U.S. patent application number 14/877885 was filed with the patent office on 2016-08-04 for methods of selection, reporting and analysis of genetic markers using broad-based genetic profiling applications.
The applicant listed for this patent is Omicia, Inc.. Invention is credited to Martin G. Reese, Charles White.
Application Number | 20160224722 14/877885 |
Document ID | / |
Family ID | 33303054 |
Filed Date | 2016-08-04 |
United States Patent
Application |
20160224722 |
Kind Code |
A1 |
Reese; Martin G. ; et
al. |
August 4, 2016 |
Methods of Selection, Reporting and Analysis of Genetic Markers
Using Broad-Based Genetic Profiling Applications
Abstract
Disclosed is a method for determining whether an individual has
an enhanced, diminished, or average probability of exhibiting one
or more phenotypic attributes and related methods of selecting a
set of genetic markers; for providing relevant genetic information
to an individual; of evaluating the probability that progeny of two
individuals of the opposite sex will exhibit one or more phenotypic
attributes; and for determining the genomic ethnicity of an
individual.
Inventors: |
Reese; Martin G.; (Oakland,
CA) ; White; Charles; (Oakland, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Omicia, Inc. |
Oakland |
CA |
US |
|
|
Family ID: |
33303054 |
Appl. No.: |
14/877885 |
Filed: |
October 7, 2015 |
Related U.S. Patent Documents
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Application
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Filing Date |
Patent Number |
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14542524 |
Nov 14, 2014 |
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14877885 |
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13170088 |
Jun 27, 2011 |
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14542524 |
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10552665 |
Oct 11, 2005 |
8417459 |
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PCT/US2004/010905 |
Apr 9, 2004 |
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13170088 |
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60461740 |
Apr 9, 2003 |
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60492707 |
Aug 4, 2003 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16B 40/00 20190201;
G16B 50/00 20190201; C12Q 2600/172 20130101; G16B 35/00 20190201;
C12Q 2600/124 20130101; G16B 20/00 20190201; C12Q 2600/106
20130101; G16B 30/00 20190201; C12Q 1/6883 20130101; G16C 20/60
20190201; C12Q 2600/156 20130101 |
International
Class: |
G06F 19/18 20060101
G06F019/18; G06F 19/24 20060101 G06F019/24; G06F 19/22 20060101
G06F019/22; C40B 30/02 20060101 C40B030/02 |
Claims
1-30. (canceled)
31. A computer-implemented method for providing an evaluation
indicative of a probability that an individual will exhibit one or
more phenotypic attributes, comprising: (a) evaluating markers of
the individual that are preselected based on association or other
studies to be linked with the one or more phenotypic attributes,
wherein the markers map to at least 1,000 sites; (b) evaluating a
pattern of the markers to determine a genomic ethnicity of the
individual; (c) determining a probability that the individual will
exhibit the one or more phenotypic attributes based at least in
part on the pattern of the markers and the genomic ethnicity of the
individual; and (d) generating a report with an evaluation
indicative of the probability that the individual will exhibit the
one or more phenotypic attributes.
32. The method according to claim 31, wherein the genomic ethnicity
is determined with respect to haplotype blocks.
33. The method according to claim 32, wherein the haplotype blocks
are determined based on a presence of the markers.
34. The method according to claim 33, further comprising evaluating
common variants associated with complex diseases, wherein the
common variants are present within the haplotype blocks identified
by the markers.
35. The method according to claim 31, further comprising
determining a likely applicability of clinical research results
based on the genomic ethnicity of the individual.
36. The method according to claim 31, wherein the markers comprise
a plurality of exon/intron junction sequences.
37. The method according to claim 36, wherein at least about 20% of
the markers in the preselected set are exon/intron junction
sequences.
38. The method according to claim 31, wherein the markers comprise
a plurality of promoter sequences.
39. The method according to claim 38, wherein at least about 20% of
the markers are promoter sequences.
40. The method according to claim 31, further comprising formatting
the report according to an organizational matrix that determines a
grouping and presentation of information on the report.
41. The method according to claim 40, wherein the organizational
matrix (i) groups together phenotypic attributes for which the
individual has an enhanced probability, or (ii) groups together
phenotypic attributes related to similar physiological systems.
42. The method according to claim 40, wherein the organizational
matrix (i) ranks phenotypic attributes as a function of potential
impact on a lifestyle or quality of life of the individual, or (ii)
ranks phenotypic attributes as a function of the genomic ethnicity
of the individual.
43. The method according to claim 31, wherein in the report, an
identity of the individual is not associated with data
corresponding to any genotypic characteristics or the probability
that the individual will exhibit the one or more phenotypic
attributes.
44. The method according to claim 31, wherein the markers map to at
least about 1,000 discrete loci.
45. The method according to claim 31, further comprising placing
associations between the one or more phenotypic attributes and the
markers on a two-dimensional grid for display on the report.
46. The method according to claim 31, further comprising performing
a targeted assay to identify the markers, which assay includes
nucleic acid amplification of a biological sample from the
individual.
47. The method according to claim 31, further comprising providing
the report for display on a graphical user interface.
48. The method according to claim 31, wherein in (a), the markers
are preselected based on association or other studies to be linked
with at least 10 phenotypic attributes, and wherein the markers map
to at least 1,000 sites.
49. The method according to claim 31, further comprising,
subsequent to (b), obtaining a marker score by comparing the
markers and the genomic ethnicity of the individual to a
multivariate scoring matrix that relates patterns of markers and
genomic ethnicity with probabilities of exhibiting phenotypic
attributes, which phenotypic attributes include the one or more
phenotypic attributes.
50. The method according to claim 49, wherein the multivariate
scoring matrix scores the markers at least with respect to three or
more criteria selected from the group consisting of penetrance of a
given marker in a population of interest, ontological
classification, conservation of mutated sequence sites at conserved
or less conserved sites, and biological significance.
51. The method according to claim 49, wherein the multivariate
scoring matrix comprises a combination of one or more scoring
matrix vectors selected from the group consisting of a descriptor
of family history, a descriptor of general medical physiological
values, a descriptor of mRNA expression levels, a descriptor of
methylation profiles, a descriptor of protein expression levels, a
descriptor of enzyme activity, and a descriptor of antibody
load.
52. The method according to claim 49, further comprising using the
marker score to determine the probability that the individual will
exhibit the one or more phenotypic attributes.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation application of patent
application Ser. No. 14/542,524, filed Nov. 14, 2014, which
application is a continuation application of U.S. patent
application Ser. No. 13/170,088, filed Jun. 27, 2011, which
application is a divisional application of U.S. patent application
Ser. No. 10/552,665, filed Oct. 11, 2005, now U.S. Pat. No.
8,417,459, which application is a National Stage filing of
International Application No. PCT/US2004/10905, filed Apr. 9, 2004,
which application claims the benefit of U.S. Provisional
Application No. 60/461,740, filed Apr. 9, 2003 and U.S. Provisional
Application No. 60/492,707, filed Aug. 4, 2003, each of which is
hereby incorporated by reference in its entirety.
BACKGROUND OF INVENTION
[0002] Personalized human health care products and services that
enable individuals to more actively manage their health based on
their genetic profiles have been increasingly heralded following
the publication of a draft human genome sequence in June 2000
(Venter, J C, Funct Integr Genomics. 2000 November; 1(3):154-5) and
a substantially complete sequence of the human genome in February
2001. (Venter, J C et al., Science 291(5507):1304-51 [2001]; Lander
E S et al., Nature 409(6822):860-921 [2001]). To date, however, the
commercial availability of personalized genetic profile products
and services has been extremely limited and costly.
[0003] The "genome" of an individual member of a species comprises
that individual's complete set of genes. Particular locations
within the genome of a species are referred to as "loci" or
"sites". "Alleles" are varying forms of the genomic DNA located at
a given site. In the case of a site where there are two distinct
alleles in a species, referred to as "A" and "B", each individual
member of the species can have one of four possible combinations:
AA; AB; BA; and BB. The first allele of each pair is inherited from
one parent, and the second from the other.
[0004] The "genotype" of an individual at a specific site in the
individual's genome refers to the specific combination of alleles
that the individual has inherited. A "genetic profile" for an
individual includes information about the individual's genotype at
a collection of sites in the individual's genome. As such, a
genetic profile is comprised of a set of data points, where each
data point is the genotype of the individual at a particular
site.
[0005] Genotype combinations with identical alleles (e.g., AA and
BB) at a given site are referred to as "homozygous"; genotype
combinations with different alleles (e.g., AB and BA) at that site
are referred to as "heterozygous." It has to be noted that in
determining the allele in a genome using standard techniques AB and
BA cannot be differentiated, meaning it is impossible to determine
from which parent a certain allele was inherited, given solely the
genomic information of the individual tested. Moreover, variant AB
parents can pass either variant A or variant B to their children.
While such parents may not have a predisposition to develop a
disease, their children may. For example, two variant AB parents
can have children who are variant AA, variant AB, or variant BB.
For example, one of the two homozygotic combinations in this set of
three variant combinations may be associated with a disease. Having
advance knowledge of this possibility allows potential parents to
make the best possible decisions about their children's health.
[0006] Diseases are often associated with the collection of atoms,
molecules, macromolecules, cells, tissues, organs, structures,
fluids, metabolic, respiratory, pulmonary, neurological,
reproductive or other physiological function, reflexes, behaviors
and other physical characteristics observable in the individual
through various means. The "phenotype" of an individual refers to
one or more of these observable physical characteristics. An
individual's phenotype is driven in large part by constituent
proteins in the individual's proteome, the collection of all
proteins produced by the cells comprising the individual and coded
for in the individual's genome.
[0007] In many cases, a given phenotype can be associated with a
specific genotype. For example, an individual with a certain pair
of alleles for the gene that encodes for a particular lipoprotein
associated with lipid transport may exhibit a phenotype
characterized by a susceptibility to a hyperlipidemous disorder
that leads to heart disease.
[0008] While efforts have been undertaken to create new
personalized active health management products and services based
on genetic profiles, several shortcomings characterize the existing
art. Among these shortcomings are the following:
[0009] First, the mix of existing products and services are in the
aggregate narrowly focused on a small set of disease phenotypes,
making them inefficient in enabling health management practices
that encompass a broad set of phenotypes;
[0010] Second, existing genetic testing products and services are
each focused on a genetic indication for one or a small set of
diseases;
[0011] Third, until the high cost of sequencing the genome of an
individual human declines by several orders of magnitude, an
alternative to genome sequencing technology must be used as the
basis for genetic profile products and services, and currently
available alternatives require substantial modification in order to
be integrated into the array of technologies and logistics
necessary to provide genetic profile products and services
encompassing a comprehensive set of diseases;
[0012] Fourth, existing informatics and database management tools
do not scale efficiently or effectively to the dynamic and
exponential growth of reported scientific research and clinical
findings underlying genetic profile products and services,
resulting in a high degree of information obsolescence;
[0013] Fifth, existing genetic profile products and services are
designed to be used at key life events, such as disease onset,
family disease onset, preconception and prenatal events, and
typically by one or more members of a family with an already-known
history a particular disease among its generations, rather than as
part of a comprehensive personalized health management program;
and
[0014] Sixth, genetic counseling practices, focused on point tests
assessed at key life events must be significantly altered to
support the increase in information volume and complexity arising
from broad-based genetic profiling.
[0015] The objective of personalized genetic profile health
management products and services is to provide individuals with
information about their predisposition to diseases. Armed with this
information, individuals can, in many instances, make decisions
about their dietary practices, pharmaceutical use, exercise, and
other lifestyle habits that are designed to better manage their
predisposition to diseases.
[0016] From individual to individual within any species, genes are
characterized by a very high degree of conservation in the sequence
of nucleotide base pairs comprising them. At certain locations in
many sites, however, the specific nucleotides that comprise a gene
can undergo alteration, or mutation. Mutations can be inherited
from a parent or acquired during a person's life. A hereditary
mutation will be present in all of a person's cells and will be
passed on to future generations, because the person's reproductive
cells (sperm or egg) will contain the mutation. An acquired
mutation can arise in the DNA of individual cells as a result of
many possible factors. For example, mutations in the DNA of skin
cells can be caused by exposure to the sun's UV radiation. Genetic
mutations in other cells can arise from errors that occur just
prior to cell division, during which a cell makes a copy of its DNA
before dividing into two. Genetic profile products and services
tend to focus on hereditary mutations.
[0017] The situation in which two or more sequence variants of an
allele exist at a site across different members of a population is
called a "polymorphism," typically defined as having an occurrence
frequency of larger than 1% within that population. Several
different types of polymorphisms are known in the art. By far the
most common form of polymorphisms are those involving single
nucleotide variations between individuals of the same species; such
polymorphisms are called "single nucleotide polymorphisms", or
"SNPs". To date, at least 1.42 million SNPs have been identified in
the human genome. (Sachidanandam R et al., Nature 409(6822):928-33
[2001]). While it is believed that the great preponderance of these
SNPs are harmless, there is a substantial number that have been
associated with various diseases.
[0018] SNPs that occur in the protein coding regions of genes that
give rise to the expression of variant or defective proteins are
potentially the cause of a genetic-based disease. Even SNPs that
occur in non-coding regions can result in altered mRNA and/or
protein expression. Examples are SNPs that defective splicing at
exon/intron junctions. Exons are the regions in genes that contain
three-nucleotide codons that are ultimately translated into the
amino acids that form proteins. Introns are regions in genes that
can be transcribed into pre-messenger RNA but do not code for amino
acids. In the process by which genomic DNA is transcribed into
messenger RNA, introns are often spliced out of pre-messenger RNA
transcripts to yield messenger RNA.
[0019] For example, in the "healthy" form of the protein
hemoglobin, the amino acid at the sixth position in the protein's
beta chain is glutamic acid. This amino acid is encoded in the
hemoglobin gene by the DNA codon guanine-adenine-guanine (GAG). In
some individuals, however, the adenine nucleotide in this codon is
replaced with the thymine nucleotide, resulting in a GTG codon
which codes for the amino acid valine. This substitution of valine
for glutamic acid alters the normal shape of the hemoglobin
protein. Red blood cells that contain these abnormally shaped
hemoglobin proteins exhibit a sickle shape and are unable to
perform the oxygen-transport function normally associated with red
blood cells. Individuals who are GTG homozygous (i.e., have
inherited a GTG variant from each parent) suffer from sickle cell
anemia.
[0020] In addition to sickle cell anemia, SNPs have been associated
with diseases such as cystic fibrosis, Huntington's chorea,
beta-thalassemia, muscular dystrophy, fibro muscular displasia,
pheny ketonuria, Type II diabetes, a hyperlipidemous disorder
associated with Apolipoprotein E2, at least one form of
hypertension, and some forms of migraine headaches. These
disease-associated SNPs are inherited through classic Mendelian
mechanisms. This type of SNP, however, is not believed to be the
predominant form of SNPs associated with the most common diseases.
This view is supported by the theory that common mutations in the
protein coding regions would entirely dysfunction protein
structures and therefore completely shutdown a specific pathway or
parts of such pathways, a result which is not supported by
observation. Nevertheless, it is believed that functional variants
associated with phenotypes further associated with diseases should
be clustered around non-coding sites that play an important role in
the functioning of the genome.
[0021] An example of such functional, non-coding sites are the
"splice sites" at which pre-messenger RNA transcripts are spliced
into messenger RNA (mRNA). The need for splicing arises from the
fact that within the pre-messenger RNA transcripts are RNA base
pairs that correspond to introns in the genomic DNA from which the
pre-messenger RNA transcript derives. The complex of proteins and
RNA at which splicing occurs is called the "spliceosome". (See,
e.g., Fairbrother et al. 2002).
[0022] A few different methods are commonly used to analyze DNA for
polymorphisms and genotype. The most definitive method is to
sequence the DNA to determine the actual base sequence (see, A. M.
Maxam and W. Gilbert, Proc. Natl. Acad. Sci. USA 74:560 (1977);
Sanger et al., Proc. Natl. Acad. Sci. USA 74:5463 (1977)). Patent
application 20020082869, "Method and system for providing and
updating customized health care information based on an
individual's genome", Anderson, Glen J., describes a system for
delivering personalized genetic profiling information based on
sequencing. Although such a method is the most definitive it is
also the most expensive and time-consuming method. Accordingly, the
sequencing of the human genome has only been performed for research
purposes such as the Human Genome Project on samples from a very
small number of individual humans, and at a cost of millions of
dollars per individual. While the cost of sequencing the genome of
an individual human has been following a steeply declining
price/performance curve, where performance is measured in terms of
accuracy and time, the substantial cost that still stands today
prohibits its use on a broad commercial scale. Until the cost of
sequencing technologies declines substantially further, the
delivery of genetic profiles to a significantly large number of
individuals cannot be cost effectively based on genome sequencing.
Moreover, as described below, simply being able to sequence an
individual's genome is not sufficient to generate and provide a
comprehensive genetic profile product or service to the
individual.
[0023] Another method of analyzing DNA for polymorphisms and
genotype is restriction mapping analysis. With this method genomic
DNA is digested with a restriction enzyme and the resulting
fragments are analyzed on an electrophoresis gel or with a Southern
blot to determine the presence or absence of a polymorphism that
changes the recognition site for the restriction enzyme. This
method can also be used to determine the presence or absence of
gross insertions or deletions in genomic DNA by observing the
lengths of the resulting DNA fragments. In this respect,
restriction mapping analysis has limited use in the type of
genome-wide search for polymorphisms and genotyping analysis
required for providing genetic profile products and services of the
type contemplated by the present invention.
[0024] Another method of determining the genotype of an individual
at a given site is to detect the presence of one or more nucleotide
sequences at that site known to be associated with a
predisposition, disease or other phenotypic abnormality. These
sites, also called "genetic markers," can be detected using various
tagged oligonucleotide hybridization technologies that are
significantly less costly than genomic sequencing and
allele-specific hybridization. Means now exist for constructing and
performing large-scale, multiplexed genetic marker hybridization
tests on biological samples from individuals, such as samples of
blood, saliva and urine. These means, such as very dense chip and
bead arrays, can enable a sample from an individual to be tested
simultaneously for the presence of thousands of genetic markers.
(Oliphant A et al., Biotechniques Suppl:56-8, 60-1 [2002]; and
Fodor S P, Science 251(4995):767-73 [1991]).
[0025] Splice junctions in pre-messenger RNA, 5-prime (exon to
intron transition) and 3-prime (intron to exon transitions), are
the sequence regions that are used as recognition sites for the
spliceosome and contain certain sequence motifs which typically are
conserved between related species. Nucleotide changes in these
binding sites can have a substantial effect on the spliced mRNA
product, depending on the effect of the mutation on the overall
binding affinity of the spliceosome components with the mRNA
sequence. Known mis-splicing behavior arises from exon skipping,
alternative splicing, protein coding truncation through the
introduction of a frame shift, and the disruption of the entire
mRNA production process. These changes have significant effects in
the mRNA and protein processing step and can totally change their
production. In addition, smaller changes can partially regulate and
influence quantitatively the splicing behavior of certain genes.
Additional sites known to be involved and sometimes even known to
regulate splicing, are the branch-point, enhancer and silencer
sequences (Fairbrother et al. 2002). Splice sites constitute
locations in the genome for evolutionary pressure to function
through nucleotide mutations.
[0026] Similarly, promoter regions in genes constitute locations in
the genome where the presence of a SNP can be used for determining
an individual's genotype. As gene-expression regulatory mechanisms,
promoter regions include the transcription start site and various
transcription factor-binding sites, including all the regions that
are involved in gene regulation.
[0027] The determination of the presence of polymorphisms or, less
frequently, mutations, in DNA has become a very important tool for
a variety of purposes. Detecting mutations that are known to cause
or to predispose persons to disease is one of the more important
uses of determining the possible presence of a mutation. One
example is the analysis of the gene named BRCA1 that may result in
breast cancer if it is mutated (see, Miki et al., Science,
266:66-71, 1994). Several known mutations in the BRCA1 gene have
been causally linked with breast cancer. It is now possible to
screen women for these known mutations to determine whether they
are predisposed to develop breast cancer. Some other uses for
determining polymorphisms or mutations are for genotyping and for
mutational analysis for positional cloning experiments.
[0028] In some cases, as illustrated in the case of the hemoglobin
SNP and sickle cell anemia, the association of a SNP with a disease
is direct and well-established and can be simply diagnosed. In many
other cases, however, the association of a SNP with a phenotype
that gives rise to disease or other adverse medical condition is
not well-established and different diseases, disorders have
different associations with different genotypes and SNPs. In these
cases, the association between genotype and phenotype can vary from
individual to individual in a complex manner that depends on the
individual's genome, age, family history, life style habits, and
other personal health and demographic factors. Consequently, direct
testing of the individual's DNA is not accurate 100% of the time in
predicting the onset of a genetically-based disease or other
adverse medical condition. In these more complex cases, there is a
probabilistic relationship between genotype (as characterized by
different variants and SNPs) and phenotype (as characterized by the
association of phenotypes with different diseases). In these cases,
the presence of a SNP at a given genetic site is not sufficient by
itself for the development of a pathological condition. In
addition, not all persons possessing a given SNP in a given variant
will develop a disease associated with that SNP. The onset of a
genetically-based disease may also depend on exposure to certain
conditions in a person's environment. Moreover, the same disease,
disorder, or other adverse medical condition associated with a
given SNP in a given variant may result from a different SNP at
another site. Consequently, comprehensive analysis of the
relationship between an individual's genotype and phenotype
requires a scoring matrix of variables along various dimensions and
a method of using this matrix to determine the probability that a
given genotype in a given individual will result in a given
phenotype.
[0029] To further illustrate the complexity of associating
genotypes with phenotypes, it is currently believed that the human
genome comprises approximately 30,000 genes while the human
proteome comprises potentially millions of proteins. The process by
which the information contained in the DNA comprising 30,000
distinct genes is transcribed into messenger RNA, which is in turn
translated into the sequence of amino acids comprising potentially
millions of distinct proteins, therefore adds significant
complexity to associations of genotypes with phenotypes. In
addition, in the search for unknown disease-causing variants,
whole-genome association scans using hundreds of thousands of
genetic markers simultaneously are likely to face serious
theoretical-statistical challenges, as well as practical
difficulties associated with the management of data sets of
enormous size and complexity. One obvious problem is the fact that,
the more genetic markers are used, the higher the expected number
of apparent, spurious associations that are the result of
statistical chance as opposed to true association stemming from
shared genealogy between genetic marker and causative allele.
[0030] Beyond this complexity of associating genotypes with
phenotypes, there has been rapid growth of data on the existence of
SNPs, their locations in the human genome, and associations of SNPs
with phenotypes that are further associated with various diseases.
This data arises from research on genomics, proteomics, preclinical
and clinical studies of pharmaceuticals and related research
gathered from laboratories, hospitals and medical clinics around
the world.
[0031] Genomics has the potential to change the way medicine is
practiced and impact the health of individuals. (E.g., Guttmacher A
E, Collins F S, Genomic medicine--a primer, N Engl J Med 347:
1512-20 and Collins [2002]; Varmus, Getting ready for gene-based
medicine, N Engl J Med 347: 1526-7 [2002]). Through sequencing and
genotyping, extensive personal genetic information is expected to
continue to be generated in large quantities in coming years.
(Trager R S, DNA sequencing. Venter's next goal: 1000 human
genomes, Science 298: 947 [2002]). The rapid growth in volume of
available genomic and proteomic data has been characterized by
substantial disorder and information obsolescence. For example,
currently, the Gene Ontology (GO) consortium (Ashburner, M et al.,
Gene ontology: tool for the unification of biology. The Gene
Ontology Consortium. Nat Genet 25: 25-9 [2000]) and the National
Library of Medicine's MeSH (Schulman J-L, What's New for 2001 MeSH,
NLM Tech Bull. 317 [2001]) are two of the best-known ontologies in
the bioinformatics domain. Neither of these ontologies, however,
currently contains the necessary information to support research
about the relationships between genes and disease in the context of
the human genome. While GO is well suited to classify a gene
product in terms of its function, process and location, it has no
terms to describe human diseases and the relations between them,
whereas MeSH, while rich in descriptions and classifications of
human disease, contains no information about sequences, little
information about genes, and no information about disease causing
mutations and SNPs. This is an unfortunate situation, especially in
light of the recent completion of the human genome sequence, and
its annotation.
[0032] A key aspect of research in genetics is the association of
sequence variation with disease genes and phenotypes. Sequence
variation data are currently available, for example, from OMIM,
HGMD (Hamosh A, et al., Online Mendelian Inheritance in Man (OMIM),
a knowledgebase of human genes and genetic disorders, Nucleic Acids
Res 30: 52-5 [2002]; Krawczak, M et al., Human gene mutation
database--a biomedical information and research resource, Hum Mutat
15: 45-51 [2000]; McKusick V A, Online Mendelian Inheritance in
Man, OMIM (TM), McKusick-Nathans Institute for Genetic Medicine,
Johns Hopkins [2000]) and others, both of which provide phenotypic
information and describe amino acid variation. Unfortunately, in
most cases these variation references do not provide sufficient
information to support their direct mapping onto current genomic
sequences and the associated annotated genes. Single nucleotide
polymorphism (SNP) data are held in dbSNP and other publicly
accessible databases. (E.g., Sachidanandam, R et al., A map of
human genome sequence variation containing 1.42 million single
nucleotide polymoiphisms, Nature 409: 928-33 [2001]; Sherry, S T et
al., dbSNP: the NCBI database of genetic variation, Nucleic Acids
Res 29: 308-11.2001). While these databases contain millions of
entries each including the position of the SNP on the genome, they
do not provide significant phenotypic information about the SNPs at
the levels which need to be reached, namely from the genome to the
phenotype and the clinic.
[0033] Moreover, as the volume of genomic and proteomic data grows
there are requirements to synthesize vast amounts of information to
enable clearer understanding of an individual's genetic profile.
Patent application 20020052761 ("Method and system for genetic
screening data collection, analysis, report generation and access",
Fey, Christopher T.; et al.) describes a system for generating
highly complex personal health reports to individuals concerning
their genetic test results, based on an aggregate set of genetic
markers and phenotypes.
[0034] Over the years most genomic and clinical advances have been
published in scientific journals. Molecular biology advances and
finding have been published predominantly in molecular biology
journals (e.g., Cell, Nat. Genetics, Am J. Hum. Genetics, etc.),
and clinical phenotype related findings have been published
predominantly in medical journals (e.g., N Eng J Medicine, Lancet,
etc.). Because of these different journals are directed to
different communities, large communication gaps have been created.
Thus, there now exists in the public domain two distinct
information resources, and neither is as valuable as it potentially
could be because current research efforts require their
integration. One part includes all large public genomic databases
and the other is the vast amount of clinical research data, mostly
held in publication, but increasingly accessible electronically.
There is a clear tendency in the community for a computer-based
classification of disease through ontologies and relating medical
diagnostic classification schemes such as the ICD-9 with gene
diseases (e.g., the NuGene project; Chisholm, R et al., The Nugene
Project [2003]).
[0035] There are currently various standardization efforts
occurring within molecular biology, most notably the gene ontology
(GO) consortium efforts. (Ashburner, M et al., Gene ontology: tool
for the unification of biology. The Gene Ontology Consortium. Nat
Genet 25: 25-9 [2000]). Additional ontologies such as the sequence
ontology, the mutation ontology and others are in work in process
(for an overview of ontology development please see the "global
open biological ontologies" (GOBO) web site
(www.geneontology.org/doc/gobo.html).
[0036] Databases containing information on polymorphisms are also
expected to have an important impact in the field of
pharmacogenomics. Pharmacogenomics is an area of research focused
on how variations in a patient's DNA can cause pharmaceuticals to
respond differently. The importance of understanding these
variations is underscored by the number of hospitalizations and
deaths that occur each year that are caused by adverse drug
reactions. One method of characterizing the genetic basis of drug
response is by cataloging variations in drug response as a function
of SNPs. The more SNPs cataloged, the more robust and effective the
database. However, collecting and sorting the SNPs becomes a huge
undertaking. In U.S. Patent application 20020049772, Reinhoff, et
al, provides a broad overview of polymorphisms, pharmacogenomics,
and classifying populations based upon sets of polymorphisms.
[0037] In addition to the scientific, technological and medical
complexities that characterize the development and
commercialization of genetic profile products and services, there
are growing legal and regulatory complexities. For example, patient
privacy has been a growing concern in multiple jurisdictions. In
Europe, the European Union Directive 95/46/EC is designed to
protect individuals with regard to the processing and movement of
their personal data. In the United States, under the Health
Insurance Portability and Accountability Act of 1996, commonly
referred to as "HIPAA", regulations have been adopted that set
forth "Standards for Privacy of Individually Identifiable Health
Information". The purpose of these regulations is to help guarantee
privacy and confidentiality of patient medical records. These
Standards are quite extensive and apply to health care providers,
insurers, payers and employers.
[0038] The confluence of all of the factors discussed above leads
to the conclusion that what has been lacking from the art, but
necessary for viable broad-based commercial provision of
personalized health management products and services based on
genetic profiling, is a method that satisfies the following
requisites:
[0039] (1) the genotype of the individual to whom such products and
services are being provided must be accurately and economically
determinable at a large number of sites in that individual's genome
relevant to a broad selection of different diseases;
[0040] (2) a large, dynamic, well-curated, database containing the
associations between diverse genotypes and phenotypes must be
maintained, easily accessed, and updated at very frequent
intervals;
[0041] (3) for each individual to whom such products and services
are being provided, the individual's genotype at each such site
must be easily analyzed and filtered through such database to
determine the individual's phenotype and construct the individual's
genetic profile;
[0042] (4) the genetic profile so constructed for each individual
and its implications must be easily communicated to the individual
and the individual's physicians and medical/health care counselors
in an effective manner that complies with health care, privacy and
other laws and regulations.
[0043] Various means exist for practicing each of these separately.
Each such means, however, suffers from various deficiencies, and a
method of collectively optimizing their combined practice is
required in order to provide health care management products and
services on a broad commercial scale at prices that are
economically attractive to both provider and customer. The present
invention provides these and other benefits.
SUMMARY OF INVENTION
[0044] The present invention relates to a method for determining an
individual's probability, whether enhanced, diminished, or average
probability, of exhibiting one or more phenotypic attributes
through evaluating genomic markers from that individual for
zygosity for the members of a preselected set of genetic markers.
In accordance with the method, the markers are compared to a
multivariate scoring matrix to obtain a marker score, from which it
is determined whether an enhanced, diminished, or average
probability of exhibiting one or more phenotypic attributes is
indicated. The multivariate scoring matrix correlates patterns of
marker zygosity with probabilities of exhibiting phenotypic
attributes.
[0045] The present invention is further directed to a method of
selecting a set of genetic markers. The method involves filtering
markers for inclusion in the set, based on a determination of
measures of phenotypic value and/or prioritization, such as but not
limited to, penetrance of the marker in a population or
subpopulation of interest; the degree of linkage of the marker to a
particular phenotype; the relative contribution of the marker to
communicating the phenotype; and the degree of statistical or
scientific confidence to be placed in any data associated with any
of the measures of phenotypic value or priority used.
[0046] The present invention further relates to a method for
providing relevant genetic information to an individual, or about
an individual to another interested party (e.g., a physician,
veterinarian, researcher, breeder, or owner). The method involves
identifying genotypic characteristics of the individual that
correlate with a relative probability of exhibiting one or more
phenotypic characteristics. The method also involves determining
for each of the one or more phenotypic characteristics whether the
individual has an enhanced, diminished, or average probability, of
exhibiting the characteristic by evaluating genomic markers for
zygosity (for example, but not limited to heterozygosity or
homozygosity) at each member of a preselected set of markers and
comparing the zygosity of the markers to a multivariate matrix that
correlates patterns of marker zygosity with probabilities of
exhibiting phenotypic attributes, determining whether the marker
score resulting from this comparison indicates an enhanced,
diminished, or average probability of exhibiting the one or more
phenotypic attributes. Then one or more selection criteria is
applied for each of the one or more phenotypic characteristics.
Each selection criterion applied imposes a total, a partial, or no
limitation on the information communicated to the individual.
Subsequently, information is identified that is relevant to the
individual's probabilities of exhibiting the one or more phenotypic
characteristics and is consistent with the limitations imposed by
the selection criteria, and the information is communicated to the
individual.
[0047] The present invention is further directed to a method of
evaluating the genetic profile combination (i.e, "compatibility")
of two individuals of the opposite sex (i.e., male and female) of
the same species, whether human or a non-human species, in other
words, a method of evaluating the probability that progeny of two
individuals of the opposite sex will exhibit one or more phenotypic
attributes. The method involves evaluating genomic markers from
each of the two individuals for zygosity (for example, but not
limited to marker heterozygosity or homozygosity) at each member of
a preselected set of markers, determining a probability
distribution for the zygosity for each member of the preselected
set of markers in the genomes of the progeny of the two
individuals, and comparing the probability distributions to a
multivariate matrix to obtain a probability distribution score,
wherein the matrix correlates patterns of marker zygosity with
probabilities of exhibiting phenotypic attributes, and determining
whether the probability distribution score indicates that the
progeny of the two individuals have an enhanced, diminished, or
average probability of exhibiting one or more phenotypic
attributes.
[0048] The present invention further provides a method for
determining the genomic ethnicity of an individual comprising
evaluating genomic markers from an individual at each member of a
preselected set of genetic markers, comparing the genotype for each
of the markers to a multivariate matrix, wherein the matrix
correlates patterns of genotypes with probabilities of exhibiting
phenotypic attributes, and determining the genomic ethnicity of the
individual as a pattern of the probabilities of exhibiting the
phenotypic attributes.
[0049] The inventive methods can be used to provide individualized
health management products and services based on an individual's
genetic profile as well as for pharmacogenomic studies in human or
other populations or subpopulations of varying sizes and
compositions (e.g., members of defined ethnicity, gender, age, or
disease or other physiologically abnormal condition, etc.). They
can also be applied to the analysis and reporting of genetic
profiles of various human populations and subpopulations, as well
as populations and subpopulations of other non-human species.
[0050] Thus, the present invention provides the benefits of
economical and effective genetic profiling that involves (i)
genotyping each individual who seeks such products and services for
a broad set of genetic variants, (ii) scoring these variants for
their association with the individual's phenotypic susceptibility
to the later onset of various diseases, (iii) providing information
on such genotype and phenotype information in a manner that can be
understood by the individual and the individual's physicians and
medical/health care counselors so that appropriate health
management steps can be taken prior to onset, (iv) tracking
on-going advances in research tailored specifically for individuals
based on the their genotypes and other personal information, and
(v) providing information on these on-going advances to the
individuals and their physicians and medical/health care
counselors. The practice of the present invention is applicable to
the provision of genetic profile-based health management products
and services characterized by logistical efficiencies and economies
of scale as well accuracy of analysis, interpretation and reports,
and the effectiveness of such tracking of related scientific
research and medical advances, and which can be offered in
accordance with applicable health care, privacy and related laws
and regulations. Moreover, the practice of the present invention is
a valuable tool for pharmacogenomics.
[0051] These and other advantages and features of the present
invention will be described more fully in a detailed description of
the preferred embodiments which follows.
BRIEF DESCRIPTION OF THE DRAWINGS
[0052] FIG. 1 illustrates the way lexica, thesauri, and ontologies
are used to semantically classify and define genomics data.
(Modified from Ashburner M et al., Gene ontology: tool for the
unification of biology. The Gene Ontology Consortium, Nat Genet 25:
25-29 [2000]).
[0053] FIG. 2 illustrates database schematic of the invention.
[0054] FIG. 3 represents BIOSQL schematic of the invention.
[0055] FIG. 4 represents a schematic overview of the SNP table of
the invention.
[0056] FIG. 5 represents a schematic overview of the DiseaseGene
module of the invention.
[0057] FIG. 6 represents a schematic overview of the OMIM-R module
of the invention.
[0058] FIG. 7 represents a portion of a MeSH disease ontology
("C04"), which has been populated with human disease genes.
[0059] FIG. 8 shows a variation map for the MTRR gene. Nonsense
coding mutations are shown in dark arrows (e.g., "Arg114Stp"),
missense coding mutations are shown in grey arrows (e.g.,
"Ile22Met") and silent mutations are shown with light grey arrows
(e.g., "Leu179Leu"). Intronic mutations are shown with light grey
arrows and upstream and downstream mutations within .+-.5 kb are
shown with black arrows at the top of FIG. 8 (original code from
Stein L et al., WormBase: network access to the genome and biology
of Caenorhabditis elegans, Nucleic Acids Res 29: 82-6 [2001]).
[0060] FIG. 9 (A-D) shows an exemplary list of 2236 genes that can
be included, in any combination of subsets, in a preselected set of
markers in accordance with the invention.
[0061] FIG. 10 shows an example report.
[0062] FIG. 11 shows an example detailed report.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0063] The present invention relates to a method for determining
whether an individual has an enhanced, diminished, or average
probability of exhibiting one or more phenotypic attributes. In
accordance with the method, genomic markers from an individual are
evaluated for their zygosity at each member of a preselected set of
markers.
[0064] For purposes of the present invention, an "individual" can
be of any species of interest, including a human or a non-human
species.
[0065] For purposes of the present invention, an enhanced,
diminished, or average probability of exhibiting one or more
phenotypic attributes, or a "relative probability," is with respect
to the general population in a particular geographical area or
areas, or with respect to a defined subpopulation thereof, for
example, but not limited to, a particular gender, age grouping, or
ethnicity, or some other identifying feature.
[0066] "Zygosity" of a marker is its genotype with respect to any
combination of heterozygosity or homozygosity for one or more
possible alleles at a locus, including, but not limited to, a
condition of heterozygosity or homozygosity for a particular allele
or alleles between chromosomes, or allele heterozygosity among
several possible alleles at a locus on one chromosome.
[0067] In accordance with the present invention, the preselection
of the set of markers is based on genotype/phenotype associations
with disease conditions or predispositions for disease conditions.
The association of genotypes with phenotypes and further
associating such phenotypes with diseases and predispositions
includes utilization of existing data in the relevant literature
and can also include data acquired from additional studies
incorporating the practice of the present invention. FIG. 9 shows
an exemplary set of 2236 genes that can be included (in its
entirety or in any combination of subsets) in a preselected set of
markers in accordance with the invention. The skilled practitioner
is aware of other markers that can be included in the preselected
set of markers.
[0068] In one embodiment, preselecting the set of markers can be
achieved using haplotype mapping to capture the majority of
historic recombination events that have occurred since the most
recent common ancestor of the sample group or population analyzed.
An example of such a haplotype map is by the National Institute for
Genome Research, which comprises a set of a few hundred thousand
polymorphic markers covering the entire human genome with
sufficient density along each chromosome to measure the diversity
of common haplotypes prevailing in each local region. Nevertheless,
haplotypes also can be defined as a block or region of DNA that has
been inherited as a unit. SNPs within that block of DNA will also
have been inherited together. These SNPs can then be used to
identify the presence of a particular block. This block can have
any size from a few base pairs to large hundreds of kilobases of
nucleotides. (Stephens J C, Mol Diagn. 4(4):309-17 [1999]). In one
embodiment of the present invention these markers are used to find
common alleles or variants associated with complex diseases and are
used to interpret local variation patterns and provide information
on the evolutionary origin of known polymorphisms that are the
cause of functional differences between alleles or variants. The
markers are prioritized in order to construct or "preselect" a
reduced marker set to avoid the data management and statistical
problems that attend whole genome scans using hundreds of thousands
of genetic markers simultaneously. In a preferred embodiment of the
present invention, certain genotypes are associated with certain
phenotypes.
[0069] In another embodiment, the predicted and/or measured effect
of different genetic markers on the DNA/RNA sequence changes that
occur in the splicing process at exon/intron junctions is used to
classify and preselect the set of genetic markers. Such
classification can be accomplished by scoring each genetic marker
candidate through the application of one or more predictive models
applied to the data in a multivariate scoring matrix for that
candidate. In this embodiment, all splice sites relevant to a
variant are first identified by mRNA identification and alignments
to the genomic DNA either through existing annotations or new
annotations, thereby enabling both informatic and chemical
retrieval of 5' and 3' splice site sequence regions. Second,
variants with SNPs within these splice site regions and the
associated sites are identified by the integrating and mapping of
public and private mutations within these splicing associated
sites. Third, the wild-type or original sequences and the mutated
sequences are scored with one or more predictive models selected
from the universe of: statistical models, such as weight matrix,
Bayesian, hidden Markov models, semi- or general hidden Markov
models; artificial intelligence models; and discriminative and
predictive models related to binding affinity. This process results
in splicing strength scores for the original variant and the
variant with the SNP. The difference of these scores or any other
metric applied to these scores can then be used to preselect
markers for genotyping analysis and to score, rank, and prioritize
factors in the construction of a multivariate scoring matrix.
[0070] In one embodiment of the present invention, the preselected
set of genetic markers comprises a plurality of exon/intron
junction sequences. In another embodiment of the present invention
at least about 20% of the genetic markers in the preselected set of
genetic markers are exon/intron junction sequences. In another
embodiment of the present invention at least about 40% of the
genetic markers in the preselected set of genetic markers are
exon/intron junction sequences. In another embodiment of the
present invention at least about 60% of the genetic markers in the
preselected set of genetic markers are exon/intron junction
sequences. In another embodiment of the present invention at least
about 80% of the genetic markers in the preselected set of genetic
markers are exon/intron junction sequences.
[0071] In addition to applying predictive models to such splice
sites for purposes of preselecting genetic markers and for
constructing genotyping assays, such predictive models can also be
applied for such purposes to large-scale promoter regions of
genomic DNA that can contain one or more SNPs, functional binding
sites within the 5' or 3'untranslated regions ("UTRs"), RNAi-genes,
and miRNAs. In a preferred embodiment of the present invention, a
method is provided of subjecting genetic markers within promoter
regions to the same analytic selection process as specified above
for splice sites. In one preferred embodiment of the present
invention, the preselected set of genetic markers comprises a
plurality of promoter sequences. In another preferred embodiment of
the present invention, at least about 20% of the markers in the
preselected set are promoter sequences. In another preferred
embodiment of the present invention, at least about 40% of the
markers in the preselected set are promoter sequences. In another
preferred embodiment of the present invention, at least about 60%
of the markers in the preselected set are promoter sequences. In
another preferred embodiment of the present invention, at least
about 80% of the markers in the preselected set are promoter
sequences.
[0072] In another embodiment of the present invention, genetic
markers of interest are identified and selected for inclusion in a
preselected set, based upon a matrix with dimensions comprising
genotypic and phenotypic criteria. These criteria can include,
among others: base pair sequence homology to another known genetic
marker sequence of interest; the presence of two or more regions of
DNA on the same chromosome or genetic marker (synteny); relevance
to the description of the molecular function, biological process
and cellular component of the protein coded by the gene under
investigation (ontology) and ontological classification;
conservation of mutated sequence sites at conserved or less
conserved sequence homology sites in the genome; quality of
research on the genotype, genetic marker and phenotype under
investigation; biological significance of the genetic marker (for
example, whether the marker specifies a protein coding change); and
regulatory value and classifications of the amino acid(s) specified
by the genetic marker.
[0073] "Lexica", "thesauri", and "ontologies" are used to
semantically classify and define genomics data. A "lexicon" is a
list of terms belonging to the same semantic class: BMP4 and DPP,
for example, both belong to the semantic class of "BMP" (i.e., bone
morphogenetic factor). A "thesaurus" provides a listing of the
synonyms for a term, or semantic class, and hierarchical
"ontologies" are used to "define" the terms contained in a lexicon
and a thesaurus. Long a cornerstone of computer science, ontologies
have recently become a major focus of research in bioinformatics.
Like controlled vocabularies, ontologies also enable data sharing
but, because they contain hierarchical relationships between their
terms, ontologies enable logical inference and deduction (see FIG.
1) on the data they contain, making them powerful tools for
hypothesis generation. The definition of a term is produced by
tracing the path from a term to the root of the ontology (FIG. 1,
panel c, path starting from "BMP"). The simple ontology shown in
panel c of FIG. 1, for example, defines "BMP" as "a TGF-.beta.
growth factor". Definitions apply to all members of a semantic
class and their synonyms, and can be used as a basis for logical
inference: e.g., "DPP is a DVR, a DVR is a BMP, a BMP is a
TGF-.beta., and a TGF-.beta. is a growth factor"; therefore it can
be inferred that "DPP is a growth factor" even if no document
explicitly states this fact. Note that the ontology shown in panel
c of FIG. 1 is a particular type known as an `isa-hierarchy`; other
types of ontologies exist, not all of which are suitable for
definition.
[0074] In one embodiment the selection of markers is based on
microsatellite markers. Microsatellite include simple sequence
repeats, short tandem repeats and simple sequence length
polymorphisms that are characterized as relatively short tandem
repeat nucleotide sequences, e.g. (TG)n or (AAT)n, generally less
than 5 base pairs in length. Microsatellite markers have adequate
physical densities for family-based (linkage) studies where the
number of recombination events between closely related family
members is small, hence markers even as distant as mega bases apart
are likely to be co-inherited. In addition, microsatellite markers
are generally useful in genetic studies because of their
hypervariability, co-dominance and reproducibility of
microsatellite markers.
[0075] The present invention is also directed to a method of
selecting a set of genetic markers which can be employed, for
example, in preselecting the set of genetic markers in accordance
with the method for determining whether an individual has an
enhanced, diminished, or average probability of exhibiting one or
more phenotypic attributes. Selection of a set of genetic markers
according to the present invention results in large set of genetic
markers that map to splice site regulation loci and promoter loci
for gene clusters and gene families associated with specific
phenotypes as well as complete genome-wide splice site and promoter
site genetic marker profiles.
[0076] Selecting the set of genetic markers is optimized by a
meaningful marker prioritization, which requires the incorporation
of additional facets of information to characterize the local
genome context. One useful approach is to employ existing lists of
candidate genes thought to be involved in a given disease. By
computational mining of the disease literature, identifying sets of
genes already implicated in related clinical phenotypes (or
homologous genes involved in related phenotypes in model
organisms), and by locating disease loci and disease-causing
mutations within the common frame of reference of the genome
sequence one can hope to extend these lists. Although extended,
such lists still drastically limit the number of loci one needs to
scan for disease association, while they are, hopefully, still
inclusive enough to reduce the risk of omission of true causative
loci. Within the targeted regions, typically a gene locus, one has
the choice of either including all known polymorphic markers, or
trying to make further reductions.
[0077] Some other useful considerations include marker location
relative to the functional units of the gene (coding, UTR, splice
site, regulatory, intron, etc.), marker zygosity (related to
population frequency), or ease of assay development (local repeat
structure, sequence composition for oligo design). Typically,
focusing on extended candidate lists reduces the DNA search space
from the entire genome to about one thousand to a few thousand
loci, or roughly 5% of the genome. Additional pruning can introduce
an additional 5-fold reduction. The result is a scenario where, at
the cost of genotyping 1% of all the markers available for a
genome-wide scan, one types all candidate loci, with the reasonable
expectation that at least half of the causative loci are
investigated. In addition to the reduction in genotyping cost, an
advantageous reduction of false positive associations results,
which false positive associations have been known to plague even
very large clinical association studies and have been cited as an
important cause of irreproducible reports of disease
association.
[0078] In a preferred embodiment of the present invention, the
preselected set of markers comprises genetic markers that map to at
least about 1,000 discrete loci. In another preferred embodiment of
the present invention, the preselected set of markers comprises
genetic markers that map to at least about 2,000 discrete loci. In
another preferred embodiment of the present invention, the
preselected set of markers comprises genetic markers that map to at
least about 3,000 to about 5,000 discrete loci. In another
preferred embodiment of the present invention, the preselected set
of markers comprises genetic markers that map to at least about
5,000 to about 10,000 discrete loci. In another preferred
embodiment of the present invention, the preselected set of markers
comprises genetic markers that map to at least about 10,000 to
about 15,000 discrete loci. In another preferred embodiment of the
present invention, the preselected set of markers comprises genetic
markers that map to least about 15,000 to about 30,000 discrete
loci.
[0079] In accordance with the invention and the method for
determining whether an individual has an enhanced, diminished, or
average probability of exhibiting one or more phenotypic
attributes, genomic markers corresponding to at least the
preselected set of genetic markers from an individual are evaluated
for zygosity. For purposes of the present invention, zygosity for a
marker is detected in a biological sample collected from the
individual that contains the individual's genomic DNA (such as, but
not limited to, a blood, saliva, or tissue biopsy sample, which
biological sample can be freshly collected or suitably stored to
preserve the DNA) by employing suitable biochemical genotyping
analytical assay means. Analytical hybridization or polynucleotide
sequencing means are typically employed, optionally after
amplification of DNA in the biological sample, for example, by
using PCR-based amplification means. High throughput analyses can
optionally be achieved by multiplexing techniques known in the art.
The genotyping analytical assay means can optionally be performed
with commonly available robotic apparati and/or very dense array
detection apparati.
[0080] The determined zygosity of the markers for the individual is
compared to the multivariate scoring matrix to obtain a marker
score, wherein the multivariate scoring matrix correlates patterns
of marker zygosity with probabilities of exhibiting phenotypic
attributes. In accordance with the invention, the multivariate
scoring matrix correlates patterns of marker zygosity with
probabilities of exhibiting phenotypic attributes, based on scoring
matrix vectors that can include descriptors of: family history,
general medical physiological measures or values (such as, but not
limited to, cholesterol levels, triglyceride levels, blood
pressure, heart rate, HGH or other hormone levels, red blood cells,
bone density, CD scan results, etc.), mRNA expression profiles,
methylation profiles, protein expression profiles, enzyme activity,
antibody load, and the like. The comparison with the multivariate
scoring matrix can be done manually or, preferably, by employing a
suitable computer software instantiation in which the multivariate
scoring matrix is algorithmically constructed and manipulated via a
programming language, for example, but not limited to, Java, Perl,
or c++.
[0081] Based on this comparison it is determined whether the marker
score indicates an enhanced, diminished, or average probability of
exhibiting one or more phenotypic attributes, relative to a
reference population, e.g., the general population of a chosen
geographical area, or another chosen subpopulation thereof in terms
of ethnicity, gender, age, or other identifying feature of
interest.
[0082] The present invention further provides a means for providing
relevant genetic information to an individual whose genetic profile
has been determined in accordance with the present invention.
[0083] The method for providing relevant genetic information to an
individual, and for generating information reports to be
communicated, includes identifying the individual's genotypic
characteristics by correlating those genotypic characteristics with
a relative probability of exhibiting one or more phenotypic
characteristics and determining for each of the one or more
phenotypic characteristics whether the individual has an enhanced,
diminished, or average probability of exhibiting the phenotypic
characteristic, in accordance with the present invention as
described hereinabove.
[0084] The method for providing relevant genetic information to an
individual then includes applying one or more selection criteria
for each such phenotypic characteristic, wherein each selection
criterion imposes total, partial, or no limitation on the
information communicated to the individual, identifying information
that is relevant to the individual's probabilities of exhibiting
such phenotypic characteristics and consistent with the limitations
imposed by the selection criteria, and communicating the
information to the individual in a report as described above.
[0085] In one embodiment of the present invention, the same or
different selection criteria are applied one or more additional
times to the determined probabilities of exhibiting each of the
phenotypic characteristics, information is identified that is
relevant to the individual's probabilities of exhibiting the one or
more phenotypic characteristics and consistent with the limitations
imposed by the selection criteria, and the information is
communicated to the individual.
[0086] In one embodiment of the present invention, at least one of
the selection criteria is specified in advance by the
individual.
[0087] In another embodiment of the present invention, at least one
of the selection criteria is a function of the availability of
treatments effective to modify the phenotypic characteristic.
[0088] In other embodiments, at least one of the selection criteria
is a function of the scope and quality of known research relating
to the phenotypic characteristic; or at least one of the selection
criteria is a function of the probability determination(s) for one
or more other phenotypic characteristics.
[0089] The information is then communicated to the individual,
whether directly, or indirectly via an appropriate intermediary, in
a report that generally includes an explanation of relevant
terminology; a summary of the genetic profile of such individual
subject; an explanation of each genotype assay performed on
biological samples from the individual, typically as categorized by
types of disease (such as, but not limited to, cancer,
cardiovascular disease, and the like), and in relation, where
applicable, to specific body organs, tissues and metabolic,
reproductive or other bodily functions and systems; a summary and
detailed results for each such genotype assay performed; a health
risk appraisal for such individual subject; general information
about genetics and genomics; and references for further
information. Such report can include summary sections of genes and
gene families important to individuals based on their specific
phenotypic impact and the demographic, ethnic, gender, age, and
related characterizations of each individual.
[0090] For example, the categories for such characterization can
include, but are not limited to, aging, women's health, and drug
interactions. These summaries can include (i) an assessment of the
overall genetic health of the individual in each health category
presented and the overall genetic health of the individual and (ii)
cross references to the more detailed information within the
report.
[0091] The information report can be communicated orally, but is
preferably communicated in a documentary format, whether written or
electronic, and in any suitable order of report presentation. Prior
to communicating the information to the individual, the information
is preferably, but not necessarily, formatted to present the
relevant phenotypic attributes according to an organizational
matrix, wherein the organizational matrix determines the grouping
and presentation of information to the individual. More preferably
the organizational matrix groups the various phenotypic
characteristics for which the individual has an enhanced
probability together. In some embodiments the organizational matrix
groups phenotypic characteristics related to similar physiological
systems together. In other embodiments, the organizational matrix
ranks the phenotypic characteristics as a function of the potential
impact on the individual's lifestyle or quality of life, or the
organizational matrix ranks the phenotypic characteristics as a
function of the "genomic ethnicity" of the individual, as described
herein.
[0092] In a preferred embodiment of the present invention, the
information report relates to a broad selection of diseases. Such
diseases may include, among others, cancer and those relating to
the following organs, tissues, and metabolic, reproductive and
other bodily functions and systems involved in human health,
including, but not limited to, cardiovascular, respiratory, kidney
and urinary tract; immune system, gastrointestinal, neurological,
psychoneurological, and hematological functions and systems.
[0093] In a preferred embodiment of the present invention, the
report for each such disease, disorder and other adverse medical
condition comprises information about the the relevant genes, the
sites assayed in such gene, the clinical association of variants at
such sites with relevant disease, the genotype of the individual
subject at each such site, information about the association of
such genotype with the phenotype associated with such disease, and
information about drug interactions for the individual subject
based on such genotype.
[0094] Prior to communicating the information to the individual,
the identity of the individual need not be associated with the data
corresponding to the genotypic characteristics, the relative
probabilities of exhibiting the phenotypic characteristics, or the
identified relevant information. A suitable coding or "blind"
system can be employed to shield the identity of the individual, if
appropriate.
[0095] The present invention provides that the format and substance
of the information report can be modified from time to time on an
ongoing basis, particularly in response to questions from
individuals, physicians and medical counselors about genetic
markers, genotypes, genotype assays, the association of genotype
with phenotype and diseases, and other aspects of such reports. In
a preferred embodiment of the present invention, such questions are
stored in a database and the overall effectiveness of communication
of the text and graphics presented in such reports are from time to
time assessed. In a preferred embodiment of the present
information, the report generator is constructed so that changes to
the information report format and substance can be effected
efficiently and economically.
[0096] The present invention also relates to a method of evaluating
the genetic profile combination (i.e., "compatibility") of two
individuals of the opposite sex of the same species, in other
words, a method of evaluating the probability that progeny of two
individuals of the opposite sex will exhibit one or more phenotypic
attributes. The method is applicable to providing genetic
counseling to pairs of individuals of the opposite sex relating to
preselected risks engendered by their respective genotypes, or
genetic profiles, to their progeny, whether prospectively or after
the birth of the progeny. Alternatively, the method is applicable
to providing genetic counseling to a recipient parent (or recipient
parents) of progeny resulting from, or intended to result from, the
use of donor gametes, e.g., donor sperm and/or ova.
[0097] The method involves evaluating genomic markers from each of
the two individuals whose gametes are to contribute, or
contributed, to the genetic inheritance of the progeny by the
formation of a zygote, i.e., the genetic or so-called "biological"
parents. Zygosity at each member of a preselected set of markers is
evaluated, and a determination is made of a probability
distribution for the zygosity for each member of the preselected
set of markers in the genomes of the progeny of the two
individuals. The probability distributions are compared to a
multivariate matrix to obtain a probability distribution score. The
multivariate matrix correlates patterns of marker zygosity with
probabilities of exhibiting phenotypic attributes, as described
hereinabove. Then it is determined whether the probability
distribution score indicates that the progeny of the two
individuals would have an enhanced, diminished, or average
probability of exhibiting one or more phenotypic attributes.
[0098] The present invention further provides a method for
determining the genomic ethnicity of an individual for the purpose
of determining the likely applicability of clinical research
results based upon the genetic profile of the individual. Clinical
research results are often only found in particular populations or
subpopulations, and these results are frequently correlated with
the ethnicity of the population or subpopulation. "Genomic
ethnicity" means a genetic profile of an individual having a
distribution of genetic markers in a preselected set of markers,
based on genotype/phenotype associations with disease conditions,
that is preponderantly consistent with the distribution of those
markers that is determined to be or is known in the art to be
characteristic of a particular ethnic population or subpopulation.
The determination of genomic ethnicity in accordance with the
method can often provide more useful information than an
individual's mere self-reporting of ethnicity.
[0099] The method involves evaluating genomic markers from an
individual at each member of a preselected set of markers,
comparing the genotype for each of the markers to a multivariate
matrix, wherein the multivariate matrix correlates patterns of
genotypes with probabilities of exhibiting particular phenotypic
attributes, and determining the genomic ethnicity of the individual
as a pattern of the probabilities of exhibiting the phenotypic
attributes.
[0100] In should be borne in mind that the genotype-phenotype
relation requires two axes, the phenotype (e.g., a particular
disease) and the mutations. Most known disease-causing mutations
are found in single-gene disorders because these have been the
easiest to find. Most of the OMIM disease entries are in this
class; hence most OMIM mutations cause single-gene disorders. These
disorders are usually very rare in the general population (although
can be much more prevalent in a given family or population group).
For diseases in this group, allele state often predicts disease
with an almost certainty. But there are more complex scenarios: (i)
with the cm-rent state of knowledge, genotype-phenotype association
can only be cast in statistical terms (i.e. having the "bad" allele
does not necessarily mean the individual will ever develop the
disease, but means a heightened chance or predisposition, relative
to the general population or a defined subpopulation, of developing
it in the future), or (ii) the association is only seen in some
studies but not in others, or (iii) the association is only seen in
a single family, in a population isolate, or one of the world
populations, with no knowledge of relevance for other populations.
Also typical in these cases is that the associated marker is almost
certainly not itself causative, merely an associated marker for the
disease. In accordance with the methods of the present invention,
each of these scenarios are taken into account as appropriate in
constructing the multivariate matrix, or multivariate scoring
matrix, and as the available information improves concerning
genotype/haplotype relationships with a given phenotype, the
multivariate matrix, or multivariate scoring matrix, can be
updated, expanded, thereby enhancing its predictive value when
employed within the inventive methods. However, the practice of the
inventive methods is not limited by the precise contents of the
multivariate matrix at any given time, which will vary as new
information is incorporated.
[0101] The information about the genetic profiles of individuals
for whom genetic profiles have been generated of the type produced
in accordance with the present invention can facilitate delivery of
appropriate therapies to the individuals, can enable the further
identification of novel genotype-phenotype relationships to support
the discovery of new therapies, and can provide novel indicators of
health. Thus the present invention can be applied to
pharmacogenomic analysis and the development of more effectively
directed drug therapies.
[0102] In another application, the information generated in
accordance with the present invention can be maintained in an
updatable database. The database can be constructed using widely
available database management tools, such as Oracle or open source
database tools such as postgres. The information in the database
can be used to select individuals for whom investigational
therapies or newly commercially available therapies could probably
be most effective, based on their genetic profiles. For example,
such selection can be made positively on an indication that
individuals with a disease or predisposition to disease can benefit
from particular therapies, or negatively on an indication that such
individuals are at risk with a particular therapy. Such
classification schemes can be used to identify subsets of
populations with complex disease for purposes of course of therapy
decisions.
EXAMPLES
Example 1
Use of a Genetic Marker as an Index to Literature
[0103] A genetic marker, and a variant found at that marker, can be
characterized by a number of factors. These can include a unique
sequence surrounding it, a fixed offset from a known reference
sequence point, or a specific amino acid change in a particular
protein. The clinical literature refers to markers, mutations, or
polymorphisms in a variety of different ways. In accordance with
the present invention, individual markers and variants can be
characterized using these and other attributes in order to use them
to index clinical literature. This index can be constructed of a
single marker, or of a set of markers. By indexing literature in
this way, a consolidated report can be constructed based upon these
markers. If for example, there exists a genotyped, or sequenced
database of markers that have been found to be present in an
individual, this marker database can be used to create an extract
of clinical literature that is particularly relevant to the
individual. This use of an individual's markers or variants as a
literature index, is a useful component in providing personalized
communication of information from literature and other databases,
based upon an individual's genetic profile.
Example 2
Association Representation
[0104] As genetic disease associations become more complex,
interpreting the implications of these associations becomes more
complicated. The overall quality of the association, in a number of
dimensions, becomes increasingly important. In addition, in order
to understand the importance of a particular association to an
individual, an association, in all of its texture, needs to be
viewed in the overall context of the individual's genetic profile.
Characterizing and representing this quality, both independent of
the individual and in the context of the individual's genetic
profile become fundamental to the appropriate usage of the
information.
[0105] The factors that shape the implications of a genetic
association are diverse. They range from the causal linkage seen
with the association (e.g., the percentage of disease cases that
are associated with a particular association and the percentage of
individuals with a given marker who develop the disease), to
factors related to the breadth of the research (e.g., number of
studies, number of individuals studied, and number of ethnicities
studied). By characterizing and representing the associations in
this manner, individuals, their counselors, and their health care
providers can better understand, appropriately value, and make use
of them.
[0106] One method of representing the quality of an association,
for the purposes of analyzing, evaluating, determining,
identifying, comparing, or formatting the information and/or of
communicating or presenting the information to the individual, is
to place associations on a two dimensional grid. The axes of this
grid would be the percentage of disease cases that are associated
with a particular association and the percentage of individuals
with a given marker who develop the disease. Each association would
be represented, e.g., by a colored circle, on this grid; the color
can represent the number of studies of this association, with for
example, red representing a low number, yellow representing an
intermediate number and green representing a large number of
studies. The darkness of the circle can represent the number of
ethnicities studied, and the size of the circle can represent the
number of individuals. In this way, at a glance, a viewer can
understand how a particular association compares to others, and the
overall quality of the association itself. For example, a light,
red, circle close to the origin is a very weak association. A dark,
green circle in the top right of the graph is very significant,
well-studied in large populations.
[0107] The same graph can be constructed of associations for those
markers that have been found in an individual's genetic profile.
With this, an individual or his or her counselor or health care
provider is immediately focused on those associations that are most
significant.
[0108] Of course, this multi-dimensional representation can take
many graphical forms. An alternative method might use a radar
graph. A radar graph is a two dimensional polar graph that enables
one to simultaneously display many variables. It does this by
plotting each variable along a different radial axis emanating from
the origin of the polar plot. If one has ten variables, then there
will be ten radial axes thirty six degrees apart. Small values are
near the center of the polar plot and large values near the outer
circumference. An experiment might result in multiple measured
variables and the investigator wants to compare the results of this
experiment repeated under different conditions. If one connects the
variable values on each radial axis with a straight line then a
distorted star-like pattern (or "polygon") is created. One can then
visually compare the patterns created by the lines representing the
different conditions for the experiment. In addition, several
polygons can be plotted on the same set of radial arms through the
use of one color or line pattern per polygon.
[0109] The preceding are illustrative, and not exhaustive examples,
of how graphical representation of a number of factors that
characterize a genetic association can support an enhanced
understanding of the association, both individually and in the
context of other associations, as well as independently and in the
context of an individual's genetic profile.
Example 3
Characterizing Patterns of Associations Found in an Individual's
Genetic Profile
[0110] Genetic profiles, defined as a set of markers found in an
individual's genome ranging from a small set of markers to the
entire genetic sequence, are used to generate or predict attributes
of individuals that can be used for a variety of applications.
These applications can range from identity verification to
determining disease predisposition and drug interactions to feature
prediction (e.g., height, hair color or eye color). In addition,
the attributes can range from being absolutely correlated to the
genetic profile, to being shaped by the genetic profile, to being
unpredictable from the genetic profile. Understanding and
characterizing the degree to which a genetic profile specifies an
attribute has great utility. For example, the generation of a
profile from DNA of a suspected criminal is much more useful when
the degree to which the feature can be predicted is
well-understood. If red hair is almost assured, but height much
less specified by the profile, searches can be better refined.
Capturing and representing this information becomes much more
important as the number of attributes to be analyzed grows. The
methods that can be used to extract and capture these patterns
include statistical models, Bayesian networks, hidden Markov
models, and other machine learning techniques.
Example 4
Constructing Multivariate Matrix
[0111] A goal of the present invention is to bridge the time gap
that currently exists between the latest research advances and the
final benefit to the consumers in the health care system. Through
genetic testing and the information delivery this gap can be
bridged and shorten by a huge factor. For example, individuals can
be tested for a newly identified functional polymorphism (e.g., the
RANTES gene; Hizawa, N. et al., A functional polymorphism in the
RANTES gene promoter is associated with the development of
late-onset asthma, Am J Respir Crit Care Med 166: 686-90 [2002];
see Table 1 below), information about which can be added to the
multivariate matrix, or multivariate scoring matrix in accordance
with the present invention. Suitable genotyping assays can be
developed for the newly identified polymorphisms described in the
original article in a matter of weeks. After the genetic testing
with the genotyping assay, the result can be communicated to the
tested individual about her or his genetic profile and the relevant
clinical phenotype information like specific risk factors--in the
RANTES gene example its relationship with asthma--that are
correlated with a specific genetic profile. The complexity of the
clinical information for the RANTES gene is not limited to this
article but there exist many more articles describing the
relationship between genetic markers in the RANTES gene and a
clinical phenotype. One useful database to capture more
comprehensively genetic literature information concerning a disease
can be found in the "Online Mendelian Inheritance of Man" (OMIM)
database (Hamosh, A et al., Online Mendelian Inheritance in Man
(OMIM), a knowledgebase of human genes and genetic disorders,
Nucleic Acids Res 30: 52-5 [2002]; McKusick V A, Online Mendelian
Inheritance in Man, OMIM (TM). McKusick-Nathans Institute for
Genetic Medicine, Johns Hopkins [2000]). The OMIM database contains
textual information and references on inherited diseases and
genetic disorders. It also contains copious links to MEDLINE and
sequence records in the Entrez system, and links to additional
related resources at NCBI and elsewhere. This database is
accessible on the internet
(www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM), as well as in a
hard copy book. The electronic version is also distributed in XML
format under an NIH license from the National Library of Medicine.
While the OMIM database is very useful for clinical and academic
research it is still far too technical for an individual and
her/his healthcare provider (see as an example the OMIM entry for
the RANTES gene in Table 2 below).
[0112] A prototype system was developed that allows scientists to
position alleles described in the Online Mendelian Inheritance in
Man (OMIM) database on the build 33 assembly of the Human Genome
from the National Center for Biomedical Information (NCBI). Once
placed on the assembly, the system automatically classifies the
alleles within the context of a gene and its genomic structure;
i.e., coding (synonymous/non-synonymous), UTR, etc. The OMIM
database is currently the most comprehensive and relevant database
for human genetic disease, and we have therefore focused our
efforts on mapping the OMIM alleles to the genome. Toward this end
we have developed a `deterministic` algorithm for OMIM allele
mapping. Most of the alleles described within OMIM are clinically
relevant, and have links to the appropriate scientific
publications; in an attempt to better manage the clinical data
associated with these alleles, we have also developed a concise and
high-level disease ontology.
[0113] All five subsystems of the prototype system have been tested
with a set of known, well-characterized disease genes. Specific
care and focus have been given to the user-friendliness and user
interaction of the annotation station, an extendable and flexible
design for the database infrastructure and a practical approach for
the OMIM allele mutation mapping algorithm, to capture as many OMIM
mutations within the genome as possible, by avoiding false
positives. During the genetic disease ontology development, we
largely made use of existing medical textbook classifications, due
to the widespread familiarity of these classifications within the
medical community. The following sections detail the software and
the science developed.
(a) Prototype Visualization Annotation Station
[0114] We built a prototype SNP and mutation oriented genome
annotation station, in which we leveraged existing open source
genome browser software as developed in the wormbase project
(Stein, L. et al., WormBase: network access to the genome and
biology of Caenorhabditis elegans, Nucleic Acids Res 29: 82-6
[2001]). For additional data manipulation work we have used PERL
code from the open source BioPerl project (Stajich, J E et al., The
bioperl toolkit: perl modules for the life sciences, Genome Res 12:
1611-8 [2002]). The annotation station is centered on genes, and
with a gene symbol the genomic region for the specified gene can be
viewed.
[0115] Our "GeneViewer" can display different types of genomic
sequence variations within a graphical user interface. The display
now shows graphically a genomic gene region with 5 kb sequence
upstream and downstream of the start and stop codon of a gene. It
also displays neighboring genes that are located within this +/-5
kb window. The gene structure is displayed using the "traditional"
exon-intron viewer, in which exons are linked with arcs that span
the introns. Currently the display color-codes and groups missense,
nonsense, coding and non-coding mutations onto the gene structure.
The variation symbols list the nucleotide and/or protein amino acid
change in a small text glyph. A variation map screen shot of this
GeneViewer can be seen in FIG. 8, which shows the MTRR gene.
[0116] Following the GeneViewer is a short "Gene Summary", which
tabulates all the general information about a gene, such as NCBI's
LocusLink identifier(s), genomic Genbank records, RefSeq links,
full textual gene names as well as internal disease classifications
from our Ontology (see below) that are assigned by the
annotators.
[0117] Following the Gene Summary is the "Variation Summary", which
seeks to list in table format all known genetic markers within this
gene from different mutation databases. This table currently lists
OMIM alleles and dbSNP entries including their sequence position
within the genomic sequence as well as the protein sequence.
Furthermore, codon position, codon changes, and a short functional
characterization (missense, non-sense, exonic, intronic, 5'UTR,
3'UTR, upstream or downstream) are included. A snapshot for the
MTRR gene can be seen in FIG. 8.
[0118] In the next two sections, the "mRNA Summary" and the "CDS
Summary", raw mRNA and coding sequences (CDS) including the Genbank
annotations. On demand, the genomic sequence can also be
displayed.
[0119] The "Protein Summary" follows, which besides the translated
coding region lists under the wild type sequence the protein
mutations caused by the sequence variation lists in the Variation
Summary table. This is very useful, because for each occurring
nucleotide change the resulting protein mutation can be seen.
[0120] At the end of each gene page, the corresponding OMIM entry
is listed from a local OMIM copy from our database to avoid the
reloading of every OMIM entry from the NCBI site through the
internet ("OMIM Summary"). This allows the annotators to review the
verbal gene annotation within the same display.
[0121] The data required for effective and accurate annotation are
diverse. To address this diversity, the visual annotation station
is split into these different views. The prototype also allows to
add other information sources and even to generate new summary
views if necessary. Improvements can be added, including various
hyperlinks with the interface, that can link to external (NCBI:
dbSNP, RefSeq, Genbank) as well as internal sources (OMIM, filtered
version of PUBMED; see text mining algorithms below). For example,
this can be addressed by downloading the OMIM database from the NIH
and incorporating the OMIM locus entries within the genome
annotation station. This allows to cross-link mutations within a
gene document and get fast access to the allele variation
description in OMIM on the same page.
(b) Prototype Annotation Pipeline Software
[0122] We have built a selection of PERL scripts to download the
latest human genome assembly as well as annotations from Genbank
and RefSeq. The current prototype system has loaded the build 33
assembly, but is easily updated. Furthermore, dbSNP and the OMIM
databases have been licensed and downloaded from the NCBI ftp
site.
[0123] To capture and identify all known human genes predisposing
to disease, we have begun with a widely accepted, recently
published paper tabulating and summarizing all known human disease
genes (Jimenez-Sanchez, G et al., Human disease genes, Nature 409:
853-5 [2001]). This set consisted of the non-somatic genetic
associations contained in the OMIM database. This set of 923 human
disease genes was expanded with a collection of genes derived from
an in-house analysis of hundreds of current clinical research
papers, selecting those associations that were most significant and
"actionable" by the clients of our service. Any hint of an
association between a novel disease gene and a clinical phenotype
within an abstract triggered the collection of the paper from the
library or online databases. The original papers were then reviewed
and if significant, the gene was manually added to our collection
of disease genes. Additional papers that justify the selection of a
certain gene into our disease gene set were noted as additional
evidence. A broad disease classification scheme (see "high-level
disease ontology section") was developed during this process as
well. The total number of genes in our system is currently 1,732.
Given that the current estimate of the total number of human genes
is between 25,000 and 30,000, this would mean that between 5.5% and
7.0% of human genes can be classified to be disease related. Of
course, this is only a snapshot of the current clinical research
and we expect the percentage to increase over time. 98% of these
genes already had OMIM entries.
[0124] Of the 1,732 hand-selected genes, only 1,614 could be
uniquely located in the latest genome assembly (see Table 3). The
remaining 118 genes were either annotated at multiple loci within
the human genome or were missing from both RefSeq and Genbank. The
location of these 118 genes within the human genome sequence is
next to be manually determined, as many belong to small paralogous
families that have significant clinical implications.
[0125] During the loading process of the flat file OMIM entries, we
have parsed out the OMIM allele identifiers. It has been very
difficult to develop this PERL parser due to the incredible mixture
of formats for allele annotations. The allele identifiers have then
been used to locate the protein mutations within the human genome
assembly and the Genbank annotations. As can be seen in Table 3,
57.29% of the OMIM alleles could be localized directly within the
genome assembly through the Genbank annotations using only the
protein variation allele identifiers. This procedure of comparing
OMIM alleles with the Genbank annotations is a quality check for
the Genbank annotations. The main reason why not all of the OMIM
alleles could be easily mapped to the genome has primarily to do
with the different protein sequences, in which the first mutations
have been described. Case studies for some of these genes have
shown that the underlying protein sequence has usually been
extended on the 5' end of the coding sequence over time. In
previous publications only the 3' part of a gene sequence has been
known--typically because of the early 3' EST sequencing
efforts--and old, incorrect amino acids variation annotations have
been propagated through the literature until today. In an attempt
to circumvent this problem we have developed a `deterministic`
algorithm for OMIM allele mutation mapping; see below.
[0126] The integration of dbSNP polymorphisms has been straight
forward, because they are linked to a specific gene sequence within
Genbank using the dbSNP identifiers ("rs" and "ss" numbers).
Surprisingly only a very small number, 1.87%, of OMIM alleles have
a dbSNP entry (see Table 3). We speculate that there are two
reasons for this. First, most of the alleles in OMIM are Mendelian
mutations and have a very low minor-allele frequency. Secondly,
SNPs have only recently been discovered during the human genome
project and the SNP consortium efforts and clinical studies showing
clinical correlations are still under way and they therefore have
no allele entry within OMIM.
[0127] A preliminary study encouraged by the surprising low overlap
between OMIM alleles and dbSNP entries performed using HGMD showed
that OMIM alleles are represented in HGMD. We could find
approximately 40% of the OMIM alleles from our 1,732 disease genes
annotated in HGMD. This is a most promising finding and allows us
to conclude that a lot of information can be gained by using the
annotations from HGMD to link OMIM with the human genome
assembly.
(c) Prototype Database Infrastructure
[0128] A prototype database infrastructure was built. Existing
public domain database schemas were adapted to fulfill specific
variation related needs. Furthermore, software was developed to
load and update the specific genome and clinical databases. All
annotation pipeline software as well as the visualization
annotation station are driven by this database system. The detail
of the prototype database system is as follows.
[0129] The database uses a loosely-coupled modular architecture
(FIG. 2). Within each module, tables are tightly-coupled by means
of relational foreign keys. Between modules, tables are implicitly
linked by means of dbxrefs (database accessions) and gene
symbols.
[0130] This modular architecture facilitates better software
engineering and database management. Modules can be swapped in and
out easily. Loading large data bulks or data management can be
carried out on any module independent of the others. For example,
if NCBI releases a new human genome assembly build, or a new dbSNP
build, we can bulkload this without perturbing our marker data.
[0131] We used the Postgresql version 7.3 relational database
management system (RDBMS). Postgresql has the advantage of being
open source, as well as extremely robust, with a large community of
users and developers who are eager to provide support for free. It
is much simpler to manage and administer than commercial RDBMSs
such as Oracle, and has many advantages over the other open source
RDBMS like MySQL. We believe MySQL is not suited to a production
environment because of its lack of full SQL92 support, such as
views, sub-selects and foreign key integrity.
[0132] We have taken care not to incorporate any Postgresql
specific functionality into the design, which means that a port to
a commercial RDBMS such as Oracle should in the future be a
relatively uncomplicated task.
(d) BioSQL Module
[0133] BioSQL (obda.open-bio.org) is a third party open source
database schema and a PERL API (Application Programmer Interface).
It is a generic database of sequences and sequence features. It is
ideal for representing sequence data such as that from GenBank, the
EMBL sequence database, SwissProt and RefSeq. We used it to store
the NCBI Human Genome and NCBI human RefSeq. Data, loaded from NCBI
flat file format using a PERL script that comes with the BioSQL
distribution.
[0134] The BioSQL data model is based around the concept of
`bioentries` and `seq_features`. This is equivalent to a
Genbank/RefSeq record and feature table entry, respectively. One of
these records typically contains features of type gene, mRNA, CDS
and variation. The intersection between these locations tells us
where the markers such as SNPs are with respect to exons, introns,
untranslated regions (UTRs) and up/downstream regions. This data
model is also suitable for housing the locations of the HGMD and
OMIM variations mapped onto genomic coordinates. The most relevant
part of the BioSQL schema can be seen in FIG. 3.
[0135] The snp table (FIG. 4) stores single base pair and other
variation features. Any one marker can have multiple effects, at
the genomic, transcriptional and protein levels--this is what the
snp_function table is for. It allows us to easily query for markers
that affect a particular genomic area (exon, intron, UTR, protein,
intergenic) or for protein-affecting markers what is the amino acid
modification. Each of these effects is with respect to a particular
gene; we have a weak (a non-foreign key that is not enforced) link
to the gene table (in the DiseaseGene module, see below) via the
official gene symbol.
(e) DiseaseGene Module
[0136] The DiseaseGene module (FIG. 5) is primarily for storing
literature based curation of genes implicated in diseases, and
associated information such as likely markers. Note that the
gene_to_snp table is primarily for storing information on the
literature curator's notes on possible markers, and is only weakly
linked (implied via locational correspondence) with the snp table
in the so-named OMICIASNP module. The gene table uses the official
gene symbol; alternate symbols are stored in the gene synonym
table. Each gene can have multiple disease categories, associated
diseases or OMIM IDs.
(f) Phenotype Module
[0137] The characteristics of the Web based graphical user
interface (GUI) that support the manual mutation curation and
mapping of phenotype information to the current genome assembly,
are of a client/server design, with a graphical representation of a
gene sequence with the associated mutation data through a Web
browser, delivered through an integrated database. Using a web
browser, the user interface can be accessed internally and secured
externally for annotation and reviewing. From a user's perspective,
the diverse analytic tools and algorithms, described below, appear
as a single unified application. Users are able to edit the data
and add annotations to the gene sequence throughout the
process.
(g) OMIM-R
[0138] The OMIM-R module (FIG. 6) is a normalized relational model
for housing data imported from OMIM. This module is focused around
the OMIM disease table, which has an OMIM identifier as a primary
key; this also serves as a weak key for integration with other
modules in the OMICIADB schema. The cryptically named columns are
named after multi-valued OMIM fields of the same name. Each disease
can have multiple phenotypes attached. Each disease can also have
multiple known mutations, and each of these can have multiple amino
acid modifications.
(h) DbSNP-xml Module
[0139] This is a fairly simple module, as we opted to store each
dbSNP entry as a single denormalised XML `blob`, accessed by a
dbSNP identifier. This module is a single table, with a column for
the dbSNP identifier, and the XML stored as a text field.
(i) Dataflow and Software Architecture for Database Loading
[0140] OMIM records are parsed into XML and then loaded into the
OMIM-R module; any mutational information from the OMIM allele
entries is mapped from protein coordinate space onto genomic
coordinates from Genbank, and the resulting features are stored in
the BioSQL module. The NCBI human genome build and RefSeq database
are loaded into the BioSQL module. dbSNP is loaded into the
dbSNP-xml module. Any disease gene information from the literature
curation process is parsed into XML and then stored in the
DiseaseGene module.
[0141] After any of these steps, which can be carried out
independently, all genes in the DiseaseGene module are iterated
through. For every gene we look for the corresponding gene
annotation in BioSQL and find nearby marker features. We
instantiate marker entries for all of these markers within a gene
in the so-named OMICIASNP module. We also create a so-named
OMICIAGeneSummary XML file, which integrates information from all
modules into one XML document. The Annotation Station web interface
code accesses this cached data for building the web displays. This
is achieved through two Application Programming Interfaces
(APIs)--the open source BioSQL PERL API and a PERL API. A
proprietary PERL API developed by Omicia (so-named "OMICIA API")
provides a unified access layer for all so-named OMICIADB modules
and a simplified layer on top of the BioSQL API. However, the
skilled artisan is able to construct his or her own API.
(j) Deterministic Algorithm for OMIM Allele Mutation Mapping
[0142] Many OMIM alleles could not be mapped directly, usually
because some of the literature references were based on outdated
versions of the reference sequence, or actually predated the
availability of any reference sequence. When less than 50% of OMIM
alleles for a gene could not be mapped, a simple codon position
shift/deterministic algorithm was applied to get a higher
percentage of mapped OMIM alleles. The algorithm is as follows: For
a given gene, all OMIM allele codon positions are ordered and
stored in a hash. Every neighboring OMIM protein allele is compared
to the original protein sequence from Genbank. If any of these
pairs match with the Genbank annotation, all other OMIM protein
alleles are "shifted" according to the offset into the Genbank
annotation as well. By using this deterministic algorithm, an
additional 12.11% of OMIM alleles were mapped (see Table 4).
[0143] In order to evaluate software system, 97 genes were selected
for annotation. The annotation consisted of reviewing the clinical
associations for each marker within a gene, assessing it for
significance, and then updating the database with a clinical
reference and rationale for selection. Biologists supported by
computer scientists performed this annotation process. The process
was assessed and modified throughout the effort as bottlenecks were
identified. More expeditious approaches were developed on the basis
of overcoming these obstacles. Table 4 shows a summary of the
selected 97 genes. OMIM had annotated a total of 529 alleles for
these sequences. This corresponds in average to 5.45 allele
annotations per gene. This is significantly higher than for our
total set of disease genes, which had in average 3.25 alleles per
gene (see Table 3). We believe this bias is due to the selection
process, by which we mostly used well-known and therefore
well-studied genes, for which many clinical studies have been
performed over the years. Typical genes in our set include APOE,
CFTR, APC, MTTR and all genes starting with the letter "R". Even
with this result, about 30.60% of the OMIM alleles remain unmapped.
More sophisticated algorithms can be developed to map a higher
percentage of the remaining OMIM alleles.
[0144] We found that on average, the annotation of each gene took
approximately 2 hours, but this time can be reduced through the use
of text mining tools, and the development and refinement of a
ranking scheme for the markers, as the integration of the clinical
and pharmaceutical information, which is of great value to the
individual client, is the most burdensome.
[0145] On the average 4.24 markers per gene were selected. The
criteria for marker selection were based on the association
significance of a marker with a disease. Markers were selected that
were believed to be informative and useful for a typical
individual. Based on this initial study, it is estimated that a set
of approximately 7,344 (1,732 total disease genes.times.4.24)
informative disease markers is supported by the current clinical
literature.
(k) a "High-Level" Genetic Disease Ontology
[0146] The genetic associations were placed into high-level
categories, based upon a leading medical text, Harrison's
Principles of Internal Medicine. The categories used were: Cancer;
Cardiovascular; Endocrinology and metabolism; Gastrointestinal
system; Hematology/blood disorders; Kidney and urinary tract;
Immune system; Neurological and psychiatric disorders; Respiratory;
and Others. This effort made clear that a uniform terminology does
not exist within the medical literature. Therefore, the
associations are preferably categorized using the MeSH disease
ontology (see Example 5 below).
TABLE-US-00001 TABLE 1 A typical disease gene research abstract for
the RANTES gene (published in American Journal of Respiratory and
Critical Care Medicine citation). Original Article A Functional
Polymorphism in the RANTES Gene Promoter Is Associated with the
Development of Late-Onset Asthma Nobuyuki Hizawa, Etsuro Yamaguchi,
Satoshi Konno, Yoko Tanino, Eisei Jinushi and Masaharu Nishimura
First Department of Medicine, Hokkaido University School of
Medicine, Sapporo, Japan Correspondence: Correspondence and
requests for reprints should be addressed to Nobuyuki Hizawa, First
Department of Medicine, Hokkaido University School of Medicine,
Kita-Ku, N-15 N-7, Sapporo, Japan 060-8638. E-mail:
nhizawa@med.hokudai.ac.jp The CC chemokine regulated upon
activation, normal T-cell expressed and secreted (RANTES) attracts
eosinophils, basophils, and T cells during inflammation and immune
response, indicating a possible role for this chemokine in asthma.
Both the -403A and -28G alleles of the RANTES promoter region
exhibit significantly enhanced promoter activity in reporter
constructs in vitro. We therefore investigated the genetic
influence of these alleles on the development of asthma using a
case-control analysis in a Japanese population (298 patients with
asthma and 311 control subjects). Given the evidence for
heterogeneity of asthma according to age at onset, we divided
patients with asthm into three subgroups: 117 late-onset patients
with asthma (onset at more than 40 years of age), 83 middle-onset
patients with asthma (onset at 20 to 40 years of age), and 98
early-onset patients with asthma (onset at less than 20 years of
age). The -28G allele was significantly associated with late-onset
asthma (odds ratio = 2.033; 95% confidence interval, 1.379- 2.998;
corrected p < 0.0025) but was not associated with the other two
asthma subgroups. The -403A allele was not associated with any of
the asthma subgroups. Further evidence of the importance of the
-28G allele was a significant increase in the production of RANTES
in vitro in individuals who carried this allele. Our findings
suggest that, among Japanese, the -28G allele of the RANTES
promoter region confers susceptibility to late-onset asthma. Key
Words: late-onset asthma .cndot. RANTES .cndot. single nucleotide
polymorphism (SNP)
TABLE-US-00002 TABLE 2 OMIM entry 18701 for the RANTES gene.
*187011 CHEMOKINE, CC MOTIF, LIGAND 5; CCLS Alternative titles;
symbols SMALL INDUCIBLE CYTOKINE A5, FORMERLY; SCYA5, FORMERLY
REGULATED UPON ACTIVATION, NORMALLY T-EXPRESSED, AND PRESUMABLY
SECRETED; RANTES T CELL-SPECIFIC RANTES T CELL-SPECIFIC PROTEIN
p228; TCP228 Gene map locus 17q11.2-q12 CLONING Using a human cDNA
library that was enriched by subtractive hybridization for
sequences expressed by T lymphocytes but not B lymphocytes, Schall
et al. (1988) isolated a gene (D17S136E), which they designated
RANTES, that encodes a novel T cell-specific molecule. (RANTES is
an acronym for `Regulated upon Activation, Normally T-Expressed,
and presumably Secreted.`) The gene product was predicted to be a
10-kD protein which, after cleavage of the signal peptide, could be
expected to be approximately 8 kD. Of the 68 residues, 4 are
cysteines, and there are no sites for N-linked glycosylation.
Significant homology (30 to 70%) was found between the RANTES
sequence and several other T-cell genes, suggesting that they
constitute a family of small, secreted T-cell molecules. Schall et
al. (1988) found that RANTES, also designated p228 (TCP228), was
expressed in 10 functional T-cell lines, but not in 8 hematopoietic
tumor lines or in 6 T-cell tumor lines. Its expression was
increased more than 10-fold in peripheral blood lymphocytes 3 to 5
days following mitogenic or antigenic stimulation. GENE FUNCTION
CD8-positive T lymphocytes are involved in the control of human
immunodeficiency virus (HIV) infection in vivo. Cocchi et al.
(1995) demonstrated that the chemokines RANTES, MIP-1-alpha
(182283), and MIP-1-beta (182284) are the major HIV-suppressive
factors produced by CD8- positive T cells. HIV-suppressive factor
activity produced by either immortalized or primary CD8- positive T
cells was completely blocked by a combination of neutralizing
antibodies against these 3 cytokines. On the other hand,
recombinant forms of the 3 human cytokines induced a dose-
dependent inhibition of different strains of HIV-1, HIV-2, and
simian immunodeficiency virus (SIV). Cocchi et al. (1995)
speculated that chemokine-mediated control of HIV may occur either
directly, through their inherent anti-lentiretroviral activity, or
indirectly, through their ability to chemoattract T cells and
monocytes to the proximity of the infection foci. However, this
latter mechanism may also have the opposite effect of providing
new, uninfected targets for HIV infection. The authors noted that
the findings may be relevant for the prevention and therapy of
AIDS. Arenzana-Seisdedos et al. (1996) investigated a derivative of
RANTES as a possible therapeutic agent for inhibition of HIV
infection. The derivative, called RANTES (9-68), lacks the first 8
N- terminal amino acids and has no chemotactic or
leukocyte-activating properties. RANTES (9-68) was a potent
receptor antagonist and inhibited infection of macrophage-tropic
HIV. The anti-HIV activity was somewhat lower than that of RANTES
itself, which correlated with its lower affinity for CC chemokine
receptors. Arenzana-Seisdedos et al. (1996) found that the anti-HIV
activity of RANTES and RANTES (9-68) showed some variability
depending on the donor cells. The authors concluded that structural
modification of a chemokine can yield variants lacking activation
properties but retaining both high-affinity for chemokine receptors
and the ability to block HIV infection. Pritts et al. (2002)
investigated the effect of PPAR-gamma ligands upon transcription
and translation of RANTES in human endometrial stromal cells. Three
putative PPAR response elements (PPREs) were found in the human
RANTES promoter. In cells transfected both with RANTES promoter
vectors containing 958 by and 3 PPREs, the addition of 2 PPAR-gamma
ligands inhibited promoter activity by 60% (P less than 0.01) and
48% (P less than 0.02), respectively. Truncation of the gene
promoter to delete all putative PPREs abrogated the ligand-induced
inhibition. Stromal cells showed a 40% decrease in RANTES protein
secretion when treated with a PPAR-gamma ligand (P less than 0.01).
The authors concluded that use of PPAR-gamma ligands to reduce
chemokine production and inflammation may be a productive strategy
for future therapy of endometrial disorders, such as endometriosis.
MAPPING By analysis of somatic cell hybrids and by in situ
hybridization using the cDNA probe, Donlon et al. (1990) assigned
the RANTES locus to 17q11.2-q12. A secondary hybridization peak was
noted in the region 5q31-q34, which may represent the location of
other members of the gene family. The region on chromosome 5
overlaps with the location of an extended linked cluster of growth
factor and receptor genes, some of which may be coregulated with
members of the RANTES gene family. MOLECULAR GENETICS RANTES is one
of the natural ligands for the chemokine receptor CCR5 (CMKBR5;
601373) and potently suppresses in vitro replication of the R5
strains of HIV-1, which use CCR5 as a coreceptor. Previous studies
showing that peripheral blood mononuclear cells or CD4+ lymphocytes
obtained from different individuals have wide variations in their
ability to secrete RANTES prompted Liu et al. (1999) to analyze the
upstream noncoding region of the RANTES gene, which contains
cis-acting elements involved in RANTES promoter activity, in 272
HIV-1-infected and 193 non-HIV-1-infected individuals in Japan.
They found 2 polymorphic positions, 1 of which was associated with
reduced CD4+ lymphocyte depletion rates during untreated periods in
HIV-1-infected individuals. This -28G mutation of the RANTES gene
(187011.0001) occurred at an allele frequency of approximately 17%
in the non-HIV-1-infected Japanese population and exerted no
influence on the incidence of HIV-1 infection. Functional analyses
of RANTES promoter activity indicated that the -28G mutation
increases transcription of the RANTES gene. Taken together, these
data suggested that the -28G mutation increases RANTES expression
in HIV-1-infected individuals and thus delays the progression of
the HIV-1 disease. ALLELIC VARIANTS (selected examples) .0001 HUMAN
IMMUNODEFICIENCY VIRUS TYPE 1, DELAYED DISEASE PROGRESSION WITH
INFECTION BY [SCYA5, -28C-G] In a large Japanese cohort of
HIV-1-infected and non-HIV-1-infected individuals, Liu et al.
(1999) identified a C-to-G transversion at position -28 in the
promoter of the SCYA5 gene, also referred to as the RANTES gene.
The -28G allele had a frequency of approximately 17% in the
Japanese population and appeared to have no influence on the
incidence of HIV-1 infection. However, functional analyses
indicated that the -28G mutation increased transcription of the
RANTES gene. Liu et al. (1999) suggested that the -28G mutation
increases RANTES expression in HIV-1-infected individuals and thus
delays the progression of the HIV-1 disease. They showed that the
-28G mutation is associated with reduced rates of depletion of CD4+
lymphocytes in HIV-1-infected individuals, thus confirming that
this polymorphism delays HIV-1 disease progression. .0002 HUMAN
IMMUNODEFICIENCY VIRUS TYPE 1, RAPID DISEASE PROGRESSION WITH
INFECTION BY [SCYA5, 168923, T/C]. Among 7 SNPs within the RANTES
gene investigated by An et al. (2002), one was the intronic RANTES
regulatory element, In1.1T/C (168923T/C). They found that
In1.1C-bearing genotypes accounted for 37% of the attributable risk
for rapid progression to AIDS among African Americans. Because 36%
of African Americans carry the In1.1C allele, it is likely that
In1.1C may have a significant impact on the AIDS epidemic in sub-
Saharan Africa.
REFERENCES
[0147] 1. An, P.; Nelson, G. W.; Wang, L.; Donfield, S.; Goedert,
J. J.; Phair, J.; Vlahov, D.; Buchbinder, S.; Farrar, W. L.; Modi,
W.; O'Brien, S. J.; Winkler, C. A.: Modulating influence on
HIV/AIDS by interacting RANTES gene variants. Proc. Nat. Acad. Sci.
99: 1002-1007, 2002.
PubMed ID: 11792860
[0147] [0148] 2. Arenzana-Seisdedos, F.; Virelizier, J.-L.;
Rousset, D.; Clark-Lewis, I.; Loetscher, P.; Moser, B.; Baggiolini,
M.: Table 2 Cont. HIV blocked by chemokine antagonist. (Letter)
Nature 383: 400 only, 1996.
PubMed ID: 8837769
[0148] [0149] 3. Bakhiet, M.; Tjernlund, A.; Mousa, A.; Gad, A.;
Stromblad, S.; Kuziel, W. A.; Seiger, A.; Andersson, J.: RANTES
promotes growth and survival of human first-trimester forebrain
astrocytes. Nature Cell Biol. 3: 150-157, 2001.
TABLE-US-00003 [0149] TABLE 3 Statistical summary on the initial
OMIM mapping efforts. Disease genes Percentage Total "disease
genes" 1,732 Total "disease genes" mapped to build 30 1,614 93.19%
Non-uniquely mapped "disease genes" 118 6.81% Total OMIM alleles:
5,624 OMIM alleles per gene: 3.25 OMIM alleles mapped to build30
3,222 57.29% OMIM alleles mapped to build30 using 681 12.11%
deterministic algorithm Total OMIM alleles mapped: 3,903 69.40%
OMIM alleles that are in dbSNP (incl. 105 1.87% deterministic shift
algorithm) OMIM alleles that are in HGMD (incl. 2,276 40.47%
deterministic shift algorithm) Total OMIM alleles either in dbSNP
or HGMD 2,381 42.34%
TABLE-US-00004 TABLE 4 Annotated test set of 97 disease genes.
Annotated Genes Total # of genes: 97 5.60% Total OMIM alleles: 529
OMIM alleles per gene: 5.45 Total OMIM alleles either in dbSNP or
HGMD 432 81.66% Total markers picked for annotated genes 411
Markers picked per gene 4.24
Example 5
Ontologies for Marker Selection
[0150] In one embodiment of selecting one or more of the markers to
be included in a preselected set of markers, in accordance with the
present invention, an example of applicable techniques that can be
applied follows. Briefly, `porting` the MeSH ontology to GO format
facilitates the use of various software applications that have been
developed for manual curation, editing and maintenance of the Gene
Ontology (GO), allowing the rapid addition of human gene and
disease information to MeSH, and where necessary, allowing the
alteration of the MeSH ontology itself so as to render it a more
suitable container for this information. A further integration of
the GO terminology and refinement of the disease ontology for
genetic disorders can facilitate the preselection of the set of
markers.
[0151] We outline a methodology for doing so below, and also
propose a text-mining approach (Yandell M D, Majoros W H, Genomics
and natural language processing, Nat Rev Genet 3: 601-10 [2002])
that makes use of the HGMD database to jump-start the entire effort
by rapidly assigning the 1,037 human disease causing genes
contained in HGMD to MeSH via an automatic procedure. Preliminary
studies indicate that assigning the human disease genes to MeSH can
bring many, as yet unappreciated relationships, between sequence
homology and disease to light. FIG. 7 illustrates that in several
cases genes involved in hand deformities are also involved in foot
deformities, and that in some cases these genes are homologous to
one another, e.g., PAX3, HOXA13, and MSX1 (all are homeodomain
proteins); whereas FOXC2 and FOXE1 are paralogous genes of the Fork
head class. In other cases, genes involved in related diseases are
involved in the interacting signaling pathways and developmental
processes, e.g., FGFR2 and GDF5 (for reviews see, Blundell T L et
al., Protein protein interactions in receptor activation and
intracellular signallingg, Biol Chem 381: 955-9 [2000]; Buxton P et
al., Growth/differentiation factor-5 (GDF-5) and skeletal
development, J Bone Joint Surg Am 83-A: S23-30 [2001]; Thiery J P,
Role of growth factor signaling in epithelial cell plasticity
during development and in carcinogenesis, Bull Acad Natl Med 185:
1279-92 [2001]). By systemically assigning all known human disease
genes to MeSH, previously unnoticed relationships between
disease-causing genes. In many cases, genes homologous to a known
disease gene--but not yet implicated in that disease--may prove to
be involved in causing those same diseases. Tying genes to MeSH
provides an important resource allowing hypothesis generation for
human disease research. Statistically significant associations
between SNPs, haplotypes, and complex diseases can be significantly
simplified by first assigning genes to MeSH.
[0152] In accordance with the invention, the assignment of genes to
MeSH provides a logical starting point by which to choose sets of
genetic markers already implicated in a similar clinical phenotype,
thereby revealing significant correlations between these gene
marker sets and (related) disease phenotypes using SNP and
haplotype data--and thereby, circumventing much of the statistical
noise associated with large scale studies that attempt to identify
groups of genes (without prior knowledge) using polymorphism data
for all, or a randomly chosen subset of genes. Placing genes in
MeSH also provides a means by which to cross-validate more
traditional approaches to SNP and haplotype analysis.
[0153] The core database system consists of four major database
components: [0154] 1. Disease marker database including genomic
sequence and variation database [0155] 2. Individualized
genome-profile mutation database [0156] 3. Clinical disease
information database [0157] 4. Genetic profile report database
[0158] The disease marker database has very many unstructured data
points from genome sequence strings to disease ontologies such as
MeSH (Schulman J-L, What's New for 2001 MeSH, NLM Tech Bull. 317
[2001]). While loading these databases from public data
repositories, various data consistency checks need to be
implemented due to the unreliable and error-prone data stored in
these databases. Further attention has to be paid to the fact that
there is much redundancy in gene databases, which is mostly due to
the unstructured gene nomenclature, only recently subject to
efforts at unification.
[0159] For individualized genome-profiles there is physically
separate storage of genetic data and the individual's geographical
data due to extremely important privacy concerns.
[0160] The genetic profile report database is a virtual joint over
many entries from all the databases above. Nevertheless, any
updates made by hand-editors such as genetic counselors should
propagate back to the origin of the information storage. This
versioning system is important to reduce redundant
hand-editing.
[0161] The relationship between genes including their DNA
sequences, mRNA sequences and protein sequences, and clinical
disease phenotypes is annotated by automatic text mining algorithms
to search and pre-filter literature database such as MEDLINE,
employing a computer interface for disease marker phenotype
annotation using disease ontology and clinical study abstracts
(see, e.g., Example 4 above).
Example 6
Sample of Organizational Matrix for Communicating the Genetic
Information to the Individual
[0162] Existing disease ontologies such as MeSH (Schulman Schulman,
J-L, What's New for 2001 MeSH, NLM Tech Bull. 317 [2001]) are very
technical and therefore not generally suited for the communication
to individuals. The following example (Table 5) illustrates one
embodiment of an organizational matrix for communicating genetic
information to the individual in accordance with the invention.
Specific focus is given to the most significant markers, most
informative markers, markers specifically relevant for the gender,
markers related to aging and markers that have a well-described
pharmacogenomics application. Here, the individual is a
hypothetical "Ms. Smith," to whom the terminology and structure of
the report is explained in overview, and then detailed information
is provided. Examples of other sections that are not shown in table
5 but which can be included are a glossary, a list of references
for further information, a health risk appraisal, and an index. A
"Rare Genetic Conditions or Carrier Report" at the end of the
sample report lists, in alphabetical order, a panoply of genetic
conditions for which testing was done, but is presented here in
truncated form for the sake of brevity.
* * * * *