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Platelet-specific collagen receptor glycoprotein VI gene variants affect recurrent pregnancy loss
Platelet-specific collagen receptor glycoprotein VI gene variants affect recurrent pregnancy loss Anjurani Siddesh, M.S., Farah Parveen, Ph.D., Maneesh Kumar Misra, M.Sc., Shubha R. Phadke, D.M., and Suraksha Agrawal, Ph.D. Department of Medical Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India
Objective: To determine whether platelet-specific collagen receptor glycoprotein VI (GP6) gene variants are associated with recurrent miscarriages (RM). Design: Genetic association study. Setting: Tertiary care referral hospital. Patient(s): A total of 200 women with at least three unexplained spontaneous abortions before 20 weeks of gestation and 300 healthy parous women. Intervention(s): Determination of variants of GP6 single-nucleotide polymorphisms (SNPs) namely; rs1671153, rs1654410, rs1654419, and rs1613662 was based on polymerase chain reaction–restriction fragment-length polymorphism. Main Outcome Measure(s): Genotypes and haplotypes frequencies were compared in RM case subjects versus control subjects. Result(s): We observed significantly higher occurrence of rare alleles of SNPs in GP6, namely, rs1671153, rs1654410, rs1654419, and rs1613662, among RM cases, revealing risk association for fetal losses. The synergistic effects of haplotype combinations were also evaluated and showed that four haplotypes G-T-G-G, T-C-A-A, G-C-G-A, and G-T-A-A were more prevalent among RM cases, revealing increased risk for fetal losses. In silico analysis revealed that GP6 has an impact on biologic pathways and significant influence in collagen binding. Gene-gene interaction network analysis revealed that GP6 consisted of a total of 25 interactions with 13 genes in the human genome. Conclusion(s): These results suggest that variants of GP6 SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662, may be associated with risk of recurrent miscarriage. In silico analyses demonstrated the influence of GP6 in biologic pathways, molecular function, including collagen binding, and gene-gene interaction in the human genome. Use your smartphone (Fertil SterilÒ 2014;102:1078–84. Ó2014 by American Society for Reproductive Medicine.) to scan this QR code Key Words: Recurrent miscarriages, fetal losses, platelets, glycoprotein VI, single-nucleotide and connect to the polymorphism Discuss: You can discuss this article with its authors and other ASRM members at http:// fertstertforum.com/siddesha-glycoprotein-vi-gene-rpl/
R
ecurrent miscarriage (RMOMIM: 614389) classically refers to three or more consecutive losses of clinically recognized pregnancies before the 20th week of gestation. The incidence of RM is estimated to be 1%–3% among couples attempting
pregnancy (1). Current clinical practice includes testing of several factors potentially increasing the risk of RM, e.g., parental chromosomal anomalies, maternal thrombophilias, and endocrine, anatomic, and immunologic disorders (1–4). Approximately 50% of
Received April 27, 2014; revised May 27, 2014; accepted July 1, 2014; published online July 30, 2014. A.S. has nothing to disclose. F.P. has nothing to disclose. M.K.M. has nothing to disclose. S.R.P. has nothing to disclose. S.A. has nothing to disclose. The first three authors contributed equally to this work. Supported by the Indian Council of Medical Research, Government of India, New Delhi, India [63/8/ 2010-BMS]. Reprint requests: Prof. Suraksha Agrawal, Ph.D., Department of Medical Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow, Uttar Pradesh 226014, India (E-mail: [email protected]). Fertility and Sterility® Vol. 102, No. 4, October 2014 0015-0282/$36.00 Copyright ©2014 American Society for Reproductive Medicine, Published by Elsevier Inc. http://dx.doi.org/10.1016/j.fertnstert.2014.07.002 1078
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RM cases have no deviations in the appropriate diagnostic test and are considered to be idiopathic in nature (5). In addition to clinical, environmental, and lifestyle risk factors, there is growing evidence that RM also involves genetic susceptibility, although the exact genetic cause of RM has yet to be identified. It has been suggested that platelets have a major role in patients with RM (6, 7). Glycoprotein VI (GP6) is a crucial platelet membrane glycoprotein for adequate platelet activation, adhesion, and aggregation (8). Human GP6 (Gene ID: 51206; MIM no.: 605546) maps to band q13.4 of VOL. 102 NO. 4 / OCTOBER 2014
Fertility and Sterility® human chromosome 19 with a nucleotide size of 24.56 kb and is composed of 8 exons. It is a member of the immunoglobulin superfamily with a 58-kDa platelet transmembrane glycoprotein consisting of 319 amino acids, located in the platelet membrane in noncovalent complex with FcRg subunit (9). Platelets are essential for primary hemostasis, but they also play a key role in atherogenesis and thrombus formation (10). Platelet activation and increased production of thromboxane and decreased sensitivity to antiaggregation effects of prostacyclin increase the prothrombotic state of pregnancy. The damage of blood vessel wall exposes the subendothelial component collagen to platelets in the blood flow. Interaction of platelets with collagen via the GP6 receptor results in platelet activation and adhesion, processes that are essential for thrombus formation. In vitro and in vivo studies have shown that GP6 is essential for activation of integrin for stable adhesion and subsequent signal transduction (via activation of phosphatidylinositol-3-kinase and phospholipase Cg2), which leads to granule release, activation of GPIIb/IIIa via inside-out signaling, and platelet aggregation (11). Previously, it has been shown that the patients with unexplained RM have significantly increased platelet aggregation (12). Recently, numerous polymorphisms in the GP6 gene affecting the platelet aggregation have been identified (13). The importance of variability of the GP6 gene sequences in platelet aggregation was identified by a recent genome-wide meta-analysis by Johnson et al. (14). Considering the critical role of GP6 in collagen-initiated signal transduction and platelet procoagulant activity, the observed variations in GP6 content may influence the risk for recurrent miscarriages. The precise mechanisms regulating GP6 gene molecular variations and aggregation function are largely unknown; however, few studies have suggested that genetic alteration in GP6 may influence receptor density and platelet function (15, 16). Approximately 55% of RM is caused by procoagulant defects which induce thrombosis and infarction of placental vessels (6). To the best our knowledge, to date only one study has evaluated the association of GP6 variants with fetal losses in sticky platelet syndrome patients among the northwestern population of Czechoslovakia (13). There is no such study reported in the northern Indian population. In the present study, we tested the hypothesis that single-nucleotide polymorphisms (SNPs) in GP6 may modulate its expression and molecular function and thereby may influence the risk of RM. The selection of tagged SNPs was based on the Hapmap database (http://hapmap.ncbi.nlm. nih.gov/index.html.en) with a minor allele frequency R1%. Four SNPs in GP6 were selected because of the evidence from published literature illustrating their potential role in thrombosis, RM, or sticky platelet syndrome. The aim of the present study was to evaluate the association of these SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662 with rRM among a northern Indian population.
Institute of Medical Sciences, Lucknow, Uttar Pradesh, India, for the evaluation of RM. All of the subjects were registered from April 2011 to March 2013. RM cases were primary aborters with a history of at least three consecutive miscarriages. The inclusion and exclusion criteria for the selection of RM case subjects and control subjects are described below. All the cases were screened for various known causes of miscarriages with appropriate investigations, including karyotypes of the couple, day 21 progesterone levels, antiphospholipid antibodies including lupus anticoagulant (PLR 0.8–1.05) and anticardiolipin antibodies 0–12 IgG anticardiolipin units, 0–5 IgM anticardiolipin units, and prothrombotic risk factors including factor V Leiden and prothrombin mutations, prolactin level, glycemic curve, and thyroid hormone levels. Uterine causes and cervical incompetence were ruled out by history, ultrasonography, and appropriate investigations. A well designed study protocol was used to record the detailed clinical information and pregnancy history of both RM and control subjects before inclusion in this study. Of the 516 initially screened individuals, 38.75% (n ¼ 200) had unknown cause of RM and were included in this study. Among these 200 women with unknown cause of RM, 96.5% (n ¼ 193) had three to five pregnancy losses, and 3.5% (n ¼ 7) had six or more pregnancy losses. The control group consisted of 300 healthy parous women with the same ethnic distribution as the RM cases with at least two live births and no history of miscarriage, preeclampsia, ectopic pregnancy, or preterm delivery. All the subjects included in this study lived in the Uttar Pradesh state of northern India and fell within the same linguistic group (Indo-European speakers), which indicates the homogeneity of the sample, i.e., case and control subjects were of the same ethnicity. This study was approved by the Ethics Committee of our institute and performed according to the standards laid down by the Declaration of Helsinki for medical research involving human subjects. Written informed consent to participate in this study was obtained from each of the participants.
Selection of SNPs in GP6 Gene Region The online Tagger software was used for tagged SNP selection with the pairwise tagging algorithm r2 R0.8 and minor allele frequency (MAF) R1% (17). The choice of this MAF served to strike a balance for the following factors: 1) the number of SNPs to be genotyped; 2) the available sample size; and 3) a reasonable power of the study (R80%). Furthermore, SNPs in the GP6 region were selected for which evidence has been published to illustrate their potential role in thrombosis, RM, or sticky platelet syndrome. Accordingly, four SNPs in the GP6 region, namely, rs1671153, rs1654410, rs1654419, and rs1613662, were selected in the present study. Details of the SNPs under investigation in this study are provided in Supplemental Table 1 (available online at www.fertstert.org.
MATERIALS AND METHODS Study Population and Inclusion/Exclusion Criteria
Blood Collection, DNA Extraction, and Genotyping
All of the samples were collected from cases attending the outpatient department of Sanjay Gandhi Post Graduate
Blood samples (5 mL) from control subjects, RM women, and male partners of case and control subjects were collected in
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ORIGINAL ARTICLE: EARLY PREGNANCY EDTA-coated collection vials. Genomic DNA was obtained with the use of Qiagen DNA extraction kits. The four SNPs in GP6, namely, rs1671153, rs1654410, rs1654419, and rs1613662, were genotyped with the use of polymerase chain reaction (PCR) followed by restriction fragment–length polymorphism analysis assay with the use of primers and restriction enzyme as described earlier (13). The amplification reaction was carried out with the use of a thermal cycler (PTC 200 Thermal Cycler; Bio Rad) with 50 ng genomic DNA in 10 mL PCR buffer (67 mmol/L Tris-HCl, pH 8.8, 16 mmol/L (NH4)2SO4, 2 mmol/L MgCl2, 0.01% Tween-20, and 100 mmol/L dNTP) containing 10 pmol of each primer and 0.5 U Taq DNA polymerase. The digested product was visualized under ultraviolet light and screened by two independent observers. Genotyping was performed without the knowledge of the subject's case or control status. For each assay, control DNA samples with known genotypes (positive control) and nontemplate (water) as a negative control were included in each typing run to ensure that genotypes were called correctly throughout the study. Two researchers independently read the gel pictures and performed repeated assays if they did not reach a consensus on the tested genotype.
Statistical Analysis The sample size for both RM case subjects and control subjects were calculated with the use of Quanto (version 1.2; http://hy dra.usc.edu/gxe) under the guidance of a statistician. The power of the study was 80% with type 1 error a ¼ 0.05. Allele and genotype frequencies, heterozygosities, and likelihood ratios were tested for Hardy-Weinberg equilibrium with the use of Popgen version 16 (www.ualberta.ca/fyeh/popge ne_download.html). A correlation test was used to evaluate the degree of correlation between the incidence of RM and the GP6 genotypes of the RM women. Genotype associations were analyzed with the use of an additive model (minor-allele homozygous and heterozygous individually with major-allele homozygous), dominant model (minor-allele homozygous and heterozygous taken together compared with majorallele homozygous), and recessive model (comparing minorallele homozygous with major-allele homozygous and heterozygous taken together). The differences in the genotype, allele, and haplotype distributions between the RM and control groups were tested for significance with the use of c2 test or Fisher exact test. The allelic frequencies were computed in a single strand of measured DNA. Haplotypes were generated for four SNPs of GP6 (rs1671153, rs1654410, rs1654419, rs1613662) from multilocus diploid data based on a Gibbs sampling strategy with the use of the Arlequin v3.5 software package. To minimize the possibility of spurious association or chance findings, P values were corrected for the number of comparisons (Pcorr) with the use of the Bonferroni inequality method (Pcorr ¼ 1 [1 P]n, where n is the number of comparisons). The magnitude of the effect was estimated by odds ratio (OR) and its 95% confidence interval (CI). Statistical analysis was performed with the use of SPSS version 20.0 for Windows. Statistical significance was considered to be when the P value was %.05. 1080
In Silico Analysis of Biological Pathways, Molecular Functions, and Gene-gene Interaction Network of GP6 ‘‘In silico’’ is an expression used to mean ‘‘performed on computer or via computer simulation,’’ which was used for the biologic pathways, molecular functions, and gene-gene interaction network analysis. Biologic pathways and molecular functions were identified with the use of Toppgene (18) and Ingenuity (19) pathway analysis software. Gene-gene (protein-protein) interaction network of GP6 recognized by Toppgene (18) were graphically represented with the use of Cytoscope (20). Functional annotation was carried out with the use of enrichment (negative binomial) test, a P value of %.05 being considered to be significant. The P value of each annotation of the respective test gene is derived through random sampling of the whole genome. The protein-protein interaction network–based candidate gene (GP6) prioritization was carried out on the basis of the K-Step Markov method (extended versions of the Pagerank and HITS algorithms).
RESULTS Demographic Profile and Clinical Features of Case and Control Subjects Two hundred RM and 300 fertile control women were screened for the evaluation of four SNPs in the locus of GP6. The demographic profiles of both case and control subjects are shown in Supplemental Table 2 (available online at www.fertstert.org). The control subjects were age and ethnically matched with the case subjects.
Distribution of GP6 Gene Variants Different alleles and their genotypes, in recessive, dominant, and additive models for the GP6 SNPs under study, namely, rs1671153, rs1654410, rs1654419, and rs1613662, were investigated (Table 1). Studied GP6 SNPs were in HardyWeinberg equilibrium in both RM case subjects and control subjects. We observed twofold increased risk for rs1671153 and rs1613662 SNPs under a dominant model in RM cases. In additive, dominant, and recessive models, rs1654410 SNP revealed nearly twofold risk, whereas under additive and recessive models, risk association for rs1654419 SNP ranged from almost threefold to twofold, respectively, among RM cases. Furthermore, the risk association for fetal losses ranged from 1.36- to 1.54-fold for minor allele carrier RM women compared with control women for GP6 SNPs rs1671153, rs1654410, rs1654419, and rs1613662 (Table 1).
Haplotype Combination Analysis The combined analysis was performed to evaluate the synergistic effect of all the possible haplotype combinations for rs1671153, rs1654410, rs1654419, rs1613662 SNPs (Table 2). We observed sixteen haplotypes among RM cases and controls (Table 2). Increased risk was seen for RM cases against four haplotypes viz. G-T-G-G (OR 2.55, 95% CI 1.29–5.02; P¼ .0061), T-C-A-A (OR 3.03, 95% CI 1.56–5.85; P¼ .0009), G-C-G-A (OR 1.60, 95% CI 1.06–2.42; P¼ .0256) VOL. 102 NO. 4 / OCTOBER 2014
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TABLE 1 Distribution of glycoprotein VI gene variants among recurrent miscarriage (RM) case subjects and control subjects, n (%). Genotype dbSNP ID rs1671153T>G Genotype frequency TT TG (additive model) GG (additive model) GG þ TG vs. TT (dominant model) GG vs. TG þ TT (recessive model) Allele frequency T G dbSNP ID rs1654410T>C Genotype frequency TT TC (additive model) CC (additive model) CC þ TC vs. TT (dominant model) CC vs. TC þ TT (recessive model) Allele frequency T C dbSNP ID rs1654419G>A Genotype frequency GG GA (additive model) AA (additive model) AA þ GA vs. GG (dominant model) AA vs. GA þ GG (recessive model) Allele frequency G A dbSNP ID rs1613662A>G Genotype frequency AA AG (additive model) GG (additive model) GG þ AG vs. AA (dominant model) GG vs. AG þ AA (recessive model) Allele frequency A G
RM (n [ 200)
Control (n [ 300)
P value
OR (95% CI)
74 (32.0%) 105 (52.5%) 21 (10.5%)
141 (47.0%) 139 (46.3%) 20 (6.4%)
1 .0645 .0521 .0274a .1364
1.43 (0.98–2.10) 2.00 (1.02–3.92) 1.51 (1.04–2.17) 1.64 (0.86–3.11)
253 (63.3%) 147 (36.7%)
421 (70.2%) 179 (29.8%)
.0232b .0232a
0.73 (0.55–0.95) 1.36 (1.04–1.78)
40 (20.0%) 92 (46.0%) 68 (34.0%)
85 (28.3%) 146 (48.6%) 69 (23.0%)
1 .2509 .0040a .0357a .0078a
1.33 (0.84–2.11) 2.09 (1.26–3.46) 1.58 (1.03–2.42) 1.72 (1.15–2.56)
172 (43.0%) 228 (57.0%)
316 (52.7%) 284 (47.3%)
.0030b .0030a
0.67 (0.52–0.87) 1.47 (1.14–1.90)
96 (48.0%) 82 (41.0%) 22 (11.0%)
168 (56.0%) 117 (39.0%) 15 (5.0%)
1 .2907 .0110a .0831 .0145a
1.22 (0.84–1.78) 2.56 (1.27–5.18) 1.37 (0.96–1.97) 2.34 (1.18–4.64)
274 (68.5%) 126 (31.5%)
453 (75.5%) 147 (24.5%)
.0168b .0168a
0.70 (0.53–0.93) 1.41 (1.07–1.87)
124 (62.0%) 61 (30.5%) 15 (7.5%)
216 (72.0%) 72 (24.0%) 12 (4.0%)
1 .0764 .0629 .0242a .1068
1.47 (0.98–2.21) 2.17 (0.98–4.81) 1.57 (1.07–2.30) 1.94 (0.89–4.25)
309 (77.3%) 91 (22.7%)
504 (84.0%) 96 (16.0%)
.0081b .0081a
0.64 (0.46–0.89) 1.54 (1.12–2.12)
Note: The difference in genotype/allele frequencies between the case and control groups were analyzed for statistical significance at the 95% confidence interval (CI) with the use of c2 test or Fisher exact test with Bonferroni correction under additive, recessive, and dominant models of inheritance. Odds ratios (ORs) were calculated and reported with 95% confidence limits. Additive model: comparing mutant homozygous and heterozygous genotypes individually with wild-type homozygous genotypes; recessive model: comparing mutant homozygous genotype with wild-type homozygous and heterozygous genotype taken together; dominant model: mutant homozygous and heterozygous genotype taken together compared with wild-type homozygous genotype. a Statistically significant risk-associated genotypes/alleles for RM. b Statistically significant protective alleles for RM. Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
and G-T-A-A (OR 2.68, 95% CI 1.11–6.46; P¼ .0274). Interestingly, wild-type haplotype combinations T-T-G-A (OR 0.44, 95% CI 0.26–0.76; P¼ .0037) revealed a protective effect among RM cases.
Impact of GP6 on Biologic Pathway, Molecular Functions, and Gene-gene Interaction Network in the Human Genome To see the relevance of our results in terms of biologic pathway, molecular functions and gene-gene interaction network we used an in silico approach as described in the Materials and Methods section with the use of Toppgene (18) and Ingenuity (19), pathway analysis software and Cytoscope (20). The detailed description of biologic pathways VOL. 102 NO. 4 / OCTOBER 2014
is available in the Biosystems databases (www.ncbi.nlm.nih. gov/biosystems) with unique Biosystems IDs (BSIDs). In silico analysis revealed the following top-ranked biologic pathways that are significantly associated with GP6: 1) platelet adhesion to exposed collagen: BSID 106030 (P¼1.21E3); 2) GP6-mediated activation cascade: BSID 106036 (P¼2.98E3); 3) ECM-receptor interaction: BSID 83068 (P¼8.01E3); 4) cell surface interactions at the vascular wall: BSID 106062 (P¼9.13E3); and 5) platelet activation, signaling, and aggregation: BSID 106034 (P¼1.99E2). Molecular function of GP6 included collagen binding (P¼3.01E3). Gene-gene interaction network analysis revealed that the GP6 gene consists of a total of 25 interactions with 13 other genes in the human genome (Fig. 1). The functional annotation of gene-gene interaction is presented in 1081
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TABLE 2 Combined haplotype analysis of glycoprotein VI SNPs dbSNP ID rs1671153T>G, dbSNP ID rs1654410T>C, dbSNP ID rs1654419G>A, and dbSNP ID rs1613662A>G among recurrent miscarriage (RM) case subjects and control subjects, n (%). Haplotype
RM (n [ 400)
Control (n [ 600)
TCGA GTGG TTGA GCAA TCAA GCGA TTAA GTAA G CG G TCAG GTGA TCGG TTGG GTAG GCAG TTAG
89 (22.3%) 137 (22.8%) 23 (5.7%) 14 (2.3%) 40 (10.0%) 111 (18.5%) 5 (1.3%) 10 (1.7%) 27 (6.7%) 14 (2.3%) 52 (13.0%) 51 (8.5%) 54 (13.5%) 101 (16.8%) 14 (3.5%) 8 (1.3%) 22 (5.5%) 22 (3.7%) 15 (3.7%) 12 (2.0%) 28 (7.0%) 72 (12.0%) 17 (4.2%) 37 (6.2%) 3 (0.7%) 9 (1.5%) 2 (0.5%) 1 (0.16%) 1 (0.25%) 1 (0.16%) 8 (2.0%) –
OR
95% CI
P value
0.96 2.55 0.48 0.74 3.03 1.60 0.77 2.68 1.52 1.90 0.55 0.67 0.49
0.71–1.31 1.29–5.02 0.33–0.72 0.25–2.20 1.56–5.85 1.06–2.42 0.53–1.10 1.11–6.46 0.83–2.81 0.88–4.12 0.34–0.87 0.37–1.21 0.13–1.84
.8774 .0061a .0002b .7918 .0009a .0256a .1808 .0274a .2075 .1116 .0098b .2018 .3806
Note: Haplotypes were generated from multilocus diploid data based on a Gibbs sampling strategy (Arlequin v3.5). The difference in haplotype frequencies between the case and control groups for the prevalent haplotypes (prevalence >5% in either case or control subjects) was analyzed for statistical significance with the use of c2 test or Fisher exact test with Bonferroni correction, and odds ratios (ORs) are reported with 95% confidence limits. a Statistically significant risk-associated haplotype combination. b Statistically significant protective haplotype combination. Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
Table 3. The significant terms (gene count and P value) for gene-gene interaction are shown in Supplemental Figure 1.
DISCUSSION Recurrent miscarriages are characterized by defective placentation and microthrombi in the placental vasculature. Thrombophilic disorders resulting in microthrombi in placental vasculature are considered to be an important cause of RM. It has been suggested that platelets are essential for primary hemostasis as well as play a crucial role in atherogenesis and thrombus formation (10). GP6 is a platelet transmembrane glycoprotein that plays a significant role in collagen-initiated signal transduction and platelet procoagulant activity; therefore, the observed variation in the GP6 gene region may influence risk for thromboembolic disorders (21). There is growing evidence implicating congenital and acquired thrombophilia in the pathophysiologic processes underlying RM. Keeping these facts in mind, the present study was designed to investigate the impact of four GP6 SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662, in RM cases from a northern Indian population. We observed significantly higher occurrence of mutant genotypes of GP6 SNPs, namely rs1671153, rs1654410, rs1654419, and rs1613662, in RM cases, suggesting a risk association for fetal losses (Table 1). Earlier studies have shown risk associations of mutant genotypes at rs1671153, rs1654419, and rs1613662 SNPs with thrombotic disorders (22), which is in concordance with our findings of high prevalence in RM cases. Sokol et al. (13) have shown a significantly higher frequency of mutant genotype GG of 1082
rs1671153 SNP in sticky platelet syndrome patients with fetal losses. They also observed higher frequencies of mutant genotypes of rs1654419 and rs1613662 SNPs, but the differences were not statistically significant (13). This may be due to the small sample size (n ¼ 27) because of the rarity of sticky platelet syndrome, which is an autosomal dominant disorder associated with arterial and venous thromboembolic events. It is the second most common thrombophilia that causes recurrent spontaneous abortions or fetal loss syndrome (6, 7). We have conducted a study on GP6 on a large cohort of well characterized 200 RM case subjects and 300 healthy parous women. Increased occurrence of minor allele carriers for GP6 SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662, was seen in RM women compared with control women. We have made an attempt to evaluate the synergistic effect of these SNPs and found that four haplotypes, G-T-G-G, T-C-A-A, G-C-G-A, and G-T-A-A, were more prevalent among RM case subjects, revealing increased risk for fetal loss. It is already known that there exists an association of GP6 with platelet procoagulant activity, and the variation in GP6 gene content may contribute to risk of hemorrhagic or thromboembolic disorders (22), which are also well established factors in repeated miscarriages. To date, to the best of our knowledge, there is only one report on sticky platelet syndrome patients with fetal losses and there is no earlier study reporting on GP6 in recurrent miscarriage, which makes our study more relevant. The distribution of global MAF (Supplemental Table 1) of GP6 SNPs (according to the 1,000 Genomes database) has been included to demonstrate the relevance of this study in its application to different populations and to know the MAF data for this gene in other world populations (23). This information was provided to illustrate the importance of ethnic differences from the global perspective. The influence of GP6 in biologic pathways, molecular function, and interaction with other genes in the human genome is not yet very clear. The variation in GP6 gene content may influence the expression of this gene and directly or indirectly influence the functions of other genes, which are in gene-gene (at protein level) interaction pathways as illustrated in Figure 1. Therefore, we carried out an in silico analysis to determine the relevance of GP6 on biologic pathways, molecular functions, and the gene-gene interaction network. The results revealed that the GP6 gene has an impact on biologic pathways, namely, platelet adhesion to exposed collagen, GP6-mediated activation cascade, ECM-receptor interaction, cell surface interactions at the vascular wall, and platelet activation, signaling, and aggregation. It also has significant influence in collagen binding. Gene-gene interaction network analysis revealed that GP6 consists of a total of 25 interactions with 13 neighboring genes (Fig. 1). The functional annotation of gene-gene interaction is presented in Table 3. Gene counts and P values for gene-gene interaction are shown in Supplemental Figure 1. Pregnancy itself is notably a hypercoagulable state, owing at least in part to the physiologic changes in the coagulation and fibrinolytic systems; this has the potential for interaction with an acquired or heritable thrombophilia to cause adverse experiences (24). Platelet response to collagen is a primary event in hemostasis and thrombosis VOL. 102 NO. 4 / OCTOBER 2014
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FIGURE 1
Gene-gene (protein-protein) interaction network analysis consists of a total of 25 interactions with 13 neighboring genes in the human genome. The protein-protein interaction network–based candidate gene (glycoprotein VI [GP6]) prioritization was carried out on the basis of the K-Step Markov method (extended versions of the Pagerank and HITS algorithms). Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
but the molecular events that underlie these responses remain poorly understood. The receptor GP6 has a major role in collagen-induced platelet signaling (25). Therefore, the observed variations in GP6 may influence platelet reactivity toward collagen and therefore influence platelet function and the risk of fetal losses among women of reproductive age. Recently, a genome-wide meta-analysis carried out by Johnson et al. (14) highlighted the importance of gene variability of the GP6 gene on platelet aggregation. Johnson et al. (14) focused on the assessment of the genetic influence on platelet functions and recognized seven loci linked with platelet aggregation to physiologic agonists (ADP, collagen, and EPI). One of the loci within the region of the GP6 gene was strongly linked with increased aggregation to collagen (14). Furthermore, it has been shown that patients with unexplained RM have significantly increased platelet aggregation (12). This provides an attractive explanation to the findings of our study about the observed increased risk for fetal losses among minor allele carrier women for GP6 SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662. Taken together, it may be concluded that RM women with minor allele carrier status for GP6 SNPs may have increased platelet aggregation, which may contribute to increased risk of RM. VOL. 102 NO. 4 / OCTOBER 2014
However, this needs further validation in other ethnic groups. This is the first study from northern India and only the second study that has found an association of GP6 SNPs with RM. It must be seen to be a preliminary study, consisting of important observations, and needs further studies in an independent cohort to confirm the findings. The role of platelet hyperaggregability has not been well demonstrated as a possible risk factor for RM. It has been shown that increased platelet aggregation is a contributing factor for RM (12), decreased platelet aggregation has also been reported in RM cases (6), and no correlation between platelet hyperaggregability and RM has been seen (26). To date, the role of platelet hyperaggregability has not been elucidated in the RM population. Therefore, the current clinical practice at our center does not include the platelet hyperaggregability test in pregnancy. It has been reported that GP6 is associated with platelet procoagulant activity, and the variation in GP6 gene sequences may contribute to risk of thromboembolic disorders (22). Furthermore, it has been observed that the platelet procoagulant activity influences the incidence of RM (12). A future area of research would be to evaluate whether any of the SNPs under investigation in our study are involved in the alteration in the ability of GP6 to bind to collagen. 1083
ORIGINAL ARTICLE: EARLY PREGNANCY 3.
TABLE 3 Functional annotation of gene-gene interactions.
2207 51206 2214 2212 1401 7041 653361 9618 7525 2185 2534 4067 801
4.
Gene name
P value
Fc fragment of IgE, high-affinity I, receptor for; gamma polypeptide [FCER1G] interactions Glycoprotein IV (GP6) interactions Fc fragment of IgG, low-affinity IIIa, receptor (CD16a) [FCGR3A] interactions Fc fragment of IgG, low-affinity IIa, receptor (CD32) [FCGR2A] interactions C-reactive protein, pentraxin-related [CRP] interactions Transforming growth factor beta 1–induced transcript 1 [TGFB1I1] interactions Neutrophil cytosolic factor 1 [NCF1] interactions TNF receptor–associated factor 4 [TRAF4] interactions v-yes-1 Yamaguchi sarcoma viral oncogene homolog 1 [YES1] interactions Protein tyrosine kinase 2 beta [PTK2B] interactions FYN oncogene related to SRC, FGR, YES [FYN] interactions v-yes-1 Yamaguchi sarcoma viral–related oncogene homologue [LYN] interactions Calmodulin 1 (phosphorylase kinase, delta) [CALM1] interactions
6.996E4
Gene ID
5. 6.
9.095E4 1.119E3 7.
1.189E3 1.399E3
8.
2.659E3
9.
3.218E3 3.918E3
10.
3.988E3
11.
6.017E3
12.
1.385E2 1.455E2 1.931E2
Note: Functional annotation was carried out with the use of enrichment (negative binomial) test; a P value of %.05 was considered to be significant. The P value of each annotation of the respective test gene was derived with the use of random sampling of the whole genome.
13.
14.
15.
Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
16.
A limitation of our study is that we did not evaluate the protein concentrations of the platelet collagen-signaling receptor GP6 against its variants. This is an outstanding point of uncertainty and will need to be addressed in future studies to fully elucidate the role of GP6 in the pathophysiology of RM. Correlation of the variant risk alleles with blood levels of GP6 in women with RM may be informative. The pharmacologic interventions that alter these concentrations could be examined for their role in risk prediction of RM. In conclusion, our study has found an association of GP6 SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662, with increased risk of RM. In silico analysis revealed quite significant observations that demonstrated the influence of GP6 in biologic pathways, molecular function, including collagen binding, and gene-gene interaction in the human genome. In the future, more studies should be undertaken to evaluate to the association of GP6 SNPs with RM.
REFERENCES 1. 2.
Branch DW, Gibson M, Silver RM. Clinical practice. Recurrent miscarriage. N Engl J Med 2010;363:1740–7. Bricker L, Farquharson RG. Types of pregnancy loss in recurrent miscarriage: implications for research and clinical practice. Hum Reprod 2002; 17:1345–50.
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24. 25.
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Christiansen OB, Steffensen R, Nielsen HS, Varming K. Multifactorial etiology of recurrent miscarriage and its scientific and clinical implications. Gynecol Obstet Invest 2008;66:257–67. Tang AW, Quenby S. Recent thoughts on management and prevention of recurrent early pregnancy loss. Curr Opin Obstet Gynecol 2010;22:446–51. Rull K, Nagirnaja L, Laan M. Genetics of recurrent miscarriage: challenges, current knowledge, future directions. Front Genet 2012;3:34. Bick RL. DRW Metroplex Recurrent Miscarriage Syndrome Cooperative Group. Recurrent miscarriage syndrome due to blood coagulation protein/ platelet defects: prevalence, treatment and outcome results. Clin Appl Thromb Hemost 2000;6:115–25. Bick RL, Hoppensteadt D. Recurrent miscarriage syndrome and infertility due to blood coagulation protein/platelet defects: a review and update. Clin Appl Thromb Hemost 2005;11:1–13. Varga-Szabo D, Pleines I, Nieswandt B. Cell adhesion mechanisms in platelets. Arterioscler Thromb Vasc Biol 2008;28:403–12. Jandrot-Perrus M, Busfield S, Lagrue AH, Xiong X, Debili N, Chickering T, et al. Cloning, characterization, and functional studies of human and mouse glycoprotein VI: a platelet-specific collagen receptor from the immunoglobulin superfamily. Blood 2000;96:1798–807. de Gaetano G. Historical overview of the role of platelets in hemostasis and thrombosis. Haematologica 2001;86:349–56. Clemetson KJ, Clemetson JM. Platelet collagen receptors. Thromb Haemost 2001;86:189–97. Mete Ural U, Bayoglu Tekin Y, Balik G, Kir Sahin F, Colak S. Could platelet distribution width be a predictive marker for unexplained recurrent miscarriage? Arch Gynecol Obstet 2014;290:233–6. Sokol J, Biringer K, Skerenova M, Hasko M, Bartosova L, Stasko J, et al. Platelet aggregation abnormalities in patients with fetal losses: the GP6 gene polymorphism. Fertil Steril 2012;98:1170–4. Johnson AD, Yanek LR, Chen MH, Faraday N, Larson MG, Tofler G, et al. Genome-wide meta-analyses identifies seven loci associated with platelet aggregation in response to agonists. Nat Genet 2010;42:608–13. Yee DL, Bray PF. Clinical and functional consequences of platelet membrane glycoprotein polymorphisms. Semin Thromb Hemost 2004;30:591–600. Joutsi-Korhonen L, Smethurst PA, Rankin A, Gray E, IJsseldijk M, Onley CM, et al. The low-frequency allele of the platelet collagen signaling receptor glycoprotein VI is associated with reduced functional responses and expression. Blood 2003;101:4372–9. de Bakker PI, Yelensky R, Pe’er I, Gabriel SB, Daly MJ, Altshuler D. Efficiency and power in genetic association studies. Nat Genet 2005;37:1217–23. Chen J, Xu H, Aronow BJ, Jegga AG. Improved human disease candidate gene prioritization using mouse phenotype. BMC Bioinformatics 2007; 8:392. Kramer A, Green J, Pollard J Jr, Tugendreich S. Causal analysis approaches in Ingenuity pathway analysis. Bioinformatics 2014;30:523–30. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498–504. Furihata K, Kunicki TJ. Characterization of human glycoprotein VI gene 50 regulatory and promoter regions. Arterioscler Thromb Vasc Biol 2002;22: 1733–9. Kotulicova D, Chudy P, Skerenova M, Ivankova J, Dobrotova M, Kubisz P. Variability of GP6 gene in patients with sticky platelet syndrome and deep venous thrombosis and/or pulmonary embolism. Blood Coagul Fibrinolysis 2012;23:543–7. Genomes Project C, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012;491:56–65. Greer IA. Thrombophilia: implications for pregnancy outcome. Thromb Res 2003;109:73–81. Heemskerk JW, Siljander PR, Bevers EM, Farndale RW, Lindhout T. Receptors and signalling mechanisms in the procoagulant response of platelets. Platelets 2000;11:301–6. Beyan C, Kaptan K, Ifran A. Platelet aggregation abnormalities in patients with recurrent fetal losses. Thromb Res 2007;121:327–31.
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SUPPLEMENTAL FIGURE 1
Illustration of significant terms (gene count and P value) for gene-gene interaction. Functional annotation was carried out with the use of enrichment (negative binomial) test; a P value of %.05 was considered to be significant. The P value of each annotation of the respective test gene was derived with the use of random sampling of the whole genome. Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
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SUPPLEMENTAL TABLE 1 Minor allele frequency (MAF) of GP6 SNPs in world population, according to the 1,000 Genomes database. SNP ID rs1671153 rs1654410 rs1654419 rs1613662 a
SNP position on chromosome 19
Major/minor allele
SNP sequence
Global MAFa
55527189 55524813 55535881 55536595
T/G T/C G/A A/G
CCGAACACACACACACATGG[G/T]GAGGCACAATTCCACAGCAT TAAAATTATTTTTCCTCTGT[C/T]TTCTGATAGCATCTCAGTAT TTGGTGCTTCACTCTGAGAC[A/G]CAGGGAATGTTAGACACGCC GCAGAACCTACCTGCTACCG[A/G]GGAAGGTGGTTCTGTTGGTA
0.2383 0.4995 0.2277 0.1309
Considering the 1,092 worldwide individuals from the 1,000 Genome project (22).
Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
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SUPPLEMENTAL TABLE 2 Characteristics of recurrent miscarriage (RM) patients and control subjects. Parameter Mean age (y) Ethnicity Previous miscarriages No. of live births No. of primary aborters No. of secondary aborters Body mass index (kg/m2) Smokersa Alcohol consumersa
RM
Control
28.4 5.9 North Indian Mean 4 (3–7) 0 100% 0 23.3 4.1 None None
31.9 7.3 North Indian 0 >2 NA NA 23.9 3.5 None None
Note: NA ¼ not applicable. a Smoking and alcohol consumption status ever. Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
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Objective: To determine whether platelet-specific collagen receptor glycoprotein VI (GP6) gene variants are associated with recurrent miscarriages (RM). Design: Genetic association study. Setting: Tertiary care referral hospital. Patient(s): A total of 200 women with at least three unexplained spontaneous abortions before 20 weeks of gestation and 300 healthy parous women. Intervention(s): Determination of variants of GP6 single-nucleotide polymorphisms (SNPs) namely; rs1671153, rs1654410, rs1654419, and rs1613662 was based on polymerase chain reaction–restriction fragment-length polymorphism. Main Outcome Measure(s): Genotypes and haplotypes frequencies were compared in RM case subjects versus control subjects. Result(s): We observed significantly higher occurrence of rare alleles of SNPs in GP6, namely, rs1671153, rs1654410, rs1654419, and rs1613662, among RM cases, revealing risk association for fetal losses. The synergistic effects of haplotype combinations were also evaluated and showed that four haplotypes G-T-G-G, T-C-A-A, G-C-G-A, and G-T-A-A were more prevalent among RM cases, revealing increased risk for fetal losses. In silico analysis revealed that GP6 has an impact on biologic pathways and significant influence in collagen binding. Gene-gene interaction network analysis revealed that GP6 consisted of a total of 25 interactions with 13 genes in the human genome. Conclusion(s): These results suggest that variants of GP6 SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662, may be associated with risk of recurrent miscarriage. In silico analyses demonstrated the influence of GP6 in biologic pathways, molecular function, including collagen binding, and gene-gene interaction in the human genome. Use your smartphone (Fertil SterilÒ 2014;102:1078–84. Ó2014 by American Society for Reproductive Medicine.) to scan this QR code Key Words: Recurrent miscarriages, fetal losses, platelets, glycoprotein VI, single-nucleotide and connect to the polymorphism Discuss: You can discuss this article with its authors and other ASRM members at http:// fertstertforum.com/siddesha-glycoprotein-vi-gene-rpl/
R
ecurrent miscarriage (RMOMIM: 614389) classically refers to three or more consecutive losses of clinically recognized pregnancies before the 20th week of gestation. The incidence of RM is estimated to be 1%–3% among couples attempting
pregnancy (1). Current clinical practice includes testing of several factors potentially increasing the risk of RM, e.g., parental chromosomal anomalies, maternal thrombophilias, and endocrine, anatomic, and immunologic disorders (1–4). Approximately 50% of
Received April 27, 2014; revised May 27, 2014; accepted July 1, 2014; published online July 30, 2014. A.S. has nothing to disclose. F.P. has nothing to disclose. M.K.M. has nothing to disclose. S.R.P. has nothing to disclose. S.A. has nothing to disclose. The first three authors contributed equally to this work. Supported by the Indian Council of Medical Research, Government of India, New Delhi, India [63/8/ 2010-BMS]. Reprint requests: Prof. Suraksha Agrawal, Ph.D., Department of Medical Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow, Uttar Pradesh 226014, India (E-mail: [email protected]). Fertility and Sterility® Vol. 102, No. 4, October 2014 0015-0282/$36.00 Copyright ©2014 American Society for Reproductive Medicine, Published by Elsevier Inc. http://dx.doi.org/10.1016/j.fertnstert.2014.07.002 1078
discussion forum for this article now.*
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RM cases have no deviations in the appropriate diagnostic test and are considered to be idiopathic in nature (5). In addition to clinical, environmental, and lifestyle risk factors, there is growing evidence that RM also involves genetic susceptibility, although the exact genetic cause of RM has yet to be identified. It has been suggested that platelets have a major role in patients with RM (6, 7). Glycoprotein VI (GP6) is a crucial platelet membrane glycoprotein for adequate platelet activation, adhesion, and aggregation (8). Human GP6 (Gene ID: 51206; MIM no.: 605546) maps to band q13.4 of VOL. 102 NO. 4 / OCTOBER 2014
Fertility and Sterility® human chromosome 19 with a nucleotide size of 24.56 kb and is composed of 8 exons. It is a member of the immunoglobulin superfamily with a 58-kDa platelet transmembrane glycoprotein consisting of 319 amino acids, located in the platelet membrane in noncovalent complex with FcRg subunit (9). Platelets are essential for primary hemostasis, but they also play a key role in atherogenesis and thrombus formation (10). Platelet activation and increased production of thromboxane and decreased sensitivity to antiaggregation effects of prostacyclin increase the prothrombotic state of pregnancy. The damage of blood vessel wall exposes the subendothelial component collagen to platelets in the blood flow. Interaction of platelets with collagen via the GP6 receptor results in platelet activation and adhesion, processes that are essential for thrombus formation. In vitro and in vivo studies have shown that GP6 is essential for activation of integrin for stable adhesion and subsequent signal transduction (via activation of phosphatidylinositol-3-kinase and phospholipase Cg2), which leads to granule release, activation of GPIIb/IIIa via inside-out signaling, and platelet aggregation (11). Previously, it has been shown that the patients with unexplained RM have significantly increased platelet aggregation (12). Recently, numerous polymorphisms in the GP6 gene affecting the platelet aggregation have been identified (13). The importance of variability of the GP6 gene sequences in platelet aggregation was identified by a recent genome-wide meta-analysis by Johnson et al. (14). Considering the critical role of GP6 in collagen-initiated signal transduction and platelet procoagulant activity, the observed variations in GP6 content may influence the risk for recurrent miscarriages. The precise mechanisms regulating GP6 gene molecular variations and aggregation function are largely unknown; however, few studies have suggested that genetic alteration in GP6 may influence receptor density and platelet function (15, 16). Approximately 55% of RM is caused by procoagulant defects which induce thrombosis and infarction of placental vessels (6). To the best our knowledge, to date only one study has evaluated the association of GP6 variants with fetal losses in sticky platelet syndrome patients among the northwestern population of Czechoslovakia (13). There is no such study reported in the northern Indian population. In the present study, we tested the hypothesis that single-nucleotide polymorphisms (SNPs) in GP6 may modulate its expression and molecular function and thereby may influence the risk of RM. The selection of tagged SNPs was based on the Hapmap database (http://hapmap.ncbi.nlm. nih.gov/index.html.en) with a minor allele frequency R1%. Four SNPs in GP6 were selected because of the evidence from published literature illustrating their potential role in thrombosis, RM, or sticky platelet syndrome. The aim of the present study was to evaluate the association of these SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662 with rRM among a northern Indian population.
Institute of Medical Sciences, Lucknow, Uttar Pradesh, India, for the evaluation of RM. All of the subjects were registered from April 2011 to March 2013. RM cases were primary aborters with a history of at least three consecutive miscarriages. The inclusion and exclusion criteria for the selection of RM case subjects and control subjects are described below. All the cases were screened for various known causes of miscarriages with appropriate investigations, including karyotypes of the couple, day 21 progesterone levels, antiphospholipid antibodies including lupus anticoagulant (PLR 0.8–1.05) and anticardiolipin antibodies 0–12 IgG anticardiolipin units, 0–5 IgM anticardiolipin units, and prothrombotic risk factors including factor V Leiden and prothrombin mutations, prolactin level, glycemic curve, and thyroid hormone levels. Uterine causes and cervical incompetence were ruled out by history, ultrasonography, and appropriate investigations. A well designed study protocol was used to record the detailed clinical information and pregnancy history of both RM and control subjects before inclusion in this study. Of the 516 initially screened individuals, 38.75% (n ¼ 200) had unknown cause of RM and were included in this study. Among these 200 women with unknown cause of RM, 96.5% (n ¼ 193) had three to five pregnancy losses, and 3.5% (n ¼ 7) had six or more pregnancy losses. The control group consisted of 300 healthy parous women with the same ethnic distribution as the RM cases with at least two live births and no history of miscarriage, preeclampsia, ectopic pregnancy, or preterm delivery. All the subjects included in this study lived in the Uttar Pradesh state of northern India and fell within the same linguistic group (Indo-European speakers), which indicates the homogeneity of the sample, i.e., case and control subjects were of the same ethnicity. This study was approved by the Ethics Committee of our institute and performed according to the standards laid down by the Declaration of Helsinki for medical research involving human subjects. Written informed consent to participate in this study was obtained from each of the participants.
Selection of SNPs in GP6 Gene Region The online Tagger software was used for tagged SNP selection with the pairwise tagging algorithm r2 R0.8 and minor allele frequency (MAF) R1% (17). The choice of this MAF served to strike a balance for the following factors: 1) the number of SNPs to be genotyped; 2) the available sample size; and 3) a reasonable power of the study (R80%). Furthermore, SNPs in the GP6 region were selected for which evidence has been published to illustrate their potential role in thrombosis, RM, or sticky platelet syndrome. Accordingly, four SNPs in the GP6 region, namely, rs1671153, rs1654410, rs1654419, and rs1613662, were selected in the present study. Details of the SNPs under investigation in this study are provided in Supplemental Table 1 (available online at www.fertstert.org.
MATERIALS AND METHODS Study Population and Inclusion/Exclusion Criteria
Blood Collection, DNA Extraction, and Genotyping
All of the samples were collected from cases attending the outpatient department of Sanjay Gandhi Post Graduate
Blood samples (5 mL) from control subjects, RM women, and male partners of case and control subjects were collected in
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ORIGINAL ARTICLE: EARLY PREGNANCY EDTA-coated collection vials. Genomic DNA was obtained with the use of Qiagen DNA extraction kits. The four SNPs in GP6, namely, rs1671153, rs1654410, rs1654419, and rs1613662, were genotyped with the use of polymerase chain reaction (PCR) followed by restriction fragment–length polymorphism analysis assay with the use of primers and restriction enzyme as described earlier (13). The amplification reaction was carried out with the use of a thermal cycler (PTC 200 Thermal Cycler; Bio Rad) with 50 ng genomic DNA in 10 mL PCR buffer (67 mmol/L Tris-HCl, pH 8.8, 16 mmol/L (NH4)2SO4, 2 mmol/L MgCl2, 0.01% Tween-20, and 100 mmol/L dNTP) containing 10 pmol of each primer and 0.5 U Taq DNA polymerase. The digested product was visualized under ultraviolet light and screened by two independent observers. Genotyping was performed without the knowledge of the subject's case or control status. For each assay, control DNA samples with known genotypes (positive control) and nontemplate (water) as a negative control were included in each typing run to ensure that genotypes were called correctly throughout the study. Two researchers independently read the gel pictures and performed repeated assays if they did not reach a consensus on the tested genotype.
Statistical Analysis The sample size for both RM case subjects and control subjects were calculated with the use of Quanto (version 1.2; http://hy dra.usc.edu/gxe) under the guidance of a statistician. The power of the study was 80% with type 1 error a ¼ 0.05. Allele and genotype frequencies, heterozygosities, and likelihood ratios were tested for Hardy-Weinberg equilibrium with the use of Popgen version 16 (www.ualberta.ca/fyeh/popge ne_download.html). A correlation test was used to evaluate the degree of correlation between the incidence of RM and the GP6 genotypes of the RM women. Genotype associations were analyzed with the use of an additive model (minor-allele homozygous and heterozygous individually with major-allele homozygous), dominant model (minor-allele homozygous and heterozygous taken together compared with majorallele homozygous), and recessive model (comparing minorallele homozygous with major-allele homozygous and heterozygous taken together). The differences in the genotype, allele, and haplotype distributions between the RM and control groups were tested for significance with the use of c2 test or Fisher exact test. The allelic frequencies were computed in a single strand of measured DNA. Haplotypes were generated for four SNPs of GP6 (rs1671153, rs1654410, rs1654419, rs1613662) from multilocus diploid data based on a Gibbs sampling strategy with the use of the Arlequin v3.5 software package. To minimize the possibility of spurious association or chance findings, P values were corrected for the number of comparisons (Pcorr) with the use of the Bonferroni inequality method (Pcorr ¼ 1 [1 P]n, where n is the number of comparisons). The magnitude of the effect was estimated by odds ratio (OR) and its 95% confidence interval (CI). Statistical analysis was performed with the use of SPSS version 20.0 for Windows. Statistical significance was considered to be when the P value was %.05. 1080
In Silico Analysis of Biological Pathways, Molecular Functions, and Gene-gene Interaction Network of GP6 ‘‘In silico’’ is an expression used to mean ‘‘performed on computer or via computer simulation,’’ which was used for the biologic pathways, molecular functions, and gene-gene interaction network analysis. Biologic pathways and molecular functions were identified with the use of Toppgene (18) and Ingenuity (19) pathway analysis software. Gene-gene (protein-protein) interaction network of GP6 recognized by Toppgene (18) were graphically represented with the use of Cytoscope (20). Functional annotation was carried out with the use of enrichment (negative binomial) test, a P value of %.05 being considered to be significant. The P value of each annotation of the respective test gene is derived through random sampling of the whole genome. The protein-protein interaction network–based candidate gene (GP6) prioritization was carried out on the basis of the K-Step Markov method (extended versions of the Pagerank and HITS algorithms).
RESULTS Demographic Profile and Clinical Features of Case and Control Subjects Two hundred RM and 300 fertile control women were screened for the evaluation of four SNPs in the locus of GP6. The demographic profiles of both case and control subjects are shown in Supplemental Table 2 (available online at www.fertstert.org). The control subjects were age and ethnically matched with the case subjects.
Distribution of GP6 Gene Variants Different alleles and their genotypes, in recessive, dominant, and additive models for the GP6 SNPs under study, namely, rs1671153, rs1654410, rs1654419, and rs1613662, were investigated (Table 1). Studied GP6 SNPs were in HardyWeinberg equilibrium in both RM case subjects and control subjects. We observed twofold increased risk for rs1671153 and rs1613662 SNPs under a dominant model in RM cases. In additive, dominant, and recessive models, rs1654410 SNP revealed nearly twofold risk, whereas under additive and recessive models, risk association for rs1654419 SNP ranged from almost threefold to twofold, respectively, among RM cases. Furthermore, the risk association for fetal losses ranged from 1.36- to 1.54-fold for minor allele carrier RM women compared with control women for GP6 SNPs rs1671153, rs1654410, rs1654419, and rs1613662 (Table 1).
Haplotype Combination Analysis The combined analysis was performed to evaluate the synergistic effect of all the possible haplotype combinations for rs1671153, rs1654410, rs1654419, rs1613662 SNPs (Table 2). We observed sixteen haplotypes among RM cases and controls (Table 2). Increased risk was seen for RM cases against four haplotypes viz. G-T-G-G (OR 2.55, 95% CI 1.29–5.02; P¼ .0061), T-C-A-A (OR 3.03, 95% CI 1.56–5.85; P¼ .0009), G-C-G-A (OR 1.60, 95% CI 1.06–2.42; P¼ .0256) VOL. 102 NO. 4 / OCTOBER 2014
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TABLE 1 Distribution of glycoprotein VI gene variants among recurrent miscarriage (RM) case subjects and control subjects, n (%). Genotype dbSNP ID rs1671153T>G Genotype frequency TT TG (additive model) GG (additive model) GG þ TG vs. TT (dominant model) GG vs. TG þ TT (recessive model) Allele frequency T G dbSNP ID rs1654410T>C Genotype frequency TT TC (additive model) CC (additive model) CC þ TC vs. TT (dominant model) CC vs. TC þ TT (recessive model) Allele frequency T C dbSNP ID rs1654419G>A Genotype frequency GG GA (additive model) AA (additive model) AA þ GA vs. GG (dominant model) AA vs. GA þ GG (recessive model) Allele frequency G A dbSNP ID rs1613662A>G Genotype frequency AA AG (additive model) GG (additive model) GG þ AG vs. AA (dominant model) GG vs. AG þ AA (recessive model) Allele frequency A G
RM (n [ 200)
Control (n [ 300)
P value
OR (95% CI)
74 (32.0%) 105 (52.5%) 21 (10.5%)
141 (47.0%) 139 (46.3%) 20 (6.4%)
1 .0645 .0521 .0274a .1364
1.43 (0.98–2.10) 2.00 (1.02–3.92) 1.51 (1.04–2.17) 1.64 (0.86–3.11)
253 (63.3%) 147 (36.7%)
421 (70.2%) 179 (29.8%)
.0232b .0232a
0.73 (0.55–0.95) 1.36 (1.04–1.78)
40 (20.0%) 92 (46.0%) 68 (34.0%)
85 (28.3%) 146 (48.6%) 69 (23.0%)
1 .2509 .0040a .0357a .0078a
1.33 (0.84–2.11) 2.09 (1.26–3.46) 1.58 (1.03–2.42) 1.72 (1.15–2.56)
172 (43.0%) 228 (57.0%)
316 (52.7%) 284 (47.3%)
.0030b .0030a
0.67 (0.52–0.87) 1.47 (1.14–1.90)
96 (48.0%) 82 (41.0%) 22 (11.0%)
168 (56.0%) 117 (39.0%) 15 (5.0%)
1 .2907 .0110a .0831 .0145a
1.22 (0.84–1.78) 2.56 (1.27–5.18) 1.37 (0.96–1.97) 2.34 (1.18–4.64)
274 (68.5%) 126 (31.5%)
453 (75.5%) 147 (24.5%)
.0168b .0168a
0.70 (0.53–0.93) 1.41 (1.07–1.87)
124 (62.0%) 61 (30.5%) 15 (7.5%)
216 (72.0%) 72 (24.0%) 12 (4.0%)
1 .0764 .0629 .0242a .1068
1.47 (0.98–2.21) 2.17 (0.98–4.81) 1.57 (1.07–2.30) 1.94 (0.89–4.25)
309 (77.3%) 91 (22.7%)
504 (84.0%) 96 (16.0%)
.0081b .0081a
0.64 (0.46–0.89) 1.54 (1.12–2.12)
Note: The difference in genotype/allele frequencies between the case and control groups were analyzed for statistical significance at the 95% confidence interval (CI) with the use of c2 test or Fisher exact test with Bonferroni correction under additive, recessive, and dominant models of inheritance. Odds ratios (ORs) were calculated and reported with 95% confidence limits. Additive model: comparing mutant homozygous and heterozygous genotypes individually with wild-type homozygous genotypes; recessive model: comparing mutant homozygous genotype with wild-type homozygous and heterozygous genotype taken together; dominant model: mutant homozygous and heterozygous genotype taken together compared with wild-type homozygous genotype. a Statistically significant risk-associated genotypes/alleles for RM. b Statistically significant protective alleles for RM. Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
and G-T-A-A (OR 2.68, 95% CI 1.11–6.46; P¼ .0274). Interestingly, wild-type haplotype combinations T-T-G-A (OR 0.44, 95% CI 0.26–0.76; P¼ .0037) revealed a protective effect among RM cases.
Impact of GP6 on Biologic Pathway, Molecular Functions, and Gene-gene Interaction Network in the Human Genome To see the relevance of our results in terms of biologic pathway, molecular functions and gene-gene interaction network we used an in silico approach as described in the Materials and Methods section with the use of Toppgene (18) and Ingenuity (19), pathway analysis software and Cytoscope (20). The detailed description of biologic pathways VOL. 102 NO. 4 / OCTOBER 2014
is available in the Biosystems databases (www.ncbi.nlm.nih. gov/biosystems) with unique Biosystems IDs (BSIDs). In silico analysis revealed the following top-ranked biologic pathways that are significantly associated with GP6: 1) platelet adhesion to exposed collagen: BSID 106030 (P¼1.21E3); 2) GP6-mediated activation cascade: BSID 106036 (P¼2.98E3); 3) ECM-receptor interaction: BSID 83068 (P¼8.01E3); 4) cell surface interactions at the vascular wall: BSID 106062 (P¼9.13E3); and 5) platelet activation, signaling, and aggregation: BSID 106034 (P¼1.99E2). Molecular function of GP6 included collagen binding (P¼3.01E3). Gene-gene interaction network analysis revealed that the GP6 gene consists of a total of 25 interactions with 13 other genes in the human genome (Fig. 1). The functional annotation of gene-gene interaction is presented in 1081
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TABLE 2 Combined haplotype analysis of glycoprotein VI SNPs dbSNP ID rs1671153T>G, dbSNP ID rs1654410T>C, dbSNP ID rs1654419G>A, and dbSNP ID rs1613662A>G among recurrent miscarriage (RM) case subjects and control subjects, n (%). Haplotype
RM (n [ 400)
Control (n [ 600)
TCGA GTGG TTGA GCAA TCAA GCGA TTAA GTAA G CG G TCAG GTGA TCGG TTGG GTAG GCAG TTAG
89 (22.3%) 137 (22.8%) 23 (5.7%) 14 (2.3%) 40 (10.0%) 111 (18.5%) 5 (1.3%) 10 (1.7%) 27 (6.7%) 14 (2.3%) 52 (13.0%) 51 (8.5%) 54 (13.5%) 101 (16.8%) 14 (3.5%) 8 (1.3%) 22 (5.5%) 22 (3.7%) 15 (3.7%) 12 (2.0%) 28 (7.0%) 72 (12.0%) 17 (4.2%) 37 (6.2%) 3 (0.7%) 9 (1.5%) 2 (0.5%) 1 (0.16%) 1 (0.25%) 1 (0.16%) 8 (2.0%) –
OR
95% CI
P value
0.96 2.55 0.48 0.74 3.03 1.60 0.77 2.68 1.52 1.90 0.55 0.67 0.49
0.71–1.31 1.29–5.02 0.33–0.72 0.25–2.20 1.56–5.85 1.06–2.42 0.53–1.10 1.11–6.46 0.83–2.81 0.88–4.12 0.34–0.87 0.37–1.21 0.13–1.84
.8774 .0061a .0002b .7918 .0009a .0256a .1808 .0274a .2075 .1116 .0098b .2018 .3806
Note: Haplotypes were generated from multilocus diploid data based on a Gibbs sampling strategy (Arlequin v3.5). The difference in haplotype frequencies between the case and control groups for the prevalent haplotypes (prevalence >5% in either case or control subjects) was analyzed for statistical significance with the use of c2 test or Fisher exact test with Bonferroni correction, and odds ratios (ORs) are reported with 95% confidence limits. a Statistically significant risk-associated haplotype combination. b Statistically significant protective haplotype combination. Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
Table 3. The significant terms (gene count and P value) for gene-gene interaction are shown in Supplemental Figure 1.
DISCUSSION Recurrent miscarriages are characterized by defective placentation and microthrombi in the placental vasculature. Thrombophilic disorders resulting in microthrombi in placental vasculature are considered to be an important cause of RM. It has been suggested that platelets are essential for primary hemostasis as well as play a crucial role in atherogenesis and thrombus formation (10). GP6 is a platelet transmembrane glycoprotein that plays a significant role in collagen-initiated signal transduction and platelet procoagulant activity; therefore, the observed variation in the GP6 gene region may influence risk for thromboembolic disorders (21). There is growing evidence implicating congenital and acquired thrombophilia in the pathophysiologic processes underlying RM. Keeping these facts in mind, the present study was designed to investigate the impact of four GP6 SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662, in RM cases from a northern Indian population. We observed significantly higher occurrence of mutant genotypes of GP6 SNPs, namely rs1671153, rs1654410, rs1654419, and rs1613662, in RM cases, suggesting a risk association for fetal losses (Table 1). Earlier studies have shown risk associations of mutant genotypes at rs1671153, rs1654419, and rs1613662 SNPs with thrombotic disorders (22), which is in concordance with our findings of high prevalence in RM cases. Sokol et al. (13) have shown a significantly higher frequency of mutant genotype GG of 1082
rs1671153 SNP in sticky platelet syndrome patients with fetal losses. They also observed higher frequencies of mutant genotypes of rs1654419 and rs1613662 SNPs, but the differences were not statistically significant (13). This may be due to the small sample size (n ¼ 27) because of the rarity of sticky platelet syndrome, which is an autosomal dominant disorder associated with arterial and venous thromboembolic events. It is the second most common thrombophilia that causes recurrent spontaneous abortions or fetal loss syndrome (6, 7). We have conducted a study on GP6 on a large cohort of well characterized 200 RM case subjects and 300 healthy parous women. Increased occurrence of minor allele carriers for GP6 SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662, was seen in RM women compared with control women. We have made an attempt to evaluate the synergistic effect of these SNPs and found that four haplotypes, G-T-G-G, T-C-A-A, G-C-G-A, and G-T-A-A, were more prevalent among RM case subjects, revealing increased risk for fetal loss. It is already known that there exists an association of GP6 with platelet procoagulant activity, and the variation in GP6 gene content may contribute to risk of hemorrhagic or thromboembolic disorders (22), which are also well established factors in repeated miscarriages. To date, to the best of our knowledge, there is only one report on sticky platelet syndrome patients with fetal losses and there is no earlier study reporting on GP6 in recurrent miscarriage, which makes our study more relevant. The distribution of global MAF (Supplemental Table 1) of GP6 SNPs (according to the 1,000 Genomes database) has been included to demonstrate the relevance of this study in its application to different populations and to know the MAF data for this gene in other world populations (23). This information was provided to illustrate the importance of ethnic differences from the global perspective. The influence of GP6 in biologic pathways, molecular function, and interaction with other genes in the human genome is not yet very clear. The variation in GP6 gene content may influence the expression of this gene and directly or indirectly influence the functions of other genes, which are in gene-gene (at protein level) interaction pathways as illustrated in Figure 1. Therefore, we carried out an in silico analysis to determine the relevance of GP6 on biologic pathways, molecular functions, and the gene-gene interaction network. The results revealed that the GP6 gene has an impact on biologic pathways, namely, platelet adhesion to exposed collagen, GP6-mediated activation cascade, ECM-receptor interaction, cell surface interactions at the vascular wall, and platelet activation, signaling, and aggregation. It also has significant influence in collagen binding. Gene-gene interaction network analysis revealed that GP6 consists of a total of 25 interactions with 13 neighboring genes (Fig. 1). The functional annotation of gene-gene interaction is presented in Table 3. Gene counts and P values for gene-gene interaction are shown in Supplemental Figure 1. Pregnancy itself is notably a hypercoagulable state, owing at least in part to the physiologic changes in the coagulation and fibrinolytic systems; this has the potential for interaction with an acquired or heritable thrombophilia to cause adverse experiences (24). Platelet response to collagen is a primary event in hemostasis and thrombosis VOL. 102 NO. 4 / OCTOBER 2014
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FIGURE 1
Gene-gene (protein-protein) interaction network analysis consists of a total of 25 interactions with 13 neighboring genes in the human genome. The protein-protein interaction network–based candidate gene (glycoprotein VI [GP6]) prioritization was carried out on the basis of the K-Step Markov method (extended versions of the Pagerank and HITS algorithms). Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
but the molecular events that underlie these responses remain poorly understood. The receptor GP6 has a major role in collagen-induced platelet signaling (25). Therefore, the observed variations in GP6 may influence platelet reactivity toward collagen and therefore influence platelet function and the risk of fetal losses among women of reproductive age. Recently, a genome-wide meta-analysis carried out by Johnson et al. (14) highlighted the importance of gene variability of the GP6 gene on platelet aggregation. Johnson et al. (14) focused on the assessment of the genetic influence on platelet functions and recognized seven loci linked with platelet aggregation to physiologic agonists (ADP, collagen, and EPI). One of the loci within the region of the GP6 gene was strongly linked with increased aggregation to collagen (14). Furthermore, it has been shown that patients with unexplained RM have significantly increased platelet aggregation (12). This provides an attractive explanation to the findings of our study about the observed increased risk for fetal losses among minor allele carrier women for GP6 SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662. Taken together, it may be concluded that RM women with minor allele carrier status for GP6 SNPs may have increased platelet aggregation, which may contribute to increased risk of RM. VOL. 102 NO. 4 / OCTOBER 2014
However, this needs further validation in other ethnic groups. This is the first study from northern India and only the second study that has found an association of GP6 SNPs with RM. It must be seen to be a preliminary study, consisting of important observations, and needs further studies in an independent cohort to confirm the findings. The role of platelet hyperaggregability has not been well demonstrated as a possible risk factor for RM. It has been shown that increased platelet aggregation is a contributing factor for RM (12), decreased platelet aggregation has also been reported in RM cases (6), and no correlation between platelet hyperaggregability and RM has been seen (26). To date, the role of platelet hyperaggregability has not been elucidated in the RM population. Therefore, the current clinical practice at our center does not include the platelet hyperaggregability test in pregnancy. It has been reported that GP6 is associated with platelet procoagulant activity, and the variation in GP6 gene sequences may contribute to risk of thromboembolic disorders (22). Furthermore, it has been observed that the platelet procoagulant activity influences the incidence of RM (12). A future area of research would be to evaluate whether any of the SNPs under investigation in our study are involved in the alteration in the ability of GP6 to bind to collagen. 1083
ORIGINAL ARTICLE: EARLY PREGNANCY 3.
TABLE 3 Functional annotation of gene-gene interactions.
2207 51206 2214 2212 1401 7041 653361 9618 7525 2185 2534 4067 801
4.
Gene name
P value
Fc fragment of IgE, high-affinity I, receptor for; gamma polypeptide [FCER1G] interactions Glycoprotein IV (GP6) interactions Fc fragment of IgG, low-affinity IIIa, receptor (CD16a) [FCGR3A] interactions Fc fragment of IgG, low-affinity IIa, receptor (CD32) [FCGR2A] interactions C-reactive protein, pentraxin-related [CRP] interactions Transforming growth factor beta 1–induced transcript 1 [TGFB1I1] interactions Neutrophil cytosolic factor 1 [NCF1] interactions TNF receptor–associated factor 4 [TRAF4] interactions v-yes-1 Yamaguchi sarcoma viral oncogene homolog 1 [YES1] interactions Protein tyrosine kinase 2 beta [PTK2B] interactions FYN oncogene related to SRC, FGR, YES [FYN] interactions v-yes-1 Yamaguchi sarcoma viral–related oncogene homologue [LYN] interactions Calmodulin 1 (phosphorylase kinase, delta) [CALM1] interactions
6.996E4
Gene ID
5. 6.
9.095E4 1.119E3 7.
1.189E3 1.399E3
8.
2.659E3
9.
3.218E3 3.918E3
10.
3.988E3
11.
6.017E3
12.
1.385E2 1.455E2 1.931E2
Note: Functional annotation was carried out with the use of enrichment (negative binomial) test; a P value of %.05 was considered to be significant. The P value of each annotation of the respective test gene was derived with the use of random sampling of the whole genome.
13.
14.
15.
Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
16.
A limitation of our study is that we did not evaluate the protein concentrations of the platelet collagen-signaling receptor GP6 against its variants. This is an outstanding point of uncertainty and will need to be addressed in future studies to fully elucidate the role of GP6 in the pathophysiology of RM. Correlation of the variant risk alleles with blood levels of GP6 in women with RM may be informative. The pharmacologic interventions that alter these concentrations could be examined for their role in risk prediction of RM. In conclusion, our study has found an association of GP6 SNPs, namely, rs1671153, rs1654410, rs1654419, and rs1613662, with increased risk of RM. In silico analysis revealed quite significant observations that demonstrated the influence of GP6 in biologic pathways, molecular function, including collagen binding, and gene-gene interaction in the human genome. In the future, more studies should be undertaken to evaluate to the association of GP6 SNPs with RM.
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Branch DW, Gibson M, Silver RM. Clinical practice. Recurrent miscarriage. N Engl J Med 2010;363:1740–7. Bricker L, Farquharson RG. Types of pregnancy loss in recurrent miscarriage: implications for research and clinical practice. Hum Reprod 2002; 17:1345–50.
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Christiansen OB, Steffensen R, Nielsen HS, Varming K. Multifactorial etiology of recurrent miscarriage and its scientific and clinical implications. Gynecol Obstet Invest 2008;66:257–67. Tang AW, Quenby S. Recent thoughts on management and prevention of recurrent early pregnancy loss. Curr Opin Obstet Gynecol 2010;22:446–51. Rull K, Nagirnaja L, Laan M. Genetics of recurrent miscarriage: challenges, current knowledge, future directions. Front Genet 2012;3:34. Bick RL. DRW Metroplex Recurrent Miscarriage Syndrome Cooperative Group. Recurrent miscarriage syndrome due to blood coagulation protein/ platelet defects: prevalence, treatment and outcome results. Clin Appl Thromb Hemost 2000;6:115–25. Bick RL, Hoppensteadt D. Recurrent miscarriage syndrome and infertility due to blood coagulation protein/platelet defects: a review and update. Clin Appl Thromb Hemost 2005;11:1–13. Varga-Szabo D, Pleines I, Nieswandt B. Cell adhesion mechanisms in platelets. Arterioscler Thromb Vasc Biol 2008;28:403–12. Jandrot-Perrus M, Busfield S, Lagrue AH, Xiong X, Debili N, Chickering T, et al. Cloning, characterization, and functional studies of human and mouse glycoprotein VI: a platelet-specific collagen receptor from the immunoglobulin superfamily. Blood 2000;96:1798–807. de Gaetano G. Historical overview of the role of platelets in hemostasis and thrombosis. Haematologica 2001;86:349–56. Clemetson KJ, Clemetson JM. Platelet collagen receptors. Thromb Haemost 2001;86:189–97. Mete Ural U, Bayoglu Tekin Y, Balik G, Kir Sahin F, Colak S. Could platelet distribution width be a predictive marker for unexplained recurrent miscarriage? Arch Gynecol Obstet 2014;290:233–6. Sokol J, Biringer K, Skerenova M, Hasko M, Bartosova L, Stasko J, et al. Platelet aggregation abnormalities in patients with fetal losses: the GP6 gene polymorphism. Fertil Steril 2012;98:1170–4. Johnson AD, Yanek LR, Chen MH, Faraday N, Larson MG, Tofler G, et al. Genome-wide meta-analyses identifies seven loci associated with platelet aggregation in response to agonists. Nat Genet 2010;42:608–13. Yee DL, Bray PF. Clinical and functional consequences of platelet membrane glycoprotein polymorphisms. Semin Thromb Hemost 2004;30:591–600. Joutsi-Korhonen L, Smethurst PA, Rankin A, Gray E, IJsseldijk M, Onley CM, et al. The low-frequency allele of the platelet collagen signaling receptor glycoprotein VI is associated with reduced functional responses and expression. Blood 2003;101:4372–9. de Bakker PI, Yelensky R, Pe’er I, Gabriel SB, Daly MJ, Altshuler D. Efficiency and power in genetic association studies. Nat Genet 2005;37:1217–23. Chen J, Xu H, Aronow BJ, Jegga AG. Improved human disease candidate gene prioritization using mouse phenotype. BMC Bioinformatics 2007; 8:392. Kramer A, Green J, Pollard J Jr, Tugendreich S. Causal analysis approaches in Ingenuity pathway analysis. Bioinformatics 2014;30:523–30. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498–504. Furihata K, Kunicki TJ. Characterization of human glycoprotein VI gene 50 regulatory and promoter regions. Arterioscler Thromb Vasc Biol 2002;22: 1733–9. Kotulicova D, Chudy P, Skerenova M, Ivankova J, Dobrotova M, Kubisz P. Variability of GP6 gene in patients with sticky platelet syndrome and deep venous thrombosis and/or pulmonary embolism. Blood Coagul Fibrinolysis 2012;23:543–7. Genomes Project C, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012;491:56–65. Greer IA. Thrombophilia: implications for pregnancy outcome. Thromb Res 2003;109:73–81. Heemskerk JW, Siljander PR, Bevers EM, Farndale RW, Lindhout T. Receptors and signalling mechanisms in the procoagulant response of platelets. Platelets 2000;11:301–6. Beyan C, Kaptan K, Ifran A. Platelet aggregation abnormalities in patients with recurrent fetal losses. Thromb Res 2007;121:327–31.
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SUPPLEMENTAL FIGURE 1
Illustration of significant terms (gene count and P value) for gene-gene interaction. Functional annotation was carried out with the use of enrichment (negative binomial) test; a P value of %.05 was considered to be significant. The P value of each annotation of the respective test gene was derived with the use of random sampling of the whole genome. Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
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SUPPLEMENTAL TABLE 1 Minor allele frequency (MAF) of GP6 SNPs in world population, according to the 1,000 Genomes database. SNP ID rs1671153 rs1654410 rs1654419 rs1613662 a
SNP position on chromosome 19
Major/minor allele
SNP sequence
Global MAFa
55527189 55524813 55535881 55536595
T/G T/C G/A A/G
CCGAACACACACACACATGG[G/T]GAGGCACAATTCCACAGCAT TAAAATTATTTTTCCTCTGT[C/T]TTCTGATAGCATCTCAGTAT TTGGTGCTTCACTCTGAGAC[A/G]CAGGGAATGTTAGACACGCC GCAGAACCTACCTGCTACCG[A/G]GGAAGGTGGTTCTGTTGGTA
0.2383 0.4995 0.2277 0.1309
Considering the 1,092 worldwide individuals from the 1,000 Genome project (22).
Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
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SUPPLEMENTAL TABLE 2 Characteristics of recurrent miscarriage (RM) patients and control subjects. Parameter Mean age (y) Ethnicity Previous miscarriages No. of live births No. of primary aborters No. of secondary aborters Body mass index (kg/m2) Smokersa Alcohol consumersa
RM
Control
28.4 5.9 North Indian Mean 4 (3–7) 0 100% 0 23.3 4.1 None None
31.9 7.3 North Indian 0 >2 NA NA 23.9 3.5 None None
Note: NA ¼ not applicable. a Smoking and alcohol consumption status ever. Siddesh. Glycoprotein VI gene variants in RM. Fertil Steril 2014.
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