U.S. patent application number 13/764803 was filed with the patent office on 2014-02-13 for single nucleotide polymorphisms and use of same in predicting male-specific prenatal loss.
This patent application is currently assigned to Medical Diagnostic Laboratories, LLC. The applicant listed for this patent is Charronne Davis, Thuy Ngoc Do, Mehmet Tevik Dorak, Esma Ucisik-Akkaya. Invention is credited to Charronne Davis, Thuy Ngoc Do, Mehmet Tevik Dorak, Esma Ucisik-Akkaya.
Application Number | 20140045710 13/764803 |
Document ID | / |
Family ID | 42057864 |
Filed Date | 2014-02-13 |
United States Patent
Application |
20140045710 |
Kind Code |
A1 |
Dorak; Mehmet Tevik ; et
al. |
February 13, 2014 |
SINGLE NUCLEOTIDE POLYMORPHISMS AND USE OF SAME IN PREDICTING
MALE-SPECIFIC PRENATAL LOSS
Abstract
The present invention is directed to a panel of single
nucleotide polymorphisms (SNPs) in specific genes that serve as
biomarkers for sex-specific prenatal loss of a conceptus or embryo.
There is provided herein methods and reagents for assessing the
specific SNPs in those genes. The method useful in applying these
SNPs in predicting an increased risk of prenatal loss is also
disclosed.
Inventors: |
Dorak; Mehmet Tevik;
(Hamilton, NJ) ; Ucisik-Akkaya; Esma; (Hamilton,
NJ) ; Davis; Charronne; (Mahwah, NJ) ; Do;
Thuy Ngoc; (Hamilton, NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Dorak; Mehmet Tevik
Ucisik-Akkaya; Esma
Davis; Charronne
Do; Thuy Ngoc |
Hamilton
Hamilton
Mahwah
Hamilton |
NJ
NJ
NJ
NJ |
US
US
US
US |
|
|
Assignee: |
Medical Diagnostic Laboratories,
LLC
Hamilton
NJ
|
Family ID: |
42057864 |
Appl. No.: |
13/764803 |
Filed: |
February 12, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
12386106 |
Apr 14, 2009 |
8394587 |
|
|
13764803 |
|
|
|
|
Current U.S.
Class: |
506/9 ;
435/6.11 |
Current CPC
Class: |
C12Q 1/6883 20130101;
C12Q 2600/172 20130101; C12Q 2600/156 20130101 |
Class at
Publication: |
506/9 ;
435/6.11 |
International
Class: |
C12Q 1/68 20060101
C12Q001/68 |
Claims
1. A method for predicting prenatal loss of a conceptus or embryo,
comprising the steps of: (a) providing a biological sample from a
pregnant woman; (b) isolating a nucleic acid from said biological
sample; (c) performing a polymerase chain reaction (PCR) on said
isolated nucleic acid to produce an amplicon; (d) assessing said
amplicon for the presence of a combination of SNPs, said SNP
combination consisting of HLA-DQA1 rs1142316, HLA-DRA rs7192, and
HSPA1B rs1061581; and (e) predicting an increased risk of prenatal
loss of said male conceptus or embryo in said pregnant woman by
said presence of said SNP combination, wherein the presence of said
SNP combination is indicative of an increased risk of prenatal loss
for male conceptus or embryo.
2. The method of claim 1, wherein said biological sample is derived
from a conceptus or amniocentesis.
3. The method of claim 1, wherein said nucleic acid is selected
from the group consisting of genomic DNA, mRNA and isolated
DNA.
4. The method of claim 1, wherein said assessing step is performed
by polymerase chain reaction-restriction fragment length
polymorphism assay or TaqMan allelic discrimination assay.
5. The method of claim 4, wherein said assessing step is performed
by polymerase chain reaction-restriction fragment length
polymorphism assay.
6. The method of claim 4, wherein said assessing step is performed
by TaqMan allelic discrimination assay.
7. The method of claim 4, wherein said assessing step is performed
by a process which comprises subjecting said isolated nucleic acid
to a PCR flanking the region of said SNP.
8. The method of claim 1, wherein said assessing step is performed
on the presence of a SNP further selected from the group consisting
of RXRB rs421446, BRD2 rs635688, HLA-E rs1264456, IRF4 rs12203592,
IRF4 rs872071, LIF rs929271, TP53 rs1042522, MDM2 rs2279744,
SLC11A2 rs422982, SLC40A1 rs1439814, and RRM2 rs1130609.
9. A method of predicting prenatal survival probability of a
prospective offspring of a couple, comprising the steps of: (a)
providing a biological sample from a pregnant woman; (b) isolating
a nucleic acid from said biological sample; (c) performing a
polymerase chain reaction (PCR) on said isolated nucleic acid to
produce an amplicon; (d) assessing said amplicon for the presence
of a combination of SNPs, said SNP combination consisting of
HLA-DQA1 rs1142316, HLA-DRA rs7192, and HSPA1B rs1061581; and (e)
predicting a decreased prenatal survival probability of said
prospective male offspring of said couple by said presence of said
SNP combination, wherein the presence of said SNP combination is
indicative of a decreased prenatal survival probability of a
prospective offspring.
10. The method of claim 9, wherein said biological sample is
derived from a conceptus or amniocentesis.
11. The method of claim 9, wherein said nucleic acid is selected
from the group consisting of genomic DNA, mRNA and isolated
DNA.
12. The method of claim 9, wherein said assessing step is performed
by polymerase chain reaction-restriction fragment length
polymorphism assay or TaqMan allelic discrimination assay.
13. The method of claim 12, wherein said assessing step is
performed by polymerase chain reaction-restriction fragment length
polymorphism assay.
14. The method of claim 12, wherein said assessing step is
performed by TaqMan allelic discrimination assay.
15. The method of claim 12, wherein said assessing step is
performed by a process which comprises subjecting said isolated
nucleic acid to a PCR flanking the region of said SNP.
16. The method of claim 9, wherein said assessing step is performed
on the presence of a SNP further selected from the group consisting
of RXRB rs421446, BRD2 rs635688, HLA-E rs1264456, IRF4 rs12203592,
IRF4 rs872071, LIF rs929271, TP53 rs1042522, MDM2 rs2279744,
SLC11A2 rs422982, SLC40A1 rs1439814, and RRM2 rs1130609.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a divisional application of the U.S.
Utility application Ser. No. 12/386,106 filed Apr. 14, 2009, which
claims the benefit under 35 U.S.C. .sctn.119(e) to U.S. Provisional
Applications Nos. 61/124,111 filed Apr. 14, 2008 and 61/132,634
filed Jun. 20, 2008, the content of which is incorporated by
reference herein in their entirety.
BACKGROUND OF THE INVENTION
[0002] Prenatal loss is a common occurrence. The survival
probability of human conceptions from fertilization to term is
estimated to be less than 25% (Roberts & Lowe, 1975). Vatten et
al. described a primary male-to-female ratio of 120-165:100 at the
time of fertilization, but the ratio decreases to 106:100 at the
time of birth. This suggests that prenatal loss concerns males more
than that of females, albeit the underlying mechanism is not
clear.
[0003] There have always been interests and efforts in discovering
the contribution of genetic factors to pregnancy loss. A
conventional approach is to use population genetics to assess
sex-specific prenatal loss. This population genetic approach
involves genotyping women experiencing repeated pregnancy loss.
Although some positive findings were obtained, the results have
been inconsistent. Even if positive findings were obtained, whether
miscarriages represent the whole spectrum of repeated pregnancy
loss is doubtful. Miscarriages represent only a fraction of the
total prenatal loss, and thus rendering the past studies
underpowered. Thus, the population genetic approach is suboptimal
at best.
[0004] Single nucleotide polymorphism (SNP) is a common form of
genetic polymorphisms. SNP may influence gene functions and
modifies an individual's susceptibility to diseases. Almost any
diseases have a genetic component in its etiology and most are
being unraveled in genetic association studies. In some instances,
a single SNP may be sufficient to confer susceptibility, while in
others multiple SNPs may act jointly to influence disease
susceptibility. An estimated 20 million SNPs are present in human
genome. This astronomical number precludes individual screening one
at a time because of the huge work and cost.
[0005] To the best of the present inventors' knowledge, there are
no reliable genetic markers for prenatal selection (i.e., fetal
survival) that have clinical utility. Genetic tests used in in
vitro fertilization (IVF) clinics in pre-implantation genetic
screening do not contain a genetic marker to predict the survival
probability of pregnancy but screens for chromosomal
abnormality.
[0006] Accordingly, there is a continuing need for a genetic marker
to predict the probability of pregnancy success as well as
sex-specific prenatal selection. The need for a reliable SNP
biomarker for sex-specific prenatal selection is expected to have
utility in the application in IVF and infertility clinics.
BRIEF SUMMARY OF THE INVENTION
[0007] The present invention is based on the present discovery of
particular SNPs in selected genes that represent biomarker
candidates in regulating prenatal development for male and female
offspring. In accordance with the present invention, the SNPs
influence prenatal selection individually or in particular
combinations of genotypes, and hence contribute to differential
viability of male and female embryos or fetuses. There is disclosed
herein SNPs that contribute to differential viability of male and
female embryos or fetuses by their different frequencies in healthy
newborn males and females.
[0008] In one aspect, the present invention provides a panel of
such SNPs that predict sex-specific prenatal selection and methods
of using these SNPs in assessing the propensity of prenatal loss
probability.
[0009] In one aspect, the present invention provides a candidate
gene approach and identifying a subset of single nucleotide
polymorphisms (SNPs) that is useful to predict the probability of
prenatal loss for a given offspring.
[0010] In one aspect, the present invention provides a method for
predicting prenatal loss of a conceptus or embryo, comprising the
steps of: (a) providing a biological sample; (b) isolating nucleic
acid from said sample; and (c) assessing the presence of a SNP
selected from the group consisting of RXRB rs2076310, HLA-DQA1
rs1142316, HLA-DRA rs7192, HSPA1B rs1061581, GTF2H4 rs3909130,
HIST1H1T rs198844, IFNG rs2069727, IL-6 rs1800796, KLRK1
rs10772266, KLRK1 rs2617160, KLRK1 rs2617171, TMPRSS6 rs733655, and
HMOX1 rs2071748, wherein the presence of said SNP is indicative of
an increased risk of prenatal loss for male conceptus or embryo.
Preferably, the biological sample is derived from a conceptus or
amniocentesis. The nucleic acid may be genomic DNA, mRNA or
isolated DNA.
[0011] In one aspect, the present invention provides a method
whereby an assessing step for SNPs is performed by polymerase chain
reaction-restriction fragment length polymorphism assay or TaqMan
allelic discrimination assay. The assessing step is performed
preferably by a process which comprises subjecting said nucleic
acid to an PCR amplification flanking the region of said SNP.
[0012] In one aspect, the present invention provides a method for
predicting prenatal loss of a conceptus or embryo by assessing the
presence of a combination of SNPs of HLA-DQA1 rs1142316, HLA-DRA
rs7192, and HSPA1B rs1061581, wherein the presence of such a
combination is indicative of an increased risk of prenatal loss for
male conceptus or embryo.
[0013] In one aspect, the present invention provides a method of
predicting prenatal loss of a conceptus or embryo by assessing the
presence of a combination of SNPs of KLRK1 rs10772266, KLRK1
rs2617160, and KLRK1 rs2617171 wherein the presence of such a
combination is indicative of an increased risk of prenatal loss for
male conceptus or embryo.
[0014] In one aspect, the present invention provides a method for
predicting prenatal loss of a conceptus or embryo, comprising the
steps of: (a) providing a biological sample; (b) isolating nucleic
acid from said sample; and (c) assessing the presence of a SNP
further selected from the group consisting of RXRB rs421446, BRD2
rs635688, HLA-E rs1264456, IRF4 rs12203592, IRF4 rs872071, LIF
rs929271, TP53 rs1042522, MDM2 rs2279744, SLC11A2 rs422982, SLC40A1
rs1439814, and RRM2 rs1130609.
[0015] In one aspect, the present invention provides a method for
predicting prenatal loss of a conceptus or embryo by assessing the
presence of a combination of SNPs of LIF rs929271, TP53 rs1042522,
and MDM2 rs2279744, wherein the presence of such a combination is
indicative of an increased risk of prenatal loss for male conceptus
or embryo.
[0016] In one aspect, the present invention provides a method for
predicting prenatal loss of a conceptus or embryo by assessing the
presence of a combination of SNPs of IRF4 rs12203592, and IRF4
rs872071, wherein the presence of such a combination is indicative
of an increased risk of prenatal loss for male conceptus or
embryo.
[0017] In yet another aspect, the present invention provides a
method of predicting prenatal survival probability of a prospective
offspring of a couple, comprising the steps of: (a) providing a
biological sample; (b) isolating nucleic acid from said sample; and
(c) assessing the presence of a SNP selected from the group
consisting of RXRB rs2076310, HLA-DQA1 rs1142316, HLA-DRA rs7192,
HSPA1B rs1061581, GTF2H4 rs3909130, HIST1H1T rs198844, IFNG
rs2069727, IL-6 rs1800796, KLRK1 rs10772266, KLRK1 rs2617160, KLRK1
rs2617171, TMPRSS6 rs733655, and HMOX1 rs2071748, wherein the
presence of said SNP is indicative of a decreased prenatal survival
probability of a prospective offspring.
[0018] In one aspect, the present invention provides a method for
predicting a decreased prenatal survival probability of a
prospective offspring by assessing the presence of a combination of
SNPs of KLRK1 rs10772266, KLRK1 rs2617160, and KLRK1 rs2617171,
wherein the presence of such a combination is indicative of a
decreased prenatal survival probability of a prospective
offspring.
[0019] In one aspect, the present invention provides a method for
predicting a decreased prenatal survival probability of a
prospective offspring of a couple, comprising the steps of: (a)
providing a biological sample; (b) isolating nucleic acid from said
sample; and (c) assessing the presence of a SNP further selected
from the group consisting of RXRB rs421446, BRD2 rs635688, HLA-E
rs1264456, IRF4 rs12203592, IRF4 rs872071, LIF rs929271, TP53
rs1042522, MDM2 rs2279744, SLC11A2 rs422982, SLC40A1 rs1439814, and
RRM2 rs1130609.
[0020] In one aspect, the present invention provides a method of
predicting a decreased prenatal survival probability of a
prospective offspring by assessing the presence of a combination of
SNPs of LIF rs929271, TP53 rs1042522, and MDM2 rs2279744, wherein
the presence of such a combination is indicative of a decreased
prenatal survival probability of a prospective offspring.
[0021] In one aspect, the present invention provides a method of
predicting a decreased prenatal survival probability of a
prospective offspring by assessing the presence of a combination of
SNPs of IRF4 rs12203592, and IRF4 rs872071, wherein the presence of
such a combination is indicative of a decreased prenatal survival
probability of a prospective offspring.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] FIG. 1 depicts the genomic location of the single nucleotide
polymorphisms (SNPs) evaluated for their values to predict
sex-specific prenatal selection by genotyping healthy newborns.
[0023] FIG. 2 depicts the individual and additive predictive power
of the independent predictive subset of single nucleotide
polymorphisms (SNPs) as biomarkers for sex-specific prenatal
loss.
DETAILED DESCRIPTION OF THE INVENTION
[0024] The present inventors cured the prior art deficiency and
used a novel approach to identify biomarkers in predicting
sex-specific prenatal loss. The present invention provides genetic
markers in male and female newborns. The present invention provides
comparison of genotype frequencies that provide clues for the
involvement of genes in prenatal selection. Selected gene candidate
in biologically plausible targets in HLA complex, immune
system-related genes (NKG2D and cytokines) and iron-related genes
were genotyped in healthy newborns. The present inventors
discovered that specific single nucleotide polymorphisms (SNPs) in
these genes represent good predictors for sex-specific prenatal
selection, and that the prenatal selection acts strongly against
male fetuses.
DEFINITIONS
[0025] Various terms used throughout this specification shall have
the definitions set forth herein.
[0026] The term "polymorphism" refers to the occurrence of two or
more alternative genomic sequences or alleles between or among
different genomes or individuals.
[0027] The term "polymorphic" refers to the condition in which two
or more variants of a specific genomic sequence found in a
population.
[0028] The term "polymorphic site" is the locus at which the
variation occurs. A polymorphic site generally has at least two
alleles, each occurring at a significant frequency in a selected
population. A polymorphic locus may be as small as one base pair,
in which case it is referred to as single nucleotide polymorphism
(SNP). The first identified allelic form is arbitrarily designated
as the reference, wild-type, common or major form, and other
allelic forms are designated as alternative, minor, rare or variant
alleles.
[0029] The term "genotype" refers to a description of the alleles
of a gene contained in an individual or sample.
[0030] The term "single nucleotide polymorphism" ("SNP") refers to
a site of one nucleotide that varies between alleles.
[0031] The term "oligonucleotide" is used interchangeable with
"primer" or "polynucleotide."
[0032] The term "primer" refers to an oligonucleotide that acts as
a point of initiation of DNA synthesis in a PCR reaction. A primer
is usually about 15 to about 35 nucleotides in length and
hybridizes to a region complementary to the target sequence.
[0033] The term "probe" refers to an oligonucleotide that
hybridizes to a target nucleic acid in a PCR reaction. Target
sequence refers to a region of nucleic acid that is to be analyzed
and comprises the polymorphic site of interest.
[0034] The term "TaqMan allelic discrimination assay" (also known
as the 5' nuclease PCR assay) is a technology that exploits the
5'-3' nuclease activity of Taq DNA polymerase to allow direct
detection of the PCR product by the release of a fluorescent report
as a result of PCR. The TaqMan allelic discrimination assay permits
discrimination between the alleles of a two-allele system. It
represents a sensitive and rapid means of genotyping SNPs.
[0035] The term "functional SNPs" refers to those SNPs that produce
alterations in gene expression or in the expression or function of
a gene product, and therefore are most predictive of a possible
clinical phenotype. The alterations in gene function caused by
functional SNPs may include changes in the encoded polypeptide,
changes in mRNA stability, binding of transcriptional and
translation factors to the DNA or RNA, and the like.
[0036] The term "PCR-RFLP" refers to polymerase chain
reaction-restriction fragment length polymorphism. PCR-RFLP is
technique to detect a variation in the DNA sequence of a genome by
breaking the DNA into pieces with restriction enzymes and analyzing
the size of the resulting fragments by gel electrophoresis.
PCR-RFLP is one type of genotyping for detecting SNP by
visualization of fragments on a gel following restriction
endonuclease digestion of the PCR product.
[0037] The term "repeated pregnancy loss" is defined clinically as
failure of established pregnancies before a live birth more than
two times.
[0038] The term "an increased risk of prenatal loss" refers to a
situation where the survival probability of a male offspring is
reduced compared to that of a female. For purposes of this
application, it refers to an odds ratio <0.50 (i.e., more than
two-fold increased risk) and has a statistically significance of P
.ltoreq.0.05 indicate strongly increased risk.
[0039] The term "95% confidence interval" (or "95% CI") refers to
the range of values surrounding the odds ratio (OR) within which
the true value is believed to lie with 95% certainty.
[0040] The term "conceptus" refers to the embryo in the uterus,
during the early stage of pregnancy. The term "embryo" refers an
unborn human baby, especially in the first eight weeks from
conception, after implantation but before all the organs are
developed. For purposes of this application, "conceptus" and
"embryo" are used interchangeably.
[0041] The term "Hardy-Weinberg equilibrium" refers to a principle
that allele and genotype frequencies in a population remain
constant; that is, they are in equilibrium--from generation to
generation unless specific disturbing influences are introduced.
Those disturbing influences include non-random mating, mutations,
selection, limited population size, random genetic drift and gene
flow. In the simplest case of a single locus with two alleles: one
allele is denoted "A" and the other "a" and their frequencies are
denoted by p and q; freq(A)=p; freq(a)=q; p+q=1. According to the
Hardy-Weinberg principle, when the population is in equilibrium,
then we will have freq(AA)=p.sup.2 for the AA homozygotes in the
population, freq(aa)=q.sup.2 for the aa homozygotes, and
freq(Aa)=2pq for the heterozygotes.
[0042] The term "haplotype tagging SNPs" (htSNPs) refers to a
subset of SNPs in each gene that provides sufficient information
about genetic variation in a gene as genotyping all of the SNPs in
a gene. They basically represent other SNPs in their vicinity and
make the others redundant in terms of providing additional
information about genetic variation.
[0043] The term "linkage disequilibrium" refers to the non-random
association in population genetics of alleles at two or more loci.
Linkage disequilibrium describes a situation in which some
combinations of alleles or genetic markers occur more or less
frequently in a population than would be expected from a random
formation of haplotypes from alleles based on their frequencies.
Non-random associations between polymorphisms at different loci are
measured by the degree of linkage disequilibrium.
[0044] The term "odds ratio" (OR) refers to the ratio of the
frequency of the disease in individuals having a particular marker
(allele or polymorphism) to the frequency of the disease in
individuals without the marker (allele or polymorphism).
[0045] The term "multivariable analysis" refers to an analysis used
to assess the independent contribution of each of the multiple risk
factors that contribute to a disease condition. That is,
multivariable analysis helps to determine the most informative
minimal set of independent (uncorrelated) multiple risk markers
(variables). In situations where two SNPs from the same gene show
statistically significant association, but when tested together in
a multivariable analysis, if they are correlated, one of them loses
significance and the other one is called an independent marker. The
one that is no longer significantly associated is still useful in
estimation of the risk in the absence of any other marker, but its
association is only due to its correlation with a stronger marker.
Since human diseases are often influenced by multiple genes, it is
usual to find associations with many SNPs from many genes. In this
case, a multivariable analysis is used to eliminate any
redundancy.
[0046] The term "adjusted odds ratio" refers to an odds ratio that
is adjusted with another factor (e.g., age). When all independent
risk markers are analyzed together in a multivariable analysis, the
odds ratio for each marker may be slightly different from the odds
ratios obtained from analysis of each SNP on its own. These new
odds ratios are called adjusted odds ratios. Since no SNP acts on
its own in reality, these adjusted odds ratios represent a more
realistic estimate of the risk. These are odds ratios calculated by
statistical algorithms that take into account individual
contributions of any other risk marker (variable) included in the
multivariable analysis.
[0047] In one embodiment, the present invention provides a panel of
SNPs that exhibit associations with sex-specific prenatal
selection. The SNPs identified are present in specific candidate
genes. In another embodiment, the present invention provides a
method of using genotyping approach to identify a panel of SNPs
listed in Table 4 out of all the 244 SNPs listed in Table 1.
[0048] In accordance with the present invention, one of a skilled
artisan understands that SNPs have two alternative alleles, each
corresponds to a nucleotide that may exist in the chromosome. Thus,
a SNP is characterized by two nucleotides out of four (A, C, G, T).
An example would be that a SNP has either allele C or allele T at a
given position on each chromosome. This is shown as C>T or C/T.
The more commonly occurring allele is shown first (in this case, it
is C) and called the major, common or wild-type allele. The
alternative allele that occurs less commonly instead of the common
allele (in this case, it is T) is called minor, rare or variant
allele. To avoid confusion, in this patent application, we adopted
to use wild-type and variant allele to define the common and rare
alleles. Since humans are diploid organisms meaning that each
chromosome occurs in two copies, each individual has two alleles at
a SNP. These alleles may be two copies of the same allele (CC or
TT) or they may be different ones (CT). The CC, CT and TT are
called genotypes. Among these CC and TT are characterized by having
two copies of the same allele and are called homozygous genotypes.
The genotype CT has different alleles on each chromosome and is a
heterozygous genotype. Individuals bearing homozygote or
heterozygote genotypes are called homozygote and heterozygote,
respectively.
[0049] The present inventors discovered that by examining genotype
frequencies of polymorphisms in newborns, clues may be obtained as
to which genes are involved in prenatal loss. This can be achieved
by comparing genotype frequencies in newborn males and females for
sex-specific selection.
[0050] In one embodiment, the present invention provides a method
of using genotype data rather than sequence data, SNPs are
identified to support the findings in the association study.
Hardy-Weinberg equilibrium (HWE) and Ewens-Watterson (E-W) tests
are used in the present genotype-based tools to search evidence for
selection.
[0051] HWE tests check the agreement between observed genotype
frequencies and expected frequencies calculated from observed
allele frequencies. A perfect agreement is expected when several
assumptions are met. One of the assumptions is the absence of
selection. A statistically significant result in the
goodness-of-fit test examining the agreement suggests
disequilibrium. The cause for this is change in genotype
distribution in the population is usually selection. In practice,
however, the most common cause for Hardy-Weinberg disequilibrium is
genotyping errors. It is often possible to distinguish between
selection and genotyping error when HWE is violated. Genotyping
errors are unlikely to be selective. This means if HWE is violated
in males but not in females, it points out towards a selective
event that has occurred exclusively in males.
[0052] In one embodiment, the present invention provides of method
of using a statistical test (e.g., HWE) to obtain evidence for
sex-specific prenatal selection using genotype frequencies in male
and female newborns. The statistical tests for HWE often yield
significant results. The present invention also provides other
statistical tests (e.g., E-W test) to complement the HWE test. One
of ordinary skill in the art would recognize that detail of the HWE
test is publicly available in Haploview version 4.0
(http://www.broad.mit.edu/mpg/haploview).
[0053] In one embodiment, the present invention provides another
statistical test (i.e., E-W test) that can be used when population
genetic data is available. E-W test shares some common feature as
that of HWE test. When the E-W test attains a statistically
significance, it is an indication of selection. Besides association
tests, the present inventors tested the data with both HWE and E-W
tests in order to obtain additional evidence for prenatal selection
in genotype data generated from healthy newborns. For the E-W test,
a publicly available PopGen software version 1.32
(http://www.ualberta.ca/.about.fyeh) is used.
[0054] In accordance with the present invention, there is disclosed
an optimal approach that utilizes genotyping to provide direct
evidence for sex-specific prenatal loss. In this approach, if a
genotype has a deleterious effect on the prenatal development of a
male offspring, newborn males will have a reduced frequency for
that genotype compared with female newborns. The present approach
has advantages of examining healthy newborns who survived the
prenatal selection till the end of pregnancy, thus providing
summary data regarding all forms of prenatal selection (i.e.,
implantation failure, embryonic development errors, and fetal
loss). The present approach is therefore superior to other
approaches (e.g., population genetics) in prenatal selection that
focuses on miscarriages. Studying couples experiencing repeated
pregnancy loss to find genetic markers is disadvantageous because
miscarriages represent a minority of total prenatal loss.
[0055] In one embodiment, the present invention provides a method
of utilizing an individual SNP to predict susceptibility to
prenatal loss of males. In accordance with the present invention,
the assessing techniques to determine the presence of a SNP are
known in the field of molecular genetics. Further, many of the
methods involve amplification of nucleic acids. (See, PCR
Technology: Principles and Applications for DNA Amplification (Ed.
H. A. Erlich, Freeman Press, NY, N.Y., 1992), and Current Protocols
in Molecular Biology, Ausubel, 1999).
[0056] In one embodiment, the detection of the presence of a SNP in
a particular gene is genotyping. One of the many suitable
genotyping procedures is the TaqMan allelic discrimination assay.
In this assay, one may utilize an oligonucleotide probe labeled
with a fluorescent reporter dye at the 5' end of the probe and a
quencher dye at the 3' end of the probe. The proximity of the
quencher to the intact probe maintains a low fluorescence for the
reporter. During the PCR reaction, the 5' nuclease activity of DNA
polymerase cleaves the probe, and separates the dye and quencher.
Thus resulting in an increase in fluorescence of the reporter.
Accumulation of PCR product is detected directly by monitoring the
increase in fluorescence of the reporter dye. The 5' nuclease
activity of DNA polymerase cleaves the probe between the reporter
and the quencher only if the probe hybridizes to the target and is
amplified during PCR. The probe is designed to straddle a target
SNP position and hybridize to the nucleic acid molecule only if a
particular SNP allele is present.
[0057] Genotyping is performed using oligonucleotide primers and
probes. Oligonucleotides may be synthesized and prepared by any
suitable methods (such as chemical synthesis), which are known in
the art. Oligonucleotides may also be conveniently available
through commercial sources. One of the skilled artisans would
easily optimize and identify primers flanking the gene of interest
in a PCR reaction. Commercially available primers may be used to
amplify a particular gene of interest for a particular SNP. A
number of computer programs (e.g., Primer-Express) is readily
available to design optimal primer/probe sets. It will be apparent
to one of skill in the art that the primers and probes based on the
nucleic acid information provided (or publically available with
accession numbers) can be prepared accordingly.
[0058] The labeling of probes is known in the art. The labeled
probes are used to hybridize within the amplified region during the
amplification region. The probes are modified so as to avoid them
from acting as primers for amplification. The detection probe is
labeled with two fluorescent dyes, one capable of quenching the
fluorescence of the other dye. One dye is attached to the 5'
terminus of the probe and the other is attached to an internal
site, so that quenching occurs when the probe is in a
non-hybridized state.
[0059] As appreciated by one of skill in the art, other suitable
genotyping assays may be used in the present invention. This
includes hybridization using allele-specific oligonucleotides,
primer extension, allele-specific ligation, sequencing,
electrophoretic separation techniques, and the like. Exemplary
assays include 5' nuclease assays, molecular beacon allele-specific
oligonucleotide assays, and SNP scoring by real-time pyrophosphate
sequences.
[0060] Determination of the presence of a particular SNP is
typically performed by analyzing a nucleic acid sample present in a
biological sample obtained from an individual. Biological sample is
derived from a conceptus or amniocentesis. The nucleic acid sample
comprises genomic DNA, mRNA or isolated DNA. The nucleic acid may
be isolated from blood samples, cells or tissues. Protocols for
isolation of nucleic acid are known. When RNA is used, the analysis
can be performed by first reverse-transcribing the target RNA
using, for example, a viral reverse transcriptase, and then
amplifying the resulting cDNA.
[0061] PCR-RFLP represents an alternative genotyping method used in
the invention. PCR-RFLP can yield unambiguous results provided that
there is a suitable endonuclease that will cut the amplified PCR
product containing a SNP if it contains one of the alternative
nucleotides but not the others. Results of PCR-RFLP may be achieved
by visualization of fragments on a gel following restriction
endonuclease digestion of the PCR product. Thus, a fragment of DNA
containing the SNP is first amplified using two oligonucleotides
(primers) and is subject to digestion by the variant
allele-specific restriction endonuclease enzyme. If the fragment
contains the variant allele it is cut into two or more pieces and
in the absence of the variant allele, the PCR product remains
intact. By visualizing the end-products of the digestion process by
agarose or polyacrylamide gel electrophoresis, the presence or
absence of the variant allele is easily detected. Other suitable
methods that are known in the art such as single-base extension
assay, oligonucleotide ligation assay, DNA microarray,
pyrosequencing, high-resolution melting method, denaturing
high-performance liquid chromatography, mass spectrometry,
microsphere-based suspension array platform (Luminex)-based assays
and the like can be used in the present invention to detect the
presence of SNP.
[0062] In one embodiment, the present invention provides a panel of
individual SNPs that are useful in predicting sex-specific prenatal
loss. This panel of SNPs includes RXRB rs2076310, HLA-DQA1
rs1142316, HLA-DRA rs7192, HSPA1B rs1061581, GTF2H4 rs3909130,
HIST1H1T rs198844, IFNG rs2069727, IL-6 rs1800796, KLRK1
rs10772266, KLRK1 rs2617160, KLRK1 rs2617171, TMPRSS6 rs733655, and
HMOX1 rs2071748.
[0063] In another embodiment, the present invention further
provides an additional panel of individual SNPs useful in
predicting sex-specific prenatal loss. This additional panel
includes RXRB rs421446, BRD2 rs635688, HLA-E rs1264456, IRF4
rs12203592, IRF4 rs872071, LIF rs929271, TP53 rs1042522, MDM2
rs2279744, SLC11A2 rs422982, SLC40A1 rs1439814, and RRM2
rs1130609.
[0064] In another embodiment, the present invention provides a
method of utilizing multiple SNPs that would exert joint effects
and alter the individual's susceptibility to sex-specific prenatal
loss.
[0065] In one embodiment, the present invention provides a method
of using haplotype tagging SNPs (i.e., htSNPs). hsSNPs represent a
cluster of SNPs in their vicinity; together, they provide
additional information about genetic variation. The present
invention provides a method of using the htSNP approach. When there
is no already known functional SNP available in a candidate gene,
the present invention provides a method of using htSNPs to predict
individual's susceptibility to sex-specific prenatal loss. The goal
is to use functional SNPs that are known to affect either the
function or expression of a gene. The use of functional SNPs may
yield a positive association. On the other hand, a non-functional
SNP may also be a marker to predict the outcome.
[0066] Haplotype tagging SNPs are capable of representing other
SNPs. This is because of a phenomenon called linkage disequilibrium
(LD). An htSNP and other SNPs tagged or represented by the htSNP
form a group that are equally informative when genotyped
individually. Any pair of SNPs that are in linkage disequilibrium
may provide the same information. If one SNP is associated with a
disease condition, the other SNP is similarly associated with the
same disease condition. This generates a situation in genetic
association studies where an association may be replicated by using
a different SNP that is in the linkage disequilibrium with the
original SNP. Accordingly, the SNPs in the present panel may be
replaced by other SNPs to yield the same information. The linkage
disequilibrium information is available in public resources such as
HapMap (http://www.hapmap.org) or genome variation server (GVS:
http://gvs.gs.washington.edu/GVS).
[0067] In one embodiment, the present invention provides a panel of
SNPs, when in combination, produces a synergistic effect on
sex-specific prenatal loss. While an individual SNP alone has no
effect, the combined SNPs together exert a significant effect. In
an exemplary embodiment, the presence of a combination of SNPs of
HLA-DQA1 rs1142316, HLA-DRA rs7192, and HSPA1B rs1061581 is
indicative of a sex-specific prenatal loss. In another exemplary
embodiment, the presence of a combination of SNPs of KLRK1
rs10772266, KLRK1 rs2617160, and KLRK1 rs2617171 is indicative of a
sex-specific prenatal loss.
[0068] In yet another exemplary embodiment, the presence of a
combination of LIF rs929271, TP53 rs1042522, and MDM2 rs2279744 is
indicative of a sex-specific prenatal loss.
[0069] In another exemplary embodiment, the presence of a
combination of SNPs of IRF4 rs12203592, and IRF4 rs872071 is
indicative of a sex-specific prenatal loss.
[0070] The SNP's individual and combined effects on sex-specific
prenatal loss against male are similar to that in decreasing
prenatal survival probability of a prospective offspring.
[0071] As will be apparent to one of skill in the art, one utility
of the present invention relates to the field of in vitro
fertilization (IVF). After a fertilized egg undergoes cell division
to become multiple cell stages (i.e., 8-cell stage), the cells can
be separated. The single cell can be used to perform multiple
genotyping. This can be achieved by whole genome amplification
(WGA). The technology for amplifying DNA from a single cell is
known. The resulting whole genome amplified DNA can be used for
PCR-based genotyping. The use of WGA in pre-implantation genetic
testing on single cell biopsies from 8-cell stage embryo is known
in the art. (See, e.g., Zhang et al. Proc. Natl. Acad. Sci. USA
89(13): 5847-51 (1992), Snabes et al., Proc. Natl. Acad. Sci. USA
91(13): 6181-5 (1994), and Coskun et al., Prenat. Diagn. 27(4):
297-302 (2007). After the genotyping assessment of the presence of
specific SNPs, a physician can thereby predict the risk of
sex-specific prenatal loss or chance of prenatal survival
probability of a prospective offspring. The present invention
provides a useful tool in deciding to implant a particular
fertilized embryo based on the genotyping results.
EXPERIMENTAL STUDIES
Example 1
Characteristics of Population Samples
[0072] To obtain evidence for sex-specific prenatal selection, we
examined genotype frequencies in male and females newborns and
compared these frequencies for differences by statistical methods.
Any difference found suggested differential viability for male and
female fetuses bearing that genotype.
[0073] The population samples consisted of 388 cord blood samples
form 201 girls and 187 boys. The cord blood samples were collected
in EDTA-containing tubes. White blood cells were isolated using
standard protocols. DNA was extracted from white blood cells using
standard phenol-chloroform extraction method or equivalent methods.
DNA samples were re-suspended in double distilled H.sub.2O at 100
nanograms per microliter and kept frozen at -20.degree. C. until
used for genotyping. Further details of the samples are provided in
detailed experimental procedures section.
[0074] Table 1 lists all of the 244 SNPs from the candidate genes
we selected to test for their predictive value for prenatal
selection. The table provides the gene name, the SNP ID number
(beginning with rs) as listed in National Center for Biotechnology
Information (NCBI) Entrez SNP
(http://www.ncbi.nlm.nih.gov/sites/entrez?db=snp), chromosomal
location and the position in the chromosome as nucleotide number
beginning from the tip of the short arm of a chromosome.
[0075] Each one of the 244 SNPs from our candidate genes were
genotyped in newborns and genotype frequencies were compared
between male and female newborns. Any difference between the
frequencies was considered to be an indication of differential
viability of male and female offspring.
Example 2
Selection of Genes for Testing their Role in Prenatal Selection
[0076] To the best of the present inventors' knowledge, despite few
published reports (Healey et al., 2000; Denschlag et al., 2004;
Pietrowski et al., 2005; Goodman et al., 2009), there are no
genetic polymorphisms for prediction prenatal selection (i.e.,
fetal survival) in clinical use. Past studies designed to correlate
genetic markers to prenatal selection using couples who had
experienced recurrent miscarriages. However, these miscarriages
represent only a fraction of the total prenatal loss, and thus
rendering the past studies underpowered.
[0077] The present inventors used a new approach. We noted that
male-to-female ratio is high at the time of fertilization in
humans; however, the male-to-female ratio diminishes by the time of
birth (i.e., from up to 165 males-to-100 females to 106
males-to-100 females). We postulated and tested the hypothesis that
prenatal selection is sex-specific; that is, prenatal selection
acts strongly against male fetuses. To test this hypothesis, we
examined genetic markers in male and female newborns.
[0078] While any gene may have a role in embryonic or fetal
viability, we stratified the genes for the probability of their
involvement in prenatal selection and used a candidate gene
approach. Besides known physiologic roles of genes, we also
exploited our own findings in childhood leukemia since
susceptibility to leukemia and prenatal selection share genetic
risk markers. Furthermore, childhood leukemia is more common in
males and since we explored markers for sex-specific prenatal
selection, we included leukemia risk markers. Most of these markers
are from the HLA complex and iron regulatory genes but also
included selected cytokine genes IFNG, IL-10, IL-6 and LIF (See,
Table 1 and FIG. 1). These two groups of genes represent plausible
gene candidates for prenatal selection.
[0079] We chose to examine additional gene candidates. These
include heme oxygenase I (i.e., HMOX1), leukemia inhibitory factor
(i.e., LIF) and natural killer cell receptor (i.e., NKG2D). We
analyzed selected polymorphisms of these relevant genes in the
potential genetic marker list (See, Table 1, and FIG. 1).
[0080] Furthermore, we examined selected polymorphisms of TP53,
IL-6, IL-10, IL-1B. These genes have been suggested to associate
with prenatal selection (TP53) and repeated pregnancy loss (HMOX1,
1L-6, 1L-10, IL-1B).
Example 3
Genotypings of Single Nucleotide Polymorphisms
[0081] Genotypings of SNPs were achieved by a variety of methods.
They usually provide equivalent results. The choice was based on
availability of the necessary instruments and expertise, budget
available for the study and convenience. Our choice of method was
TaqMan allelic discrimination assay for ordinary SNP genotyping.
All TaqMan assays were purchased from ABI (California) (See Table
6).
[0082] When TaqMan allelic discrimination assay was not possible to
use, we chose an alternative method. This happened for MDM2
rs2279744, HSPA1B rs1061581 and HLA-DQA1 rs1142316. For these
polymorphisms, we used a PCR based restriction fragment length
polymorphism assay. The details of these methods used to genotype
polymorphisms within our candidate genes are given in the detailed
experimental procedures section.
[0083] Table 2 shows the 24 SNPs either showed an individual
difference in genotype frequencies between male and female healthy
newborns or contributed to a combination of regional genotype
combinations that showed frequency differences. The gene name, SNP
ID number, alternative name for the SNP according to Genome
Variation Society (HGV), when available, SNP location within the
gene and nucleotide change are shown.
Example 4
Natural Killer Cell Receptor KLRK1 (NKG2D) and Prenatal Loss
[0084] The major role played by NK cells in maternal tolerance to
fetus is well recognized (Sargent et al, 2006; Hanna et al, 2006).
It has been shown that maternal immune tolerance to developing
offspring, which is immunologically foreign to maternal immune
system, is achieved by natural killer cells. Natural killer cell
activity is regulated by multiple molecules and receptor systems.
Among those, the most powerful is the NKG2D receptor encoded by the
KLRK1 gene (Raulet D H, 2003). The KLRK1 gene is polymorphic and
these polymorphisms are associated with cancer susceptibility
(Hayashi et al, 2006).
[0085] We obtained genotype and allele frequencies in the healthy
newborns. In overall analysis, all loci were in HWE with the
exception of rs10772266 (P=0.004) and in sex-specific analysis,
this distortion was evident in boys only (P=0.02) suggesting a
selection event affecting males during prenatal period. In most
other SNPs, HWE was mildly violated in boys (rs1049174, rs2617160,
rs2617170, rs2617171) while all SNPs remaining in equilibrium in
girls. The E-W neutrality test showed statistically significant
evidence for selection only for rs10772266 and only in boys.
[0086] The same KLRK1 haplotype that was described as associated
with low natural cytotoxic activity in Japan (Hayashi et al, 2006)
was the commonest haplotype also in the Caucasian sample analyzed
here. Notably, all the SNPs within the haplotype block described by
Hayashi et al. showed differences between boys and girls in their
frequencies. Inspection of genotype frequencies revealed that there
were differences in heterozygous frequencies between boys and girls
and boys had consistently lower rates for heterozygous genotypes.
The stronger violation of HWE in boys suggested that selection was
stronger in boys and the statistical assessment showed that the
deviation was heterozygote deficit in boys rather than excess in
girls.
[0087] The magnitude of deficit in boys was 18.4% for rs2617170,
which is a coding region variant (N104S) in KLRC4 immediately 3' to
the KLRK1 gene. This variant is also an htSNP for the 3' end of
KLRK1 in the HapMap project. Since the sample was healthy newborns,
we interpreted this finding as suggestive evidence for the
involvement of KLRK1 in feto-maternal interactions and possibly in
sex-specific prenatal selection. The strong evidence for a
functional role played by KLRK1 in feto-maternal interactions is
the demonstration of the secretion of soluble MHC class I
chain-related molecules (MIC) by placental trophoblastic cells to
counteract the maternal NK cell activity by blocking KLRK1
receptors.
Example 5
Genetic Markers in HLA-Complex that Correlate with Prenatal
Selection
[0088] We identified three genetic markers that bear high
correlation with prenatal selection in homozygosity representing
main lineages of HLA haplotypes. These genetic markers are: (i)
HSPA1B rs1061581; (ii) HLA-DRA rs7192; and (iii) HLA-DQA1
rs1142316. The major alleles of these SNPs characterize the
ancestral HLA-DRB4 lineage (i.e., HLA-DR4, HLA-DR7 and HLA-DR9).
The minor alleles of these SNPs characterize the HLA-DRB3 lineage
(i.e., HLA-DR3, HLA-DR11/12 and HLA-DR13/14). The frequency in male
newborns who were homozygote for either the major alleles or minor
alleles of the three SNPs was 5.9%. In contrast, the frequency in
female newborns was 14.6%. The comparison between the frequencies
in male and female newborns was statistically significant
(P=0.006). The more than two-fold deficit in homozygosity of SNPs
in male newborns is consistent with the hypothesis that there
exists a prenatal selection against male offspring bearing these
SNP haplotypes.
[0089] We hypothesized that transcription factors encoded within
the HLA complex may also be relevant in prenatal selection. There
are several embryo-expressed and evolutionarily conserved
transcription factor genes within the HLA complex. Of these, SNPs
from RXRB, BRD2 and GTF2H4 showed statistically significant
frequency differences between male and female newborns and RXRB2
and GTF2H4 retained their significance in the multivariable model
as independent markers of prenatal selection. Besides these, HLA-E
and HIST1H1T also showed frequency differences between males and
females with HIST1H1T remaining in the final model (these results
are presented in Tables 3 and 4).
Example 6
Iron-Related Gene Polymorphisms and that Correlate with Prenatal
Selection
[0090] Iron is a required element for cellular proliferation. One
iron-related gene HMOX1 (heme oxygenase 1) has been shown to affect
recurrent miscarriage susceptibility (Denschlag et al., 2004). This
association was, however, with a promoter region microsatellite
marker, which is not as easy to type as a SNP marker. We studied
the HMOX1 gene SNPs to search associations with prenatal
selection.
[0091] The SNP rs2071748 showed a sex-specific frequency difference
in newborns. The frequency in male newborns who were homozygote for
the minor allele of rs2071748 was 14.7%. In contrast, the frequency
in female newborns was 22.1%. The difference between the
frequencies in male and female newborns reached borderline
statistical significance (P=0.06). The .about.two-fold deficit in
homozygosity of the SNP in male newborns is consistent with the
hypothesis that there is a prenatal selection against male fetuses
bearing this SNP genotype.
[0092] The present inventors screened iron regulatory pathway genes
and detected associations with sex-specific prenatal loss
(OR.ltoreq.0.67 or P.ltoreq.0.05). These SNPs were from the genes
SLC11A2 (also known as NRAMP2), SLC40A1, RRM2 and TMPRSS6. The SNPs
and accompanying statistics are listed in Table 3.
Example 7
Leukemia Inhibitory Factor (LIF) and Sex-Specific Prenatal
Selection
[0093] We examined LIF and its natural genetic variation to search
for variants as markers for prenatal loss. LIF interacts with TP53
and TP53 interacts with MDM2. We found functional polymorphisms of
TP53 its interaction with MDM2 to produce joint effects.
[0094] Individually LIF, TP53 and MDM2 SNPs did not show a
statistically significant association with sex-specific prenatal
loss. The only suggestive association was with wild-type
homozygosity for the LIF SNP rs929271, which yielded an odds ratio
of 0.71 (P=0.10). However, when all three SNPs were analyzed
together, there was a significant finding. The combination of
having wild-type homozygote genotypes in each of the three SNPs at
LIF, TP53 and MDM2 showed a deficit in newborn males compared with
girls (OR=0.30, 95% CI=0.12 to 0.75; P=0.009). This finding
confirmed the involvement of LIF in the success of pregnancy and
also the interactions with TP53/MDM2 as expected form their
biologic interaction. The present investigation showed the
sex-specificity of this effect in that having the wild-type
homozygote genotypes at these three SNPs has a deleterious effect
for male offspring and such offspring have three-times reduced
chance of reaching the end of pregnancy.
Example 8
Associations of Cytokine Genes Interferon-Gamma (IFNG) and
Interleukin-6 (IL-6) Polymorphisms With Sex-Specific Prenatal
Loss
[0095] We examined IFNG SNP in our candidate SNPs because of its
sex-specific expression patterns. To investigate their association
with sex-specific prenatal selection, we genotyped selected SNPs
from IL-6, IL-10 and IFNG genes. Two of those, IFNG rs2069727 and
IL-6 rs1800796, showed different genotype frequencies between male
and female newborns. These results are shown in Table 3. The effect
of these SNPs was strong enough to remain in the multivariable
model in the presence of other markers of prenatal loss. The
adjusted odds ratios were less than 0.50 for both SNPs (Table 4)
meaning reduced chance of survival for male offspring during
pregnancy.
Example 9
Heterozygote Advantage in Sex-Specific Prenatal Selection
[0096] In this series of study, we examined heterozygosity at all
SNPs for its effect on sex-specific prenatal selection. As already
mentioned in different sections above, HLA-E rs1264456
individually, IRF4 SNPs rs12203592 and rs872071 in combination, and
KLRK1 SNPs rs2617160 and rs2617171 in combination showed reduced
frequencies in male newborns compared with female newborns. The
HLA-E and IRF4 SNPs were not retained in the final model but the
two KLRK1 region SNPs in combination with rs10772266 remained
statistically significant.
[0097] Altogether, the present inventors discovered genetic markers
including KLRK1 region, individual HLA complex genes, cytokine
genes, HMOX1 and other iron regulatory genes as predictors in
sex-specific prenatal selection.
Example 10
Prediction of Propensity to Prenatal Loss: Individual SNP
Analysis
[0098] Individually, RXRB rs421446, RXRB rs2076310, BRD2 rs635688,
GTF2H4 rs3909130, HIST1H1T rs198844, SLC11A2 rs422982, SLC40A1
rs1439814, RRM2 rs1130609, IFNG rs2069727 showed frequency
differences between males and females for wild-type allele or
variant allele positivity (dominant genetic model). This is
interpreted as being positive for a certain allele of these SNPs
was unfavorable for male offspring and they were less likely to
reach the end of pregnancy. As the odds ratios lie between 0.37 and
0.67, male offspring bearing any of these genotypes have at least
33% reduced chance of surviving pregnancy.
[0099] As will be seen in Table 3, some genotypes did not reach
statistical significance using the conventional criterion
(P.ltoreq.0.05) but they are still listed if the association was
marginally significant (P=0.06 to 0.10) and odds ratios were 0.67
or smaller. This was only done to be able to assess those SNPs in
the multivariable models (in which they may reach statistical
significance because of their small odds ratios).
[0100] Two RXRB SNPs reached statistical significance in their
association with prenatal loss. When this happens, it is customary
to examine their independence from each other because most common
reason for this is that the two SNPs are correlated. In genetic
data analysis, this means they are in linkage disequilibrium (LD).
An examination of LD between RXRB SNPs rs421446 and rs2076310
showed extremely significant correlation (correlation
coefficient=0.81, P<10.sup.-10). Multivariable modeling showed
that the primary association was with rs2076310 and the other SNP
showed an association simply because of its LD with rs2076310.
Consequently, only RXRB rs2076310 was considered for further
analysis in the next step (multivariable modeling).
[0101] TMPRSS6 rs733655, HMOX1 rs2071748 and IL-6 rs1800796 also
showed individual associations with male-specific prenatal loss but
with homozygous genotypes (homozygosity for the variant allele or
the wild-type allele as indicated in Table 3). The odds ratios for
these SNPs were between 0.38 and 0.61.
[0102] Finally, HLA-E rs1264456 also showed reduced frequency in
males for its heterozygosity rate. This association had a
borderline statistical significance (P=0.05) and an odds ratio of
0.67. This association represented a deleterious effect of
heterozygosity for male offspring, which can be translated into
heterozygous advantage for female offspring. All genotypes that
showed associations with heterozygosity are discussed below.
[0103] The other SNPs listed there did not show any individual
association but in combinations they were markers for male-specific
prenatal loss. The three SNPs from the HLA complex (HLA-DQA1
rs1142316, HLA-DRA rs7192 and HSPA1B rs1061581) characterize the
major HLA complex genetic lineages as first described by Dorak et
al. (2006). In the present study, combined homozygosity for
ancestral lineages showed a decreased frequency in male newborns
compared with the homozygosity rate in female newborns (5.9% in
males vs 14.6%, P=0.006, OR=0.36, 95% CI=0.18 to 0.75). The
combinations that gave rise to this strong association are
homozygosity for wild-type alleles in all three SNPs and
homozygosity for variant allele in all three SNPs. Because of its
strength, this association remained statistically significant in
the multivariable model for prenatal loss as presented below (and
in Table 4).
[0104] Table 4 lists the nine genotypes identified as independent
markers for survival of a male offspring. Seven of those are
individual SNP genotypes and two are particular genotype
combinations of three SNPs, one in the HLA complex
(HLA-DQA1-DRA-HSPA1B) and another in the KLRK1 region. The
frequencies in male and female newborns as well as resulting odds
ratios and P values are presented.
[0105] Likewise, heterozygosity at two IRF4 SNPs rs12203592 and
rs872071 did not show an association individually but in
combination (i.e., heterozygosity at both SNPs) (10.1% in males vs
19.4% in females, P=0.01, OR=0.47, 95% CI=0.26 to 0.85). This IRF4
combined genotype marker was included in the multivariable model
for assessment of its independence but did not remain statistically
significant.
[0106] Three KLRK1 (NKG2D) region SNPs listed at the end of Table 3
also showed statistically significant or marginally significant
associations with odds ratios between 0.60 and 0.69. Since they
were from the same gene region (KLRK1), their genotypes were
combined to be used as a single marker. The combination included
wild-type allele positivity for rs10772266 and heterozygosity for
both rs2617160 and rs2617171 (21.7% in males vs 33.3% in females,
P=0.01, OR=0.56, 95% CI=0.35 to 0.88). This KLRK1 region combined
genotype remained statistically significant as an independent
marker in the multivariable model.
Example 11
Multivariable SNP Analysis and Generation of Final Predictive
Model
[0107] The outcome of pregnancy is not determined by a single
genotype and our single marker analysis revealed multiple
statistically significant associations. We therefore proceeded to
the next step and analyzed the statistically significant
associations by multivariable modeling to identify the most
informative minimal subset of markers. These would be the
statistically most significant and independent associations.
Independence is important to avoid redundancy in testing samples
and also for contributions to the additive model. Markers that are
correlated and therefore not independent do not add to the
information obtained from one of them and does not change the odds
ratio when included in the multivariable final model.
[0108] The multivariable modeling yielded the independence and
statistical significance of the nine markers listed in Table 4. The
frequencies for each SNP in male and female newborns are replicated
from Table 3 and the frequencies for two of the combined genotypes
that remained statistically significant in the multivariable model
are given in Table 4. In this final model, all but one adjusted
odds ratios were smaller than 0.50 and therefore associated with
less than 50% likelihood of a male offspring to reach the end of
pregnancy.
[0109] Next, we assessed the value of this subset of markers in
predicting the prenatal loss jointly. Since all associations were
arranged to be in the same direction, it was possible to examine
the additive effect of the sum of markers without any further
manipulation. Each individual was simply given a score for the
number of markers possessed. In the newborn group examined, there
was no newborn who lacks all of the markers (score=0) or having all
of them (score=9). Thus, the scores were between 1 and 8. The
newborns were stratified into three groups: the baseline group
consisted of newborns possessing any 1 to 3 of the nine markers
(n=176), the next group consisted of 141 newborns who possessed any
4 of the nine markers and the third group was 96 newborns positive
for any 5 or more of the nine markers.
[0110] Examination of the additive effect of these nine SNPs
revealed a stepwise decrease in odds ratio corresponding decreasing
likelihood of survival for male offspring as the number of markers
possessed increases. The overall model reached extreme statistical
significance (P<10.sup.-10). This was because, in reference to
the baseline group of newborns possessing 1 to 3 of the markers,
having 4 markers was associated with male-specific prenatal loss
with an odds ratio of 0.37 (95% CI=0.23 to 0.59; P=0.00001) and
again in reference to the baseline group, having 5 or more of the
markers was associated with prenatal loss even more strongly
(OR=0.20, 95% CI=0.11 to 0.34; P<0.00001). In other words, these
figures translate into three-times decreased chance of survival for
boys possessing 4 of the nine markers and five-times decreased
chance of survival for boys possessing 5 or more compared with boys
possessing 3 or fewer markers.
[0111] It is important that if such a model is useful in clinical
use, the markers should occur at appreciable frequencies in the
population. Frequencies of individual markers in newborn males and
females are given in Tables 3 and 4. Table 3 shows the results of
the analysis of 24 SNPs in newborns. The frequencies for the
genotypes shown in male and female newborns, and their statistical
evaluation as odds ratio, its 95% confidence interval and P value
are shown.
[0112] In the cumulative risk model, possession of 4 markers
occurred in 38.8% of female newborns, and possession of 5 or more
markers in 32.7% of them. It is expected that at the beginning of
pregnancy before selection occurs, males have similar frequencies
(although at the end of pregnancy, these frequencies are 29.1% and
13.1%, respectively). Thus, at the beginning or at early phases of
pregnancy, the risk markers will be present at a considerable
frequency in offspring to allow risk stratification.
Experimental Protocols
I. Characterization of Clinical Samples
[0113] The population sample analyzed in this study consisted of
anonymously collected cord blood samples from newborns in South
Wales (United Kingdom). Random, anonymous umbilical cord blood
samples were obtained from full-term babies born in the University
Hospital of Wales and Llandough Hospital in Cardiff, UK over a
period of 12 months from 1996. This practice of collection of
surplus biological material for research purposes anonymously was
in compliance with the regulations of the local institutional
ethics committee.
[0114] It was not practically possible to obtain samples from every
newborn over this period but no newborn was intentionally excluded
on the basis of any selection criteria. The samples were collected
until the number in both sex groups exceeded 200. In the final
group of 415 newborns, there were 201 boys and 214 girls. This
gives a male-to-female (M:F) ratio of 0.939 that is slightly lower
than the expected M:F ratio (1.056) in newborns (statistically
non-significant).
[0115] These samples were previously used to describe the first
marker for sex-specific prenatal loss. In the present study, 388 of
the originally collected 415 samples were genotyped due to limited
DNA availability (201 girls and 187 boys). No data are available
about the newborns (such as gestational age, birth order, birth
weight, parental age) other than their sex and that they were born
via natural vaginal birth. No newborn born via cesarean section was
included.
II. Genotyping Procedures
[0116] A) Allelic Discrimination Assays
[0117] TaqMan allelic discrimination assay utilizes an
oligonucleotide probe labeled with a fluorescent reporter dye at
the 5' end of the probe and a quencher dye at the 3' end of the
probe. The proximity of the quencher to the reporter in the intact
probe maintains a reduced fluorescence for the reporter. During the
PCR reaction, the 5' nuclease activity of DNA polymerase cleaves
the probe, thereby separating the reporter dye and the quencher dye
and resulting in increased fluorescence of the reporter.
Accumulation of PCR product is detected directly by monitoring the
increase in fluorescence of the reporter dye. The 5' nuclease
activity of DNA polymerase cleaves the probe between the reporter
and the quencher only if the probe hybridizes to the target and is
amplified during PCR. The probe is designed to straddle a target
SNP position and hybridize to the nucleic acid molecule only if a
particular SNP allele is present.
[0118] TaqMan allelic discrimination assays were performed on
Stratagene MX3000P instruments. The standard thermal profile
protocol was used with the modification of 90 seconds at 60.degree.
C. for 50 cycles. TaqMan.RTM. SNP genotyping assays purchased from
ABI as 40.times. were diluted to 20.times. by adding Tris-HCl and
EDTA at pH 8.0. 96-well plates were set up by adding 1.5 .mu.l DNA
(10 ng/.mu.l), 4.625 .mu.l ddH.sub.2O and 6.25 .mu.l TaqMan.RTM.
genotyping master mix (ABI) and 0.625 .mu.l assay reagents. Each
plate contained intra and inter-plate controls and no-template
controls. Built-in Stratagene Mx3000P software was used to assign
genotypes.
[0119] B) Polymerase Chain Reaction--Restriction Fragment Length
Polymorphism (PCR-RFLP) Analysis
[0120] In these series of study, PCR-RFLP analysis was performed to
genotype the HSPA1B SNP rs1061581. In this analysis,
oligonucleotides 5'-CAT CGA CTT CTA CAC GTC CA-3' (SEQ ID NO: 1)
and 5'-CAA AGT CCT TGA GTC CCA AC-3' (SEQ ID NO: 2) and the
restriction endonuclease PstI were used. In the first step, using
the oligonucleotides, a 1,117 bp fragment was amplified by PCR. The
fragments were then subjected to restriction endonuclease digestion
by using the PstI enzyme. This enzyme cuts the fragment into two
fragments of 934 bp and 183 bp when there is a nucleotide G in the
SNP position but fails to cut it when there is a nucleotide A in
the SNP position. Samples with only 934 bp and 183 bp fragments
were classified as homozygote for allele G and samples with only
the 1,117 bp fragment were classified as homozygote for allele A.
Samples that contained 1,117 bp, 934 bp and 183 bp fragments were
classified as heterozygote for alleles A and G.
[0121] PCR-RFLP analysis was performed to genotype the HLA-DQA1
3'UTR SNP rs1142316. In this analysis, oligonucleotides 5'-CAA GGG
CCA TTG TGA ATC YCC AT-3' (SEQ ID NO: 3) and 5'-TGG GYG GCA RTG CCA
A-3'(SEQ ID NO: 4) and the restriction endonuclease BglII were
used. In the first step, using the oligonucleotides, a 726 bp
fragment was amplified by PCR. PCR was done under standard
conditions using 20 ng of genomic DNA and annealing temperature of
57.degree. C. The fragments were then subjected to restriction
endonuclease digestion by using the BglII enzyme. This enzyme cuts
the fragment into two fragments of 513 bp and 213 bp when there is
a nucleotide C in the SNP position but fails to cut it when there
is a nucleotide A in the SNP position. Samples with only 513 bp and
213 bp fragments were classified as homozygote for allele C and
samples with only the 726 bp fragment were classified as homozygote
for allele A. Samples that contained 726 bp, 513 bp and 213 bp
fragments were classified as heterozygote for alleles A and C.
[0122] PCR-RFLP analysis was also used to genotype MDM2 SNP
rs2279744. For the MspA1I RFLP analysis, primers 5'-CGG GAG TTC AGG
GTA AAG GT-3' (SEQ ID NO: 5) and oligonucleotide 5'-AGC AAG TCG GTG
CTT ACC TG-3' (SEQ ID NO: 6) were used. PCR was done under standard
conditions using 20 ng of genomic DNA and annealing temperature of
66.degree. C. The resulting PCR product (351 bp) was digested by
MspA1I. MspA1I cleaves final PCR product on two sites, one is
constitutive that served as an internal control of enzymatic
digestion and allele G of SNP309 generates specific MspA1I
restriction site.
[0123] Table 5 shows the flanking DNA sequence of each SNP. The
SNPs are shown as the wild-type and variant alleles. Table 6 lists
the different genotyping methods used to genotype SNPs analyzed in
this invention.
III. Statistical Analysis
[0124] The statistical analysis of a SNP association may be
performed using the following statistical models. It may be of
importance to have the variant allele in homozygous or heterozygous
combination as long as there is at least one copy of it in the
genotype (CT and TT). In this case, individuals with CT or TT
genotypes are pooled together and coded as 1 in a variable that are
going to be used in the statistical analysis. The code 1 indicates
presence of the susceptibility marker. In this case, individuals
who have the homozygous wild-type genotype are coded as 0 meaning
the lack of the susceptibility marker. This model that pools
heterozygotes and homozygotes together is called dominant genetic
model.
[0125] In recessive model, the interest in on homozygous genotype
of the variant allele (TT) and individuals with the TT genotype are
coded as 1 while all other genotypes are coded as 0. There are
certain situations in which the number of variant allele possessed
is important because having 1 or 2 copies of the variant allele
correlates with the degree of susceptibility. In this case,
individuals with genotype CT (one copy of the variant allele) have
increased susceptibility and individuals with genotype TT (two
copies of the variant allele) have an even higher degree of
susceptibility. This model is called the additive model and
demonstrates a gene-dosage effect. In most cases, statistical
significance for this model is usually an indication of an
association with dominant or recessive model. In our analysis that
follows, we have presented dominant or recessive model associations
for each SNP. Variables with P values of less than 0.05 were
considered statistically significant. Statistical association
analysis was carried out using logistic regression with Stata
version 10 statistical software.
[0126] One exceptional situation is that the heterozygous genotype
CT may be of importance. Heterozygosity in the genome is shown to
be a beneficial trait for prevention from many common diseases
including infections and cancer. This situation is called
`heterozygote advantage` and is characterized by decreased
frequency or underrepresentation of a heterozygous genotype among
cases with a disease compared with normal controls because of its
protective effect from the condition. In prenatal selection,
heterozygote advantage confers survival benefit is observed at
higher frequency. The dependency of the HLA complex-mediated
heterozygote advantage on sex in prenatal selection has already
been reported.
[0127] As mentioned above, each individual is coded as 0 or 1 based
on the absence or presence of the susceptibility genotype(s) for
each SNP before statistical association. A SNP may have a
deleterious or beneficial effect on a condition. In the present
invention, the outcome of interest was sex-specific prenatal
survival. In this case, beneficial genotypes are overrepresented in
the favored sex and deleterious genotypes for a particular sex are
underrepresented in that sex group among newborns who have survived
the selection. To avoid intricate mathematical manipulations while
constructing a statistical model to find the most informative
subset of SNPs, it is desirable that all SNPs are beneficial or
deleterious. This means, it is easier to construct a model if the
direction of the effect is the same for each SNP. In the case of
SNP associations, this is achieved easily. Since each individual is
coded as 0 or 1, when necessary, an association that is beneficial
for one sex can be converted to a deleterious one by simply
reversing the statistical codes. Males are under greater selective
pressure during pregnancy and our aim was to find deleterious
genotypes for males. We were interested in genotypes that were
underrepresented in male newborns compared with female newborns.
All results presented here are in this direction and the genotypes
that give rise to an association in that direction (i.e.,
deleterious for males) are given in the text and tables. In terms
of the odds ratio, which is a measure of the strength of
association, they are all less than 1.0. The odds ratio
approximates the survival chance of a male fetus to the end of
pregnancy. Thus, a value of 0.49 suggests, a male conceptus with
this genotype has a survival probability of 49% as opposed to 100%
for the comparison group who are female newborns.
[0128] All patents, publications, accession numbers, and patent
application described supra in the present application are hereby
incorporated by reference in their entirety.
[0129] Although the foregoing invention has been described in some
detail by way of illustration and example for purposes of clarity
of understanding, it will be readily apparent to those of ordinary
skill in the art in light of the teachings of this invention that
certain changes and modifications may be made thereto without
departing from the spirit or scope of the appended claims.
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TABLE-US-00001 [0162] TABLE 1 List of Genes and SNP Evaluated for
Their Predictive Value as Markers for Prenatal Loss Chromosome
Genes and SNP Position SNP ID position HFE2 (HJV)-5'FLANK rs4970862
chr1: 144132834 HFE2 (HJV)-3'FLANK rs16827043 chr1: 144106797 IL10
rs1800896 chr1: 205013520 PKR (EIF2AK2)-IVS2 rs2270414 chr2:
37216952 PKR (EIF2AK2)-IVS1 rs12712526 chr2: 37224339 PKR
(EIF2AK2)-5'UTR rs2254958 chr2: 37229795 RRM2-5'UTR rs1130609 chr2:
10180371 STEAP3-5'UTR rs1562256 chr2: 119687643 STEAP3-ivs1
rs865688 chr2: 119699720 STEAP3-IVS1 rs865108 Chr2: 119702854
CYBRD1-IVS1 rs960748 chr2: 172088182 CYBRD1-IVS1 rs6759240 chr2:
172089044 CYBRD1-ex4 (G266A) rs10455 chr2: 172119519 SLC40A1(V221V)
rs2304704 chr2: 190138422 SLC40A1-IVS2 rs1439812 chr2: 190148793
SLC40A1-IVS2 rs1439814 chr2: 190151138 SLC40A1-IVS7 rs1439816 chr2:
190152875 SLC11A1/NRAMP1-IVS4 rs3731865 chr2: 218958247
SLC11A1/NRAMP1-5'UTR rs1059823 chr2: 218968088 TF-P589S (Ex 15)
rs1049296 chr3: 134977044 TF-L524L (Ex 13) rs8649 chr3: 134969648
TF-5' UTR rs1130459 chr3: 134947973 TF-5' FLANK rs4481157 chr3:
134947374 TF-5' FLANK rs16840812 chr3: 134945497 CP-E543D (Ex 9)
rs701753 chr3: 150398925 CP-IVS1 rs7652826 chr3: 150421640
TFRC-S142G (Ex 4) rs3817672 chr3: 197285208 TFRC (5'UTR) rs11915082
chr3: 197293536 NFKB1-IVS6 rs4648022 chr4: 103715475 DAXX-IVS1
rs2073524 chr6: 33398525 DAXX-Y379Y (ex4) rs1059231 33396249
DAXX-IVS4 rs2239839 33396053 RXRB-5'FLANK rs421446 33282761
RXRB-5'FLANK rs365339 33280883 RXRB-IVS3 rs2076310 33274012
RXRB-F384F rs6531 33271429 BRD2-3'UTR rs1049414 33056585 BRD2-IVS7
rs11908 33052724 BRD2-IVS3 rs635688 33051129 BRD2-5'FLANK rs206786
33043157 TAP2 rs241453 32904204 HLA-DQB2 rs1573649 32839236
HLA-DQA2 rs2227128 32819378 HLA-DQA1-3'UTR rs1142316 32686523
HLA-DQA1-IVS2 rs9272723 32717405 HLA-DQA1-IVS1 rs17426593 32716055
HLA-DRB1 to DQA1 rs17599077 32699036 HLA-DRB1 to DQA1 rs3129763
32698903 HLA-DRB1 to DQA1 rs9271586 32698877 HLA-DRB1 to DQA1
rs2395225 32698602 HLA-DRB1 to DQA1 rs3135005 32693997 DRA-3'FLANK
rs3135388 32521029 DRA-3'UTR rs7194 32520458 DRA-L242V (exon 4)
rs7192 32519624 DRA-I134I (exon 3) rs8084 32519013 DRA-V16L (exon
1) rs16822586 32515751 DRA-5'UTR rs14004 32515687 BTNL2 (Q350Q)
rs9268480 32471822 BTNL2 rs2076530 chr6: 32471794 BTNL2 rs3129953
32469799 C6orf10 rs9268428 32452951 C6orf10-IVS6 rs1265758 32431507
NOTCH4-5'FLANK rs3096690 32302608 NOTCH4-5'FLANK rs3096702 32300309
NOTCH1-IVS1 rs396960 32299559 NOTCH4-K117Q (exon 3) rs915894
32298368 NOTCH4-S244L (exon 4) rs8192585 32296801 NOTCH4-IVS11
rs3134799 32292199 PBX2-IVS4 rs204993 32263559 PBX2-3'FLANK
rs1800684 32259972 EGFL8-3'UTR rs1061808 32244525 EGFL8-R86K
rs3096697 32242488 TNXB-3'UTR rs8283 32191278 TNXB-3'FLANK
rs3130342 32188124 TNXB-H1248R rs185819 32158045 CYP21A2-V282L
rs6471 32115866 CYP21A2-R103K rs6474 32114865 SKIV2L-Y1067Y (exon
26) rs410851 32044647 SKIV2L-IVS6 rs419788 32036778 SKIV2L-IVS6
rs2280774 32036670 SKIV2L Ex5 Q151R rs438999 32036285 SKIV2L-IVS2
rs440454 32035321 CFB-IVS14 rs1270942 32026839 CBF-R32W rs12614
32022158 HSPA1B-Q351Q rs1061581 31904759 HSPA1B-5'FLANK (-1136)
rs2763979 31902571 HSPA1A-5'UTR (-27G > C) rs1043618 31891486
HSPA1L-T493M rs2227956 chr6: 31886251 HSPA1L-G602K rs2075800
31885925 MSH5 rs1802127 31837904 MSH5-Q716Q rs707938 31837338 MSH5
rs3131378 31833264 MSH5 rs707939 31834667 MSH5 rs28381349 31817024
MSH5 rs2075789 31816307 CLIC1 rs3131383 31812273 CLICI rs2272592
31806331 BAT3-IVS6 rs805303 31724345 BAT3-IVS12 rs2077102 31719819
BAT3-3'FLANK rs2736155 31713178 AIF1-IVS4/R15W rs2269475 31691910
AIF1-5'UTR/IVS3 rs2259571 31691806 AIF1-IVS1 rs2844475 31691134
NCR3-5'UTR rs986475 31664688 NCR3-3'UTR rs1052248 31664560
NCR3-3'FLANK rs2256965 31663109 TNF (promoter-238) rs361525
31651080 TNF-5'FLANK (promoter-857) rs1799724 31650461 LTA-IVS1
rs909253 31648292 NFKBIL1 rs2071592 31623319 MICA-V152M rs1051792
31486956 HLA-C-5'FLANK rs9264942 31382359 POU5F1-IVS1 (Ex1 - M1R)
rs3130932 31241922 POU5F1-IVS4 rs2394882 31240628 TCF19-P219P
rs2073722 31237621 TCF19-IVS1 rs6905862 31235581 TCF19-IVS1
rs1150765 chr6: 31235541 GTF2H4-IVS11 rs1264307 30988736 GTF2H4
rs1264309 30983878 GTF2H4-5'FLANK rs3909130 30982144 DDR1 rs1049623
30972808 DDR1 rs1264323 30963886 DDR1 rs1264327 30958561 DDR1
rs1264328 30958121 IER3-3'UTR rs10947089 30818114 HLA-E-3'FLANK
rs1264456 30570063 HLA-E-5'FLANK rs1264459 30563899 ZNRD1 rs9261269
30138093 HLA-G-3'UTR rs1704 29906560 HLA-G-5'FLANK rs1736939
29901364 UBD (5'FLANK) rs1233405 29637733 UBD (IVS1) rs2534790
29632147 UBD (Ex2-T68C) rs2076485 29631931 UBD (Ex2-C160S) rs8337
29631655 HIST1H1T-V14L rs198844 26216261 HIST1H4C-5'FLANK rs198853
26212075 HIST1H4C-5' & HFE-3'FLANK rs17596719 26205173
HIST1H4C-5' & HFE-3'FLANK rs12346 26205025 HFE-3'FLANK rs707889
26203910 HFE-IVS5 rs2858996 26202005 HFE-C282Y rs1800562 26201120
HFE-H63D rs1799945 26199158 HFE-S65C rs1800730 26199164 HFE-IVS2
rs2071303 26199315 HFE-IVS1 rs9366637 26197077 HFE-5'FLANK
rs2794719 chr6: 26196869 HFE-5'FLANK rs2794720 26195181 HFE-5'FLANK
rs1800702 26194442 HFE-5'FLANK rs4529296 26191114 HFE-HIST1H1C
intergenic rs2050947 26178058 HIST1H1C-5'FLANK rs807212 26173600
HIST1H1C-5'FLANK rs9358903 26169928 HIST1H1C 5'FLANK rs9393682
26165029 HIST1H1C-S36S rs10425 26164528 HIST1H1C-P195P rs8384
26164051 HIST11H2AB-L97L rs2230655 26141485 HIST1H3B (3'UTR)
rs2213284 26139847 HIST1H4A (5'FLANK) rs9467664 26129792 SLC17A3
rs1165165 25970445 PRL (promoter) rs1341239 22412183 CDKAL1
rs6908425 20836710 Ch6: 20099022 rs965036 20099022 EDN1-3'FLANK
rs4714384 12405839 EDN1-3'FLANK rs4714383 12405468 EDN1 (K198N -
Ex5) rs5370 12404241 EDN1-IVS4 rs1626492 12403489 EDN1-IVS2
rs1476046 12401207 EDN1-5'FLANK rs3756863 12397016 Ch6: 9559183
rs10484246 9559183 IRF4 rs4985288 327246 IRF4 rs9405192 327537
IRF4-5'FLANK rs1033180 328546 IRF4-IVS4 rs12203592 341321 IRF4
rs3778607 348799 IRF4 rs2001508 chr6: 349632 IRF4 rs7768807 353246
IRF4 rs1877175 355493 IRF4-3'UTR rs9392502 355608 IRF4-3'UTR
rs872071 356064 IRF4 rs11242865 356954 IRF4 rs7757906 357741 IRF4
rs9378805 362727 IGFBP3-5'FLANK rs2854744 chr7: 45927600 TFR2-IVS17
rs10247962 chr7: 100057865 TFR2-IVS3 rs7385804 chr7: 10073906
TFR2-5'FLANK rs4434553 chr7: 10078127 SLC39A14-5'FLANK rs4872476
chr8: 22266179 SLC39A14-5'FLANK rs11136002 chr8: 22273027
SLC39A14-L33C rs896378 Chr8: 22318266 SLC39A14-IVS8 rs10101909
chr8: 22332985 H19 rs217727 chr11: 1973484 RRM1-IVS2 rs232054
chr11: 4680003 KLRK1 3' rs10772266 chr12: 10397436 KLRK1 3'
rs1049174 chr12: 10416632 KLRK1 (intron 1) rs2617160 chr12:
10436864 KLRK1 (intron 1) rs2246809 chr12: 10448311 KLRC4 (intron
3) rs2734565 chr12: 10451858 KLRC4 S104N (ex3) rs2617170 chr12:
10452224 KLRC4 (intron 2) rs2617171 chr12: 10452546 KLRC4 S29I
(ex1) rs1841958 chr12: 10453356 KLRC1-5'FLANK rs1983526 chr12:
10499280 KLRC1-5'FLANK rs2900421 chr12: 10513314 SLC11A2
(NRAMP2)-IVS4 rs224589 chr12: 49685317 SLC11A2 (NRAMP2)-IVS1
rs422982 chr12: 49692621 SLC11A2 (NRAMP2)-IVS1 rs224575 chr12:
49705888 IFNG-3' FLANK rs2069727 chr12: 66834490 MDM2-IVS1 (SNP309)
rs2279744 chr12: 67488847 IGF1 Exon 4-3'UTR rs6220 chr12: 101318645
IGF1-IVS3 rs1520220 chr12: 101320652 IREB2 rs2656070 chr15:
76517307 IGFIR-Exon 16-E1043E rs2229765 chr15: 97295748 HP_5'UTR
rs9924964 chr16: 70643062 HP_5'UTR rs7203426 Chr16: 70644056
HP_IVS1 rs2070937 chr16: 70647241 TP53_Ex4 R72P rs1042522 chr17:
7520197 BRIP1-IVS4 rs4968451 chr17: 57282089 HAMP-5'FLANK rs1882694
chr19: 40463222 HAMP-5'FLANK rs10414846 chr19: 40464311 HAMP-IVS1
rs8101606 ch19: 40466396 HAMP-IVS1 rs7251432 chr19: 40467281
BMP2-3'FLANK rs235756 chr20: 6715111 LIF-3'UTR rs929271 chr22:
28968226 LIF-IVS2 rs737921 chr22: 28970214 LIF-IVS2 rs929273 chr22:
28970595 LIF-5'FLANK rs2267153 chr22: 28973609 LIF-5'FLANK
rs3761427 chr22: 28974826 LIF-5'FLANK rs9606708 chr22: 28976126
HMOX1-IVS1 rs2071748 chr22: 34107618 HMOX1-IVS2 rs9607267 chr22:
34111207 HMOX1-IVS3 rs2071749 chr22: 34113413 HMOX1-3'UTR rs743811
chr22: 34122974 TMPRSS6-Y739Y rs2235321 chr22: 35792872
TMPRSS6-V736A (Ex17) rs855791 chr22: 35792882 TMPRSS6-D511D (Ex13)
rs4820268 chr22: 35799537 TMPRSS6-IVS2 rs733655 chr22: 35824997
TMPRSS6-5'UTR rs5756515 chr22: 35829638 HEPH-5'FLANK rs5919015 X
chr: 65299410 HEPH-5'UTR rs1028348 X chr: 65300888 HEPH-IVS7
rs760866 X chr: 65330706 HEPH-Exon 13 (Y498Y) rs806607 X chr:
65343765 HEPH-Exon 13 (T526T) rs809363 X chr: 65343849 HEPH-IVS14
rs708966 X chr: 65370647 HEPH-IVS18 rs4827365 X chr: 65397067
HEPH-IVS18 rs2198868 X chr: 65399577
TABLE-US-00002 TABLE 2 Characteristics of Single Nucleotide
Polymorphisms and Other Polymorphisms Found To Be Predictors of
Prenatal Loss in Univariable Statistical Association Tests Position
in Genes SNP name Alternative Name Gene/Change RXRB rs421446
NT_007592.14: g.24033033A > G 5' flanking region, T > C RXRB
rs2076310 NT_007592.14: g.24024284A > G intron 3, T > C BRD2
rs635688 NT_007592.14: g.23801401T > C intron 3, C > T
HLA-DQA1 rs1142316 no alternative name 3'UTR, A > C HLA-DRA
rs7192 NT_007592.14: g.23269895T > G exon 4, G > T (L242V)
HSPA1B rs1061581 no alternative name exon 1, A > G (Q351Q)
GTF2H4 rs3909130 NT_007592.14: g.21732416A > G 5' flanking
region, C > T HLA-E rs1264456 no alternative name 3' flanking
region, C > T HIST1H1T rs198844 NT_007592.14: g.16966532C > G
exon 1, C > G (L14V) IRF4 rs12203592 NT_034880.3: g.336321C >
T intron 4, C > T IRF4 rs872071 NT_034880.3: g.351064A > G
3'UTR, G > A LIF rs929271 NT_011520.11: g.10028795T > G
3'UTR, T > G TP53 rs1042522 NT_010718.15: g.7176820G > C exon
4, C > G (R72P) MDM2 rs2279744 NT_029419.11: g.31345886T > G
intron 1, T > G (SNP309) SLC11A2 rs422982 NT_029419.11:
g.13549660T > A intron 1, T > A (NRAMP2) SLC40A1 rs1439814
NT_005403.16: g.40652310C > T intron 2, T > C RRM2 rs1130609
NT_005334.15: g.5097055T > G 5'UTR, G > T TMPRSS6 rs733655
NT_011520.11: g.16885566T > C intron 2, T > C HMOX1 rs2071748
NT_011520.11: g.15168187G > A intron 1, G > A IFNG rs2069727
NT_029419.11: g.30691529T > C 3' flanking region, A > G IL6
rs1800796 NT_007819.16: g.22255204G > C promoter, G > C KLRK1
region rs10772266 no alternative name intergenic KLRK1 region
rs2617160 NT_009714.16: g.3304571A > T intron 1, A > T KLRK1
region rs2617171 NT_009714.16: g.3320253C > G intron 2, C >
G
TABLE-US-00003 TABLE 3 Individual Predictive Value of the Single
Nucleotide Polymorphisms and Other Polymorphisms or Their
Combinations Frequency in Univariable Odds Males vs Ratio (95% CI)
Genes/SNP/Genotypes Females (%) and P value RXRB rs421446/ 45.2 vs
55.3 OR = 0.66 variant allele positivity (0.44 to 0.99), P = 0.05
RXRB rs2076310/ 35.7 vs 49.0 OR = 0.58 variant allele positivity
(0.38 to 0.87), P = 0.009 BRD2 rs635688/ 71.5 vs 80.2 OR = 0.62
wildtype allele positivity (0.38 to 0.99), P = 0.05 HLA-DQA1
rs1142316*/ 58.7 vs 59.1 OR = 0.99 combined homozygous genotypes
(0.64 to 1.50), P = 0.94 HLA-DRA rs7192*/combined 52.4 vs 58.7 OR =
0.78 homozygous genotypes (0.52 to 1.16), P = 0.22 HSPA1B
rs1061581*/combined 50.0 vs 54.0 OR = 1.17 homozygous genotypes
(0.80 to 1.73), P = 0.42 GTF2H4 rs3909130/ 89.0 vs 95.7 OR = 0.37
wildtype allele positivity (0.16 to 0.82), P = 0.02 HLA-E
rs1264456/ 37.6 vs 47.3 OR = 0.67 heterozygosity (0.45 to 1.00), P
= 0.05 HIST1H1T rs198844/ 18.1 vs 28.9 OR = 0.54 variant allele
positivity (0.33 to 0.89), P = 0.02 IRF4 rs12203592/ 35.7 vs 32.7
OR = 0.73 heterozygosity (0.48 to 1.13), P = 0.16 IRF4 rs872071/
45.5 vs 52.9 OR = 0.74 heterozygosity (0.49 to 1.12), P = 0.16 LIF
rs929271**/ 42.4 vs 51.0 OR = 0.71 wild-type homozygosity (0.47 to
1.07), P = 0.10 TP53 rs1042522**/ 55.0 vs 58.0 OR = 0.88 wild-type
homozygosity (0.59 to 1.31), P = 0.54 MDM2 rs2279744**/ 45.2 vs
45.2 OR = 1.00 wild-type homozygosity (0.67 to 1.47), P = 0.99
SLC11A2 rs422982/ 40.3 vs 50.2 OR = 0.67 variant allele positivity
(0.45 to 1.0), P = 0.05 SLC40A1 rs1439814/ 57.4 vs 67.5 OR = 0.65
variant allele positivity (0.43 to 0.98), P = 0.04 RRM2 rs1130609/
38.2 vs 48.1 OR = 0.66 variant allele positivity (0.44 to 1.01), P
= 0.06 TMPRSS6 rs733655/ 2.03 vs 5.16 OR = 0.38 variant allele
homozygosity (0.12 to 1.22), P = 0.10 HMOX1 rs2071748/ 14.7 vs 22.1
OR = 0.61 variant allele homozygosity (0.36 to 1.02), P = 0.06 IFNG
rs2069727/ 80.1 vs 87.7 OR = 0.56 wild-type allele positivity (0.33
to 0.97), P = 0.04 IL6 rs1800796/ 81.4 vs 88.0 OR = 0.60 wildtype
homozygosity (0.34 to 1.04), P = 0.07 KLRK1 rs10772266***/ 70.9 vs
80.1 OR = 0.60 wild-type allele positivity (0.38 to 0.97), P = 0.04
KLRK1 rs2617160***/ 34.8 vs 43.6 OR = 0.69 heterozygosity (0.46 to
1.05), P = 0.08 KLRK1 rs2617171***/ 35.9 vs 44.6 OR = 0.69
heterozygosity (0.46 to 1.05), P = 0.08 *These SNPs make up the
HLA-DQA1-DRA-HSPA1B haplotype. Individually they have no effect on
prenatal loss. **These SNPs do not show any effect on prenatal loss
but in interaction with MDM2 and TP53 SNPs, the LIF SNP influences
viability of male offspring. ***Individually, these SNPs do not
show any effect individually but in combination of the genotypes
shown, they are a combined KLRK1 marker for loss of male offspring
before birth.
TABLE-US-00004 TABLE 4 Single nucleotide polymorphisms and other
polymorphisms found to be independent predictors of prenatal loss
in multivariable statistical modeling Frequency in Adjusted odds
ratio Gene/SNP/Genotype Males vs Females (%) (95% CI) and P value
RXRB rs2076310/ 35.7 vs 49.0 OR = 0.45 variant allele positive
(0.27 to 0.74), P = 0.002 HLA-DQA1 rs1142316/ 5.85 vs 14.6 OR =
0.31 homozygosity (0.13 to 0.77), HLA-DRA rs7192/ P = 0.01
homozygosity HSPA1B rs1061581/ homozygosity GTF2H4 rs3909130/ 89.0
vs 95.7 OR = 0.28 wildtype allele positive (0.09 to 0.81), P = 0.02
HIST1H1T rs198844/ 18.1 vs 28.9 OR = 0.47 variant allele positive
(0.26 to 0.85), P = 0.01 IFNG rs2069727/ 80.1 vs 87.7 OR = 0.47
wildtype allele positive (0.24 to 0.91), P = 0.03 IL6 rs1800796/
81.4 vs 88.0 OR = 0.38 wildtype homozygous (0.19 to 0.78), P =
0.008 KLRK1 rs10772266/ 21.7 vs 33.3 OR = 0.55 wildtype allele
positive (0.32 to 0.96), KLRK1 rs2617160/ P = 0.035 heterozygous
KLRK1 rs2617171/ heterozygous TMPRSS6 rs733655/ 2.03 vs 5.16 OR =
0.09 variant homozygous (0.01 to 0.78), P = 0.03 HMOX1 rs2071748/
14.7 vs 22.1 OR = 0.47 variant homozygous (0.24 to 0.90), P =
0.02
TABLE-US-00005 TABLE 5 Single Nucleotide Polymorphisms Found to
Predict Sex-Specific Prenatal Selection RXRB rs421446 C/T:
GAGGGCCACC TGTTCCAAGA CCCCCTTTCA AGGCCAGACT GGACACCAAG ATGGGGCCAT
GAACAAATCA CCCTTGGGGA CCATAAGAAC CCAGGGAGTT GGGGGGAGGG GACTGGTGCT
GCAGAACCAG TGGAAAGGGG TGACGCACGA ACCCCTCCCT C/T CAAAAAGACC
CGGAGTGTCA CGCATACACA GTGACACATA CTCTTTCCTC TCACACCCGG CGGCGGGGGT
TGCCCTGGGA GACCAGGCAG AGAAAGGGAA CAATCCTTCG GGAAAGGGAA AGGAGGGGGA
GGTGGGGAAG GGTCTGAGGG CTTGGACACA AGAAGAGCCG GAGGTGGCAG RXRB
rs2076310 C/T: AGATGTGAAG CCACCAGTCT TAGGGGTCCG GGGCCTGCAC
TGTCCACCCC CTCCAGGTGG CCCTGGGGCT GGCAAACGGC TATGTGCAAT CTGCGGGGAC
AGAAGCTCAG GTATGTGGCT CAGAGGATGA ACAGAGAGGG AGAGTCTGGG CCATGTATCA
C/T CACCTGTGGG ATTCCCAGGG CTTATGGAGT TTGGTCAGAG CAAGTGACCT
GGGGGAGGCC TGATGGGAGT AAAGAAGCTG AAGCTGAGAT GTAGGACGCG ATTGGGGGGA
AGGTCAGAGG GAAAAGGAAG CAGCGTGTAG GGTTTCTGAA CAGTGAGGAG ACTGGGACTG
GATCATCACT BRD2 rs635688 C/T: ATTTATTTAT TTTGTCCCAC AGTTTAATTG
GGGCCGCAGT TTAAGTAACT GTTCCTTTGA TGCATAGGGG GGGTGTGTGT GTGTGTGTGT
GTGTGTGAGA GTCGGGGATC GGTAGTCTCC CTATAAGCAT TTATTTTTCT GTGGTTCTGA
CCTAACATTT C/T TTTATTTAGG ATTATCACAA AATTATAAAA CAGCCTATGG
ACATGGGTAC TATTAAGAGG AGACTTGAAA ACAATTATTA TTGGGCTGCT TCAGAGTGTA
TGCAAGATTT TAATACCATG TTCACCAACT GTTACATTTA CAACAAGGTG AGTTTTTCTG
TGTGTTCATT TAGTAGGTGG HLA-DQA1 rs1142316 A/C: TAACATCGAT CTAAAATCTC
CATGGAAGCA ATAAATTCCC TTTAAGAGAT A/C TATGTCAAAT TTTTCCATCT
TTCATCCAGG GCTGACTGAA ACCGTGGCTA HLA-DRA rs7192 G/T: CTTCTTCCCA
CACTCATTAC CATGTACTCT GCCTTATTTC CCCCCAGAGT TTGATGCTCC AAGCCCTCTC
CCAGAGACTA CAGAGAACGT GGTGTGTGCC CTGGGCCTGA CTGTGGGTCT GGTGGGCATC
ATTATTGGGA CCATCTTCAT CATCAAGGGA G/T TGCGCAAAAG CAATGCAGCA
GAACGCAGGG GGCCTCTGTA AGGCACATGG AGGTGAGTTA GGTGTGGTCA GAGGAAGACG
TATATGGAGA TATCTGAGGG AGGAAAACAG GGTGGGGAAA GGAAATGTAA TGCATTTAAG
AGACAAGGTA GGAACAGATG TGGCTCTTGA TTTCTCTTTG HSPA1B rs1061581 A/G:
CCAGGGCGAG GTTCGAGGAG CTGTGCTCCG ACCTGTTCCG AAGCACCCTG GAGCCCGTGG
AGAAGGCTCT GCGCGACGCC AAGCTGGACA AGGCCCAGAT TCACGACCTG GTCCTGGTCG
GGGGCTCCAC CCGCATCCCC AAGGTGCAGA AGCTGCTGCA A/G GACTTCTTCA
ACGGGCGCGA CCTGAACAAG AGCATCAACC CCGACGAGGC TGTGGCCTAC GGGGCGGCGG
TGCAGGCGGC CATCCTGATG GGGGACAAGT CCGAGAACGT GCAGGACCTG CTGCTGCTGG
ACGTGGCTCC CCTGTCGCTG GGGCTGGAGA CGGCCGGAGG CGTGATGACT GTF2H4
rs3909130 A/G: TTAAAATCTT CAAAGAACAG CTAAAAATTG ACAGAGCTTC
TTTATGGCAA ACTTTAGGTA AGGTTGAAAG ACAATTTACA ATCTAGGAAG AAATGGTTGA
TGAAATAAAC AAAATACAAA AAGCTGTTAC AAAGCAATAA GAAAAAGAAA CATAATAGAA
A/G GATTGGGACA GACCACTGCT TACTAGTTAG CCCTGCTCAG CAAGGAGCAG
CTTAAAAAAA AAAAAAGAAG AAGAAAAGAA AAAGAAAAGA AAGAGGCCTG GCGGGGTGGC
TCAGGCCTGT AATCCCAACA CTTTGGGAGG CCAAAGAAGG TGGATCATTT TAGCTCAGGA
GTTCCAGACC HLA-E rs1264456 C/T: CACAGGAAGA AATGGCAAAG TAAAAATTCA
CACCCAGGAC TCCCTGGGCT TTCTCACCGC ACATGTTGCC TTCTTACTGG ATATCACCTG
ACAGAATGAG ACTCAGGTGA TTACAGGGAT TCACCAGGAA AACGGGAAAG TCGGCATGAC
CAGAACTAGA ACA C/T GGGCCAGTGA ATGCAGTTCT GGGTGGACCA TGGCATTGGA
AGCCAAAGGA TAGCTTGAAT GTGGTTAAAA AATTAAAACA ACAAGGCACA AAACGCACAA
ATGAAATACA AATGATGCTC AAACACAGCT TTTATTTTAC TTCAAAGTTT ACCTCAGATC
AGCCTGGGAA GGTGAGGGGA HIST1H1T rs198844 C/G: GTGACACTGA AAGGGCCTCG
GTGATCAACT TGGACACAGA GAGGTTCGGC ACTTTGCGAC TTGCACTTAT CAAGCCAGCC
GGCTTCCTCC CTCGCTTCTT GGTTGGAAGT TTCTCCATAG CGGCTA C/G ACCAGCACTG
GCAGAAGCTG CAGGCACGGT TTCAGACATA ACAACAGAGA AACGCAAGAT GTAATAACCA
GCGAAAAGCA TGAAACACCC GGGCGGCCTC GGGGCCTTAT ATAGGGTAGG GCGCGCTGTG
ATTGGTGCAT CACCTAGGCA CCGCCCCCGC CCCTTGGAGG AGGAGTATTT IRF4
rs12203592 C/T: ATGTTTTGTG GAAGTGGAAG ATTTTGGAAG TAGTGCCTTA
TCATGTGAAA CCACAGGGCA GCTGATCTCT TCAGGCTTTC TTGATGTGAA TGACAGCTTT
GTTTCATCCA CTTTGGTGGG TAAAAGAAGG C/T AAATTCCCCT GTGGTACTTT
TGGTGCCAGG TTTAGCCATA TGACGAAGCT TTACATAAAA CAGTACAAGT ATCTCCATTG
TCCTTTATGA TCCTCCATGA GTGTTTTCAC TTAGTCTGAT GAAGGGTTCA CTCCAGTCTT
TTCGGATGAT AAAATGCTTC GGCTGTCAGT CTAATAAGGG IRF4 rs872071 A/G:
TGTTTTACAT GCCCCGTTTT TGAGACTGAT CTCGATGCAG GTGGATCTCC TTGAGATCCT
GATAGCCTGT TACAGGAATG AAGTAAAGGT CAGTTTTTTT TTGTATTGAT TTTCACAGCT
TTGAGGAACA TGCATAAGAA ATGTAGCTGA AGTAGAGGGG A/G CGTGAGAGAA
GGGCCAGGCC GGCAGGCCAA CCCTCCTCCA ATGGAAATTC CCGTGTTGCT TCAAACTGAG
ACAGATGGGA CTTAACAGGC AATGGGGTCC ACTTCCCCCT CTTCAGCATC CCCCGTACCC
CACTTTCTGC TGAAAGAACT GCCAGCAGGT AGGACCCCAG AGGCCCCCAA IFNG
rs2069727 T/C: TGTGGTATTT CTTTCCACTA GCATTTTGTT GGCTTTCGCT
TTTCCAGTTA GCAGCTCTTT GAATTATCTT TCTAAGATAC AGATTTAATT ATGTCACTAT
TCAATTCAGA GGTTCTGCTA TGGAATGTAG TTTAAACTGC TTAGCTTGGC ACACAGAGAT
TTATTTCTAG CCCCTTCTCC ACCTTCCTAT TTCCTCCTTC T/C TTTCAGAATC
TTCCTCTCCC TCATCCAATG CTGGCAAACA CCAGTGGGGG TGGAGTAGTG GGTGTAAGCT
CTAGGGAGAA GGCTTGGATT GGAATCCAAG TTATTCCATT ACAAGTAGTG TGACCTTTAA
TACATTATGT ATATTGTCTA AGTTTCAGCT TTATTGTCTG AAAAAGAAAA TP53
rs1042522 C/G: TGAGGACCTG GTCCTCTGAC TGCTCTTTTC ACCCATCTAC
AGTCCCCCTT GCCGTCCCAA GCAATGGATG ATTTGATGCT GTCCCCGGAC GATATTGAAC
AATGGTTCAC TGAAGACCCA GGTCCAGATG AAGCTCCCAG AATGCCAGAG GCTGCTCCCC
C/G CGTGGCCCCT GCACCAGCAG CTCCTACACC GGCGGCCCCT GCACCAGCCC
CCTCCTGGCC CCTGTCATCT TCTGTCCCTT CCCAGAAAAC CTACCAGGGC AGCTACGGTT
TCCGTCTGGG CTTCTTGCAT TCTGGGACAG CCAAGTCTGT GACTTGCACG GTCAGTTGCC
CTGAGGGGCT MDM2 rs2279744 G/T: GGACTGGGGC TAGGCAGTCG CCGCCAGGGA
GGAGGGCGGG ATTTCGGACG GCTCTCGCGG CGGTGGGGGT GGGGGTGGTT CGGAGGTCTC
CGCGGGAGTT CAGGGTAAAG GTCACGGGGG CCGGGGGCTG CGGGGCCGCT G/T
CGGCGCGGGA GGTCCGGATG ATCGCAGGTG CCTGTCGGGT CACTAGTGTG AACGCTGCGC
GTAGTCTGGG CGGGATTGGG CCGGTTCAGT GGGCAGGTTG ACTCAGCTTT TCCTCTTGAG
CTGGTCAAGT TCAGACACGT TCCGAAACTG CAGTAAAAGG AGTTAAGTCC TGACTTGTCT
KLRK1 rs10772266 A/G: TGTTCATTCA ATATTATATT GGCTATGGGT TTGTCATAAA
TAGCTCTTAT CATTTTGAGA TATGTTCCAT CAATGCATAG TTTGAGAGTG TTTTTTTTCT
TTTTTTTTTT TAAGGCAAAT GACAAATACC TAGTTTACC A/G TCTTTACTTT
TTTAAACCTA ATGTTAACAT TAATATTTAA ACAGTTGTCA AAAATTGCTA AGTTGCCAGC
ATTCATGCAC AACTAGAAAA CATCCTTAAC TTATCTTAAA CCAGAAATGT ATTGCCATTA
ATGCATTAAT ATCTTTTACT ACTAAATACT GAAAAAAATT GAAATTATTT KLRK1
rs2617160 A/T: ATGCAGGGGC ATCTATGGCC ACACCACCAT GATGCATCCA
GTCTCGTCTG GACACGCATG GGCATATTGA AGCAGAAGTG AAATGATGAC TAATGTAAAA
GTAAAAAAGT CTGCAAACAT ATTTTAAGAA ATATGTATAT ATATATTTTC AGAACCTATT
TTCCATTCAG CTAGGTATTA A/T GTACTGGGCT ACACATACTG ACATATAATG
TTAACTGGTG TATTGTAATT ATATGAACTC AAGGCAGAGA TTCCATAAAT CTGGAATTTA
TACTTTGGGG AAAAACAGGT CATCATCTTG GCAATTAATT AATTTTCTCT GGCACAGCTT
CCTAAGCCAG GAATGATTAA ATGATTTTTT KLRK1 rs2617171 C/G: AAAATGACTT
TTCTATAAAA ATAATGAGAT CTTTAAAACA AATATTTTTA AAGCCATTAG CATAAAACTT
CACCATCTCT TATAGTATTT GATCTAACCA CTTTCAAAAA TTAATTTGTT TTTCTAAATA
TTTTTTCTCT TAAAACATGT CTTTGAGTCA TGAAATCAGA ATACATCTCT C/G
TGTGTGTGTA TCATATATAC ATATATATTT AGTACACACA AAAAAATAAA TGTTTTCTAC
AATTATTCTG TTATTTATAA ATTTGAAAAG TTCAGAAGCA GCATATTATC TTGGGGTTCA
GAGATATACA TTAAACAGAG AATTCTAATC CTCATTATTA TGAAATGTTT
CAAGGCGCTT
TABLE-US-00006 TABLE 6 Genotyping Methods for Each Single
Nucleotide Polymorphism in The SNP Panel SNP Genotyping Method
Detail RXRB rs421446 C/T Taqman allelic discrimination ABI Cat No
C_27015692_10 RXRB rs2076310 C/T Taqman allelic discrimination ABI
Cat No C_16167918_10 BRD2 rs635688 C/T Taqman allelic
discrimination ABI Cat No C_3213715_10 HLA-DQA1 rs1142316 A/C
PCR-RFLP BglII RFLP analysis HLA-DRA rs7192 G/T Taqman allelic
discrimination ABI Cat No C_8848630_20 HSPA1B rs1061581 A/G
PCR-RFLP PstI RFLP analysis GTF2H4 rs3909130 A/G Taqman allelic
discrimination ABI Cat No C_8941901_10 HLA-E rs1264456 C/T Taqman
allelic discrimination ABI Cat No C_8942134_10 HIST1H1T rs198844
G/C Taqman allelic discrimination ABI Cat No C_3266627_10 IRF4
rs12203592 C/T Taqman allelic discrimination ABI Cat No
C_31918199_10 IRF4 rs872071 A/G Taqman allelic discrimination ABI
Cat No C_8770093_10 LIF rs929271 Taqman allelic discrimination ABI
Cat No C_7545904_10 TP53 rs1042522 G/C Taqman allelic
discrimination ABI Cat No C_2403545_10 MDM2 rs2279744 T/G PCR-RFLP
MspA1I RFLP analysis SLC11A2 rs422982 Taqman allelic discrimination
ABI Cat No C_570333_10 SLC40A1 rs1439814 Taqman allelic
discrimination ABI Cat No C_2108641_10 RRM2 rs1130609 Taqman
allelic discrimination ABI Cat No C_379242_20 TMPRSS6 rs733655 T/C
Taqman allelic discrimination ABI Cat No C_3289858_1_ HMOX1
rs2071748 G/A Taqman allelic discrimination ABI Cat No C_2469922_1_
IFNG rs2069727 T/C Taqman allelic discrimination ABI Cat No
C_2683475_10 IL6 rs1800796 G/C Taqman allelic discrimination ABI
Cat No C_11326893_10 KLRK1 rs10772266 A/G Taqman allelic
discrimination ABI Cat No C_9345268_10 KLRK1 rs2617160 T/A Taqman
allelic discrimination ABI Cat No C_1841959_10 KLRK1 rs2617171 G/C
Taqman allelic discrimination ABI Cat No C_26984346_10
Sequence CWU 1
1
6120DNAHomo sapiens 1catcgacttc tacacgtcca 20220DNAHomo sapiens
2caaagtcctt gagtcccaac 20323DNAHomo sapiens 3caagggccat tgtgaatcyc
cat 23416DNAHomo sapiens 4tgggyggcar tgccaa 16520DNAHomo sapiens
5cgggagttca gggtaaaggt 20620DNAHomo sapiens 6agcaagtcgg tgcttacctg
20
* * * * *
References