graphical user interface can comprise a screen, such as an organic
light emitting diode screen, liquid crystal display screen, thin
film transistor display, and the like. The graphical user interface
can generate a wide range of colors, or a black and white screen
can be used.
[0144] In certain instances, the graphical user interface can be
touch sensitive, and it can use any technology known to skilled
artisans including, but not limited to, resistive, surface acoustic
wave, capacitive, infrared, strain gauge, optical imaging,
dispersive signal technology, acoustic pulse recognition,
frustrated total internal reflection, and diffused laser
imaging.
[0145] The methods and compositions disclosed herein are further
illustrated by the following examples. The examples are provided
for illustrative purposes only. They are not to be construed as
limiting the scope or content of the invention in any way.
Example 1
Generation of Virtual Progeny Genome
[0146] In this particular example, the generation of a Virtual
Progeny genome is a four step process. One of ordinary skill in the
art will understand that other steps may be added, combined, or
deleted as desired.
Step 1--Genome Scans
[0147] Processing is accomplished with the use of DNA microarrays,
DNA sequencing protocols, or other DNA reading technologies. In the
present example, a DNA microarray is used to generate information
relating to loci of interest. This information is utilized to
produce genome scans that include genotype information from the
plurality of loci of interest, which are defined by single base
polymorphisms ("SNPs or CNPs"), DNA sequence reads, copy number, or
other forms of personal genetic information. In the present
example, Jane Doe and John Smith provided samples, which have such
information provided for loci 01 through N (FIG. 1A).
Step 2--Expansion of Genome Scans to Generate Genome Profiles
[0148] Existing population datasets, genome scans of family
members, and a variety of computational tools and algorithms, known
to those skilled in the art, may be used in combination with each
person's genome scan to distinguish haplotypes, impute genotypes at
additional loci, and establish long-range genetic phasing. The
derived genome profile preferably incorporates phasing information
in the form of stochastic matrices between haplotypes. An example
of haplotype structure is shown in the figure below: loci 01-04 are
inherited as an indivisible block, which is a haplotype. Stochastic
matrices between loci 05 and 06 are shown in block boxes for John
and Jane (FIG. 1B).
[0149] With genome scans performed on two or more related persons,
phasing information is extended. In an example of genome analysis,
the UCSC genome browser is used to display phasing over large
maternally-inherited chromosomal segments that comprise 100 million
base pairs or more (FIG. 1C). A Monte Carlo simulation or Markov
process as described above is used to generate haplopaths through a
genome, where haplotypes are transmitted intact, and stochastic
matrices are used to move from one haplotype or locus to the next
one. In the example, John Smith's genome is converted into a series
of haplopaths by means of a Monte Carlo simulation (FIG. 1D).
[0150] Each individual genome profile is used to generate a pool of
VirtualGametes (FIG. 1E). Exemplary haplopaths in rows 1 and 7 are
illustrated.
Step 4--Virtual Progeny Permutations from Random Virtual Gametes
from Each Individual
[0151] Single Virtual Gametes from each person is chosen randomly
and combined to produce one permutation of a Virtual Progeny
genome. The process of Virtual Gamete choice and reproductive
combination to produce a diploid genome is iterated a sufficient
number of times such that the normalized sum of Virtual Progeny
permutations provides a stable estimate of the Virtual Progeny
genome probability distribution. For instance, the number of
iterations may be between about 10 and about 100. More preferably,
the number of iterations may be between about 100 and about 1000.
Most preferably, the number of iterations may be between about 1000
and about 100,000. In another aspect, the number of iterations may
be about 50 or greater. More preferably, the number of iterations
may be about 150 or greater. Most preferably, the number of
iterations may be about 3000 or greater.
EQUIVALENTS
[0152] It is to be understood that while the invention has been
described in conjunction with the detailed description thereof, the
foregoing description is intended to illustrate and not limit the
scope of the invention, which is defined by the scope of the
appended claims. Other aspects, advantages, and modifications are
within the scope of the following claims.
Sequence CWU 1
1
6118DNAHomo sapiens 1cacgcgtgcg gtgcgggc 18218DNAHomo sapiens
2caagcgtgcg atgcgggc 18318DNAHomo sapiens 3cacgcgtgcg gtacgggc
18418DNAHomo sapiens 4accatatgtg gcgtatat 18518DNAHomo sapiens
5cccgcgtgcg gtgtgggc 18618DNAHomo sapiens 6accgcgcact gtgtgtgc
18
* * * * *