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GENETIC ANALYSIS SYSTEMS AND METHODS
BACKGROUND OF THE INVENTION
[0001] Sequencing of the human genome and other recent developments in human
genomics has revealed that the genomic makeup between any two humans has over
99.9%
similarity. The relatively small number of variations in DNA between
individuals gives rise to
differences in phenotypic traits, and is related to many human diseases,
susceptibility to various
diseases, and response to treatment of disease. Variations in DNA between
individuals occur in
both coding and non-coding regions, and include changes in bases at a
particular locus in
genomic DNA sequences, as well as insertions and deletions of DNA. Changes
that occur at
single base positions in the genome are referred to as single nucleotide
polymorphisms, or
"SNPs."
[0002] While SNPs are relatively rare in the human genome, they account for a
majority
of DNA sequence variations between individuals, occurring approximately once
every 1,200
base pairs in the human genome (see International HapMap Project,
www.hapmap.org). As
more human genetic information becomes available, the complexity of SNPs is
beginning to be
understood. In turn, the occurrences of SNPs in the genome are becoming
correlated to the
presence of and/or susceptibility to various diseases and conditions.
[0003] As these correlations and other advances in human genetics are being
made,
medicine and personal health in general are moving toward a customized
approach in which a
patient will make appropriate medical and other choices in consideration of
his or her genomic
information, among other factors. Thus, there is a need to provide individuals
and their care-
givers with information specific to the individual's personal genome toward
providing
personalized medical and other decisions.
SUMMARY OF THE INVENTION
[0004] The present invention provides a method of assessing an individual's
genotype
correlations comprising: a) obtaining a genetic sample of the individual, b)
generating a genomic
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profile for the individual, c) determining the individual's genotype
correlations with phenotypes
by comparing the individual's genomic profile to a current database of human
genotype
correlations with phenotypes, d) reporting the results from step c) to the
individual or a health
care manager of the individual, e) updating the database of human genotype
correlations with an
additional human genotype correlation as the additional human genotype
correlation becomes
known, f) updating the individual's genotype correlations by comparing the
individual's genomic
profile from step c) or a portion thereof to the additional human genotype
correlation and
determining an additional genotype correlation of the individual, and g)
reporting the results
from step f) to the individual or the health care manager of the individual.
100051 The present invention further provides a business method of assessing
genotype
correlations of an individual comprising: a) obtaining a genetic sample of the
individual; b)
generating a genomic profile for the individual; c) determining the
individual's genotype
correlations by comparing the individual's genomic profile to a database of
human genotype
correlations; d) providing results of the determining of the individual's
genotype correlations to
the individual in a secure manner; e) updating the database of human genotype
correlations with
an additional human genotype correlation as the additional human genotype
correlation becomes
known; f) updating the individual's genotype correlations by comparing the
individual's
genomic profile or a portion thereof to the additional human genotype
correlation and
determining an additional genotype correlation of the individual; and g)
providing results of the
updating of the individual's genotype correlations to the individual of the
health care manager of
the individual.
[0006] Another aspect of the present invention is a method generating a
phenotype
profile for an individual comprising: a) providing a rule set comprising
rules, each rule
indicating a correlation between at least one genotype and at least one
phenotype, b) providing a
data set comprising genomic profiles of each of a plurality of individuals,
wherein each genomic
profile comprises a plurality of genotypes; c) periodically updating the rule
set with at least one
new rule, wherein the at least one new rule indicates a correlation between a
genotype and a
phenotype not previously correlated with each other in the rule set; d)
applying each new rule to
the genomic profile of at least one of the individuals, thereby correlating at
least one genotype
with at least one phenotype for the individual, and optionally, e) generating
a report comprising
the phenotype profile of the individual.
[0007] The present invention also provides a system comprising a) a rule set
comprising
rules, each rule indicating a correlation between at least one genotype and at
least one phenotype;
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b) code that periodically updates the rule set with at least one new rule,
wherein the at least one
new rule indicates a correlation between a genotype and a phenotype not
previously correlated
with each other in the rule set; c) a database comprising genomic profiles of
a plurality of
individuals; d) code that applies the rule set to the genomic profiles of
individuals to determine
phenotype profiles for the individuals; and e) code that generates reports for
each individual.
[0008] Another aspect of the present invention is transmission over a network,
in a secure
or non-secure manner, the methods and systems described above.
INCORPORATION BY REFERENCE
[0009] All publications and patent applications mentioned in this
specification are herein
incorporated by reference to the same extent as if each individual publication
or patent
application was specifically and individually indicated to be incorporated by
reference.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 is a flow chart illustrating aspects of the method herein.
[0011] FIG. 2 is an example of a genomic DNA quality control measure.
[0012] FIG. 3 is an example of a hybridization quality control measure.
[0013] FIG. 4 are tables of representative genotype correlations from
published literature
with test SNPs and effect estimates. A-I) represents single locus genotype
correlations; J)
respresents a two locus genotype correlation; K) represents a three locus
genotype correlation; L)
is an index of the ethnicity and country abbreviations used in A-K; M) is an
index of the
abbreviations of the Short Phenotype Names in A-K, the heritability, and the
references for the
heritability.
[0014] FIG. 5A-J are tables of representative genotype correlations with
effect estimates.
[0015] FIG. 6A-F are tables of representative genotype correlations and
estimated
relative risks.
[0016] FIG. 7 is a sample report.
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[0017] FIG. 8 is a schematic of a system for the analysis and transmission ot
genomic
and phenotype profiles over a network.
[0018] FIG. 9 is a flow chart illustrating aspects of the business method
herein
100191 FIG. 10: The effect of the estimate of the prevalence on the relative
risk
estimations. Each of the plots correspond to a different value of the allele
frequencies in the
populations, assuming Hardy-Weinberg Equilibrium. The two black lines
correspond to odds
ratio of 9 and 6, the two red lines correspond to 6 and 4, and the two blue
lines correspond to
odds ratio of 3 and 2.
[0020] FIG. 11: The effect of the estimate of the allele frequencies on the
relative risk
estimations. Each of the plots correspond to a different value of the
prevalence in the
populations. The two black lines correspond to odds ratio of 9 and 6, the two
red lines
correspond to 6 and 4, and the two blue lines correspond to odds ratio of 3
and 2.
100211 FIG. 12: Pairwise Comparison of the absolute values of the different
models
[0022] FIG. 13: Pairwise Comparison of the ranked values (GCI scores) based on
the
different models. The Spearman correlations between the different pairs are
given in Table 2.
[0023] FIG. 14: Effect of Prevalence Reporting on the GCI score. The Spearman
correlation between any two prevalence values is at least 0.99.
100241 FIG. 15: are illustrations of sample webpages from a personalized
portal.
[0025] FIG. 16: are illustrations of sample webpages from a personalized
portal for a
person's risk for prostate cancer.
[0026] FIG. 17: are illustrations of sample webpages from a personalized
portal for an
individual's risk for Crohn's disease.
[0027] FIG. 18: is a histogram of GCI scores for Multiple Sclerosis based on
the
HapMAP using 2 SNPs.
[0028] FIG. 19: is an individuals' lifetime risk for Multiple Sclerosis using
GCI Plus.
[0029] FIG. 20: is a histogram of GCI scores for Crohn's disease.
[0030] FIG. 21: is a table of multilocus correlations.
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[0031] FIG. 22: is a table of SNPs and phenotype correlations.
[0032] FIG. 23: is a table of phenotypes and prevalences.
[0033] FIG. 24: is a glossary for abbreviations in FIGS. 21, 22, and 25.
[0034] FIG. 25: is a table of SNPs and phenotype correlations.
DETAILED DESCRIPTION
[0035) The present invention provides methods and systems for generating
phenotype
profiles based on a stored genomic profile of an individual or group of
individuals, and for
readily generating original and updated phenotype profiles based on the stored
genomic profiles.
Genomic profiles are generated by determining genotypes from biological
samples obtained
from individuals. Biological samples obtained from individuals may be any
sample from which
a genetic sample may be derived. Samples may be from buccal swabs, saliva,
blood, hair, or any
other type of tissue sample. Genotypes may then be determined from the
biological samples.
Genotypes may be any genetic variant or biological marker, for example, single
nucleotide
polymorphisms (SNPs), haplotypes, or sequences of the genome. The genotype may
be the
entire genomic sequence of an individual. The genotypes may result from high-
throughput
analysis that generates thousands or millions of data points, for example,
microarray analysis for
most or all of the known SNPs. In other embodiments, genotypes may also be
determined by
high throughput sequencing.
[0036] The genotypes form a genomic profile for an individual. The genomic
profile is
stored digitally and is readily accessed at any point of time to generate
phenotype profiles.
Phenotype profiles are generated by applying rules that correlate or associate
genotypes with
phenotypes. Rules can be made based on scientific research that demonstrates a
correlation
between a genotype and a phenotype. The correlations may be curated or
validated by a
committee of one or more experts. By applying the rules to a genomic profile
of an individual,
the association between an individual's genotype and a phenotype may be
determined. The
phenotype profile for an individual will have this determination. The
determination may be a
positive association between an individual's genotype and a given phenotype,
such that the
individual has the given phenotype, or will develop the phenotype.
Alternatively, it may be
determined that the individual does not have, or will not develop, a given
phenotype. In other
embodiments, the determination may be a risk factor, estimate, or a
probability that an individual
has, or will develop a phenotype.
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100371 '1'he determinations may be made based on a number of rules, tor
example, a
plurality of rules may be applied to a genomic profile to determine the
association of an
individual's genotype with a specific phenotype. The determinations may also
incorporate
factors that are specific to an individual, such as ethnicity, gender,
lifestyle (for example, diet
and exercise habits), age, environment (for example, location of residence),
family medical
history, personal medical history, and other known phenotypes. The
incorporation of the specific
factors may be by modifying existing rules to encompass these factors.
Alternatively, separate
rules may be generated by these factors and applied to a phenotype
determination for an
individual after an existing rule has been applied.
[0038] Phenotypes may include any measurable trait or characteristic, such as
susceptibility to a certain disease or response to a drug treatment. Other
phenotypes that may be
included are physical and mental traits, such as height, weight, hair color,
eye color, sunburn
susceptibility, size, memory, intelligence, level of optimism, and general
disposition.
Phenotypes may also include genetic comparisons to other individuals or
organisms. For
example, an individual may be interested in the similarity between their
genomic profile and that
of a celebrity. They may also have their genomic profile compared to other
organisms such as
bacteria, plants, or other animals.
[0039] Together, the collection of correlated phenotypes determined for an
individual
comprises the phenotype profile for the individual. The phenotype profile may
be accessible by
an on-line portal. Alternatively, the phenotype profile as it exists at a
certain time may be
provided in paper form, with subsequent updates also provided in paper form.
The phenotype
profile may also be provided by an on-line portal. The on-line portal may
optionally be a secure
on-line portal. Access to the phenotype profile may be provided to a
subscriber, which is an
individual who subscribes to the service that generates rules on correlations
between phenotypes
and genotypes, determines the genomic profile of an individual, applies the
rules to the genomic
profile, and generates a phenotype profile of the individual. Access may also
be provided to
non-subscribers, wherein they may have limited access to their phenotype
profile and/or reports,
or may have an initial report or phenotype profile generated, but updated
reports will be
generated only with purchase of a subscription. Health care managers and
providers, such as
caregivers, physicians, and genetic counselors may also have access to the
phenotype profile.
[0040] In another aspect of the invention a genomic profile may be generated
for
subscribers and non-subscribers and stored digitally but access to the
phenotype profile and
reports may be limited to subscribers. In another variation, both subscribers
and non-subscribers
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may access their genotype and phenotype profiles, but have limited access, or
11ave a limited
report generated for non-subscribers, whereas subscribers have full access and
may have a full
report generated. In another embodiment, both subscribers and non-subscribers
may have full
access initially, or full initial reports, but only subscribers may access
updated reports based on
their stored genomic profile.
[0041] In another aspect of the invention information about the association of
multiple
genetic markers with one or more diseases or conditions is combined and
analyzed to produce a
Genetic Composite Index (GCI) score. This score incorporates known risk
factors, as well as
other information and assumptions such as the allele frequencies and the
prevalence of a disease.
The GCI can be used to qualitatively estimate the association of a disease or
a condition with the
combined effect of a set of Genetic markers. The GCI score can be used to
provide people not
trained in genetics with a reliable (i.e., robust), understandable, and/or
intuitive sense of what
their individual risk of a disease is compared to a relevant population based
on current scientific
research. The GCI score may be used to generate GCI Plus scores. The GCI Plus
score may
contain all the GCI assumptions, including risk (such as lifetime risk), age-
defined prevalence,
and/or age-defined incidence of the condition. The lifetime risk for the
individual may then be
calculated as a GCI Plus score which is proportional to the individual's GCI
score divided by the
average GCI score. The average GCI score may be determined from a group of
individuals of
similar ancestral background, for example a group of Caucasians, Asians, East
Indians, or other
group with a common ancestral background. Groups may comprise of at least 5,
10, 15, 20, 25,
30, 35, 40, 45, 50, 55, or 60 individuals. In some embodiments, the average
may be determined
from at least 75, 80, 95, or 100 individuals. The GCI Plus score may be
determined by
determining the GCI score for an individual, dividing the GCI score by the
average relative risk
and multiplying by the lifetime risk for a condition or phenotype. For
example, using data from
FIG. 22 and/or FIG. 25 with information in FIG. 24 to calculate GCI Plus
scores such as in
FIG. 19.
100421 The present invention encompasses using the GCI score as described
herein, and
one of ordinary skill in the art will readily recognize the use of GCI Plus
scores or variations
thereof, in place of GCI scores as described herein.
[0043] In one embodiment a GCI score is generated for each disease or
condition of
interest. These GCI scores may be collected to form a risk profile for an
individual. The GCI
scores may be stored digitally so that they are readily accessible at any
point of time to generate
risk profiles. Risk profiles may be broken down by broad disease classes, such
as cancer, heart
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disease, metabolic disorders, psychiatric disorders, bone disease, or age on-
set disorders. Broad
disease classes may be further broken down into subcategories. For example for
a broad class
such as a cancer, sub-categories of cancer may be listed such as by type
(sarcoma, carcinoma or
leukemia, etc.) or by tissue specificity (neural, breast, ovaries, testes,
prostate, bone, lymph
nodes, pancreas, esophagus, stomach, liver, brain, lung, kidneys, etc.).
[0044] In another embodiment a GCI score is generated for an individual, which
provides
them with easily comprehended information about the individual's risk of
acquiring or
susceptibility to at least one disease or condition. In one embodiment
multiple GCI scores are
generated for different diseases or conditions. In another embodiment at least
one GCI score is
accessible by an on-line portal. Altematively, at least one GCI score may be
provided in paper
form, with subsequent updates also provided in paper form. In one embodiment
access to at least
one GCI score is provided to a subscriber, which is an individual who
subscribes to the service.
In an alternative embodiment access is provided to non-subscribers, wherein
they may have
limited access to at least one of their GCI scores, or they may have an
initial report on at least
one of their GCI scores generated, but updated reports will be generated only
with purchase of a
subscription. In another embodiment health care managers and providers, such
as caregivers,
physicians, and genetic counselors may also have access to at least one of an
individual's GCI
scores.
[0045] There may also be a basic subscription model. A basic subscription may
provide
a phenotype profile where the subscriber may choose to apply all existing
rules to their genomic
profile, or a subset of the existing rules, to their genomic profile. For
example, they may choose
to apply only the rules for disease phenotypes that are actionable. The basic
subscription may
have different levels within the subscription class. For example, different
levels may be
dependent on the number of phenotypes a subscriber wants correlated to their
genomic profile, or
the number of people that may access their phenotype profile. Another level of
basic
subscription may be to incorporate factors specific to an individual, such as
already known
phenotypes such as age, gender, or medical history, to their phenotype
profile. Still another level
of the basic subscription may allow an individual to generate at least one GCI
score for a disease
or condition. A variation of this level may further allow an individual to
specify for an automatic
update of at least one GCI score for a disease or condition to be generated if
their is any change
in at least one GCI score due to changes in the analysis used to generate at
least one GCI score.
In some embodiments the individual may be notified of the automatic update by
email, voice
message, text message, mail delivery, or fax.
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[0046] Subscribers may also generate reports that have their phenotype profile
as well as
information about the phenotypes, such as genetic and medical information
about the phenotype.
For example, the prevalence of the phenotype in the population, the genetic
variant that was used
for the correlation, the molecular mechanism that causes the phenotype,
therapies for the
phenotype, treatment options for the phenotype, and preventative actions, may
be included in the
report. In other embodiments, the reports may also include information such as
the similarity
between an individual's genotype and that of other individuals, such as
celebrities or other
famous people. The information on similarity may be, but are not limited to,
percentage
homology, number of identical variants, and phenotypes that may be similar.
These reports may
further contain at least one GCI score.
[0047] The report may also provide links to other sites with further
information on the
phenotypes, links to on-line support groups and message boards of people with
the same
phenotype or one or more similar phenotypes, links to an on-line genetic
counselor or physician,
or links to schedule telephonic or in-person appointments with a genetic
counselor or physician,
if the report is accessed on-line. If the report is in paper form, the
information may be the
website location of the aforementioned links, or the telephone number and
address of the genetic
counselor or physician. The subscriber may also choose which phenotypes to
include in their
phenotype profile and what information to include in their report. The
phenotype profile and
reports may also be accessible by an individual's health care manager or
provider, such as a
caregiver, physician, psychiatrist, psychologist, therapist, or genetic
counselor. The subscriber
may be able to choose whether the phenotype profile and reports, or portions
thereof, are
accessible by such individual's health care manager or provider.
[00481 The present invention may also include a premium level of subscription.
The
premium level of subscription maintains their genomic profile digitally after
generation of an
initial phenotype profile and report, and provides subscribers the opportunity
to generate
phenotype profiles and reports with updated correlations from the latest
research. In another
embodiment, subscribers have the opportunity to generate risk profile and
reports with updated
correlations from the latest research. As research reveals new correlations
between genotypes
and phenotypes, disease or conditions, new rules will be developed based on
these new
correlations and can be applied to the genomic profile that is already stored
and being
maintained. The new rules may correlate genotypes not previously correlated
with any
phenotype, correlate genotypes with new phenotypes, modify existing
correlations, or provide
the basis for adjustment of a GCI score based on a newly discovered
association between a
genotype and disease or condition. Subscribers may be informed of new
correlations via e-mail
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or other electronic means, and if the phenotype is of interest, they may
choose to update their
phenotype profile with the new correlation. Subscribers may choose a
subscription where they
pay for each update, for a number of updates or an unlimited number of updates
for a designated
time period (e.g. three months, six months, or one year). Another subscription
level may be
where a subscriber has their phenotype profile or risk profile automatically
updated, instead of
where the individual chooses when to update their phenotype profile or risk
profile, whenever a
new rule is generated based on a new correlation.
[0049] In another aspect of the subscription, subscribers may refer non-
subscribers to the
service that generates rules on correlations between phenotypes and genotypes,
determines the
genomic profile of an individual, applies the rules to the genomic profile,
and generates a
phenotype profile of the individual. Referral by a subscriber may give the
subscriber a reduced
price on subscription to the service, or upgrades to their existing
subscriptions. Referred
individuals may have free access for a limited time or have a discounted
subscription price.
[0050] Phenotype profiles and reports as well as risk profiles and reports may
be
generated for individuals that are human and non-human. For example,
individuals may include
other mammals, such as bovines, equines, ovines, canines, or felines.
Subscribers, as used
herein, are human individuals who subscribe to a service by purchase or
payment for one or
more services. Services may include, but are not limited to, one or more of
the following:
having their or another individual's, such as the subscriber's child or pet,
genomic profile
determined, obtaining a phenotype profile, having the phenotype profile
updated, and obtaining
reports based on their genomic and phenotype profile.
[0051] In another aspect of the invention, "field-deployed" mechanisms may be
gathered
from individuals to generate phenotype profiles for individuals. In preferred
embodiments, an
individual may have an initial phenotype profile generated based on genetic
information. For
example, an initial phenotype profile is generated that includes risk factors
for different
phenotypes as well as suggested treatments or preventative measures. For
example, the profile
may include information on available medication for a certain condition,
and/or suggestions on
dietary changes or exercise regimens. The individual may choose to see, or
contact via a web
portal or phone call, a physician or genetic counselor, to discuss their
phenotype profile. The
individual may decide to take a certain course of action, for example, take
specific medications,
change their diet, etc.
[0052] The individual may then subsequently submit biological samples to
assess
changes in their physical condition and possible change in risk factors.
Individuals may have the
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changes determined by directly submitting biological samples to the facility
(or associated
facility, such as a facility contracted by the entity generating the genetic
profiles and phenotype
profiles us) that generates the genomic profiles and phenotype profiles.
Alternatively, the
individuals may use a "field-deployed" mechanism, wherein the individual may
submit their
saliva, blood, or other biological sample into a detection device at their
home, analyzed by a
third party, and the data transmitted to be incorporated into another
phenotype profile. For
example, an individual may have received an initial phenotype report based on
their genetic data
reporting the individual having an increased lifetime risk of myocardial
infarction (MI). The
report may also have suggestions on preventative measures to reduce the risk
of MI, such as
cholesterol lowering drugs and change in diet. The individual may choose to
contact a genetic
counselor or physician to discuss the report and the preventative measures and
decides to change
their diet. After a period of being on the new diet, the individual may see
their personal
physician to have their cholesterol level measured. The new information
(cholesterol level) may
be transmitted (for example, via the Internet) to the entity with the genomic
information, and the
new information used to generate a new phenotype profile for the individual,
with a new risk
factor for myocardial infarction, and/or other conditions.
[0053] The individual may also use a "field-deployed" mechanism, or direct
mechanism,
to determine their individual response to specific medications. For example,
an individual may
have their response to a drug measured, and the information may be used to
determine more
effective treatments. Measurable information include, but are not limited to,
metabolite levels,
glucose levels, ion levels (for example, calcium, sodium, potassium, iron),
vitamins, blood cell
counts, body mass index (BMI), protein levels, transcript levels, heart rate,
etc., can be
determined by methods readily available and can be factored into an algorithm
to combine with
initial genomic profiles to determine a modified overall risk estimate score.
[0054] The term "biological sample" refers to any biological sample that can
be isolated
from an individual, including samples from which genetic material may be
isolated. As used
herein, a "genetic sample" refers to DNA and/or RNA obtained or derived from
an individual.
[0055] As used herein, the term "genome" is intended to mean the full
complement of
chromosomal DNA found within the nucleus of a human cell. The term "genomic
DNA" refers
to one or more chromosomal DNA molecules occurring naturally in the nucleus of
a human cell,
or a portion of the chromosomal DNA molecules.
[0056] The term "genomic profile" refers to a set of information about an
individual's
genes, such as the presence or absence of specific SNPs or mutations. Genomic
profiles include
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the genotypes ot individuals. Genomic profiles may also be substantiallPy tne
complete genomic
sequence of an individual. In some embodiments, the genomic profile may be at
least 60%,
80%, or 95% of the complete genomic sequence of an individual. The genomic
profile may be
approximately 100% of the complete genomic sequence of an individual. In
reference to a
genomic profile, "a portion thereof' refers to the genomic profile of a subset
of the genomic
profile of an entire genome.
[0057] The term "genotype" refers to the specific genetic makeup of an
individual's
DNA. The genotype may include the genetic variants and markers of an
individual. Genetic
markers and variants may include nucleotide repeats, nucleotide insertions,
nucleotide deletions,
chromosomal translocations, chromosomal duplications, or copy number
variations. Copy
number variation may include microsatellite repeats, nucleotide repeats,
centromeric repeats, or
telomeric repeats. The genotypes may also be SNPs, haplotypes, or diplotypes.
A haplotype
may refer to a locus or an allele. A haplotype is also referred to as a set of
single nucleotide
polymorphisms (SNPs) on a single chromatid that are statistically associated.
A diplotype is a
set of haplotypes.
[0058] The term single nucleotide polymorphism or "SNP" refers to a particular
locus on
a chromosome which exhibits variability such as at least one percent (1%) with
respect to the
identity of the nitrogenous base present at such locus within the human
population For example,
where one individual might have adenosine (A) at a particular nucleotide
position of a given
gene, another might have cytosine (C), guanine (G), or thymine (T) at this
position, such that
there is a SNP at that particular position.
[0059] As used herein, the terminology "SNP genomic profile" refers to the
base content
of a given individual's DNA at SNP sites throughout the individual's entire
genomic DNA
sequence. A "SNP profile" can refer to an entire genomic profile, or may refer
to a portion
thereof, such as a more localized SNP profile which can be associated with a
particular gene or
set of genes.
[0060] The term "phenotype" is used to describe a quantitative trait or
characteristic of
an individual. Phenotypes include, but are not limited to, medical and non-
medical conditions.
Medical conditions include diseases and disorders. Phenotypes may also include
physical traits,
such as hair color, physiological traits, such as lung capacity, mental
traits, such as memory
retention, emotional traits, such as ability to control anger, ethnicity, such
as ethnic background,
ancestry, such as an individual's place of origin, and age, such as age
expectancy or age of onset
of different phenotypes. Phenotypes may also be monogenic, wherein it is
thought that one gene
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may be correlated with a phenotype, or multigenic, wherein more than one gene
is correlated
with a phenotype.
[0061] A "rule" is used to define the correlation between a genotype and a
phenotype.
The rules may define the correlations by a numerical value, for example by a
percentage, risk
factor, or confidence score. A rule may incorporate the correlations of a
plurality of genotypes
with a phenotype. A "rule set" comprises more than one rule. A "new rule" may
be a rule that
indicates a correlation between a genotype and a phenotype for which a rule
does not currently
exist. A new rule may correlate an uncorrelated genotype with a phenotype. A
new rule may
also correlate a genotype that is already correlated with a phenotype to a
phenotype it had not
been previously correlated to. A "new rule" may also be an existing rule that
is modified by
other factors, including another rule. An existing rule may be modified due to
an individual's
known characteristics, such as ethnicity, ancestry, geography, gender, age,
family history, or
other previously determined phenotypes.
[0062] Use of "genotype correlation" herein refers to the statistical
correlation between
an individual's genotype, such as presence of a certain mutation or mutations,
and the likelihood
of being predisposed to a phenotype, such as a particular disease, condition,
physical state,
and/or mental state. The frequency with which a certain phenotype is observed
in the presence
of a specific genotype determines the degree of genotype correlation or
likelihood of a particular
phenotype. For example, as detailed herein, SNPs giving rise to the
apolipoprotein E4 isoform
are correlated with being predisposed to early onset Alzheimer's disease.
Genotype correlations
may also refer to correlations wherein there is not a predisposition to a
phenotype, or a negative
correlation. The genotype correlations may also represent an estimate of an
individual to have a
phenotype or be predisposed to have a phenotype. The genotype correlation may
be indicated by
a numerical value, such as a percentage, a relative risk factor, an effects
estimate, or confidence
score.
[0063] The term "phenotype profile" refers to a collection of a plurality of
phenotypes
correlated with a genotype or genotypes of an individual. Phenotype profiles
may include
information generated by applying one or more rules to a genomic profile, or
information about
genotype correlations that are applied to a genomic profile. Phenotype
profiles may be generated
by applying rules that correlate a plurality of genotypes with a phenotype.
The probability or
estimate may be expressed as a numerical value, such as a percentage, a
numerical risk factor or
a numerical confidence interval. The probability may also be expressed as
high, moderate, or
low. The phenotype profiles may also indicate the presence or absence of a
phenotype or the risk
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of developing a phenotype. For example, a phenotype profile may indicate the
presence of blue
eyes, or a high risk of developing diabetes. The phenotype profiles may also
indicate a predicted
prognosis, effectiveness of a treatment, or response to a treatment of a
medical condition.
[0064] The term risk profile refers to a collection of GCI scores for more
than one
disease or condition. GCI scores are based on analysis of the association
between an individual's
genotype with one or more diseases or conditions. Risk profiles may display
GCI scores grouped
into categories of disease. Further the Risk profiles may display information
on how the GCI
scores are predicted to change as the individual ages or various risk factors
are adjusted. For
example, the GCI scores for particular diseases may take into account the
effect of changes in
diet or preventative measures taken (smoking cessation, drug intake, double
radical
mastectomies, hysterectomies). The GCI scores may be displayed as a numerical
measure, a
graphical display, auditory feedback or any combination of the preceding.
[0065] As used herein, the term "on-line portal" refers to a source of
information which
can be readily accessed by an individual through use of a computer and
internet website,
telephone, or other means that allow similar access to information. The on-
line portal may be a
secure website. The website may provide links to other secure and non-secure
websites, for
example links to a secure website with the individual's phenotype profile, or
to non-secure
websites such as a message board for individuals sharing a specific phenotype.
[0066] The practice of the present invention may employ, unless otherwise
indicated,
conventional techniques and descriptions of molecular biology, cell biology,
biochemistry, and
immunology, which are within the skill of the art. Such conventional
techniques include nucleic
acid isolation, polymer array synthesis, hybridization, ligation, and
detection of hybridization
using a label. Specific illustrations of suitable techniques are exemplified
and referenced herein.
However, other equivalent conventional procedures can also be used. Other
conventional
techniques and descriptions can be found in standard laboratory manuals and
texts such as
Genome Analysis: A Laboratory Manual Series (Vols. I-IV), PCR Primer: A
Laboratory Manual,
Molecular Cloning: A Laboratory Manual (all from Cold Spring Harbor Laboratory
Press);
Stryer, L. (1995) Biochemistry (4th Ed.) Freeman, New York; Gait,
"Oligonucleotide Synthesis:
A Practical Approach" 1984, IRL Press, London, Nelson and Cox (2000);
Lehninger, Principles
of Biochemistry 3rd Ed., W.H. Freeman Pub., New York, N.Y.; and Berg et al.
(2002)
Biochemistry, 5th Ed., W.H. Freeman Pub., New York, N.Y., all of which are
herein
incorporated in their entirety by reference for all purposes.
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[0067] "1'he methods of the present invention involve analysis o~an
individual's genomic
profile to provide the individual with molecular information relating to a
phenotype. As detailed
herein, the individual provides a genetic sample, from which a personal
genomic profile is
generated. The data of the individual's genomic profile is queried for
genotype correlations by
comparing the profile against a database of established and validated human
genotype
correlations. The database of established and validated genotype correlations
may be from peer-
reviewed literature and further judged by a committee of one or more experts
in the field, such as
geneticists, epidemiologists, or statisticians, and curated. In preferred
embodiments, rules are
made based on curated genotype correlations and are applied to an individual's
genomic profile
to generate a phenotype profile. Results of the analysis of the individual's
genomic profile,
phenotype profile, along with interpretation and supportive information, are
provided to the
individual of the individual's health care manager, to empower personalized
choices for the
individual's health care.
[0068] A method of the invention is detailed as in FIG. 1, where an
individual's genomic
profile is first generated. An individual's genomic profile will contain
information about an
individual's genes based on genetic variations or markers. Genetic variations
are genotypes,
which make up genomic profiles. Such genetic variations or markers include,
but are not limited
to, single nucleotide polymorphisms, single and/or multiple nucleotide
repeats, single and/or
multiple nucleotide deletions, microsatellite repeats (small numbers of
nucleotide repeats with a
typical 5-1,000 repeat units), di-nucleotide repeats, tri-nucleotide repeats,
sequence
rearrangements (including translocation and duplication), copy number
variations (both loss and
gains at specific loci), and the like. Other genetic variations include
chromosomal duplications
and translocations as well as centromeric and telomeric repeats.
[0069] Genotypes may also include haplotypes and diplotypes. In some
embodiments,
genomic profiles may have at least 100,000, 300,000, 500,000, or 1,000,000
genotypes. In some
embodiments, the genomic profile may be substantially the complete genomic
sequence of an
individual. In other embodiments, the genomic profile is at least 60%, 80%, or
95% of the
complete genomic sequence of an individual. The genomic profile may be
approximately 100%
of the complete genomic sequence of an individual. Genetic samples that
contain the targets
include, but are not limited to, unamplified genomic DNA or RNA samples or
amplified DNA
(or cDNA). The targets may be particular regions of genomic DNA that contain
genetic markers
of particular interest.
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[0070] ln step 102 of FIG. 1, a genetic sample of an individual is isolatect
trom a
biological sample of an individual. Such biological samples include, but are
not limited to,
blood, hair, skin, saliva, semen, urine, fecal material, sweat, buccal, and
various bodily tissues.
In some embodiments, tissues samples may be directly collected by the
individual, for example,
a buccal sample may be obtained by the individual taking a swab against the
inside of their
cheek. Other samples such as saliva, semen, urine, fecal material, or sweat,
may also be supplied
by the individual themselves. Other biological samples may be taken by a
health care specialist,
such as a phlebotomist, nurse or physician. For example, blood samples may be
withdrawn from
an individual by a nurse. Tissue biopsies may be performed by a health care
specialist, and kits
are also available to health care specialists to efficiently obtain samples. A
small cylinder of skin
may be removed or a needle may be used to remove a small sample of tissue or
fluids.
[0071] In some embodiments, kits are provided to individuals with sample
collection
containers for the individual's biological sample. The kit may also provide
instructions for an
individual to directly collect their own sample, such as how much hair, urine,
sweat, or saliva to
provide. The kit may also contain instructions for an individual to request
tissue samples to be
taken by a health care specialist. The kit may include locations where samples
may be taken by
a third party, for example kits may be provided to health care facilities who
in turn collect
samples from individuals. The kit may also provide return packaging for the
sample to be sent to
a sample processing facility, where genetic material is isolated from the
biological sample in step
104.
[0072] A genetic sample of DNA or RNA may be isolated from a biological sample
according to any of several well-known biochemical and molecular biological
methods, see, e.g.,
Sambrook, et al., Molecular Cloning: A Laboratory Manual (Cold Spring Harbor
Laboratory,
New York) (1989). There are also several commercially available kits and
reagents for isolating
DNA or RNA from biological samples, such as those available from DNA Genotek,
Gentra
Systems, Qiagen, Ambion, and other suppliers. Buccal sample kits are readily
available
commercially, such as the MasterAmpTM Buccal Swab DNA extraction kit from
Epicentre
Biotechnologies, as are kits for DNA extraction from blood samples such as
Extract-N-AmpTM
from Sigma Aldrich. DNA from other tissues may be obtained by digesting the
tissue with
proteases and heat, centrifuging the sample, and using phenol-chloroform to
extract the
unwanted materials, leaving the DNA in the aqueous phase. The DNA can then be
further
isolated by ethanol precipitation.
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100731 ln a preferred embodiment, genomic DNA is isolated from saliva. r or
example,
using DNA self collection kit technology available from DNA Genotek, an
individual collects a
specimen of saliva for clinical processing. The sample conveniently can be
stored and shipped at
room temperature. After delivery of the sample to an appropriate laboratory
for processing,
DNA is isolated by heat denaturing and protease digesting the sample,
typically using reagents
supplied by the collection kit supplier at 50 C for at least one hour. The
sample is next
centrifuged, and the supematant is ethanol precipitated. The DNA pellet is
suspended in a buffer
appropriate for subsequent analysis.
[0074] In another embodiment, RNA may be used as the genetic sample. In
particular,
genetic variations that are expressed can be identified from mRNA. The term
"messenger RNA"
or "mRNA" includes, but is not limited to pre-mRNA transcript(s), transcript
processing
intermediates, mature mRNA(s) ready for translation and transcripts of the
gene or genes, or
nucleic acids derived from the mRNA transcript(s). Transcript processing may
include splicing,
editing and degradation. As used herein, a nucleic acid derived from an mRNA
transcript refers
to a nucleic acid for whose synthesis the mRNA transcript or a subsequence
thereof has
ultimately served as a template. Thus, a cDNA reverse transcribed from an
mRNA, a DNA
amplified from the cDNA, an RNA transcribed from the amplified DNA, etc., are
all derived
from the mRNA transcript. RNA can be isolated from any of several bodily
tissues using
methods known in the art, such as isolation of RNA from unfractionated whole
blood using the
PAXgeneTM Blood RNA System available from PreAnalytiX. Typically, mRNA will be
used to
reverse transcribe cDNA, which will then be used or amplified for gene
variation analysis.
[0075] Prior to genomic profile analysis, a genetic sample will typically be
amplified,
either from DNA or cDNA reverse transcribed from RNA. DNA can be amplified by
a number
of methods, many of which employ PCR. See, for example, PCR Technology:
Principles and
Applications for DNA Amplification (Ed. H. A. Erlich, Freeman Press, NY, N.Y.,
1992); PCR
Protocols: A Guide to Methods and Applications (Eds. Innis, et al., Academic
Press, San Diego,
Calif., 1990); Mattila et al., Nucleic Acids Res. 19, 4967 (1991); Eckert et
al., PCR Methods and
Applications 1, 17 (1991); PCR (Eds. McPherson et al., IRL Press, Oxford); and
U.S. Pat. Nos.
4,683,202, 4,683,195, 4,800,159 4,965,188, and 5,333,675, and each of which is
incorporated
herein by reference in their entireties for all purposes.
[0076] Other suitable amplification methods include the ligase chain reaction
(LCR) (for
example, Wu and Wallace, Genomics 4, 560 (1989), Landegren et al., Science
241, 1077 (1988)
and Barringer et al. Gene 89:117 (1990)), transcription amplification (Kwoh et
al., Proc. Natl.
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WO 2008/067551 PCT/US2007/086138
Acad. Sci. USA 86:1173-1177 (1989) and W088/10315), self-sustained sequence
replication
(Guatelli et al., Proc. Nat. Acad. Sci. USA, 87:1874-1878 (1990) and
W090/06995), selective
amplification of target polynucleotide sequences (U.S. Pat. No. 6,410,276),
consensus sequence
primed polymerase chain reaction (CP-PCR) (U.S. Pat. No. 4,437,975),
arbitrarily primed
polymerase chain reaction (AP-PCR) (U.S. Pat. Nos. 5,413,909, 5,861,245)
nucleic acid based
sequence amplification (NABSA), rolling circle amplification (RCA), multiple
displacement
amplification (MDA) (U.S. Pat. Nos. 6,124,120 and 6,323,009) and circle-to-
circle amplification
(C2CA) (Dahl et al. Proc. Natl. Acad. Sci 101:4548-4553 (2004)). (See, U.S.
Pat. Nos.
5,409,818, 5,554,517, and 6,063,603, each of which is incorporated herein by
reference). Other
amplification methods that may be used are described in, U.S. Pat. Nos.
5,242,794, 5,494,810,
5,409,818, 4,988,617, 6,063,603 and 5,554,517 and in U.S. Ser. No. 09/854,317,
each of which
is incorporated herein by reference.
[0077] Generation of a genomic profile in step 106 is performed using any of
several
methods. Several methods are known in the art to identify genetic variations
and include, but are
not limited to, DNA sequencing by any of several methodologies, PCR based
methods, fragment
length polymorphism assays (restriction fragment length polymorphism (RFLP),
cleavage
fragment length polymorphism (CFLP)) hybridization methods using an allele-
specific
oligonucleotide as a template (e.g., TaqMan PCR method, the invader method,
the DNA chip
method), methods using a primer extension reaction, mass spectrometry (MALDI-
TOF/MS
method), and the like.
[0078] In one embodiment, a high density DNA array is used for SNP
identification and
profile generation. Such arrays are commercially available from Affymetrix and
Illumina (see
Affymetrix GeneChip 500K Assay Manual, Affymetrix, Santa Clara, CA
(incorporated by
reference); Sentrix humanHap650Y genotyping beadchip, Illumina, San Diego,
CA).
[0079] For example, a SNP profile can be generated by genotyping more than
900,000
SNPs using the Affymetrix Genome Wide Human SNP Array 6Ø Alternatively, more
than
500,000 SNPs through whole-genome sampling analysis may be determined by using
the
Affymetrix GeneChip Human Mapping 500K Array Set. In these assays, a subset of
the human
genome is amplified through a single primer amplification reaction using
restriction enzyme
digested, adaptor-ligated human genomic DNA. As shown in FIG. 2, the
concentration of the
ligated DNA may then be determined. The amplified DNA is then fragmented and
the quality of
the sample determined prior to continuing with step 106. If the samples meet
the PCR and
fragmentation standards, the sample is denatured, labeled, and then hybridized
to a microarray
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consisting of small DNA probes at specific locations on a coated quartz
surface. The amount of
label that hybridizes to each probe as a function of the amplified DNA
sequence is monitored,
thereby yielding sequence information and resultant SNP genotyping.
[0080] Use of the Affymetrix GeneChip 500K Assay is carried out according to
the
manufacturer's directions. Briefly, isolated genomic DNA is first digested
with either a Nspl or
Styl restriction endonuclease. The digested DNA is then ligated with a NspI or
Styl adaptor
oligonucleotide that respectively anneals to either the Nspl or Styl
restricted DNA. The adaptor-
containing DNA following ligation is then amplified by PCR to yield amplified
DNA fragments
between about 200 and 1100 base pairs, as confirmed by gel electrophoresis.
PCR products that
meet the amplification standard are purified and quantified for fragmentation.
The PCR products
are fragmented with DNase I for optimal DNA chip hybridization. Following
fragmentation,
DNA fragments should be less than 250 base pairs, and on average, about 180
base pairs, as
confirmed by gel electrophoresis. Samples that meet the fragmentation standard
are then labeled
with a biotin compound using terminal deoxynucleotidyl transferase. The
labeled fragments are
next denatured and then hybridized into a GeneChip 250K array. Following
hybridization, the
array is stained prior to scanning in a three step process consisting of a
streptavidin phycoerythin
(SAPE) stain, followed by an antibody amplification step with a biotinylated,
anti-streptavidin
antibody (goat), and final stain with streptavidin phycoerythin (SAPE). After
labeling, the array
is covered with an array holding buffer and then scanned with a scanner such
as the Affymetrix
GeneChip Scanner 3000.
[0081] Analysis of data following scanning of an Affymetrix GeneChip Human
Mapping
500K Array Set is performed according to the manufacturer's guidelines, as
shown in FIG. 3.
Briefly, acquisition of raw data using GeneChip Operating Software (GCOS)
occurs. Data may
also be aquired using Affymetrix GeneChip Command ConsoleTM. The aquisition of
raw data is
followed by analysis with GeneChip Genotyping Analysis Software (GTYPE). For
purposes of
the present invention, samples with a GTYPE call rate of less than 80% are
excluded. Samples
are then examined with BRLMM and/or SNiPer algorithm analyses. Samples with a
BRLMM
call rate of less than 95% or a SNiPer call rate of less than 98% are
excluded. Finally, an
association analysis is performed, and samples with a SNiPer quality index of
less than 0.45
and/or a Hardy-Weinberg p-value of less than 0.00001 are excluded.
[0082] As an alternative to or in addition to DNA microarray analysis, genetic
variations
such as SNPs and mutations can be detected by DNA sequencing. DNA sequencing
may also be
used to sequence a substantial portion, or the entire, genomic sequence of an
individual.
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Traditionally, common DNA sequencing has been based on polyacrylamide gel
fractionation to
resolve a population of chain-terminated fragments (Sanger et al., Proc. Natl.
Acad. Sci. USA
74:5463-5467 (1977)). Alternative methods have been and continue to be
developed to increase
the speed and ease of DNA sequencing. For example, high throughput and single
molecule
sequencing platforms are commercially available or under development from 454
Life Sciences
(Branford, CT) (Margulies et al., Nature (2005) 437:376-380 (2005)); Solexa
(Hayward, CA);
Helicos BioSciences Corporation (Cambridge, MA) (U.S. application Ser. No.
11/167046, filed
June 23, 2005), and Li-Cor Biosciences (Lincoln, NE) (U.S. application Ser.
No. 11/118031,
filed April 29, 2005).
[0083] After an individual's genomic profile is generated in step 106, the
profile is stored
digitally in step 108, such profile may be stored digitally in a secure
manner. The genomic
profile is encoded in a computer readable format to be stored as part of a
data set and may be
stored as a database, where the genomic profile may be "banked", and can be
accessed again
later. The data set comprises a plurality of data points, wherein each data
point relates to an
individual. Each data point may have a plurality of data elements. One data
element is the
unique identifier, used to identify the individual's genomic profile. It may
be a bar code.
Another data element is genotype information, such as the SNPs or nucleotide
sequence of the
individual's genome. Data elements corresponding to the genotype information
may also be
included in the data point. For example, if the genotype information includes
SNPs identified by
microarray analysis, other data elements may include the microarray SNP
identification number,
the SNP rs number, and the polymorphic nucleotide. Other data elements may be
chromosome
position of the genotype information, quality metrics of the data, raw data
files, images of the
data, and extracted intensity scores.
[0084] The individual's specific factors such as physical data, medical data,
ethnicity,
ancestry, geography, gender, age, family history, known phenotypes,
demographic data,
exposure data, lifestyle data, behavior data, and other known phenotypes may
also be
incorporated as data elements. For example, factors may include, but are not
limited to,
individual's: birthplace, parents and/or grandparents, relatives' ancestry,
location of residence,
ancestors' location of residence, environmental conditions, known health
conditions, known drug
interactions, family health conditions, lifestyle conditions, diet, exercise
habits, marital status,
and physical measurements, such as weight, height, cholesterol level, heart
rate, blood pressure,
glucose level and other measurements known in the art The above mentioned
factors for an
individual's relatives or ancestors, such as parents and grandparents, may
also be incorporated as
data elements and used to determine an individual's risk for a phenotype or
condition.
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CA 02671267 2009-05-29
[0085] WO 2008/0 1 rie specific factors may be obtained from a questionnaireC
or~Uirs m~a neaicn care
manager of the individual. Information from the "banked" profile can then be
accessed and
utilized as desired. For example, in the initial assessment of an individual's
genotype
correlations, the individual's entire information (typically SNPs or other
genomic sequences
across, or taken from an entire genome) will be analyzed for genotype
correlations. In
subsequent analyses, either the entire information can be accessed, or a
portion thereof, from the
stored, or banked genomic profile, as desired or appropriate.
Comparison of genomic profile with database of genotype correlations.
[0086] In step 110, genotype correlations are obtained from scientific
literature.
Genotype correlations for genetic variations are determined from analysis of a
population of
individuals who have been tested for the presence or absence of one or more
phenotypic traits of
interest and for genotype profile. The alleles of each genetic variation or
polymorphism in the
profile are then reviewed to determine whether the presence or absence of a
particular allele is
associated with a trait of interest. Correlation can be performed by standard
statistical methods
and statistically significant correlations between genetic variations and
phenotypic characteristics
are noted. For example, it may be determined that the presence of allele Al at
polymorphism A
correlates with heart disease. As a further example, it might be found that
the combined
presence of allele Al at polymorphism A and allele B1 at polymorphism B
correlates with
increased risk of cancer. The results of the analyses may be published in peer-
reviewed
literature, validated by other research groups, and/or analyzed by a committee
of experts, such as
geneticists, statisticians, epidemiologists, and physicians, and may also be
curated.
[0087] In FIGS. 4, 5, and 6 are examples of correlations between genotypes and
phenotypes from which rules to be applied to genomic profiles may be based.
For example, in
FIGS. 4A and B, each row corresponds to a phenotype/locus/ethnicity, wherein
FIGS. 4C
through I contains further information about the correlations for each of
these rows. As an
example, in FIG. 4A, the "Short Phenotype Name" of BC, as noted in FIG. 4M, an
index for the
names of the short phenotypes, is an abbreviation for breast cancer. In row
BC_4, which is the
generic name for the locus, the gene LSP1 is correlated to breast cancer. The
published or
functional SNP identified with this correlation is rs3817198, as shown in FIG.
4C, with the
published risk allele being C, the nonrisk allele being T. The published SNP
and alleles are
identified through publications such as seminal publications as in FIGS. 4E-G.
In the example
of LSP I in FIG. 4E, the seminal publication is Easton et al., Nature 447:713-
720 (2007). FIGS.
22 and 25 further list correlations. The correlations in FIGS. 22 and 25 may
be used to calculate
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CA 02671267 2009-05-29
an indiviaua i s risK tor a condition or phenotype, for example, for
calcuiating a200~ otig CI Plus
score. The GCI or GCI Plus score may also incorporate information such as a
condition's
prevalence, for example in FIG. 23.
[0088] Alternatively, the correlations may be generated from the stored
genomic profiles.
For example, individuals with stored genomic profiles may also have known
phenotype
information stored as well. Analysis of the stored genomic profiles and known
phenotypes may
generate a genotype correlation. As an example, 250 individuals with stored
genomic profiles
also have stored information that they have previously been diagnosed with
diabetes. Analysis
of their genomic profiles is performed and compared to a control group of
individuals without
diabetes. It is then determined that the individuals previously diagnosed with
diabetes have a
higher rate of having a particular genetic variant compared to the control
group, and a genotype
correlation may be made between that particular genetic variant and diabetes.
[0089] In step 112, rules are made based on the validated correlations of
genetic variants
to particular phenotypes. Rules may be generated based on the genotypes and
phenotypes
correlated as listed in Table 1, for example. Rules based on correlations may
incorporate other
factors such as gender (e.g. FIG. 4) or ethnicity (FIGS. 4 and 5), to generate
effects estimates,
such as those in FIGS. 4 and 5. Other measures resulting from rules may be
estimated relative
risk increase such as in FIG. 6. The effects estimates and estimated relative
risk increase may be
from the published literature, or calculated from the published literature.
Alternatively, the rules
may be based on correlations generated from stored genomic profiles and
previously known
phenotypes. In some embodiments, the rules are based on correlations in FIGS.
22 and 25.
[0090] In a preferred embodiment, the genetic variants will be SNPs. While
SNPs occur
at a single site, individuals who carry a particular SNP allele at one site
often predictably carry
specific SNP alleles at other sites. A correlation of SNPs and an allele
predisposing an
individual to disease or condition occurs through linkage disequilibrium, in
which the non-
random association of alleles at two or more loci occur more or less
frequently in a population
than would be expected from random formation through recombination.
[0091] Other genetic markers or variants, such as nucleotide repeats or
insertions, may
also be in linkage disequilibrium with genetic markers that have been shown to
be associated
with specific phenotypes. For example, a nucleotide insertion is correlated
with a phenotype and
a SNP is in linkage disequilibrium with the nucleotide insertion. A rule is
made based on the
correlation between the SNP and the phenotype. A rule based on the correlation
between the
nucleotide insertion and the phenotype may also be made. Either rules or both
rules may be
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WO 2008/067551 PCT/US2007/086138
applied to a genomic profile, as the presence of one SNP may give a certain
risk factor, the other
may give another risk factor, and when combined may increase the risk.
[0092] Through linkage disequilibrium, a disease predisposing allele
cosegregates with a
particular allele of a SNP or a combination of particular alleles of SNPs. A
particular
combination of SNP alleles along a chromosome is termed a haplotype, and the
DNA region in
which they occur in combination can be referred to as a haplotype block. While
a haplotype
block can consist of one SNP, typically a haplotype block represents a
contiguous series of 2 or
more SNPs exhibiting low haplotype diversity across individuals and with
generally low
recombination frequencies. An identification of a haplotype can be made by
identification of
one or more SNPs that lie in a haplotype block. Thus, a SNP profile typically
can be used to
identify haplotype blocks without necessarily requiring identification of all
SNPs in a given
haplotype block.
[0093] Genotype correlations between SNP haplotype patterns and diseases,
conditions
or physical states are increasingly becoming known. For a given disease, the
haplotype patterns
of a group of people known to have the disease are compared to a group of
people without the
disease. By analyzing many individuals, frequencies of polymorphisms in a
population can be
determined, and in turn these frequencies or genotypes can be associated with
a particular
phenotype, such as a disease or a condition. Examples of known SNP-disease
correlations
include polymorphisms in Complement Factor H in age-related macular
degeneration (Klein et
al., Science: 308:385-389, (2005)) and a variant near the INSIG2 gene
associated with obesity
(Herbert et al., Science: 312:279-283 (2006)). Other known SNP correlations
include
polymorphisms in the 9p2l region that includes CDKN2A and B, such as ) such as
rs10757274,
rs2383206, rs13333040, rs2383207, and rslOl 16277 correlated to myocardial
infarction
(Helgadottir et al., Science 316:1491-1493 (2007); McPherson et al., Science
316:1488-1491
(2007))
[0094] The SNPs may be functional or non-functional. For example, a functional
SNP
has an effect on a cellular function, thereby resulting in a phenotype,
whereas a non-functional
SNP is silent in function, but may be in linkage disequilibrium with a
functional SNP. The SNPs
may also be synonymous or non-synonymous. SNPs that are synonymous are SNPs in
which the
different forms lead to the same polypeptide sequence, and are non-functional
SNPs. If the
SNPs lead to different polypetides, the SNP is non-synonymous and may or may
not be
functional. SNPs, or other genetic markers, used to identify haplotypes in a
diplotype, which is 2
or more haplotypes, may also be used to correlate phenotypes associated with a
diplotype.
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Infonnation about an individual's haplotypes, diplotypes, and SNP profiies may
be in tne
genomic profile of the individual.
[0095] In preferred embodiments, for a rule to be generated based on a genetic
marker in
linkage disequilibrium with another genetic marker that is correlated with a
phenotype, the
genetic marker may have a r2 or D' score, scores commonly used in the art to
determine linkage
disequilibrium, of greater than 0.5. In preferred embodiments, the score is
greater than 0.6, 0.7,
0.8, 0.90, 0.95 or 0.99. As a result, in the present invention, the genetic
marker used to correlate
a phenotype to an individual's genomic profile may be the same as the
functional or published
SNP correlated to a phenotype, or different. For example, using BC_4, the test
SNP and
published SNP are the same, as are the test risk and nonrisk alleles are the
same as the published
risk and nonrisk alleles (FIGS. 4A and C). However, for BC_5, CASP8 and its
correlation to
breast cancer, the test SNP is different from its functional or published SNP,
as are the test risk
and nonrisk alleles to the published risk and nonrisk alleles. The test and
published alleles are
oriented relative to the plus strand of the genome, and from these columns, it
can be inferred the
homozygous risk or nonrisk genotype, which may generate a rule to be applied
to the genomic
profile of individuals such as subscribers. In some embodiments, the test SNP
may not yet be
identified, but using the published SNP information, allelic differences or
SNPs may be
identified based on another assay, such as TaqMan. For example, AMD_5 in FIG.
25A, the
published SNP is rs1061170 but a test SNP has not been identified. The test
SNP may be
identified by LD analysis with the published SNP. Alternatively, the test SNP
may not be used,
and instead, TaqMan or other comparable assay, will be used to assess an
individual's genome
having the test SNP.
[0096] The test SNPs may be "DIRECT" or "TAG" SNPs (FIGS. 4E-G, FIG. 5).
Direct
SNPs are the test SNPs that are the same as the published or functional SNP,
such as for BC 4.
Direct SNPs may also be used for FGFR2 correlation with breast cancer, using
the SNP
rs1073640 in Europeans and Asians, where the minor allele is A and the other
allele is G (Easton
et al., Nature 447:1087-1093 (2007)). Another published or functional SNP for
FGFR2
correlation to breast cancer is rs1219648, also in Europeans and Asians
(Hunter et al., Nat.
Genet. 39:870-874 (2007)). Tag SNPs are where the test SNP is different from
that of the
functional or published SNP, as in for BC_5. Tag SNPs may also be used for
other genetic
variants such as SNPs for CAMTAI (rs4908449), 9p2l (rs10757274, rs2383206,
rs13333040,
rs2383207, rslOl 16277), COL1Al (rsl 800012), FVL (rs6025), HLA-DQAl
(rs4988889,
rs2588331), eNOS (rs1799983), MTHFR (rs1801133), and APC (rs28933380).
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[0097] Databases of SNPs are publicly available from, for example, the
International
HapMap Project (see www.hapmap.org, The International HapMap Consortium,
Nature
426: 789-796 (2003), and The International HapMap Consortium, Nature 437:1299-
1320
(2005)), the Human Gene Mutation Database (HGMD) public database (see
www.hgmd.org),
and the Single Nucleotide Polymorphism database (dbSNP) (see
www.ncbi.nlm.nih.gov/SNP/).
These databases provide SNP haplotypes, or enable the determination of SNP
haplotype patterns.
Accordingly, these SNP databases enable examination of the genetic risk
factors underlying a
wide range of diseases and conditions, such as cancer, inflammatory diseases,
cardiovascular
diseases, neurodegenerative diseases, and infectious diseases. The diseases or
conditions may be
actionable, in which treatments and therapies currently exist. Treatments may
include
prophylactic treatments as well as treatments that ameliorate symptoms and
conditions, including
lifestyle changes.
[0098] Many other phenotypes such as physical traits, physiological traits,
mental traits,
emotional traits, ethnicity, ancestry, and age may also be examined. Physical
traits may include
height, hair color, eye color, body, or traits such as stamina, endurance, and
agility. Mental traits
may include intelligence, memory performance, or learning performance.
Ethnicity and ancestry
may include identification of ancestors or ethnicity, or where an individual's
ancestors originated
from. The age may be a determination of an individual's real age, or the age
in which an
individual's genetics places them in relation to the general population. For
example, an
individual's real age is 38 years of age, however their genetics may determine
their memory
capacity or physical well-being may be of the average 28 year old. Another age
trait may be a
projected longevity for an individual.
[0099] Other phenotypes may also include non-medical conditions, such as "fun"
phenotypes. These phenotypes may include comparisons to well known
individuals, such as
foreign dignitaries, politicians, celebrities, inventors, athletes, musicians,
artists, business people,
and infamous individuals, such as convicts. Other "fun" phenotypes may include
comparisons to
other organisms, such as bacteria, insects, plants, or non-human animals. For
example, an
individual may be interested to see how their genomic profile compares to that
of their pet dog,
or to a former president.
[00100] At step 114, the rules are applied to the stored genomic profile to
generate a
phenotype profile of step 116. For example, information in FIGS. 4, 5, or 6
may form the basis
of rules, or tests, to apply to an individual's genomic profile. The rules may
encompass the
information on test SNP and alleles, and the effect estimates of FIG. 4, where
the UNITS for
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effect estimate is tne units of the effect estimate, such as OR, or odds-ratio
(95,/o connaence
interval) or mean. The effects estimate may be a genotypic risk (FIGS. 4C-G)
in preferred
embodiments, such as the risk for homozygotes (homoz or RR), risk
heterozygotes (heteroz or
RN), and nonrisk homozygotes (homoz or NN). In other embodiments, the effect
estimate may
be carrier risk, which is RR or RN vs NN. In yet other embodiments, the effect
estimate may be
based on the allele, an allelic risk such as R vs. N. There may also be two
locus (FIG. 4J) or
three locus (FIG. 4K) genotypic effect estimate (e.g. RRRR, RRNN, etc for the
9 possible
genotype combinations for a two locus effect estimate). The test SNP frequency
in the public
HapMap is also noted in FIGS. 4I3 and I.
[00101] In other embodiments, information from FIGS. 21, 22, 23, and/or 25 may
be used
to generate information to apply to an individual's genomic profile. For
example, the
information may be used to generate GCI or GCI Plus scores for an individual
(for example,
FIG. 19). The scores may be used to generate information on genetic risks,
such as estimated
lifetime risk, for one or more conditions in the phenotype profile of an
individual (for example,
FIG. 15). the methods allow calculating estimated lifetime risks or relative
risks for one or more
phenotypes or conditions as listed in FIGS. 22 or 25. The risk for a single
condition may be
based on one or more SNP. For example, an estimated risk for a phenotype or
condition may be
based on at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 SNPs, wherein the SNPs
for estimating a risk
may be published SNPs, test SNPs, or both (for example, FIG. 25).
[00102] The estimated risk for a condition may be based on the SNPs as listed
in FIG. 22
or 25. In some embodiments, the risk for a condition may be based on at least
one SNP. For
example, assessment of an individual's risk for Alzheimers (AD), colorectal
cancer (CRC),
osteoarthritis (OA) or exfoliation glaucoma (XFG), may be based on 1 SNP (for
example,
rs4420638 for AD, rs6983267 for CRC, rs4911178 for OA and rs2165241 for XFG).
For other
conditions, such as obesity (BMIOB), Graves' disease (GD), or hemochromatosis
(HEM), an
individual's estimated risk may be based on at least 1 or 2 SNPs (for example,
rs993 9609 and/or
rs9291171 for BMIOB; DRB 1*0301 DQA 1*0501 and/or rs3087243 for GD; rs 1800562
and/or
rs129128 for HEM). For conditions such as, but not limited to, myocardial
infarction (MI),
multiple sclerosis (MS), or psoriasis (PS), 1, 2, or 3 SNPs may be used to
assess an individual's
risk for the condition (for example, rs1866389, rs1333049, and/or rs6922269
for MI; rs6897932,
rs12722489, and/or DRB1 *1501 for MS; rs6859018, rs11209026, and/or HLAC*0602
for PS).
For estimating an individual's risk of restless legs syndrome (RLS) or celiac
disease (CeID), 1, 2,
3, or 4 SNPs (for example, rs6904723, rs2300478, rs1026732, and/or rs9296249
for RLS;
rs6840978, rs11571315, rs2187668, and/or DQA1 *0301 DQB 1 *0302 for CeID). For
prostate
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cancer (rc:) or lupus (SLE), 1, 2, 3, 4, or 5 SNPs may be used to estimate an
inaiviauat's risk for
PC or SLE (for example, rs4242384, rs6983267, rs16901979, rs17765344, and/or
rs4430796 for
PC; rs12531711, rs10954213, rs2004640, DRB1*0301, and/or DRB1*1501 for SLE).
For
estimating an individual's lifetime risk of macular degeneration (AMD) or
rheumatoid arthritis
(RA), 1, 2, 3, 4, 5, or 6 SNPs, may be used (for example, rs10737680,
rs10490924, rs541862,
rs2230199, rs1061170, and/or rs9332739 for AMD; rs6679677, rsl 1203367,
rs6457617,
DRB*0101, DRB 1*0401, and/or DRB 1*0404 for RA). For estimating an
individual's lifetime
risk of breast cancer (BC), 1, 2, 3, 4, 5, 6 or 7 SNPs may be used (for
example, rs3803662,
rs2981582, rs4700485, rs3817198, rs17468277, rs6721996, and/or rs3803662). For
estimating
an individual's lifetime risk of Crohn's disease (CD) or Type 2 diabetes
(T2D), 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, or 11 SNPs may be used (for example, rs2066845, rs5743293,
rs10883365, rs17234657,
rs10210302, rs9858542, rs11805303, rs1000113, rs17221417, rs2542151, and/or
rs10761659 for
CD; rs13266634, rs4506565, rs10012946, rs7756992, rs10811661, rs12288738,
rs8050136,
rs1111875, rs4402960, rs5215, and/or rs1801282 for T2D). In some embodiments,
the SNPs
used as a basis for determining risk may be in linkage disequilibrium with the
SNPs as
mentioned above, or listed in FIG. 22 or 25.
[00103] The phenotype profile of an individual may comprise a number of
phenotypes. In
particular, the assessment of a patient's risk of disease or other conditions
such as likely drug
response including metabolism, efficacy and/or safety, by the methods of the
present invention
allows for prognostic or diagnostic analysis of susceptibility to multiple,
unrelated diseases and
conditions, whether in symptomatic, presymptomatic or asymptomatic
individuals, including
carriers of one or more disease/condition predisposing alleles. Accordingly,
these methods
provide for general assessment of an individual's susceptibility to disease or
condition without
any preconceived notion of testing for a specific disease or condition. For
example, the methods
of the present invention allow for assessment of an individual's
susceptibility to any of the
several conditions listed in Tables 1, FIG. 4, 5, or 6, based on the
individual's genomic profile.
Furthermore, the methods allow assessments of an individual's estimated
lifetime risk or relative
risk for one or more phenotype or condition, such as those in FIGS. 22 or 25.
[00104] The assessment preferably provides information for 2 or more of these
conditions,
and more preferably, 3, 4, 5, 10, 20, 50, 100 or even more of these
conditions. In preferred
embodiments, the phenotype profile results from the application of at least 20
rules to the
genomic profile of an individual. In other embodiments, at least 50 rules are
applied to the
genomic profile of an individual. A single rule for a phenotype may be applied
for monogenic
phenotypes. More than one rule may also be applied for a single phenotype,
such as a multigenic
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phenotype or a monogenic phenotype wherein multiple genetic variants within a
single gene
affects the probability of having the phenotype.
[00105] Following an initial screening of an individual patient's genomic
profile, updates
of an individual's genotype correlations are made (or are available) through
comparisons to
additional nucleotide variants, such as SNPs, when such additional nucleotide
variants become
known. For example, step 110 may be performed periodically, for example,
daily, weekly, or
monthly by one or more people of ordinary skill in the field of genetics, who
scan scientific
literature for new genotype correlations. The new genotype correlations may
then be further
validated by a committee of one or more experts in the field. Step 112 may
then also be
periodically updated with new rules based on the new validated correlations.
[00106] The new rule may encompass a genotype or phenotype without an existing
rule.
For example, a genotype not correlated with any phenotype is discovered to
correlate with a new
or existing phenotype. A new rule may also be for a correlation between a
phenotype for which
no genotype has previously been correlated to. New rules may also be
determined for genotypes
and phenotypes that have existing rules. For example, a rule based on the
correlation between
genotype A and phenotype A exists. New research reveals genotype B correlates
with phenotype
A, and a new rule based on this correlation is made. Another example is
phenotype B is
discovered to be associated with genotype A, and thus a new rule may be made.
[00107] Rules may also be made on discoveries based on known correlations but
not
initially identified in published scientific literature. For example, it may
be reported genotype C
is correlated with phenotype C. Another publication reports genotype D is
correlated with
phenotype D. Phenotype C and D are related symptoms, for example phenotype C
may be
shortness of breath, and phenotype D is small lung capacity. A correlation
between genotype C
and phenotype D, or genotype D with phenotype C, may be discovered and
validated through
statistical means with existing stored genomic profiles of individuals with
genotypes C and D,
and phenotypes C and D, or by further research. A new rule may then be
generated based on the
newly discovered and validated correlation. In another embodiment, stored
genomic profiles of
a number of individuals with a specific or related phenotype may be studied to
determine a
genotype common to the individuals, and a correlation may be determined. A new
rule may be
generated based on this correlation.
[00108] Rules may also be made to modify existing rules. For example,
correlations
between genotypes and phenotypes may be partly determined by a known
individual
characteristic, such as ethnicity, ancestry, geography, gender, age, family
history, or any other
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known phenotypes of the individual. Rules based on these known individual
characteristics may
be made and incorporated into an existing rule, to provide a modified rule.
The choice of
modified rule to be applied will be dependent on the specific individual
factor of an individual.
For example, a rule may be based on the probability an individual who has
phenotype E is 35%
when the individual has genotype E. However, if an individual is of a
particular ethnicity, the
probability is 5%. A new rule may be generated based on this result and
applied to individuals
with that particular ethnicity. Alternatively, the existing rule with a
determination of 35% may
be applied, and then another rule based on ethnicity for that phenotype is
applied. The rules
based on known individual characteristics may be determined from scientific
literature or
determined based on studies of stored genomic profiles. New rules may be added
and applied to
genomic profiles in step 114, as the new rules are developed, or they may be
applied
periodically, such as at least once a year.
[00109] Information of an individual's risk of disease can also be expanded as
technology
advances allow for finer resolution SNP genomic profiles. As indicated above,
an initial SNP
genomic profile readily can be generated using microarray technology for
scanning of 500,000
SNPs. Given the nature of haplotype blocks, this number allows for a
representative profile of
all SNPs in an individual's genome. Nonetheless, there are approximately 10
million SNPs
estimated to occur commonly in the human genome (the International HapMap
Project;
www.hapmap.org). As technological advances allow for practical, cost-efficient
resolution of
SNPs at a finer level of detail, such as microarrays of 1,000,000, 1,500,000,
2,000,000,
3,000,000, or more SNPs, or whole genomic sequencing, more detailed SNP
genomic profiles
can be generated. Likewise, cost-efficient analysis of finer SNP genomic
profiles and updates to
the master database of SNP-disease correlations will be enabled by advances in
computational
analytical methodology.
[00110] After generation of phenotype profile at step 116, a subscriber or
their health care
manager may access their genomic or phenotype profiles via an on-line portal
or website as in
step 118. Reports containing phenotype profiles and other information related
to the phenotype
and genomic profiles may also be provided to the subscriber or their health
care manager, as in
steps 120 and 122. The reports may be printed, saved on the subscriber's
computer, or viewed
on-line.
[00111] A sample on-line report is shown in FIG. 7. The subscriber may choose
to
display a single phenotype, or more than one phenotype. The subscriber may
also have different
viewing options, for example, as shown in FIG. 7, a "Quick View" option. The
phenotype may
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be a medical condition and different treatments and symptoms in the quick
report may link to
other web pages that contain further information about the treatment. For
example, by clicking
on a drug, it will lead to website that contains information about dosages,
costs, side effects, and
effectiveness. It may also compare the drug to other treatments. The website
may also contain a
link leading to the drug manufacturer's website. Another link may provide an
option for the
subscriber to have a pharmacogenomic profile generated, which would include
information such
as their likely response to the drug based on their genomic profile. Links to
alternatives to the
drug may also be provided, such as preventative action such as fitness and
weight loss, and links
to diet supplements, diet plans, and to nearby health clubs, health clinics,
health and wellness
providers, day spas and the like may also be provided. Educational and
informational videos,
summaries of available treatments, possible remedies, and general
recommendations may also be
provided.
[00112] The on-line report may also provide links to schedule in-person
physician or
genetic counseling appointments or to access an on-line genetic counselor or
physician,
providing the opportunity for a subscriber to ask for more information
regarding their phenotype
profile. Links to on-line genetic counseling and physician questions may also
be provided on the
on-line report.
[00113] Reports may also be viewed in other formats such as a comprehensive
view for a
single phenotype, wherein more detail for each category is provided. For
example, there may be
more detailed statistics about the likelihood of the subscriber developing the
phenotype, more
information about the typical symptoms or phenotypes, such as sample symptoms
for a medical
condition, or the range of a physical non-medical condition such as height, or
more information
about the gene and genetic variant, such as the population incidence, for
example in the world, or
in different countries, or in different age ranges or genders. For example,
FIG. 15 shows a
summary of estimated lifetime risks for a number of conditions. The individual
may view more
information for a specific condition, such as prostate cancer (FIG. 16) or
Crohn's disease (FIG.
17).
[00114] In another embodiment, the report may be of a"fun" phenotype, such as
the
similarity of an individual's genomic profile to that of a famous individual,
such as Albert
Einstein. The report may display a percentage similarity between the
individual's genomic
profile to that of Einstein's, and may further display a predicted IQ of
Einstein and that of the
individual's. Further information may include how the genomic profile of the
general population
and their IQ compares to that of the individual's and Einstein's.
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[00115] In another embodiment, the report may display all phenotypes that have
been
correlated to the subscriber's genomic profile. In other embodiments, the
report may display
only the phenotypes that are positively correlated with an individual's
genomic profile. In other
formats, the individual may choose to display certain subgroups of phenotypes,
such as only
medical phenotypes, or only actionable medical phenotypes. For example,
actionable
phenotypes and their correlated genotypes, may include Crohn's disease
(correlated with IL23R
and CARD 15), Type 1 diabetes (correlated with HLA-DR/DQ), lupus (correlated
HLA-DRB 1),
psoriasis (HLA-C), multiple sclerosis (HLA-DQA1), Graves disease (HLA-DRBl),
rheumatoid
arthritis (HLA-DRB 1), Type 2 diabetes (TCF7L2), breast cancer (BRCA2), colon
cancer (APC),
episodic memory (KIBRA), and osteoporosis (COL1A1). The individual may also
choose to
display subcategories of phenotypes in their report, such as only inflammatory
diseases for
medical conditions, or only physical traits for non-medical conditions. In
some embodiments,
the individual may choose to show all conditions an estimated risk was
calculated for the
individual by highlighting those conditions (for example, FIG. 15A, D),
highlighting only
conditions with an elevated risk (FIG. 15B), or only conditions with a reduced
risk (FIG. 15C).
[00116] Information submitted by and conveyed to an individual may be secure
and
confidential, and access to such information may be controlled by the
individual. Information
derived from the complex genomic profile may be supplied to the individual as
regulatory
agency approved, understandable, medically relevant and/or high impact data.
Information may
also be of general interest, and not medically relevant. Information can be
securely conveyed to
the individual by several means including, but not restricted to, a portal
interface and/or mailing.
More preferably, information is securely (if so elected by the individual)
provided to the
individual by a portal interface, to which the individual has secure and
confidential access. Such
an interface is preferably provided by on-line, internet website access, or in
the alternative,
telephone or other means that allow private, secure, and readily available
access. The genomic
profiles, phenotype profiles, and reports are provided to an individual or
their health care
manager by transmission of the data over a network.
[00117] Accordingly, FIG. 8 is a block diagram showing a representative
example logic
device through which a phenotype profile and report may be generated. FIG. 8
shows a
computer system (or digital device) 800 to receive and store genomic profiles,
analyze genotype
correlations, generate rules based on the analysis of genotype correlations,
apply the rules to the
genomic profiles, and produce a phenotype profile and report. The computer
system 800 may be
understood as a logical apparatus that can read instructions from media 811
and/or network port
805, which can optionally be connected to server 809 having fixed media 812.
The system
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shown in r'tts. ts includes CPU 801, disk drives 803, optional input devices
sucn as keyboard
815 and/or mouse 816 and optional monitor 807. Data communication can be
achieved through
the indicated communication medium to a server 809 at a local or a remote
location. The
communication medium can include any means of transmitting and/or receiving
data. For
example, the communication medium can be a network connection, a wireless
connection or an
internet connection. Such a connection can provide for communication over the
World Wide
Web. It is envisioned that data relating to the present invention can be
transmitted over such
networks or connections for reception and/or review by a party 822. The
receiving party 822 can
be but is not limited to an individual, a subscriber, a health care provider
or a health care
manager. In one embodiment, a computer-readable medium includes a medium
suitable for
transmission of a result of an analysis of a biological sample or a genotype
correlation. The
medium can include a result regarding a phenotype profile of an individual
subject, wherein such
a result is derived using the methods described herein.
[00118] A personal portal will preferably serve as the primary interface with
an individual
for receiving and evaluating genomic data. A portal will enable individuals to
track the progress
of their sample from collection through testing and results. Through portal
access, individuals
are introduced to relative risks for common genetic disorders based on their
genomic profile.
The subscriber may choose which rules to apply to their genomic profile
through the portal.
[00119] In one embodiment, one or more web pages will have a list of
phenotypes and
next to each phenotype a box in which a subscriber may select to include in
their phenotype
profile. The phenotypes may be linked to information on the phenotype, to help
the subscriber
make an informed choice about the phenotype they want included in their
phenotype profile.
The webpage may also have phenotypes organized by disease groups, for example
as actionable
diseases or not. For example, a subscriber may choose actionable phenotypes
only, such as
HLA-DQAI and celiac disease. The subscriber may also choose to display pre or
post
symptomatic treatments for the phenotypes. For example, the individual may
choose actionable
phenotypes with pre-symptomatic treatments (outside of increased screening),
for celiac disease,
a pre-symptomatic treatment of gluten free diet. Another example may be
Alzheimer's, the pre-
symptomatic treatment of statins, exercise, vitamins, and mental activity.
Thrombosis is another
example, with a pre-symptomatic treatment of avoid oral contraceptives and
avoid sitting still for
long periods of time. An example of a phenotype with an approved post
symptomatic treatment
is wet AMD, correlated with CFH, wherein individuals may obtain laser
treatment for their
condition.
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[00120] The phenotypes may also be organized by type or class of disease or
conditions,
for example neurological, cardiovascular, endocrine, immunological, and so
forth. Phenotypes
may also be grouped as medical and non-medical phenotypes. Other groupings of
phenotypes on
the webpage may be by physical traits, physiological traits, mental traits, or
emotional traits.
The webpage may further provide a section in which a group of phenotypes are
chosen by
selection of one box. For example, a selection for all phenotypes, only
medically relevant
phenotypes, only non-medically relevant phenotypes, only actionable
phenotypes, only non-
actionable phenotypes, different disease group, or "fun" phenotypes. "Fun"
phenotypes may
include comparisons to celebrities or other famous individuals, or to other
animals or even other
organisms. A list of genomic profiles available for comparison may also be
provided on the
webpage for selection by the subscriber to compare to the subscriber's genomic
profile.
[00121] The on-line portal may also provide a search engine, to help the
subscriber
navigate the portal, search for a specific phenotype, or search for specific
terms or information
revealed by their phenotype profile or report. Links to access partner
services and product
offerings may also be provided by the portal. Additional links to support
groups, message
boards, and chat rooms for individuals with a common or similar phenotype may
also be
provided. The on-line portal may also provide links to other sites with more
information on the
phenotypes in a subscriber's phenotype profile. The on-line portal may also
provide a service to
allow subscribers to share their phenotype profile and reports with friends,
families, or health
care managers. Subscribers may choose which phenotypes to show in the
phenotype profile they
want shared with their friends, families, or health care managers.
[00122] The phenotype profiles and reports provide a personalized genotype
correlation to
an individual. The genotype correlations provided to an individual can be used
in determining
personal health care and lifestyle choices. If a strong correlation is found
between a genetic
variant and a disease for which treatment is available, detection of the
genetic variant may assist
in deciding to begin treatment of the disease and/or monitoring of the
individual. In the case
where a statistically significant correlation exists but is not regarded as a
strong correlation, an
individual can review the information with a personal physician and decide an
appropriate,
beneficial course of action. Potential courses of action that could be
beneficial to an individual
in view of a particular genotype correlation include administration of
therapeutic treatment,
monitoring for potential need of treatment or effects of treatment, or making
life-style changes in
diet, exercise, and other personal habits/activities. For example, an
actionable phenotype such as
celiac disease may have a pre-symptomatic treatment of a gluten-free diet.
Likewise, genotype
correlation information could be applied through pharmacogenomics to predict
the likely
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response an individual would have to treatment with a particular drug or
regimen ot drugs, such
as the likely efficacy or safety of a particular drug treatment.
[00123] Subscribers may choose to provide the genomic and phenotype profiles
to their
health care managers, such as a physician or genetic counselor. The genomic
and phenotype
profiles may be directly accessed by the healthcare manager, by the subscriber
printing out a
copy to be given to the healthcare manager, or have it directly sent to the
healthcare manager
through the on-line portal, such as through a link on the on-line report.
[00124] Delivery of this pertinent information will empower patients to act in
concert with
their physician. In particular, discussions between patients and their
physicians can be
empowered through an individual's portal and links to medical information, and
the ability to tie
patient's genomic information into their medical records. Medical information
may include
prevention and wellness information. The information provided to the
individual patient by the
present invention will enable patients to make informed choices for their
health care. In this
manner, patients will be able to make choices that may help them avoid and/or
delay diseases
that their individual genomic profile (inherited DNA) makes more likely. In
addition, patients
will be able to employ a treatment regime that personally fits their specific
medical needs.
Individuals also will have the ability to access their genotype data should
they develop an illness
and need this information to help their physician form a therapeutic strategy.
[00125] Genotype correlation information could also be used in cooperation
with genetic
counseling to advise couples considering reproduction, and potential genetic
concerns to the
mother, father and/or child. Genetic counselors may provide information and
support to
subscribers with phenotype profiles that display an increased risk for
specific conditions or
diseases. They may interpret information about the disorder, analyze
inheritance patterns and
risks of recurrence, and review available options with the subscriber. Genetic
counselors may
also provide supportive counseling refer subscribers to community or state
support services.
Genetic counseling may be included with specific subscription plans. In some
embodiments,
genetic counseling may be scheduled within 24 hours of request and available
during of hours
such as evenings, Saturdays, Sundays, and/or holidays.
[00126] An individual's portal will also facilitate delivery of additional
information
beyond an initial screening. Individuals will be informed about new scientific
discoveries that
relate to their personal genetic profile, such as information on new
treatments or prevention
strategies for their current or potential conditions. The new discoveries may
also be delivered to
their healthcare managers. In preferred embodiments, the subscribers, or their
healthcare
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providers are informed of new genotype correlations and new research about the
phenotypes in
the subscriber's phenotype profiles, by e-mail. In other embodiments, e-mails
of "fun"
phenotypes are sent to subscribers, for example, an e-mail may inform them
that their genomic
profile is 77% identical to that of Abraham Lincoln and that further
information is available via
an on-line portal.
[00127] The present invention also provides a system of computer code for
generating
new rules, modifying rules, combining rules, periodically updating the rule
set with new rules,
maintaining a database of genomic profile securely, applying the rules to the
genomic profiles to
determine phenotype profiles, and for generating reports. Computer code for
notifying
subscribers of new or revised correlations new or revised rules, and new or
revised reports, for
example with new prevention and wellness information, information about new
therapies in
development, or new treatments available.
Business method
[00128] The present invention provides a business method of assessing an
individual's
genotype correlations based on comparison of the patient's genome profile
against a clinically-
derived database of established, medically relevant nucleotide variants. The
present invention
further provides a business method for using the stored genomic profile of the
individual for
assessing new correlations that were not initially known, to generate updated
phenotype profiles
for an individual, without the requirement of the individual submitting
another biological sample.
A flow chart illustrating the business method is in FIG. 9.
[00129] A revenue stream for the subject business method is generated in part
at step 101,
when an individual initially requests and purchases a personalized genomic
profile for genotype
correlations for a multitude of common human diseases, conditions, and
physical states. A
request and purchase can be made through any number of sources, including but
not limited to,
an on-line web portal, an on-line health service, and an individual's personal
physician or similar
source of personal medical attention. In an alternative embodiment, the
genomic profile may be
provided free, and the revenue stream is generated at a later step, such as
step 103.
[00130] A subscriber, or customer, makes a request for purchase of a phenotype
profile.
In response to a request and purchase, a customer is provided a collection kit
for a biological
sample used for genetic sample isolation at step 103. When a request is made
on-line, by
telephone, or other source in which a collection kit is not readily physically
available to the
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er~a cOliectlon kit is PCT/US2007/086138
custom provided by expedited delivery, such as couner service tnat provides
same-day or overnight delivery. Included in the collection kit is a container
for a sample, as well
as packaging materials for expedited delivery of the sample to a laboratory
for genomic profile
generation. The kit may also include instructions for sending the sample to
the sample
processing facility, or laboratory, and instructions for accessing their
genomic profile and
phenotype profile, which may occur through an on-line portal.
[00131] As detailed above, genomic DNA can be obtained from any of a number of
types
of biological samples. Preferably, genomic DNA is isolated from saliva, using
a commercially
available collection kit such as that available from DNA Genotek. Use of
saliva and such a kit
allows for a non-invasive sample collection, as the customer conveniently
provides a saliva
sample in a container from a collection kit and then seals the container. In
addition, a saliva
sample can be stored and shipped at room temperature.
[00132] After depositing a biological sample into a collection or specimen
container, a
customer will deliver the sample to a laboratory for processing at step 105.
Typically, the
customer may use packaging materials provided in the collection kit to
deliver/send the sample
to a laboratory by expedited delivery, such as same-day or overnight courier
service.
[00133] The laboratory that processes the sample and generates the genomic
profile may
adhere to appropriate governmental agency guidelines and requirements. For
example, in the
United States, a processing laboratory may be regulated by one or more federal
agencies such as
the Food and Drug Administration (FDA) or the Centers for Medicare and
Medicaid Services
(CMS), and/or one or more state agencies. In the United States, a clinical
laboratory may be
accredited or approved under the Clinical Laboratory Improvement Amendments of
1988
(CLIA).
[00134] At step 107, the laboratory processes the sample as previously
described to isolate
the genetic sample of DNA or RNA. Analysis of the isolated genetic sample and
generation of a
genomic profile is then performed at step 109. Preferably, a genomic SNP
profile is generated.
As described above, several methodologies may be used to generate a SNP
profile. Preferably, a
high density array, such as the commercially available platforms from
Affymetrix or Illumina, is
used for SNP identification and profile generation. For example, a SNP profile
may be
generated using an Affymetrix GeneChip assay, as described above in more
detail. As
technology evolves, there may be other technology vendors who can generate
high density SNP
profiles. In another embodiment, a genomic profile for a subscriber will be
the genomic
sequence of the subscriber.
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1001351 roiiowing generation of an individual's genomic profile, tne genotype
aata is
preferably encrypted, imported at step 111, and deposited into a secure
database or vault at step
113, where the information is stored for future reference. The genomic profile
and related
information may be confidential, with access to this proprietary information
and the genomic
profile limited as directed by the individual and/or his or her personal
physician. Others, such as
family and the genetic counselor of the individual may also be permitted
access by the
subscriber.
[00136] The database or vault may be located on-site with the processing
laboratory.
Alternatively, the database may be located at a separate location. In this
scenario, the genomic
profile data generated by the processing lab can be imported at step 111 to a
separate facility that
contains the database.
[00137] After an individual's genomic profile is generated, the individual's
genetic
variations are then compared against a clinically-derived database of
established, medically
relevant genetic variants in step 115. Alternatively, the genotype
correlations may not be
medically relevant but still incorporated into the database of genotype
correlations, for example,
physical traits such as eye color, or "fun" phenotypes such as genomic profile
similarity to a
celebrity.
[00138] The medically relevant SNPs may have been established through the
scientific
literature and related sources. The non-SNP genetic variants may also be
established to be
correlated with phenotypes. Generally, the correlation of SNPs to a given
disease is established
by comparing the haplotype patterns of a group of people known to have the
disease to a group
of people without the disease. By analyzing many individuals, frequencies of
polymorphisms in
a population can be determined, and in turn these genotype frequencies can be
associated with a
particular phenotype, such as a disease or a condition. Alternatively, the
phenotype may be a
non-medical condition.
[00139] The relevant SNPs and non-SNP genetic variants may also be determined
through
analysis of the stored genomic profiles of individuals rather than determined
by available
published literature. Individuals with stored genomic profiles may disclose
phenotypes that have
previously been determined. Analysis of the genotypes and disclosed phenotypes
of the
individuals may be compared to those without the phenotypes to determine a
correlation that
may then be applied to other genomic profiles. Individuals that have their
genomic profiles
determined may fill out questionnaires about phenotypes that have previously
been determined.
Questionnaires may contain questions about medical and non-medical conditions,
such as
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diseases previously diagnosed, family history of medical conditions,
lifestyie, physical traits,
mental traits, age, social life, environment and the like.
[00140] In one embodiment, an individual may have their genomic profile
determined free
of charge if they fill out a questionnaire. In some embodiments, the
questionnaires are to be
filled out periodically by the individuals in order to have free access to
their phenotype profile
and reports. In other embodiments, the individuals that fill out the
questionnaires may be
entitled to a subscription upgrade, such that they have more access than their
previous
subscription level, or they may purchase or renew a subscription at a reduced
cost.
[00141] All information deposited in the database of medically relevant
genetic variants at
step 121 is first approved by a research/clinical advisory board for
scientific accuracy and
importance, coupled with review and oversight by an appropriate governmental
agency if
warranted at step 119. For example, in the United States, the FDA may provide
oversight
through approval of algorithms used for validation of genetic variant
(typically SNP, transcript
level, or mutation) correlative data. At step 123, scientific literature and
other relevant sources
are monitored for additional genetic variant-disease or condition
correlations, and following
validation of their accuracy and importance, along with governmental agency
review and
approval, these additional genotype correlations are added to the master
database at step 125.
[00142] The database of approved, validated medically-relevant genetic
variants, coupled
with a genome-wide individual profile, will advantageously allow genetic risk-
assessment to be
performed for a large number of diseases or conditions. Following compilation
of an
individual's genomic profile, individual genotype correlations can be
determined through
comparison of the individual's nucleotide (genetic) variants or markers with a
database of human
nucleotide variants that have been correlated to a particular phenotype, such
as a disease,
condition, or physical state. Through comparison of an individual's genomic
profile to the
master database of genotype correlations, the individual can be informed
whether they are found
to be positive or negative for a genetic risk factor, and to what degree. An
individual will receive
relative risk and/or predisposition data on a wide range of scientifically
validated disease states
(e.g., Alzheimer's, cardiovascular disease, blood clotting). For example,
genotype correlations
in Table 1 may be included. In addition, SNP disease correlations in the
database may include,
but are not limited to, those correlations shown in FIG. 4. Other correlations
from FIGS. 5 and
6 may also be included. The subject business method therefore provides
analysis of risk to a
multitude of diseases and conditions without any preconceived notion of what
those diseases and
conditions might entail.
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1001431 In other embodiments, the genotype correlations that are coupleci to
the genome
wide individual profile are non-medically relevant phenotypes, such as "fun"
phenotypes or
physical traits such as hair color. In preferred embodiments, a rule or rule
set is applied to the
genomic profile or SNP profile of an individual, as described above.
Application of the rules to
a genomic profile generates a phenotype profile for the individual.
[00144] Accordingly, the master database of human genotype correlations is
expanded
with additional genotype correlations as new correlations become discovered
and validated. An
update can be made by accessing pertinent information from the individual's
genomic profile
stored in a database as desired or appropriate. For example, a new genotype
correlation that
becomes known may be based on a particular gene variant. Determination of
whether an
individual may be susceptible to that new genotype correlation can then be
made by retrieving
and comparing just that gene portion of the individual's entire genomic
profile.
[00145] The results of the genomic query preferably are analyzed and
interpreted so as to
be presented to the individual in an understandable format. At step 117, the
results of an initial
screening are then provided to the patient in a secure, confidential form,
either by mailing or
through an on-line portal interface, as detailed above.
[001461 The report may contain the phenotype profile as well as genomic
information
about the phenotypes in the phenotype profile, for example basic genetics
about the genes
involved or the statistics of the genetic variants in different populations.
Other information
based on the phenotype profile that may be included in the report are
prevention strategies,
wellness information, therapies, symptom awareness, early detection schemes,
intervention
schemes, and refined identification and sub-classification of the phenotypes.
Following an initial
screening of an individual's genomic profile, controlled, moderated updates
are or can be made.
[00147] Updates of an individual's genomic profile are made or are available
in
conjunction with updates to the master database as new genotype correlations
emerge and are
both validated and approved. New rules based on the new genotype correlations
may be applied
to the initial genomic profile to provide updated phenotype profiles. An
updated genotype
correlation profile can be generated by comparing the relevant portion of the
individual's
genomic profile to a new genotype correlation at step 127. For example, if a
new genotype
correlation is found based on variation in a particular gene, then that gene
portion of the
individual's genomic profile can be analyzed for the new genotype correlation.
In such a case,
one or more new rules may be applied to generate an updated phenotype profile,
rather than an
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entire rule set with rules that had already been applied. The results of the
individual's updated
genotype correlations are provided in a secure manner at step 129.
[00148] Initial and updated phenotype profiles may be a service provided to
subscribers or
customers. Varying levels of subscriptions to genomic profile analysis and
combinations thereof
can be provided. Likewise, subscription levels can vary to provide individuals
choices of the
amount of service they wish to receive with their genotype correlations. Thus,
the level of
service provided would vary with the level of service subscription purchased
by the individual.
[00149] An entry level subscription for a subscriber may include a genomic
profile and an
initial phenotype profile. This may be a basic subscription level. Within the
basic subscription
level may be varying levels of service. For example, a particular subscription
level could
provide references for genetic counseling, physicians with particular
expertise in treating or
preventing a particular disease, and other service options. Genetic counseling
may be obtained
on-line or by telephone. In another embodiment, the price of the subscription
may depend on the
number of phenotypes an individual chooses for their phenotype profile.
Another option may be
whether the subscriber chooses to access on-line genetic counseling.
[00150] In another scenario, a subscription could provide for an initial
genome-wide,
genotype correlation, with maintenance of the individual's genomic profile in
a database; such
database may be secure if so elected by the individual. Following this initial
analysis,
subsequent analyses and additional results could be made upon request and
additional payment
by the individual. This may be a premium level of subscription.
[00151] In one embodiment of the subject business method, updates of an
individual's
risks are performed and corresponding information made available to
individuals on a
subscription basis. The updates may be available to subscribers who purchase
the premium level
of subscription. Subscription to genotype correlation analysis can provide
updates with a
particular category or subset of new genotype correlations according to an
individual's
preferences. For example, an individual might only wish to learn of genotype
correlations for
which there is a known course of treatment or prevention. To aid an individual
in deciding
whether to have an additional analysis performed, the individual can be
provided with
information regarding additional genotype correlations that have become
available. Such
information can be conveniently mailed or e-mailed to a subscriber.
[00152] Within the premium subscription, there may be further levels of
service, such as
those mentioned in the basic subscription. Other subscription models may be
provided within
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the premu mi eveissror example, the highest level may provide a subscrPiner
io2uniimiie,ag updates
and reports. The subscriber's profile may be updated as new correlations and
rules are
determined. At this level, subscribers may also permit access to unlimited
number of
individuals, such as family members and health care managers. The subscribers
may also have
unlimited access to on-line genetic counselors and physicians.
[00153] The next level of subscription within the premium level may provide
more limited
aspects, for example a limited number of updates. The subscriber may have a
limited number of
updates for their genomic profile within a subscription period, for example, 4
times a year. In
another subscription level, the subscriber may have their stored genomic
profile updated once a
week, once a month, or once a year. In another embodiment, the subscriber may
only have a
limited number of phenotypes they may choose to update their genomic profile
against.
[00154] A personal portal will also conveniently allow an individual to
maintain a
subscription to risk or correlation updates and information updates or
alternatively, make
requests for updated risk assessment and information. As described above,
varying subscription
levels could be provided to allow individuals choices of various levels of
genotype correlation
results and updates and may different subscription levels may be chosen by the
subscriber via
their personal portal.
[00155] Any of these subscription options will contribute to the revenue
stream for the
subject business method. The revenue stream for the subject business method
will also be added
by the addition of new customers and subscribers, wherein the new genomic
profiles are added to
the database.
[00156] Table 1: Representative genes having genetic variants correlated with
a
phenotype.
Gene Phenotype
A2M Alzheimer's Disease
ABCAl cholesterol, HDL
ABCB1 HIV
ABCB1 11 5
ABCB1 kidney transplant complications
ABCB1 digoxin, serum concentration
ABCB1 Crohn's disease; ulcerative colitis
ABCB1 Parkinson's disease
ABCC8 Type 2 diabetes
ABCC8 diabetes, type 2
ABO myocardial infarct
ACADM medium-chain acyl-CoA deh dro enase deficiency
ACDC Type 2, diabetes
ACE Type 2 diabetes
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csene Phenotype
ACE hypertension
ACE Alzheimer's Disease
ACE myocardial infarction
ACE cardiovascular
ACE left ventricular hypertrophy
ACE coronary artery
ACE atherosclerosis, coronary
ACE retino ath , diabetic
ACE systemic lupus erythematosus
ACE blood pressure, arterial
ACE erectile d sfunction
ACE Lupus
ACE ol c stic kidney disease
ACE stroke
ACP1 diabetes, type 1
ACSM1 (LIP)c cholesterol levels
ADAM33 asthma
ADD 1 hypertension
ADD1 blood pressure, arterial
ADH1B alcohol abuse
ADH1C alcohol abuse
ADIPOQ diabetes, type 2
ADIPOQ obesity
ADORA2A panic disorder
ADRB 1 hypertension
ADRB 1 heart failure
ADRB2 asthma
ADRB2 hypertension
ADRB2 obesity
ADRB2 blood pressure, arterial
ADRB2 Type 2 Diabetes
ADRB3 obesity
ADRB3 Type 2 Diabetes
ADRB3 hypertension
AGT hypertension
AGT Type 2 diabetes
AGT Essential Hypertension
AGT myocardial infarction
AGTRI hypertension
AGTR2 hypertension
AHR breast cancer
ALAD lead toxicity
ALDH2 alcoholism
ALDH2 alcohol abuse
ALDH2 colorectal cancer
ALDRL2 Type 2 diabetes
ALOX5 asthma
ALOX5AP asthma
APBB 1 Alzheimer's Disease
APC colorectal cancer
APEX1 lung cancer
APOAl atherosclerosis, coronary
APOAI cholesterol, HDL
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APOAl coronary artery disease
APOAl Type 2 diabetes
APOA4 Type 2 diabetes
APOA5 tri1 cerides
APOA5 atherosclerosis, coronary
APOB hypercholesterolemia
APOB obesity
APOB cardiovascular
APOB coronary artery disease
APOB coronary heart disease
APOB Type 2 diabetes
APOC1 Alzheimer's Disease
APOC3 triglycerides
APOC3 Type 2 Diabetes
APOE Alzheimer's Disease
APOE Type 2 diabetes
APOE multiple sclerosis
APOE atherosclerosis, coronary
APOE Parkinson's disease
APOE coronary heart disease
APOE myocardial infarction
APOE stroke
APOE Alzheimer's disease
APOE coronary arte disease
APP Alzheimer's Disease
AR prostate cancer
AR breast cancer
ATM breast cancer
ATP7B Wilson disease
ATXN8OS spinocerebellar ataxia
BACE1 Alzheimer's Disease
BCHE Alzheimer's Disease
BDKRB2 hypertension
BDNF Alzheimer's Disease
BDNF bipolar disorder
BDNF Parkinson's disease
BDNF schizophrenia
BDNF memory
BGLAP bone density
BRAF th oid cancer
BRCAl breast cancer
BRCAl breast cancer; ovarian cancer
BRCAl ovarian cancer
BRCA2 breast cancer
BRCA2 breast cancer; ovarian cancer
BRCA2 ovarian cancer
BRIP1 breast cancer
C4A systemic lupus erythematosus
CALCR bone density
CAMTAI episodic memory
CAPN10 diabetes, type 2
CAPN10 Type 2 diabetes
CAPN3 muscular d stro h
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CARD15 Crohn's disease
CARD15 Crohn's disease; ulcerative colitis
CARD15 Inflammato Bowel Disease
CART obesity
CASR bone density
CCKAR schizophrenia
CCL2 systemic lupus erythematosus
CCL5 HIV
CCL5 asthma
CCND1 colorectal cancer
CCR2 HIV
CCR2 HIV infection
CCR2 hepatitis C
CCR2 myocardial infarct
CCR3 Asthma
CCR5 HIV
CCR5 HIV infection
CCR5 hepatitis C
CCR5 asthma
CCR5 multiple sclerosis
CD14 atopy
CD14 asthma
CD14 Crohn's disease
CD14 Crohn's disease; ulcerative colitis
CD14 periodontitis
CD14 Total IgE
CDH1 prostate cancer
CDH1 colorectal cancer
CDKN2A melanoma
CDSN psoriasis
CEBPA leukemia, m eloid
CETP atherosclerosis, coronary
CETP coronary heart disease
CETP hypercholesterolemia
CFH macular degeneration
CFTR cystic fibrosis
CFTR pancreatitis
CFTR Cystic Fibrosis
CHAT Alzheimer's Disease
CHEK2 breast cancer
CHRNA7 schizophrenia
CMA1 atopic dermatitis
CNRI schizo hrenia
COL1A1 bone density
COL1Al osteoporosis
COL1A2 bone density
COL2A1 Osteoarthritis
COMT schizophrenia
COMT breast cancer
COMT Parkinson's disease
COMT bipolar disorder
COMT obsessive compulsive disorder
COMT alcoholism
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Gene Phenotype
CR1 s stemic lupus erythematosus
CRP C-reactive protein
CST3 Alzheimer's Disease
CTLA4 Type 1 diabetes
CTLA4 Graves' disease
CTLA4 multiple sclerosis
CTLA4 rheumatoid arthritis
CTLA4 s stemic lupus e hematosus
CTLA4 lupus erythematosus
CTLA4 celiac disease
CTSD Alzheimer's Disease
CX3CRl HN
CXCL12 HIV
CXCL12 HIV infection
CYBA atherosclerosis, coronary
CYBA hypertension
CYP 11 B2 hypertension
CYP11B2 left ventricular h ertro h
CYP 17A1 breast cancer
CYP 17A1 prostate cancer
CYP 17A1 endometriosis
CYP 17A1 endometrial cancer
CYP19A1 breast cancer
CYP19A1 prostate cancer
CYP 19A1 endometriosis
CYPlAl lung cancer
CYP 1 A1 breast cancer
CYP1A1 Colorectal Cancer
CYP1A1 prostate cancer
CYPlAl eso ha eal cancer
CYP lAl endometriosis
CYPlAl c o enetic studies
CYPlA2 schizo hrenia
CYP1A2 colorectal cancer
CYP 1 B 1 breast cancer
CYP 1 B 1 glaucoma
CYP 1 B 1 prostate cancer
CYP21A2 21-h drox lase deficiency
CYP21A2 congenital adrenal h e lasia
CYP21A2 adrenal h e lasia, congenital
CYP2A6 smoking behavior
CYP2A6 nicotine
CYP2A6 lung cancer
CYP2C19 H. lori infection
CYP2C19 hen oin
CYP2C19 gastric disease
CYP2C8 malaria, plasmodium falciparum
CYP2C9 anticoagulant complications
CYP2C9 warfarin sensitivity
CYP2C9 warfarin therapy, response to
CYP2C9 colorectal cancer
CYP2C9 phenytoin
CYP2C9 acenocoumarol response
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CYP2C9 coagulation disorder
CYP2C9 hypertension
CYP2D6 colorectal cancer
CYP2D6 Parkinson's disease
CYP2D6 CYP2D6 poor metabolizer phenotype
CYP2E 1 lung cancer
CYP2E 1 colorectal cancer
CYP3A4 prostate cancer
CYP3A5 prostate cancer
CYP3A5 eso ha eal cancer
CYP46A1 Alzheimer's Disease
DBH schizophrenia
DHCR7 Smith-Lemli-Op itz syndrome
DISC1 schizophrenia
DLST Alzheimer's Disease
DMD muscular dystrophy
DRD2 alcoholism
DRD2 schizo hrenia
DRD2 smoking behavior
DRD2 Parkinson's disease
DRD2 tardive dyskinesia
DRD3 schizo hrenia
DRD3 tardive dyskinesia
DRD3 bipolar disorder
DRD4 attention deficit hyperactivity disorder
DRD4 schizophrenia
DRD4 novelty seeking
DRD4 ADHD
DRD4 personality traits
DRD4 heroin abuse
DRD4 alcohol abuse
DRD4 alcoholism
DRD4 personality disorders
DTNBP1 schizophrenia
EDN1 hypertension
EGFR lung cancer
ELAC2 prostate cancer
ENPP 1 Type 2 diabetes
EPHB2 prostate cancer
EPHX1 lung cancer
EPHX1 colorectal cancer
EPHXl c o enetic studies
EPHX1 chronic obstructive pulmonary disease/COPD
ERBB2 breast cancer
ERCC 1 lung cancer
ERCC1 colorectal cancer
ERCC2 lung cancer
ERCC2 c o enetic studies
ERCC2 bladder cancer
ERCC2 colorectal cancer
ESR1 bone density
ESR1 bone mineral density
ESR1 breast cancer
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Gene Phenotype
ESR1 endometriosis
ESR1 osteoporosis
ESR2 bone density
ESR2 breast cancer
estrogen receptor bone mineral density
F2 coronary heart disease
F2 stroke
F2 thromboembolism, venous
F2 preeclampsia
F2 thrombosis
F5 thromboembolism, venous
FS preeclampsia
F5 myocardial infarct
F5 stroke
F5 stroke, ischemic
F7 atherosclerosis, coronary
F7 m ocardial infarct
F8 hemophilia
F9 hemophilia
FABP2 Type 2 diabetes
FAS Alzheimer's Disease
FASLG multiple sclerosis
FCGR2A systemic lupus erythematosus
FCGR2A lupus erythematosus
FCGR2A periodontitis
FCGR2A rheumatoid arthritis
FCGR2B lupus erythematosus
FCGR2B s stemic lupus e hematosus
FCGR3A systemic lupus erythematosus
FCGR3A lupus erythematosus
FCGR3A periodontitis
FCGR3A arthritis
FCGR3A rheumatoid arthritis
FCGR3B periodontitis
FCGR3B periodontal disease
FCGR3B lupus erythematosus
FGB fibrinogen
FGB myocardial infarction
FGB coronary heart disease
FLT3 leukemia, myeloid
FLT3 leukemia
FMR1 Fragile X syndrome
FRAXA Fragile X Syndrome
FUT2 H. lori infection
FVL Factor V Leiden
G6PD G6PD deficiency
G6PD hyperbilirubinemia
GABRA5 bipolar disorder
GBA Gaucher disease
GBA Parkinson's disease
GCGR (FAAH, ML4R, UCP2) body mass/obesity
GCK Type 2 diabetes
GCLM F12, TLR4 atherosclerosis, myocardial infarction
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Gene Phenotype
GDNF schizophrenia
GHRL obesity
GJB 1 Charcot-Marie-Tooth disease
GJB2 deafness
GJB2 hearing loss, sensorineural nons dromic
GJB2 hearing loss, sensorineural
GJB2 hearing loss/deafness
GJB6 hearing loss, sensorineural nonsyndromic
GJB6 hearing loss/deafness
GNAS hypertension
GNB3 hypertension
GPX 1 lung cancer
GRIN1 schizo hrenia
GRIN2B schizophrenia
GSK3B bipolar disorder
GSTM1 lung cancer
GSTM1 colorectal cancer
GSTM1 breast cancer
GSTM1 prostate cancer
GSTMI c o enetic studies
GSTM1 bladder cancer
GSTM1 eso ha eal cancer
GSTM1 head and neck cancer
GSTM1 leukemia
GSTM1 Parkinson's disease
GSTMI stomach cancer
GSTP 1 Lung cancer
GSTP1 colorectal cancer
GSTP 1 breast cancer
GSTP1 c o enetic studies
GSTP1 prostate cancer
GSTT1 lung cancer
GSTT1 colorectal cancer
GSTT1 breast cancer
GSTT1 prostate cancer
GSTT1 Bladder Cancer
GSTT1 c o enetic studies
GSTT 1 asthma
GSTT 1 benzene toxicity
GSTT 1 eso ha eal cancer
GSTT1 head and neck cancer
GYS 1 Type 2 diabetes
HBB thalassemia
HBB thalassemia, beta
HD Huntin on's disease
HFE Hemochromatosis
HFE iron levels
HFE colorectal cancer
HK2 Type 2 diabetes
HLA rheumatoid arthritis
HLA Type 1 diabetes
HLA Behcet's Disease
HLA celiac disease
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Gene Phenotype
HLA psoriasis
HLA Graves disease
HLA multi le sclerosis
HLA schizophrenia
HLA asthma
HLA diabetes mellitus
HLA Lupus
HLA-A leukemia
HLA-A HN
HLA-A diabetes, type 1
HLA-A graft-versus-host disease
HLA-A multiple sclerosis
HLA-B leukemia
HLA-B Behcet's Disease
HLA-B celiac disease
HLA-B diabetes, type 1
HLA-B graft-versus-host disease
HLA-B sarcoidosis
HLA-C psoriasis
HLA-DPA1 measles
HLA-DPB1 diabetes, type 1
HLA-DPB 1 Asthma
HLA-DQAl diabetes, type 1
HLA-D Al celiac disease
HLA-D Al cervical cancer
HLA-DQA1 asthma
HLA-DQA1 multiple sclerosis
HLA-DQA1 diabetes, type 2; diabetes, type I
HLA-DQAl lupus erythematosus
HLA-D Al pregnancy loss, recurrent
HLA-D Al psoriasis
HLA-DQB1 diabetes, type 1
HLA-D B1 celiac disease
HLA-D B1 multiple sclerosis
HLA-DQB1 cervical cancer
HLA-DQB1 lupus erythematosus
HLA-D B 1 pregnancy loss, recurrent
HLA-DQB1 arthritis
HLA-D B 1 asthma
HLA-D B 1 HIV
HLA-DQB1 lymphoma
HLA-D B1 tuberculosis
HLA-DQBl rheumatoid arthritis
HLA-D Bl diabetes, type 2
HLA-DQB1 graft-versus-host disease
HLA-DQB1 narcolepsy
HLA-DQB1 arthritis, rheumatoid
HLA-D B 1 cholangitis, sclerosin
HLA-D B1 diabetes, type 2; diabetes, type I
HLA-D B 1 Graves' disease
HLA-DQB1 hepatitis C
HLA-D B 1 hepatitis C, chronic
HLA-DQB1 malaria
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Gene Phenotype
HLA-D Bl malaria, plasmodium falciparum
HLA-D B 1 melanoma
HLA-DQB1 soriasis
HLA-D B 1 S' o en's syndrome
HLA-DQB1 systemic lupus erythematosus
HLA-DRBl diabetes, type 1
HLA-DRB1 multiple sclerosis
HLA-DRB1 systemic lupus erythematosus
HLA-DRB1 rheumatoid arthritis
HLA-DRB1 cervical cancer
HLA-DRB1 arthritis
HLA-DRB 1 celiac disease
HLA-DRB1 lupus erythematosus
HLA-DRB 1 sarcoidosis
HLA-DRB1 HIV
HLA-DRB 1 tuberculosis
HLA-DRB1 Graves' disease
HLA-DRB1 lymphoma
HLA-DRB i psoriasis
HLA-DRB1 asthma
HLA-DRB1 Crohn's disease
HLA-DRB1 graft-versus-host disease
HLA-DRB1 hepatitis C, chronic
HLA-DRB1 narcol s
HLA-DRB 1 sclerosis, systemic
HLA-DRB 1 S' o en's syndrome
HLA-DRB 1 Type 1 diabetes
HLA-DRB1 arthritis, rheumatoid
HLA-DRB1 cholangitis, sclerosing
HLA-DRB1 diabetes, type 2; diabetes, type 1
HLA-DRB1 H. lori infection
HLA-DRB1 hepatitis C
HLA-DRB 1 juvenile arthritis
HLA-DRB1 leukemia
HLA-DRB1 malaria
HLA-DRB1 melanoma
HLA-DRB1 re anc loss, recurrent
HLA-DRB3 soriasis
HLA-G pregnancy loss, recurrent
HMOXI atherosclerosis, coronary
HNF4A diabetes, type 2
HNF4A type 2 diabetes
HSD11B2 hypertension
HSD 17B 1 breast cancer
HTR1A depressive disorder, major
HTR 1 B alcohol dependence
HTRIB alcoholism
HTR2A memory
HTR2A schizophrenia
HTR2A bipolar disorder
HTR2A depression
HTR2A depressive disorder, major
HTR2A suicide
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HTR2A Alzheimer's Disease
HTR2A anorexia nervosa
HTR2A hypertension
HTR2A obsessive compulsive disorder
HTR2C schizophrenia
HTR6 Alzheimer's Disease
HTR6 schizophrenia
HTRAl wet age-related macular degeneration
IAPP Type 2 Diabetes
IDE Alzheimer's Disease
IFNG tuberculosis
IFNG Type 1 diabetes
IFNG graft-versus-host disease
IFNG hepatitis B
IFNG multi le sclerosis
IFNG asthma
IFNG breast cancer
IFNG kidney transplant
IFNG kidney transplant complications
IFNG longevity
IFNG pregnancy loss, recurrent
IGFBP3 breast cancer
IGFBP3 prostate cancer
IL10 systemic lupus erythematosus
ILl 0 asthma
ILIO graft-versus-host disease
ILIO HIV
ILIO kidney transplant
ILIO kidney transplant complications
ILIO hepatitis B
ILIO 'uvenile arthritis
IL10 lon evit
ILl 0 multiple sclerosis
IL10 re anc loss, recurrent
1L10 rheumatoid arthritis
IL10 tuberculosis
IL12B Type 1 diabetes
IL12B asthma
IL13 asthma
IL13 atopy
IL13 chronic obstructive pulmonary disease/COPD
IL13 Graves' disease
IL 1 A periodontitis
IL1A Alzheimer's Disease
IL1B periodontitis
ILIB Alzheimer's Disease
IL1B stomach cancer
IL1R1 Type 1 diabetes
IL 1 RN stomach cancer
IL2 asthma; eczema; allergic disease
IL4 Asthma
IL4 atopy
IL4 HN
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IL4R asthma
IL4R atopy
IL4R Total serurn IgE
IL6 Bone Mineralization
IL6 kidney transplant
IL6 kidney transplant complications
IL6 Lon evit
IL6 multiple sclerosis
IL6 bone density
IL6 bone mineral density
IL6 Colorectal Cancer
IL6 juvenile arthritis
IL6 rheumatoid arthritis
IL9 asthma.
INHA premature ovarian failure
INS Type 1 diabetes
INS Type 2 diabetes
INS diabetes, type 1
INS obesity
INS prostate cancer
INSIG2 obesity
INSR Type 2 diabetes
INSR hypertension
INSR polycystic ovary syndrome
IPF1 diabetes, type 2
IRS 1 Type 2 diabetes
IRS 1 diabetes, type 2
IRS2 diabetes, type 2
ITGB3 myocardial infarction
ITGB3 atherosclerosis, coronary
ITGB3 coronary heart disease
ITGB3 myocardial infarct
KCNE1 EKG, abnormal
KCNE2 EKG, abnormal
KCNH2 EKG, abnormal
KCNH2 long QT syndrome
KCNJ 11 diabetes, type 2
KCNJ 11 Type 2 Diabetes
KCNN3 schizophrenia
KCNQ1 EKG, abnormal
KCNQ1 long QT syndrome
KIBRA episodic memory
KLK1 hypertension
KLK3 prostate cancer
KRAS colorectal cancer
LDLR h ercholesterolemia
LDLR hypertension
LEP obesity
LEPR obesity
LIG4 breast cancer
LIPC atherosclerosis, coronary
LPL Coron Artery Disease
LPL h erli idemia
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Gene Phenotype
LPL triglycerides
LRPl Alzheimer's Disease
LRP5 bone density
LRRK2 Parkinson's disease
LRRK2 Parkinsons disease
LTA type 1 diabetes
LTA Asthma
LTA systemic lupus erythematosus
LTA sepsis
LTC4S Asthma
MAOA alcoholism
MAOA schizophrenia
MAOA bipolar disorder
MAOA smoking behavior
MAOA personality disorders
MAOB Parkinson's disease
MAOB smoking behavior
MAPT Parkinson's disease
MAPT Alzheimer's Disease
MAPT dementia
MAPT Frontotemporal dementia
MAPT progressive supranuclear palsy
MC 1 R melanoma
MC3R obesity
MC4R obesity
MECP2 Rett syndrome
MEFV Familial Mediterranean Fever
MEFV amyloidosis
MICA Type 1 diabetes
MICA Behcet's Disease
MICA celiac disease
MICA rheumatoid arthritis
MICA s stemic lupus erythematosus
MLH1 colorectal cancer
MME Alzheimer's Disease
MMP1 Lung Cancer
MMP 1 ovarian cancer
MMP 1 periodontitis
MMP3 myocardial infarct
MMP3 ovarian cancer
MMP3 rheumatoid arthritis
MPO lung cancer
MPO Alzheimer's Disease
MPO breast cancer
MPZ Charcot-Marie-Tooth disease
MS4A2 asthma
MS4A2 atopy
MSH2 colorectal cancer
MSH6 colorectal cancer
MSRI prostate cancer
MTHFR colorectal cancer
MTHFR Type 2 diabetes
MTHFR neural tube defects
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Gene Phenotype
MTHFR homocysteine
MTHFR thromboembolism, venous
MTHFR atherosclerosis, coronary
MTHFR Alzheimer's Disease
MTHFR eso ha eal cancer
MTHFR preeclampsia
MTHFR pregnancy loss, recurrent
MTHFR stroke
MTHFR thrombosis, deep vein
MT-ND1 diabetes, type 2
MTR colorectal cancer
MT-RNRI hearing loss, sensorineural nons dromic
MTRR neural tube defects
MTRR homocysteine
MT-TL1 diabetes, type 2
MUTYH colorectal cancer
MYBPC3 cardiom o ath
MYH7 cardiorn o ath
MYOC glaucoma, primary o en-an le
MYOC glaucoma
NATI colorectal cancer
NATI Breast Cancer
NATI bladder cancer
NAT2 colorectal cancer
NAT2 bladder cancer
NAT2 breast cancer
NAT2 Lung Cancer
NBN breast cancer
NCOA3 breast cancer
NCSTN Alzheimer's Disease
NEUROD1 Type 1 diabetes
NF 1 neurofbromatosis 1
NOS 1 Asthma
NOS2A multi le sclerosis
NOS3 hypertension
NOS3 coronary heart disease
NOS3 atherosclerosis, coronary
NOS3 coronary artery disease
NOS3 myocardial infarction
NOS3 acute coronary syndrome
NOS3 blood pressure, arterial
NOS3 preeclampsia
NOS3 nitric oxide
NOS3 Alzheimer's Disease
NOS3 asthma
NOS3 Type 2 diabetes
NOS3 cardiovascular disease
NOS3 Behcet's Disease
NOS3 erectile dysfunction
NOS3 kidney failure, chronic
NOS3 lead toxicity
NOS3 left ventricular h ertro h
NOS3 re anc loss, recurrent
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Gene Phenotype
NOS3 retino ath , diabetic
NOS3 stroke
NOTCH4 schizophrenia
NPY alcohol abuse
NQO1 lung cancer
N O1 colorectal cancer
N O1 benzene toxicity
NQO1 bladder cancer
NQOi Parkinson's Disease
NR3C2 hypertension
NR4A2 Parkinson's disease
NRG1 schizophrenia
NTF3 schizo hrenia
OGG1 lung cancer
OGG1 colorectal cancer
OLRI Alzheimer's Disease
OPAI glaucoma
OPRM1 alcohol abuse
OPRM1 substance dependence
OPTN glaucoma, primaU o en-an le
P450 drug metabolism
PADI4 rheumatoid arthritis
PAH phenylketonuria/PKU
PAIl coronary heart disease
PAll asthma
PALB2 breast cancer
PARK2 Parkinson's disease
PARK7 Parkinson's disease
PDCD1 lupus e hematosus
PINK1 Parkinson's disease
PKA memory
PKC memory
PLA2G4A schizophrenia
PNOC schizophrenia
POMC obesity
PONl atherosclerosis, coronary
PON1 Parkinson's disease
PON1 Type 2 Diabetes
PON1 atherosclerosis
PONI coronary artery disease
PONl coronary heart disease
PONl Alzheimer's Disease
PONl lon evit
PON2 atherosclerosis, coronary
PON2 reterm delivery
PPARG Type 2 Diabetes
PPARG obesity
PPARG diabetes, type 2
PPARG Colorectal Cancer
PPARG hypertension
PPARGC 1 A diabetes, type 2
PRKCZ Type 2 diabetes
PRL systemic lupus e hematosus
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csene Phenotype
PRNP Alzheimer's Disease
PRNP Creutzfeldt-Jakob disease
PRNP Jakob-Creutzfeldt disease
PRODH schizo hrenia
PRSS 1 pancreatitis
PSEN1 Alzheimer's Disease
PSEN2 Alzheimer's Disease
PSMB8 Type 1 diabetes
PSMB9 Type 1 diabetes
PTCH skin cancer, non-melanoma
PTGIS hypertension
PTGS2 colorectal cancer
PTH bone density
PTPN11 Noonan syndrome
PTPN22 rheumatoid arthritis
PTPRC multiple sclerosis
PVT1 end stage renal disease
RAD51 breast cancer
RAGE retino ath , diabetic
RB 1 retinoblastoma
RELN schizo hrenia
REN hypertension
RET thyroid cancer
RET Hirschs run 's disease
RFC 1 neural tube defects
RGS4 schizo hrenia
RHO retinitis pigmentosa
RNASEL prostate cancer
RYRI malignant h erthermia
SAAI amyloidosis
SCG2 hypertension
SCG3 obesity
SCGB 1 Al asthma
SCN5A Brugada syndrome
SCN5A EKG, abnormal
SCN5A long QT syndrome
SCNNIB hypertension
SCNNIG hypertension
SERPINAI COPD
SERPINA3 Alzheimer's Disease
SERPINA3 COPD
SERPINA3 Parkinson's disease
SERPINE 1 myocardial infarct
SERPINE1 Type 2 Diabetes
SERPINE 1 atherosclerosis, coronary
SERPINE 1 obesity
SERPINE1 preeclampsia
SERPINE1 stroke
SERPINE 1 hypertension
SERPINE 1 re anc loss, recurrent
SERPINE1 thromboembolism, venous
SLC 11 A1 tuberculosis
SLC22A4 Crohn's disease; ulcerative colitis
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SLC22A5 Crohn's disease; ulcerative colitis
SLC2A1 Type 2 diabetes
SLC2A2 Type 2 diabetes
SLC2A4 Type 2 diabetes
SLC3A1 c stinuria
SLC6A3 attention deficit h eractivit disorder
SLC6A3 Parkinson's disease
SLC6A3 smoking behavior
SLC6A3 alcoholism
SLC6A3 schizophrenia
SLC6A4 depression
SLC6A4 depressive disorder, major
SLC6A4 schizo hrenia
SLC6A4 suicide
SLC6A4 alcoholism
SLC6A4 bipolar disorder
SLC6A4 personality traits
SLC6A4 attention deficit h eractivit disorder
SLC6A4 Alzheimer's Disease
SLC6A4 personality disorders
SLC6A4 panic disorder
SLC6A4 alcohol abuse
SLC6A4 affective disorder
SLC6A4 anxiety disorder
SLC6A4 smoking behavior
SLC6A4 depressive disorder, ma'or; bipolar disorder
SLC6A4 heroin abuse
SLC6A4 irritable bowel s drome
SLC6A4 migraine
SLC6A4 obsessive compulsive disorder
SLC6A4 suicidal behavior
SLC7A9 cystinuria
SNAP25 ADHD
SNCA Parkinson's disease
SODI ALS/am otro hic lateral sclerosis
SOD2 breast cancer
SOD2 lung cancer
SOD2 prostate cancer
SPINK1 pancreatitis
SPP1 multi le sclerosis
SRD5A2 prostate cancer
STAT6 asthma
STAT6 Total IgE
SULT 1 Al breast cancer
SULT 1 Al colorectal cancer
TAP1 Type 1 diabetes
TAP1 lupus erythematosus
TAP2 Type 1 diabetes
TAP2 diabetes, type 1
TBX21 asthma
TBXA2R asthma
TCF1 diabetes, type 2
TCF1 Type 2 diabetes
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TF Alzheimer's Disease
TGFB 1 breast cancer
TGFB 1 kidney transplant
TGFB 1 kidney transplant complications
TH schizophrenia
THBD m ocardial infarction
TLR4 asthma
TLR4 Crohn's disease; ulcerative colitis
TLR4 sepsis
TNF asthma
TNFA cerebrovascular disease
TNF Type 1 diabetes
TNF rheumatoid arthritis
TNF systemic lupus erythematosus
TNF kidney transplant
TNF psoriasis
TNF sepsis
TNF Type 2 Diabetes
TNF Alzheimer's Disease
TNF Crohn's disease
TNF diabetes, type 1
TNF hepatitis B
TNF kidney transplant complications
TNF multiple sclerosis
TNF schizo hrenia
TNF celiac disease
TNF obesity
TNF re anc loss, recurrent
TNFRSFIIB bone density
TNFRSFIA rheumatoid arthritis
TNFRSF 1 B Rheumatoid Arthritis
TNFRSF 1 B systemic lupus erythematosus
TNFRSF 1 B arthritis
TNNT2 cardiom o ath
TP53 lung cancer
TP53 breast cancer
TP53 Colorectal Cancer
TP53 prostate cancer
TP53 cervical cancer
TP53 ovarian cancer
TP53 smoking
TP53 eso ha eal cancer
TP73 lung cancer
TPH 1 suicide
TPH1 depressive disorder, major
TPH1 suicidal behavior
TPH1 schizo hrenia
TPMT thiopurine methyltransferase activity
TPMT leukemia
TPMT inflammato bowel disease
TPMT thiopurine S-methyltransferase phenotype
TSC1 tuberous sclerosis
TSC2 tuberous sclerosis
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uene Phenotype
TSHR Graves' disease
TYMS colorectal cancer
TYMS stomach cancer
TYMS eso ha ea1 cancer
UCHL1 Parkinson's disease
UCP 1 obesity
UCP2 obesity
UCP3 obesity
UGTIAl h erbilirubinemia
UGT1A1 Gilbert syndrome
UGT1A6 colorectal cancer
UGT1A7 colorectal cancer
UTS2 diabetes, type 2
VDR bone density
VDR prostate cancer
VDR bone mineral density
VDR Type 1 diabetes
VDR osteoporosis
VDR bone mass
VDR breast cancer
VDR lead toxicity
VDR tuberculosis
VDR Type 2 diabetes
VEGF breast cancer
Vit D rec idiopathic short stature
VKORC1 warfarin therapy, response to
WNK4 h ertension
XPA lung cancer
XPC lung cancer
XPC c o enetic studies
XRCC1 lung cancer
XRCC1 c o enetic studies
XRCC1 breast cancer
XRCC1 bladder cancer
XRCC2 breast cancer
XRCC3 breast cancer
XRCC3 c o enetic studies
XRCC3 lung cancer
XRCC3 bladder cancer
ZDHHC8 schizophrenia
The Genetic Composite Index (GCI)
[00157] The etiology of many conditions or diseases is attributed to both
genetic and
environmental factors. Recent advances in genotyping technology has provided
opportunities to
identify new associations between diseases and genetic markers across an
entire genome. Indeed,
many recent studies have discovered such associations, in which a specific
allele or genotype is
correlated with an increased risk for a disease. Some of these studies involve
the collection of a
set of test cases and a set of controls, and the comparison of allele
distribution of genetic markers
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between the two populations. In some of these studies the association between
a specitic genetic
markers and a disease is measure in isolation from other genetic markers,
which are treated as
background and are not accounted for in the statistical analysis.
[00158] Genetic markers and variants may include SNPs, nucleotide repeats,
nucleotide
insertions, nucleotide deletions, chromosomal translocations, chromosomal
duplications, or copy
number variations. Copy number variation may include microsatellite repeats,
nucleotide
repeats, centromeric repeats, or telomeric repeats.
[00159] In one aspect of the present invention information about the
association of
multiple genetic markers with one or more diseases or conditions is combined
and analyzed to
produce a GCI score. The GCI score can be used to provide people not trained
in genetics with a
reliable (i.e., robust), understandable, and/or intuitive sense of what their
individual risk of
disease is compared to a relevant population based on current scientific
research. In one
embodiment a method for generating a robust GCI score for the combined effect
of different loci
is based on a reported individual risk for each locus studied. For example a
disease or condition
of interest is identified and then informational sources, including but not
limited to databases,
patent publications and scientific literature, are queried for information on
the association of the
disease of condition with one or more genetic loci. These informational
sources are curated and
assessed using quality criteria. In some embodiments the assessment process
involves multiple
steps. In other embodiments the informational sources are assessed for
multiple quality criteria.
The information derived from informational sources is used to identify the
odds ratio or relative
risk for one or more genetic loci for each disease or condition of interest.
[00160] In an alternative embodiment the odds ratio (OR) or relative risk (RR)
for at least
one genetic loci is not available from available informational sources. The RR
is then calculated
using (1) reported OR of multiple alleles of same locus, (2) allele
frequencies from data sets,
such as the HapMap data set, and/or (3) disease/condition prevalence from
available sources
(e.g., CDC, National Center for Health Statistics, etc.) to derive RR of all
alleles of interest. In
one embodiment the ORs of multiple alleles of same locus are estimated
separately or
independently. In a preferred embodiment the ORs of multiple alleles of same
locus are
combined to account for dependencies between the ORs of the different alleles.
In some
embodiments established disease models (including, but not limited to models
such as the
multiplicative, additive, Harvard-modified, dominant effect) are used to
generate an intermediate
score that represents the risk of an individual according to the model chosen.
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1001611 1n another embodiment a method is used that analyzes muitipie moaeis
tor a
disease or condition of interest and which correlates the results obtained
from these different
models; this minimizes the possible errors that may be introduced by choice of
a particular
disease model. This method minimizes the influence of reasonable errors in the
estimates of
prevalence, allele frequencies and ORs obtained from informational sources on
the calculation of
the relative risk. Because of the "linearity" or monotonic nature of the
effect of a prevalence
estimate on the RR, there is little or no effect of incorrectly estimating the
prevalence on the final
rank score; provided that the same model is applied consistently to all
individuals for which a
report is generated.
1001621 In another embodiment a method is used that takes into account
environmental/behavzoral/demographic data as additional "loci." In a related
embodiment such
data may be obtained from informational sources, such as medical or scientific
literature or
databases (e.g., associations of smoking w/lung cancer, or from insurance
industry health risk
assessments). In one embodiment a GCI score is produced for one or more
complex diseases.
Complex diseases may be influenced by multiple genes, environmental factors,
and their
interactions. A large number of possible interactions needs to be analyzed
when studying
complex diseases. In one embodiment a procedure is used to correct for
multiple comparisons,
such as the Bonferroni correction. In an alternative embodiment the Simes's
test is used to
control the overall significance level (also known as the "familywise error
rate") when the tests
are independent or exhibit a special type of dependence (Sarkar S. (1998)).
Some probability
inequalities for ordered MTP2 random variables: a proof of the Simes
conjecture. Ann Stat
26:494-504). Simes's test rejects the global null hypothesis that all K test-
specific null
hypotheses are true ifp(k),<ak/K for any k in 1,...,K. (Simes RJ (1986) An
improved Bonferroni
procedure for multiple tests of significance. Biometrika 73:751-754.).
1001631 Other embodiments that can be used in the context of multiple-gene and
multiple-
environmental-factor analysis control the false-discovery rate-that is, the
expected proportion
of rejected null hypotheses that are falsely rejected. This approach is
particularly useful when a
portion of the null hypotheses can be assumed false, as in microarray studies.
Devlin et al. (2003,
Analysis of multilocus models of association. Genet Epidemiol 25:36-47)
proposed a variant of
the Benjamini and Hochberg (1995, Controlling the false discovery rate: a
practical and powerful
approach to multiple testing. J R Stat Soc Ser B 57:289-300) step-up procedure
that controls the
false-discovery rate when testing a large number of possible gene x gene
interactions in
multilocus association studies. The Benjamini and Hochberg procedure is
related to Simes's test;
setting k*=maxk such that p(k),<ak/K, it rejects all k* null hypotheses
corresponding to
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p(1),...,p(k ). In fact, the Benjamini and Hochberg procedure reduces to
Simes's test when all null
hypotheses are true (Benjamini Y, Yekutieli D(2001) The control of the false
discovery rate in
multiple testing under dependency. Ann Stat 29:1165-1188).
[00164] In some embodiments an individual is ranked in comparison to a
population of
individuals based on their intermediate score to produce a final rank score,
which may be
represented as rank in the population, such as the 99th percentile or 99th,
98a`, 97th, 96~', 95th, 94t1i,
93rd, 92nd, 915~, 90th, 89', 88th, 87th, 86th, 85~', 84th, 83'd, 82nd, 81St,
80'h, 79t', 78th, 77th, 76th, 75',
74th, 73', 72nd, 71St, 70u', 69th, 65', 60th, 55~', 50h 45th, 40th 40th 35th,
30', 25a' 20th, 15th, 10t'
5th, or 0th. Percentile. In another embodiment the rank may score may be
displayed as a range,
such as the 100th to 95th percentile, the 95th to 85th percentile, the 85th to
60th percentile, or any
sub-range between the I00th and 0th percentile. In yet another embodiment the
individual is
ranked in quartiles, such as the top 75th quartile, or the lowest 25th
quartile. In a further
embodiment the individual is ranked in comparison to the mean or median score
of the
population.
[00165] In one embodiment the population to which the individual is compared
to includes
a large number of people from various geographic and ethnic backgrounds, such
as a global
population. In other embodiments the population to which an individual is
compared to is limited
to a particular geography, ancestry, ethnicity, sex, age (fetal, neonate,
child, adolescent, teenager,
adult, geriatric individual) disease state (such as symptomatic, asymptomatic,
carrier, early-
onset, late onset). In some embodiments the population to which the individual
is compared is
derived from information reported in public and/or private informational
sources.
[00166] In one embodiment an individual's GCI score, or GCI Plus score, is
visualized
using a display. In some embodiments a screen (such as a computer monitor or
television screen)
is used to visualize the display, such as a personal portal with relevant
information. In another
embodiment the display is a static display such as a printed page. In one
embodiment the display
may include but is not limited to one or more of the following: bins (such as
1-5, 6-10, 11-15,
16-20, 21-25, 26-30, 31-35, 36-40, 41-45, 46-50, 51-55, 56-60, 61-65, 66-70,
71-75, 76-80, 81-
85, 86-90, 91-95, 96-100), a color or grayscale gradient, a thermometer, a
gauge, a pie chart, a
histogram or a bar graph. For example, FIGS. 18 and 19 are different displays
for MS and FIG.
20 is for Crohn's disease). In another embodiment a thermometer is used to
display GCI score
and disease/condition prevalence. In another embodiment a thermometer displays
a level that
changes with the reported GCI score, for example, FIGS. 15-17, the color
corresponds to the
risk. The thermometer may display a colorimetric change as the GCI score
increases (such as
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changing from blue, for a lower GCI score, progressively to red, for a higher
GCl score). In a
related embodiment a thermometer displays both a level that changes with the
reported GCI
score and a colorimetric change as the risk rank increases
1001671 In an alternative embodiment an individual's GCI score is delivered to
an
individual by using auditory feedback. In one embodiment the auditory feedback
is a verbalized
instruction that the risk rank is high or low. In another embodiment the
auditory feedback is a
recitation of a specific GCI score such as a number, a percentile, a range, a
quartile or a
comparison with the mean or median GCI score for a population. In one
embodiment a live
human delivers the auditory feedback in person or over a telecommunications
device, such as a
phone (landline, cellular phone or satellite phone) or via a personal portal.
In another
embodiment the auditory feedback is delivered by an automated system, such as
a computer. In
one embodiment the auditory feedback is delivered as part of an interactive
voice response (IVR)
system, which is a technology that allows a computer to detect voice and touch
tones using a
normal phone call. In another embodiment an individual may interact with a
central server via an
IVR system. The IVR system may respond with pre-recorded or dynamically
generated audio to
interact with individuals and provide them with auditory feedback of their
risk rank. In one
example an individual may call a number that is answered by an IVR system.
After optionally
entering an identification code, a security code or undergoing voice-
recognition protocols the
IVR system asks the subject to select options from a menu, such as a touch
tone or voice menu.
One of these options may provide an individual with his or her risk rank.
1001681 In another embodiment an individual's GCI score is visualized using a
display
and delivered using auditory feedback, such as over a personal portal. This
combination may
include a visual display of the GCI score and auditory feedback, which
discusses the relevance of
the GCI score to the individual's overall health and possible preventive
measures, may be
advised.
1001691 In one example the GCI score is generated using a multi-step process.
Initially,
for each condition to be studied, the relative risks from the odds ratios for
each of the Genetic
markers is calculated. For every prevalence valuep=0.01,0.02,...,0.5, the GCI
score of the
HapMap CEU population is calculated based on the prevalence and on the HapMap
allele
frequency. If the GCI scores are invariant under the varying prevalence, then
the only
assumption taken into account is that there is a multiplicative model.
Otherwise, it is determined
that the model is sensitive to the prevalence. The relative risks and the
distribution of the scores
in the HapMap population, for any combination of no-call values, are obtained.
For each new
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individual, the individual's score is compared to the HapMap distribution anct
tne resuiting score
is the individual's rank in this population. The resolution of the reported
score may be low due to
the assumptions made during the process. The population will be partitioned
into quantiles (3-6
bins), and the reported bin would be the one in which the individual's rank
falls. The number of
bins may be different for different diseases based on considerations such as
the resolution of the
score for each disease. In case of ties between the scores of different HapMap
individuals, the
average rank will be used.
[00170] In one embodiment a higher GCI score is interpreted as an indication
of an
increased risk for acquiring or being diagnosed with a condition or disease.
In another
embodiment mathematical models are used to derive the GCI score. In some
embodiments the
GCI score is based on a mathematical model that accounts for the incomplete
nature of the
underlying information about the population and/or diseases or conditions. In
some embodiments
the mathematical model includes certain at least one presumption as part of
the basis for
calculating the GCI score, wherein said presumption includes, but is not
limited to: a
presumption that the odds ratio values are given; a presumption that the
prevalence of the
condition is known; a presumption that the genotype frequencies in the
population are known;
and a presumption that the customers are from the same ancestry background as
the populations
used for the studies and as the HapMap; a presumption that the amalgamated
risk is a product of
the different risk factors of the individual genetic markers. In some
embodiments, the GCI may
also include a presumption that the mutli-genotypic frequence of a genotype is
the product of
frequencies of the alleles of each of the SNPs or individual genetic markers
(for example, the
different SNPs or genetic markers are independent across the population).
The Multiplicative Model
[00171] In one embodiment a GCI score is computed under the assumption that
the risk
attributed to the set of Genetic markers is the product of the risks
attributed to the individual
Genetic markers. This means that the different Genetic markers attribute
independently of the
other Genetic markers to the risk of the disease. Formally, there are k
Genetic markers with risk
alleles rl,...,rk and non-risk alleles nl,...,nk . In SNP i, we denote the
three possible genotype
values as rl i,ni I, and n n1. The genotype information of an individual can
be described by a
vector, (gl,...,gd , where g, can be 0,1, or 2, according to the number of
risk alleles in position
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i. We denote by,%l the relative risk of a heterozygous genotype in position i
compared to a
homozygous non-risk allele at the same position. In other words, we define A i
= P(DI ntr, I)
1 P(DI n, nr I)
r
~-_ '` 1) Under the
Similarly, we denote the relative risk of an rl 1 genotype as P(Dj n
2 P(Djn, n; l)
multiplicative model we assume that the risk of an individual with a genotype
(gl,...,gd is
k -
GCI(gl,...,gd= ~XgZ . The multiplicative model has been previously used in the
literature
i=1
in order to simulate case-control studies, or for visualization purposes.
Estimating the Relative Risk.
[00172] In another embodiment the relative risks for different Genetic markers
are known
and the multiplicative model can be used for risk assessment. However, in some
embodiments
involving association studies the study design prevents the reporting of the
relative risks. In some
case-control studies the relative risk cannot be calculated directly from the
data without further
assumptions. Instead of reporting the relative risks, it is customary to
report the odds ratio (OR)
of the genotype, which are the odds of carrying the disease given the risk
genotype (either r,.ri or
ni i} vs. the odds of not carrying the disease given the risk genotypes.
Formally,
ORl-P(Din;r,.l) 1-P(Din;n;I)
P(DI n,r l) 1- P(DI nrr I)
OR t P(Djr. re. l) 1 - P(DIni n; l)
= ~
P(DIn;n;I) 1-P(D) r,ril)
[00173] Finding the relative risks from the odds ratio may require additional
assumptions.
Such as the presumption that the allele frequencies in an entire population a
fn ~,b fn r, and
t Z Z ,
C f are known or estimated (these could be estimated from current datasets
such as the
ri,ra,
HapMap dataset which includes 120 chromosomes), and/or that the prevalence of
the disease
p=p(D) is known. From the preceding three equations can be derived:
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p=a-P(Djnlni)+b=P(Dln ri)+c=P(Dlrirl)
OR.' P(Djn.r. j) 1- P(Djn; ni ()
= " =
` P(D) n; r, () 1- P(Djn; r, l)
OR? = P(Djr;r;I) 1-P(DjnlnjI)
` P(DIn;n;I) 1-P(Djr,r;I)
1001741 By the definition of the relative risk, after dividing by the term
pP(Dlnlni) , the
first equation can be rewritten as:
1 a+bXl+c~2
P(Djn ni) p
and therefore, the last two equations can be rewritten as:
OR1 = ,~ = (a - p) + b~ + c.~.z
t
a+(b- p),~ +cXz
(1)
OR? = i1` (a - p) + b.~ + cXi
i z a+bl~ +(c-P)Ai
[00175] Note that when a=1 (non-risk allele frequency is 1), Equation system 1
is
equivalent to the Zhang and Yu formula in Zhang J and Yu K. (What's the
relative risk? A
method of correcting the odds ratio in cohort studies of common outcomes.
JAMA, 280:1690-1,
1998), which is incorporated by reference in its entirety. In contrast to the
Zhang and Yu
formula, some embodiments of the present invention take into consideration the
allele frequency
in the population, which may affect the relative risk. Further some
embodiments take into
account the interdependence of the relative risks. As opposed to computing
each of the relative
risks independently.
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1001761 Equation system 1 can be rewritten as two quadratic equations, wittl
at most four
possible solutions. A gradient descent algorithm can be used to solve these
equations, where the
starting point is set to be the odds ratio, e.g., Xi=ORi, and X2=OR2
1001771 For example:
fl(~ 1,~2)=ORI (a+(b p)~,i+c,2)-Xi =((a p)+b~,i+c~2)
f2(X 1,k2)=ORi (a+bX i+(c p)%2)-?,2((a P)+bk i+cX 2)
[00178] Finding the solution of these equations is equivalent to finding the
minimum of
the function g(X1'2, 2) f1(X1'X2)2+f2(X1'?'2)2
Thus,
d
2f1Q,12) b 2-OR2)+2f2(k1,~2)(2b~,1+cX2+a-ORlbp+ORlp)
dg _2f (?' ,~ ) c (~ -OR )+2f (~ ,~ )(2c~ +ba +a-OR c p+OR )
d~.2 2 1 2 1 1 1 1 2 2 1 2 2p
[00179) In this example we begin by setting xo ORl,yo OR2. We will set the
values
[epsilon]=10-10 to be a tolerance constant through the algorithm. In iteration
i, we define
x. y.
~y=min{0.001, z-1 , Z-1 } . We then
[epsilon]+101 d (xi 1'yi-1)l [epsilon]+10~ ~(xi-1'yi-1)~
1
set
xi xi 1-Y d(xi-1'yi-1)
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yi yi-l-7 da,2(xi-1'yi-1)
There iterations are repated until g(xi,yi)<tolerance , where tolerance is set
to 10-7
in the supplied code.
[00180] In this example these equations give the correct solution for
different values of
a,b,c,p,OR 1 , and OR2. Figure 10
Robustness of the Relative Risk Estimation.
[00181] In some embodiments the effect of different parameters (prevalence,
allele
frequencies, and odds ratio errors) on the estimates of the relative risks is
measured. In order to
measure the effect of the allele frequency and prevalence estimates on the
relative risk values,
the relative risk from a set of values of different odds ratios and different
allele frequencies is
computed (under HWE), and the results of these calculations is plotted for
prevalence values
ranging from 0 to 1. Figure 10. Additionally, for fixed values of the
prevalence, the resulting
relative risks can be plotted as a function of the risk-allele frequencies.
Figure 11. In cases when
p=0, X1=OR1, and k 2=OR2, and whenp=l, ?, 1=a,2=0. This can be computed
directly from the
equations. Additionally, in some embodiments when the risk allele frequency is
high, Xl gets
closer to a linear function, and k 2 gets closer to a concave function with a
bounded second
(OR. -1) pOR;
derivative. In the limit, when c=l, ~,2 OR2+p(l-OR2) , and ai = OR; - ' If
ORZ (1- p) + pORI
OR 1=0R2 the latter is close to a linear function as well. When risk-allele
frequency is low, k
and ?, 2 approach the behavior of the function 1/p. In the limit, when c=0,
OR l OR2
~'1 1 p+pORl'~'2 1 p+pOR2' This indicates that for high risk-allele
frequencies, incorrect
estimates of the prevalence will not significantly affect the resulting
relative risk. Further, for
low risk-allele frequency, if a prevalence value of p'=ap is substituted for
the correct prevalence
p, then the resulting relative risks will be off by a factor of at most. This
is sillustrated in
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sections (c) and (d) of Figure 11. Note that for high risk-allele frequencies
the two graphs are
quite similar and while there is a higher deviation in the difference in the
values of the relative
risks for low allele frequencies, this deviation is less than a factor of 2.
Calculating The GCI Score
[00182] In one embodiment the Genetic Composite Index is calculated by using a
reference set that represents the relevant population. This reference set may
be one of the
populations in the HapMap, or anther genotype dataset.
[00183] In this embodiment the GCI is computed as follows. For each of the k
risk loci,
the relative risk is calculated from the odds ratio using the equation system
1. Then, the
multiplicative score for each individual in the reference set is calculated.
The GCI of an
individual with a multiplicative score of s is the fraction of all individuals
in the reference dataset
with a score of s'<-s. For instance, if 50% of the individuals in the
reference set have a
multiplicative score smaller than s, the final GCI score of the individual
would be 0.5.
Other Models
[00184] In one embodiment the multiplicative model is used. In alternative
embodiments
other models that may be used for the purpose of determining the GCI score.
Other suitable
models include but are not limited to:
1001851 The Additive Model. Under the additive model the risk of an individual
with a
k t
genotype (gl,...,gd is presumed to be GCI(g,,...,gk)= Xgi
i=1
[00186] Generalized Additive Model. Under the generalized additive model it is
presumed that there is a functionf such that the risk of an individual with a
genotype (gl.... ,gd
k i
is GCI(gl,...,gd= Z.f(kg1)
i=1
[00187] Harvard Modified Score (Het). This score was derived from G.A Colditz
et al.,
so that the score that applies to genetic markers (Harvard report on cancer
prevention volume 4:
Harvard cancer risk index. Cancer Causes and Controls, 11:477-488, 2000 which
is herein
incorporated in its entirety). The Het score is essentially a generalized
additive score, although
the functionf operates on the odds ratio values instead of the relative risks.
This may be useful in
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cases where the relative risk is difficult to estimate. In order to define the
tu.nctionj, an
intermediate function g, is defined as:
1 0 1<X<-1.09
1.09<x-<1.49
g(x)= 101.49<x<-2.99
252.99<x-<6.99
1 50 6.99<x
k
equency
[00188] Next the quantity het= Ephetg(ORl) is calculated, where phet is the fr
i=1
5 of heterozygous individuals in SNP i across the reference population. The
functionf is then
k
defined as f(x)=g(x)/het, and the Harvard Modified Score (Het) is simply
defined as Zf(ORg~ .
i=1
[00189] The Harvard Modified Score (Hom). This score is similar to the Het
score,
k
except that the value het is replaced by the value hom= 2:phomg(OR1) , where
phom is the
i=1
frequency of individuals with homozygous risk-allele.
[00190] The Maximum-Odds Ratio. In this model, it is presumed that one of the
Genetic
markers (one with a maximal odds ratio) gives a lower bound on the combined
risk of the entire
panel. Formally, the score of an individual with genotypes (gl,...g" is
GCI(gl,...,gk)=maxk 1ORg1.
A comparison between the scores
[00191] In one Example the GCI score was calculated based on multiple models
across the
HapMap CEU population, for 10 SNPs associated with T2D. The relevant SNPs were
rs7754840, rs4506565, rs7756992, rs10811661, rs12804210, rs8050136, rs1111875,
rs4402960,
rs5215, rs 1801282. For each of these SNPs, an odds ratio for three possible
genotypes is reported
in the literature. The CEU population consists of thirty mother-father-child
trios. Sixty parents
from this population were used in order to avoid dependencies. One of the
individuals that had a
no-call in one of the 10 SNPs was excluded, resulting in a set of 59
individuals. The GCI rank
for each of the individuals was then calculated using several different
models.
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[00192 j o 2oosiot 6~sas observed that for this dataset different models
proaucea niogriiyogco~elated
results. Figures 12 & 13. The Spearman correlation was calculated between each
pair of models
(Table 2), which showed that the Multiplicative and Additive model had a
correlation coefficient
of 0.97, and thus the GCI score would be robust using either the additive or
multiplicative
models. Similarly, the correlation between the Harvard modified scores and the
multiplicative
model was 0.83, and the correlation coefficient between the Harvard scores and
the additive
model was 0.7. However, using the maximum odds ratio as the genetic score
yielded a
dichotomous score which was defined by one SNP. Overall these results indicate
score ranking
provided a robust framework that minimized model dependency.
Table 2: The Spearman correlations for the score distributions on the CEU data
between
model pairs.
ultiplicative Additive Harv-Het Harv-Hom MAX OR
u1t 1 0.97 0.83 0.83 0.42
dditive 0.97 1. 0.7 0.7 0.6
arv-Het 0.83 0.7 1 1 0
arv-Hom 0.83 0.7 1 1 0
AX OR 0.42 0.6 0 0 1
[00193] The effect of variation in the prevalence of T2D on the resulting
distribution was
measured. The prevalence values from 0.001 to 0.512 was varied (Figure 14).
For the case of
T2D, it was observed that different prevalence values result in the same order
of individuals
(Spearman correlation > 0.99), therefore an artificially fixed value of
prevalence 0.01 could be
presumed.
Extending the Model to an Arbitrary Number of Variants
1001941 In another embodiment the model can be extended to the situations
where an
arbitrary number of possible variants occur. Previous considerations dealt
with situations where
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there were three possible variants (nn,nr,rr). Generally, when a multi-SNr
association is known,
an arbitrary number of variants may be found in the population. For example,
when an
interaction between two Genetic markers is associated with a condition, there
are nine possible
variants. This results in eight different odds ratios values.
To generalize the initial formula, it may be assumed that there are k+l
possible variants
ao,...,ak , with frequencies fofl'"''fk , measured odds ratios of
1,OR1,...,ORk , and
unknown relative risk values l,X11 ...,k k . Further it may be assumed that
all relative risks and
P(Dl a'. )
odds ratios are measured with respect to ao, and thus, A; = P(Dl a ) and
0
P(Dl a`. ) 1- P(Dl a'. )
OR; _ = . Based on:
P(Dl ao ) 1- P(Dl ao )
k
p= ZfP(Djad,
i=0
It is determined that
k
i p
i=o
ORi ~i k
Efkl-llp
i=O
Further if it is set that C= lf.x1, this results in the equation:
i
COR.
i C p+ORp'
and thus,
k k C=ORf
C= Ef?'i E
p+oRP,
C
i=0 i=0
or
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k ORtf.
1- I C p+ORp.
i=0
[00195] The latter is an equation with one variable (C). This equation can
produce many
different solutions (essentially, up to k+l different solutions). Standard
optimization tools such
as gradient descent can be used to find the closest solution to CO= Ef.ti.
[00196] The present invention uses a robust scoring framework for the
quantification of
risk factors. While different genetic models may result in different scores,
the results are usually
correlated. Therefore the quantification of risk factors is generally not
dependent on the model
used.
EstimatinQ Relative Risk Case Control Studies
[00197] A method that estimates the relative risks from the odds ratios of
multiple alleles
in a case-control study is also provided in the present invention. In contrast
to previous
approaches, the method takes into consideration the allele frequencies, the
prevalence of the
disease, and the dependencies between the relative risks of the different
alleles. The
performance of the approach on simulated case-control studies was measured,
and found to be
extremely accurate.
Methods
[00198] In the case where a specific SNP is tested for association with a
disease D, R and
N denote the risk and non-risk alleles of this particular SNP. P(RRID),P(RNID)
and P(NNID)
denote the probability of getting affected by the disease given that a person
is homozygous for
the risk allele, heterozygous, or homozygous for the non-risk allele
respectively. fRR,fRN and fNN
are used to denote the frequencies of the three genotypes in the population.
Using these
definitions, the relative risks are defined as
P(D I RR)
~,~ P(D I NAI)
~ P(DIRN)
P(DINM
[00199] In a case-control study, the values P(RRID), P(RRI-D) can be
estimated, i.e., the
frequency of RR among the cases and the controls, as well as P(RNID), P(RNI-
D), P(NNID), and
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P(NNI-D), i.e., the frequency of RN and NN among the cases and the controls.
In order to
estimate the relative risk, Bayes law can be used to get:
_ P(RR I D) f~,~,
~~ I'(NN I D).fu
A= P(D I ~)fivlv
'~ P(D I AM.fRx
[00200) Thus, if the frequencies of the genotypes are known, one can use those
to
calculate the relative risks. The frequencies of the genotypes in the
population cannot be
calculated from the case-control study itself, since they depend on the
prevalence of disease in
the population. In particular, if the prevalence of the disease is p(D), then:
fxn = P(RR I D)p(D) + P(RR I- D)(1- p(D))
fn,v =1'(RN ~ D)p(D) + P(RAT D)(1- p(D))
fivw = P(NN I D)p(D) + P(AW h D)(l - p(D))
[00201] When p(D) is small enough, the frequencies of the genotypes can be
approximated by the frequencies of the genotypes in the control population,
but this would not be
an accurate estimate when the prevalence is high. However, if a reference
dataset is given (e.g.,
the HapMap [cite]), one can estimate the genotype frequencies based on the
reference dataset.
1002021 Most current studies do not use a reference dataset to estimate the
relative risk,
and only the odds-ratio is reported. The odds-ratio can be written as
1'(~ID)P(~I^'D)
OR,~ = P(NN I D)P(PR I^' D)
P(~ D)P(~ I ~ D)
OR~ = P(NN ~ D)P(RN D)
[00203] The odds ratios are typically advantageous since there is usually no
need to have
an estimate of the allele frequencies in the population; in order to calculate
the odds ratios
typically what is needed is the genotype frequencies in the cases and in the
controls.
[00204] In some situations, the genotype data itself is not available, but the
summary data,
such as the odds-ratios are available. This is the case when meta-analysis is
being performed
based on results from previous case-control studies. In this case, how to find
the relative risks
from the odds ratios is demonstrated. Using the fact that the following
equation holds:
P(D) = .fxnP(D I RR) + .fnral'(D I RAI) + fAwP(D I NN)
If this equation is divided by P(DINN), we get
p(D) _ .f~APR + .f~AKV + fAW
p(D I AIN)
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This allows the odds ratios to be written in the following way:
P(D I RR)(1- P(D I NN)) _ p(D)
p(DI~) P(D)
OR~ = P(D I~)(I - P(D I RR)) ~~ pc i~) - P(D)ARx
ARR .fnn Ana + .fiuv Aruv + .fivW-p(D)
.fRnARR + fRNARN + .fAW - P(D)ARR
By a similar calculation, the following system of equations results:
fi~e ~,~ + .fn~v Ax,v + .f~vtv -1~(D)
OR~ _ ~nx
.fxx a,AR + fnN AxN + fA,v - IP(D)AR
f~ A~ + .f~A~ + .f~ - P(D)
ORk,~, _ ~R,~ .fRR A,R + .fRN ARN + .fNN - P(D)=2,N
Equation 1
[00205] If the odds-ratios, the frequencies of the genotypes in the
populations, and the
prevalence of the disease are known, the relative risks can be found by
solving this set of
equations.
[00206] Note that these are two quadratic equations, and thus they have a
maximum of
four solutions. However, as shown below that there is typically one possible
solution to this
equation.
[00207] Note that when fNN = 1, Equation system 1 is equivalent to the Zhang
and Yu
formula; however, here the allele frequency in the population is taken into
account. Furthermore,
our method takes into account the fact that the two relative risks depend on
each other, while
previous methods suggest to compute each of the relative risks independently.
[00208] Relative risks for multi-allelic loci. If multi-markers or other multi-
allelic
variants are considered, the calculation is complicated slightly. ao,al,...,ak
is denoted by the
possible k+1 alleles, where ao is the non-risk allele. Allele frequencies
fo,fl,f2,...,fk in the
population for the k+1 possible alleles are assumed. For allele i, the
relative risk and odds-ratios
are defined as
.~ =P(DI a')
` P(D I ao)
ORP(D I ai)(1- P(D I ao)) 1- P(D I ao)
; = P(D a(,)(1- P(D I aj)) 1- P(D ~ a;)
The following equation holds for the prevalence of the disease:
k
P(D)=I .fP(DI ai)
r=o
Thus, by dividing both sides of the equation by p(Dlao), we get:
p(D) k
P(D I ao) ~ f'Ai
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Resulting in:
k
E fa,,-P(D)
OR; = ~,; k
I f A, - A,P(D)
i=o
k
By setting C=Ef~Ar~ , the result is At . - C- OR' . Thus, by the definition of
C, it
t_o ~ - P(D)OR; + C- p(D)
is:
k k
1=~f~ fOR;
i=0 l=o P(D)ORl + C - p(D) '
[00209] This is a polynomial equation with one variable C. Once C is
determined, the
relative risks are determined. The polynomial is of degree k+1, and thus we
expect to have at
most k+1 solutions. However, since the right-hand side of the equation is a
strictly decreasing as
a function of C, there can typically only be one solution to this equation.
Finding this solution is
k
easy using a binary search, since the solution is bounded between C=1 and C=L
ORt .
7=0
[00210] Robustness of the Relative Risk Estimation. The effect of each of the
different
parameters (prevalence, allele frequencies, and odds ratio errors) on the
estimates of the relative
risks was measured. In order to measure the effect of the allele frequency and
prevalence
estimates on the relative risk values, the relative risk was calculated from a
set of values of
different odds ratios, different allele frequencies (under HWE), and plotted
the results of these
calculations for a prevalence values ranging from 0 to 1.
[00211] Additionally, for fixed values of the prevalence, the resulting
relative risks as a
function of the risk-allele frequencies was plotted. Evidently, in all cases
when p(D) = 0, XxR =
ORxR , and Xxrr = ORRv , and when p(D) = 1, kRR =km = 0. This can be computed
directly from
Equation 1. Additionally, when the risk allele frequency is high, XRR
approaches a linear
behavior, and XR=r approaches a concave function with a bounded second
derivative. When the
risk-allele frequency is low, XxR and Xxrr approach the behavior of the
function 1/p(D). This
means that for high risk-allele frequency, wrong estimates of the prevalence
will not affect the
resulting relative risk by much.
[00212] The following examples illustrate and explain the invention. The scope
of the
invention is not limited by these examples.
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Examnle I
Generation and Analysis of SNP Profile
[00213] The individual is provided a sample tube in the kit, such as that
available from
DNA Genotek, into which the individual deposits a sample of saliva
(approximately 4 mis) from
which genomic DNA will be extracted. The saliva sample is sent to a CLIA
certified laboratory
for processing and analysis. The sample is typically sent to the facility by
overnight mail in a
shipping container that is conveniently provided to the individual in the
collection kit.
[00214] In a preferred embodiment, genomic DNA is isolated from saliva. For
example,
using DNA self collection kit technology available from DNA Genotek, an
individual collects a
specimen of about 4 ml saliva for clinical processing. After delivery of the
sample to an
appropriate laboratory for processing, DNA is isolated by heat denaturing and
protease digesting
the sample, typically using reagents supplied by the collection kit supplier
at 50 C for at least one
hour. The sample is next centrifuged, and the supernatant is ethanol
precipitated. The DNA
pellet is suspended in a buffer appropriate for subsequent analysis.
[00215] The individual's genomic DNA is isolated from the saliva sample,
according to
well known procedures and/or those provided by the manufacturer of a
collection kit. Generally,
the sample is first heat denatured and protease digested. Next, the sample is
centrifuged, and the
supernatant is retained. The supematant is then ethanol precipitated to yield
a pellet containing
approximately 5-16 ug of genomic DNA. The DNA pellet is suspended in 10 mM
Tris pH 7.6, 1
mM EDTA (TE). A SNP profile is generated by hybridizing the genomic DNA to a
commercially available high density SNP array, such as those available from
Affymetrix or
Illumina, using instrumentation and instructions provided by the array
manufacturer. The
individual's SNP profile is deposited into a secure database or vault.
[00216] The patient's data structure is queried for risk-imparting SNPs by
comparison to a
clinically-derived database of established, medically relevant SNPs whose
presence in a genome
correlates to a given disease or condition. The database contains information
of the statistical
correlation of particular SNPs and SNP haplotypes to particular diseases or
conditions. For
example, as shown in Example III, polymorphisms in the apolipoprotein E gene
give rise to
differing isoforms of the protein, which in turn correlate with a statistical
likelihood of
developing Alzheimer's Disease. As another example, individuals possessing a
variant of the
blood clotting protein Factor V known as Factor V Leiden have an increased
tendency to clot. A
number of genes in which SNPs have been associated to a disease or condition
phenotype are
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shown in Table 1. The information in the database is approved by a
research/clinical advisory
board for its scientific accuracy and importance, and may be reviewed with
governmental agency
oversight. The database is continually updated as more SNP-disease
correlations emerge from
the scientific community.
1002171 The results of the analysis of an individual's SNP profile is securely
provided to
patient by an on-line portal or mailings. The patient is provided
interpretation and supportive
information, such as the information shown for Factor V Leiden in Example IV.
Secure access
to the individual's SNP profile information, such as through an on-line
portal, will facilitate
discussions with the patient's physician and empower individual choices for
personalized
medicine.
Example II
Update of genotype correlations
[00218] In response to a request for an initial determination of an
individual's genotype
correlations, a genomic profile is generated, genotype correlations are made,
and the results are
provided to the individual as described in Example I. Following an initial
determination of an
individual's genotype correlations, subsequent, updated correlations are or
can be determined as
additional genotype correlations become known. The subscriber has a premium
level
subscription and their genotype profile and is maintained in a secure
database. The updated
correlations are performed on the stored genotype profile.
[00219] For example, an initial genotype correlation, such as described above
in Example
I, could have determined that a particular individual does not have ApoE4 and
thus is not
predisposed to early-onset Alzheimer's Disease, and that this individual does
not have Factor V
Leiden. Subsequent to this initial determination, a new correlation could
become known and
validated, such that polymorphisms in a given gene, hypothetically gene XYZ,
are correlated to a
given condition, hypothetically condition 321. This new genotype correlation
is added to the
master database of human genotype correlations. An update is then provided to
the particular
individual by first retrieving the relevant gene XYZ data from the particular
individual's
genomic profile stored in a secure database. The particular individual's
relevant gene XYZ data
is compared to the updated master database information for gene XYZ. The
particular
individual's susceptibility or genetic predisposition to condition 321 is
determined from this
comparison. The results of this determination are added to the particular
individual's genotype
correlations. The updated results of whether or not the particular individual
is susceptible or
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genetically predisposed to condition 321 is provided to the particular
individual, along with
interpretative and supportive information.
Example III
Correlation of ApoE4 Locus and Alzheimer's Disease
1002201 The risk of Alzheimer's disease (AD) has been shown to correlate with
polymorphisms in the apolipoprotein E(APOE) gene, which gives rise to three
isoforms of
APOE referred to as ApoE2, ApoE3, and ApoE4. The isoforms vary from one
another by one or
two amino acids at residues 112 and 158 in the APOE protein. ApoE2 contains
112/158 cys/cys;
ApoE3 contains 112/158 cys/arg; and ApoE4 contains 112/158 arg/arg. As shown
in Table 3,
the risk of Alzeimer's disease onset at an earlier age increases with the
number of APOE P-4 gene
copies. Likewise, as shown in Table 3, the relative risk of AD increases with
number of APOE
E4 gene copies.
[00221] Table 3: Prevalence of AD Risk Alleles (Corder et al., Science:
261:921-3, 1993)
APOE E4 Copies Prevalence Alzheimer's Risk Onset Age
0 73% 20% 84
1 24% 47% 75
2 3% 91% 68
[00222] Table 4: Relative Risk of AD with ApoE4 (Farrer et al., JAMA: 278:1349-
56,
1997)
APOE Genotype Odds Ratio
E2E2 0.6
s2E3 0.6
E3c3 1.0
E2E4 2.6
E3s4 3.2
c4c4 14.9
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Example IV
Information for Factor V Leiden Positive Patient
[00223] The following information is exemplary of information that could be
supplied to
an individual having a genomic SNP profile that shows the presence of the gene
for Factor V
Leiden. The individual may have a basic subscription in which the information
may be supplied
in an initial report.
What is Factor V Leiden?
[00224] Factor V Leiden is not a disease, it is the presence of a particular
gene that is
passed on from one's parents. Factor V Leiden is a variant of the protein
Factor V (5) which is
needed for blood clotting. People who have a Factor V deficiency are more
likely to bleed badly
while people with Factor V Leiden have blood that has an increased tendency to
clot.
[00225] People carrying the Factor V Leiden gene have a five times greater
risk of
developing a blood clot (thrombosis) than the rest of the population. However,
many people
with the gene will never suffer from blood clots. In Britain and the United
States, 5 per cent of
the population carry one or more genes for Factor V Leiden, which is far more
than the number
of people who will actually suffer from thrombosis.
How do you get Factor V Leiden?
[00226] The genes for the Factor V are passed on from one's parents. As with
all
inherited characteristics, one gene is inherited from the mother and one from
the father. So, it is
possible to inherit: -two normal genes or one Factor V Leiden gene and one
normal gene -or two
Factor V Leiden genes. Having one Factor V Leiden gene will result in a
slightly higher risk of
developing a thrombosis, but having two genes makes the risk much greater.
What are the symptoms of Factor V Leiden?
[00227] There are no signs, unless you have a blood clot (thrombosis).
What are the danger si ir~
[00228] The most common problem is a blood clot in the leg. This problem is
indicated
by the leg becoming swollen, painful and red. In rarer cases a blood clot in
the lungs (pulmonary
thrombosis) may develop, making it hard to breathe. Depending on the size of
the blood clot this
can range from being barely noticeable to the patient experiencing severe
respiratory difficulty.
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In even rarer cases the clot might occur in an arm or another part of the
bPocty. Nince these clots
formed in the veins that take blood to the heart and not in the arteries
(which take blood from the
heart), Factor V Leiden does not increase the risk of coronary thrombosis.
What can be done to avoid blood clots?
1002291 Factor V Leiden only slightly increases the risk of getting a blood
clot and many
people with this condition will never experience thrombosis. There are many
things one can do
to avoid getting blood clots. Avoid standing or sitting in the same position
for long periods of
time. When traveling long distances, it is important to exercise regularly -
the blood must not
`stand still'. Being overweight or smoking will greatly increase the risk of
blood clots. Women
carrying the Factor V Leiden gene should not take the contraceptive pill as
this will significantly
increase the chance of getting thrombosis. Women carrying the Factor V Leiden
gene should
also consult their doctor before becoming pregnant as this can also increase
the risk of
thrombosis.
How does a doctor find out if you have Factor V Leiden?
[00230] The gene for Factor V Leiden can be found in a blood sample.
[00231] A blood clot in the leg or the arm can usually be detected by an
ultrasound
examination.
[002321 Clots can also be detected by X-ray after injecting a substance into
the blood to
make the clot stand out. A blood clot in the lung is harder to find, but
normally a doctor will use
a radioactive substance to test the distribution of blood flow in the lung,
and the distribution of
air to the lungs. The two patterns should match - a mismatch indicates the
presence of a clot.
How is Factor V Leiden treated?
[00233] People with Factor V Leiden do not need treatment unless their blood
starts to
clot, in which case a doctor will prescribe blood-thinning (anticoagulant)
medicines such as
warfarin (e.g. Marevan) or heparin to prevent further clots. Treatment will
usually last for three
to six months, but if there are several clots it could take longer. In severe
cases the course of
drug treatment may be continued indefinitely; in very rare cases the blood
clots may need to be
surgically removed.
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How is Factor V Leiden treated duringpregnancy~
[00234] Women carrying two genes for Factor V Leiden will need to receive
treatment
with a heparin coagulant medicine during pregnancy. The same applies to women
carrying just
one gene for Factor V Leiden who have previously had a blood clot themselves
or who have a
family history of blood clots.
[00235] All women carrying a gene for Factor V Leiden may need to wear special
stockings to prevent clots during the last half of pregnancy. After the birth
of the child they may
be prescribed the anticoagulant drug heparin.
Prognosis
[00236] The risk of developing a clot increases with age, but in a survey of
people over
the age of 100 who carry the gene, it was found that only a few had ever
suffered from
thrombosis. The National Society for Genetic Counselors (NSGC) can provide a
list of genetic
counselors in your area, as well as information about creating a family
history. Search their on-
line database at www.nsgc.org/consumer.
[00237] While preferred embodiments of the present invention have been shown
and
described herein, it will be obvious to those skilled in the art that such
embodiments are provided
by way of example only. Numerous variations, changes, and substitutions will
now occur to
those skilled in the art without departing from the invention. It should be
understood that various
alternatives to the embodiments of the invention described herein may be
employed in practicing
the invention. It is intended that the following claims define the scope of
the invention and that
methods and structures within the scope of these claims and their equivalents
be covered thereby.
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