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Keywords:

  • deep vein thrombosis;
  • genetics;
  • risk factors;
  • single nucleotide polymorphism

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Author contributions
  8. Acknowledgements
  9. Disclosure of Conflict of Interests
  10. References
  11. Supporting Information

Summary.  Background : Recent studies have found associations between deep vein thrombosis (DVT) and single nucleotide polymorphisms (SNPs) in a 4q35.2 locus that contains genes encoding factor XI (F11), a cytochrome P450 family member (CYP4V2), and prekallikrein (KLKB1).

Objective : We investigated which of the common SNPs in this locus are independently associated with DVT.

Methods : The study populations were the Leiden Thrombophilia Study (LETS) (443 DVT cases and 453 controls) and the Multiple Environmental and Genetic Assessment of risk factors for venous thrombosis (MEGA study) (2712 DVT cases and 4634 controls). We assessed the association between DVT and 103 SNPs in a 200 kb region using logistic regression.

Results : We found that two SNPs (rs2289252 and rs2036914 in F11) were independently associated with DVT. After adjusting for age, sex, and the other SNP, the odds ratios (risk vs. non-risk homozygotes) of these two SNPs were 1.49 for rs2289252 (95% CI, 1.25–1.76) and 1.33 for rs2036914 (95% CI, 1.11–1.59). We found that rs2289252 was also associated with FXI levels, as has been previously reported for rs2036914; these two SNPs remained associated with DVT with somewhat attenuated risk estimates after adjustment for FXI levels.

Conclusion : Two SNPs, rs2289252 and rs2036914 in F11, appear to independently contribute to the risk of DVT, a contribution that is explained at least in part by an association with FXI levels.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Author contributions
  8. Acknowledgements
  9. Disclosure of Conflict of Interests
  10. References
  11. Supporting Information

Deep vein thrombosis (DVT) is a serious disease that is influenced by both genetic and acquired risk factors. More than 60% of the variation in risk for DVT has been attributed to genetic risk factors on the basis of family and twin studies [1,2]. Two well-characterized genetic risk factors for DVT are the factor V Leiden (FVL) R506Q and the prothrombin G20210A polymorphisms [3,4], which increase the risk of DVT in carriers by 3- to 5-fold [5]. Other genetic variants have been reported to be associated with DVT; however, many of them either are rare, as in the case of mutations leading to deficiencies of protein C, protein S or antithrombin [5], or are not consistently associated with DVT, as in the case of variants of methylenetetrahydrofolate reductase (MTHFR) [6,7] and plasminogen activator inhibitor-1 (PAI-1) [8].

Recent genetic studies of DVT have reported that several common SNPs in the 4q35.2 locus were associated with DVT [6,7,9]. These SNPs were located in three genes. Two of the genes are involved in the intrinsic blood coagulation cascade that encodes factor XI (F11) and prekallikrein (KLKB1). The third gene encodes cytochrome P450, family 4, subfamily V, polypeptide 2 (CYP4V2). This cytochrome P450 family member is thought to be involved in lipid metabolism [10]; however, it is not known to be involved in hemostasis or thrombosis. Previous studies [6,7] of the association between DVT and SNPs in the 4q35.2 locus had several limitations. These studies either investigated a limited number of SNPs in F11 [7], investigated a limited region of the locus [6], used an initial study population that included < 450 cases [6,7], or did not assess the degree to which the observed risk may be mediated by FXI levels [6,7], a coagulation factor that is known to be associated with risk of DVT [3].

Thus, it remains unclear how many SNPs in the 4q35.2 locus are independently associated with DVT and in which genes such SNPs are located. Therefore, we investigated which of the common SNPs in the 4q35.2 region are independently associated with DVT by intensively genotyping a larger region and employing a large study population.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Author contributions
  8. Acknowledgements
  9. Disclosure of Conflict of Interests
  10. References
  11. Supporting Information

Study populations

The Leiden Thrombophilia Study (LETS) [11] included individuals 18–70 years old without cancer: 443 cases with a first confirmed DVT and 453 controls with no history of DVT or pulmonary embolism (PE). The Multiple Environmental and Genetic Assessment of risk factors for venous thrombosis (MEGA) [12] study included individuals aged 18–70 years. We excluded MEGA subjects with a history of cancer or isolated PE and included cases with a first DVT and controls with no history of DVT or PE. For the present analysis, we split the MEGA study into two case–control studies that were based on recruitment date and sample availability (blood or buccal swab): MEGA-1 (1398 cases, 1757 controls) and MEGA-2 (1314 cases, 2877 controls). Participants of the LETS and MEGA studies were primarily Caucasians from northwestern Europe; all individuals provided informed consent. FXI antigen measurements were determined as previously described [6], and genotypes were determined by allele-specific real-time PCR in a core laboratory that was blinded to case and control status [13]; primer sequence information is available upon request.

SNP selection

The region of chromosome 4q35.2 investigated was based on rs13146272, which was previously reported to be associated with DVT [6]. The region included the SNPs in the CEPH (Utah residents with ancestry from northern and western Europe) (CEU) population in HapMap [14] data release 22 that had r2 values > 0.1 for linkage disequilibrium (LD) with rs13146272. This region also includes the SNPs in F11 that have been previously reported to be associated with DVT [6,7]. The resulting ∼ 200 kbp region was delimited by two SNPs, rs4862644 at 187 294 806 bp and rs13150040 at 187 494 774 bp on chromosome 4 (Fig. 1).

image

Figure 1.  Pattern of linkage disequilibrium (LD) in a 197715 bp segment of the chromosome 4q35.2 locus (position 187297059–187494774) based on the genotype data from the 103 SNPs in 2211 control subjects in LETS and MEGA-1 combined. The positions and exon structure of the FAM149A, CYP4V2, KLKB1 and F11 genes are shown above the Haploview plot. Each diamond represents the log odds disequilibrium (LOD) measurement calculated in Haploview, which indicates pairwise strength and significance of LD. Red coloring indicates no or minimal evidence of historical recombination, white indicates weak LD, and blue uninformative LD.

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This 200 kbp region contains 323 SNPs; 190 of these SNPs had allele frequencies > 0.02 in the CEU population [14]. Groups of SNPs that were in high LD with one another were represented by a single SNP in each group – a tagging SNP. Tagging SNPs were selected by pairwise tagging using Tagger [15] as implemented in Haploview [16] using a minimum allele frequency of 0.02 and a pair-wise r2 threshold of 0.8. In addition to tagging SNPs, we also selected missense and nonsense SNPs in CYP4V2, KLKB1 and F11 and also SNPs that were in LD (r2 > 0.2) with SNPs that had been previously reported to be associated with DVT. A total of 103 SNPs were genotyped in LETS and MEGA-1. To reduce the amount of genotyping, only SNPs found to be associated with DVT with a P value < 0.05 in both LETS and MEGA-1 studies after adjustment for the most strongly associated SNP in the region (rs2289252) were genotyped in MEGA-2. Those SNPs that were associated with DVT after adjustment for rs2289252 in MEGA-2 were included in the forward stepwise regression analysis that used genotypes from the LETS, MEGA-1 and MEGA-2 studies.

Statistical analyses

Deviation of genotype distributions from Hardy–Weinberg equilibrium expectations were assessed using an exact test [17]. Linkage disequilibrium (r2) was calculated from unphased genotype data using LDMax in the GOLD package [18]. Univariate association between SNP alleles and DVT was assessed with the chi-squared test in individual studies and the Mantel–Haenszel method was used to combine study results; the Breslow–Day test was used to test the null hypothesis that the odds ratios were homogeneous across multiple studies. Multivariate analysis of the association between SNPs and DVT was conducted using logistic regression models; these models assumed an additive effect of each additional risk allele on the log odds of DVT unless results for the individual genotypes are presented. Correction for 103 multiple comparisons was performed by the method of Bonferroni [19]. Linear regression models were used to estimate the effect of SNPs on FXI levels. Haplotype analysis was performed using the haplo.stats package [20] in the R statistical programming language (http://cran.us.r-project.org/). Population-attributable risk percentages were based on odds ratios from allelic models and population minor allele frequency calculated in controls.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Author contributions
  8. Acknowledgements
  9. Disclosure of Conflict of Interests
  10. References
  11. Supporting Information

In a combined study of LETS and MEGA-1, we investigated the association between DVT and each of the 103 SNPs (Table S1) located in a 200 kbp region containing the CYP4V2, KLKB1 and F11 genes (Fig. 1). The genotype distribution of each SNP did not deviate from Hardy–Weinberg equilibrium expectations (> 0.05). Fifty-four of these SNPs were associated with DVT (< 0.05): the seven SNPs with the lowest P-values were in the F11 gene (Table S2). The most strongly associated SNP, rs2289252, had an OR for DVT of 1.31 (95% CI, 1.20–1.43; = 3 × 10−9); rs3756011, which is in strong LD with rs2289252 (r2 = 0.98), was also associated (OR = 1.30, 95% CI, 1.19–1.42; = 5 × 10−9). Because LD between rs3756011 and rs2289252 was near perfect, further analysis included only rs2289252.

To investigate whether these 54 SNPs were associated with DVT after accounting for the most strongly associated SNP, we tested the association between each SNP and DVT in a model that adjusted for rs2289252 in the combined LETS and MEGA-1 studies. We found that 16 SNPs remained associated with DVT (= 0.046–0.0043) after adjustment for rs2289252. Six of these 16 SNPs were excluded from further analysis because they were in strong LD (r2 > 0.95) with at least one of the other 16 SNPs, leaving 11 SNPs (10 SNPs plus rs2289252) that were associated with DVT after accounting for rs2289252 (Table 1). Six of the 11 SNPs remained associated with DVT after applying a Bonferroni multiple comparison correction (Table 1).

Table 1.   SNPs associated with DVT in the combined LETS, MEGA-1 and MEGA-2 samples
SNPGeneReferenceRiskCaseControlModel 1*Model 2Model 3
OR (95% CI)POR (95% CI)PP
  1. Odds ratio and 95% confidence interval (OR (95% CI)) calculated using the non-risk raising allele as the reference group. *Adjusted for age, sex, and study. Adjusted for age, sex, study, and rs2289252. Adjusted for age, sex, study, rs2289252 and Bonferroni correction.

rs13146272CYP4V2CA68.664.21.22 (1.13–1.30)4 × 10−081.14 (1.06–1.22)0.00040.037
rs3817184CYP4V2CT46.941.71.23 (1.16–1.32)2 × 10−101.23 (1.05–1.21)0.00070.077
rs1053094CYP4V2AT55.049.31.26 (1.17–1.34)6 × 10−121.14 (1.06–1.23)0.00020.022
rs4253236CYP4V2TC67.663.91.18 (1.10–1.26)3 × 10−061.13 (1.05–1.21)0.00060.065
rs3733402KLKB1GA56.851.71.23 (1.16–1.32)3 × 10−101.14 (1.06–1.22)0.00030.027
rs4253302KLKB1GA85.983.71.19 (1.08–1.30)2 × 10−041.12 (1.03–1.23)0.0121.00
rs4253303KLKB1GA44.839.51.24 (1.17–1.33)4 × 10−111.14 (1.06–1.22)0.00040.044
rs2292423KLKB1TA46.841.11.25 (1.18–1.34)6 × 10−121.13 (1.06–1.22)0.00060.064
rs2036914F11TC59.452.11.34 (1.26–1.43)4 × 10−191.20 (1.11–1.31)1 × 10−50.0013
rs4253418F11AG96.695.61.31 (1.10–1.56)2 × 10−031.17 (0.98–1.39)0.0771.00
rs2289252F11CT48.340.61.35 (1.27–1.44)9 × 10−201.35 (1.27–1.44)N/AN/A

We then investigated which of these 11 SNPs were independently associated with DVT by using forward stepwise logistic regression to select associated SNPs in the combined LETS, MEGA-1 and MEGA-2 studies; the Breslow–Day P-value for the 11 SNPs was > 0.14. This selection required a P-value of < 0.05 for retention of a SNP in the model, which included study, sex and age as covariates. The three SNPs selected were: rs2289252 in F11, rs2036914 in F11, and rs13146272 in CYP4V2. These same three SNPs were the most strongly associated when all 11 SNPs were forced into the regression model. The risk estimates for all genotypes of these three SNPs are shown in Table 2. The LD was modest (r2 = 0.38) between the two F11 SNPs and low between either of the F11 SNPs and the CYP4V2 SNP (r2 < 0.12).

Table 2.   SNPs associated with DVT in the combined samples of LETS, MEGA-1 and MEGA-2
SNP (Gene)GenotypeCaseControlModel 1*Model 2
Count%Count%OR (95% CI)POR (95% CI)P
  1. OR (95% CI) indicates odds ratio and 95% confidence interval. *Adjusted for age, sex, and study. Adjusted for age, sex, study, and the other two SNPs.

rs2289252 (F11)TT71623.584917.11.84 (1.62–2.10)1.7 × 10−201.49 (1.25–1.76)3.8 × 10−6
TC151149.6234147.11.41 (1.27–1.57) 1.28 (1.13–1.45)1.0 × 10−4
CC81726.8178535.91.00Reference1.00Reference
rs2036914 (F11)CC108035.1136427.41.84 (1.61–2.10)6.5 × 10−191.33 (1.11–1.59)0.0018
CT149948.7245049.31.42 (1.26–1.61) 1.19 (1.03–1.38)0.019
TT49916.2115923.31.00Reference1.00Reference
rs13146272 (CYP4V2)AA139147.2198641.81.58 (1.29–1.76)2.1 × 10−71.24 (1.05–1.46)0.011
AC126342.8213244.91.28 (1.09–1.49) 1.14 (0.97–1.34)0.095
CC29510.063513.41.00Reference1.00Reference

We also performed an analysis of the haplotypes comprising these 11 SNPs in the combined study of LETS, MEGA-1 and MEGA-2. We found 12 haplotypes with allele frequencies > 2% (Table 3). The most frequent haplotype (haplotype A: 21.9%) contained the risk alleles of all 11 SNPs; thus, we tested the association between DVT and each of the haplotypes using the second highest frequency haplotype (10.6%) as the reference – this reference haplotype included the non-risk alleles of 10 of the 11 SNPs and the risk allele for rs4253418. All five of the haplotypes associated with DVT contained the risk allele of either rs2289252 or rs2036914 (Table 3). Conversely, we found that one haplotype that contained the risk alleles for all SNPs except for the rs2289252 and rs2036914 was not significantly associated with DVT (haplotype C: OR = 1.10, = 0.40; haplotype frequency = 4.1%). Haplotype H, which contains the risk allele of rs13146272 and lacks the risk alleles for rs2289252 and rs2036914, was not associated with DVT (OR = 1.02, = 0.84; haplotype frequency = 9.6%). In addition, we performed an analysis of three, five and seven SNP haplotypes centered around the rs2289252 and rs2036914 SNPs and found that no haplotype had a lower P value or greater odds ratio than either SNP by itself (data not shown).

Table 3.   Haplotype association with DVT in the combined study of LETS, MEGA-1 and MEGA-2 Thumbnail image of

SNPs in the 4q35.2 region have been reported to be associated with FXI levels. We asked if the rs2289252 SNP in F11 was also associated with FXI levels and found that the risk allele of this SNP was associated (Ptrend = 2 × 10−20) with increased FXI levels in LETS and MEGA-1 (Fig. 2). This SNP remained associated with DVT after further adjustment for FXI levels, albeit with attenuated risk estimates (Table 4).

image

Figure 2.  Association of FXI levels (%) with genotypes of rs2289252 in the F11 gene in LETS. The average level of FXI is marked by the horizontal bar (Ptrend = 2.1 × 10−20, = 895).

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Table 4.   Association of three SNPs with DVT after adjustment for factor XI levels in LETS and MEGA-1
SNPGenotypeOR (95% CI)*P*
  1. OR (95% CI) indicates odds ratio and 95% confidence interval. *Adjusted for FXI and study.

rs2289252TT1.39 (1.15–1.69)0.001
CT1.27 (1.09–1.47)0.002
CC1.00Reference
rs2036914CC1.42 (1.19–1.70)< 0.001
CT1.26 (1.07–1.48)0.006
TT1.00Reference
rs13146272AA1.40 (1.14–1.71)0.001
CA1.27 (1.04–1.56)0.018
CC1.00Reference

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Author contributions
  8. Acknowledgements
  9. Disclosure of Conflict of Interests
  10. References
  11. Supporting Information

Previous studies have noted the association between DVT and a number of SNPs in the 4q35.2 locus [6,7,9]. We found that after adjusting for other SNPs and FXI levels, two SNPs in F11 appear to be independently associated with DVT: rs2289252 and rs2036914.

The rs2289252 SNP in F11 showed the strongest association with DVT (= 9 × 10−20) and FXI levels (= 2.1 × 10−20) of any SNP tested in the study. This SNP was associated with DVT after adjustment for the rs2036914 SNP and was selected in a forward stepwise model that included all 11 SNPs. Moreover, haplotypes that contained the risk allele of rs2289252 showed the strongest association with DVT (Table 3: haplotypes A, D and F; OR > 1.45). However, one haplotype that contained the risk alleles of rs2289252 and rs2036914 was not associated with DVT (haplotype I; OR = 1.15, = 0.28, frequency 2.7%). It is possible that this haplotype was not significantly associated with DVT due to chance; alternatively, it is possible that this haplotype is in linkage disequilibrium with a variant that modifies the risk of the rs2289252 and rs2036914 SNPs.

The rs2036914 SNP was associated with DVT after adjustment for the rs2289252 SNP and was selected in a forward stepwise model that included all 11 SNPs. Haplotype analysis supported the observation that this SNP is associated with DVT independently of rs2289252: haplotypes that contained the risk allele of rs2036914 and the non-risk allele of rs2289252 remained associated with DVT, albeit with lower risk estimates (Table 3: haplotypes B and E; OR ≥ 1.22). Haplotypes that did not contain the risk allele for either rs2289252 or rs2036914 were not associated with DVT (Table 3: haplotypes C, G, H, J and K).

Although rs13146272 in CYP4V2 was associated with DVT after adjustment for rs2289252 and rs2036914 and was selected by forward stepwise regression analysis, haplotype analysis suggests that the association between rs13146272 and DVT may be explained by linkage disequilibrium with rs2289252 and rs2036914. None of the haplotypes containing the risk allele of rs13146272 were associated with DVT unless the risk alleles for rs2289252 or rs2036914 were also present. Thus, despite the low LD (r2 ≤ 0.12) between rs13146272 in CYP4V2 and the rs2289252 and rs2036914 SNPs in F11, the association of rs13146272 with both DVT and FXI levels is most likely explained by the co-incidence of the risk allele of rs13146272 with the risk alleles of rs2036914 and rs2289252 on haplotype A, the haplotype with the highest odds ratio and lowest P value.

High FXI levels are associated with elevated risk of DVT [3,6], and the DVT risk alleles of three SNPs were found to be associated with high levels of FXI (Table 4). Thus, it was somewhat surprising that these three SNPs remain associated with DVT after adjustment for FXI levels. One possible explanation is that these SNPs may more accurately assess average intra-individual FXI levels than a single FXI level measurement. That is, day to day variation in FXI levels could result in an aberrant result for a single FXI level determination while the genotype of a SNP associated with FXI levels does not change. Alternatively, these SNPs may modulate FXI expression in response to changes in age or hormonal levels. Finally, these SNPs may also affect the expression of proteins other than FXI (e.g. KLKB1), which might increase risk for DVT.

These two SNPs are common (risk allele frequencies of 41–64% in the combined controls of LETS and MEGA), suggesting that these SNPs may affect DVT risk among a sizable fraction of the population: the population attributable risk percentage (PAR%) was 27.9 for rs2036914, and 23.5 for rs2289252 in LETS, MEGA-1 and MEGA-2 combined. Furthermore, while the FVL and prothrombin G20210A risk variants are relatively rare (< 5%) and are present mainly in Caucasians, these two SNPs may have high frequencies in populations other than Caucasians – that is, 31.1% for rs2289252 risk allele in Han Chinese in Beijing, China, and 19.5% for rs2289252 risk allele in Yoruba in Ibadan, Nigeria, were reported from HapMap [14], which suggests that these SNPs may have a population attributable risk for DVT in other ethnic groups that is similar to those found in the current study; however, the association between these SNPs and DVT needs to be confirmed in other populations.

In the current study, we used a tagging approach to select SNPs to genotype in a region of chromosome 4q35.2; however, variants other than SNPs may also contribute to the disease. Although we interrogated most of the common SNPs in the region containing the CYP4V2, KLKB1 and F11 genes, we did not have enough power to detect low frequency SNPs that might be associated with DVT. For example, for an SNP with 2% allele frequency and an odds ratio of 1.35, we had 53% power to observe an association (< 0.05) in the combined LETS and MEGA studies. In the future, cost-effective resequencing technology could allow complete sequencing of the associated region to ensure that rare variants as well as insertion-deletions are detected. Nevertheless, it may still be difficult to identify causative variant(s) in some cases, given that the power to detect low frequency SNPs with modest risk estimates is limited or given a region where the linkage disequilibrium among many SNPs is high. In these cases, functional studies may be necessary to elucidate the causative SNP. In conclusion, we have identified two SNPs (rs2289252 and rs2036914 in F11) that independently contribute to the risk of DVT, a contribution that is explained at least in part by an association with FXI levels.

Author contributions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Author contributions
  8. Acknowledgements
  9. Disclosure of Conflict of Interests
  10. References
  11. Supporting Information

Design of the LETS and MEGA studies: P.H. Reitsma‡,§ and F.R. Rosendaal†,‡,§. Design of the genetic study L.A. Bare, J.J. Devlin*, Y. Li (Yonghong Li)*, P.H. Reitsma‡,§ and F.R. Rosendaal†,‡,§. Statistical analyses and interpretation of the data: A.R. Arellano*, I.D. Bezemer, J.J. Catanese*, Y. Li*, C.M. Rowland* and C.H. Tong*. Critical writing or revising the intellectual content: all authors.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Author contributions
  8. Acknowledgements
  9. Disclosure of Conflict of Interests
  10. References
  11. Supporting Information

We are indebted to the patients and volunteers. We also thank the investigators who participated in the collection of the clinical samples, and A. Grupe and S. Schrodi for critical comments on the manuscript. The Leiden Thrombophilia Study was supported by grant 89.063 from the Netherlands Heart Foundation. The Multiple Environmental and Genetic Assessment of Risk Factors for Venous Thrombosis Study was supported by grant NHS 98.113 from the Netherlands Heart Foundation, grant RUL 99/1992 from the Dutch Cancer Foundation, and grant 912-03-033| 2003 from the Netherlands Organisation for Scientific Research. Celera reimbursed the Leiden University Medical Center for aliquoting and shipping the samples. I. Bezemer received support for training in genetic epidemiology from the Leducq Foundation (Paris, France) for the development of Transatlantic Networks of Excellence in Cardiovascular Research (grant 04 CVD 02). The authors would like to thank N. Bui, D. Ross, D. Wolfson and V. Garcia for expert technical support.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Author contributions
  8. Acknowledgements
  9. Disclosure of Conflict of Interests
  10. References
  11. Supporting Information
  • 1
    Souto JC, Almasy L, Borrell M, Blanco-Vaca F, Mateo J, Soria JM, Coll I, Felices R, Stone W, Fontcuberta J, Blangero J. Genetic susceptibility to thrombosis and its relationship to physiological risk factors: the GAIT study. Genetic Analysis of Idiopathic Thrombophilia. Am J Hum Genet 2000; 67: 14529.
  • 2
    Larsen TB, Sorensen HT, Skytthe A, Johnsen SP, Vaupel JW, Christensen K. Major genetic susceptibility for venous thromboembolism in men: a study of Danish twins. Epidemiology 2003; 14: 32832.
  • 3
    Meijers JC, Tekelenburg WL, Bouma BN, Bertina RM, Rosendaal FR. High levels of coagulation factor XI as a risk factor for venous thrombosis. N Engl J Med 2000; 342: 696701.
  • 4
    Poort SR, Rosendaal FR, Reitsma PH, Bertina RM. A common genetic variation in the 3′-untranslated region of the prothrombin gene is associated with elevated plasma prothrombin levels and an increase in venous thrombosis. Blood 1996; 88: 3698703.
  • 5
    Zoller B, Garcia de Frutos P, Hillarp A, Dahlback B. Thrombophilia as a multigenic disease. Haematologica 1999; 84: 5970.
  • 6
    Bezemer ID, Bare LA, Doggen CJ, Arellano AR, Tong C, Rowland CM, Catanese J, Young BA, Reitsma PH, Devlin JJ, Rosendaal FR. Gene variants associated with deep vein thrombosis. JAMA 2008; 299: 130614.
  • 7
    Smith NL, Hindorff LA, Heckbert SR, Lemaitre RN, Marciante KD, Rice K, Lumley T, Bis JC, Wiggins KL, Rosendaal FR, Psaty BM. Association of genetic variations with nonfatal venous thrombosis in postmenopausal women. JAMA 2007; 297: 48998.
  • 8
    Tsantes AE, Nikolopoulos GK, Bagos PG, Rapti E, Mantzios G, Kapsimali V, Travlou A. Association between the plasminogen activator inhibitor-1 4G/5G polymorphism and venous thrombosis. A meta-analysis. Thromb Haemost 2007; 97: 90713.
  • 9
    Tregouet DA, Heath S, Saut N, Biron-Andreani C, Schved JF, Pernod G, Galan P, Drouet L, Zelenika D, Juhan-Vague I, Alessi MC, Tiret L, Lathrop M, Emmerich J, Morange PE. Common susceptibility allele are unlikely to contribute as strongly as the FV and ABO loci to VTE risk: results from a GWAS approach. Blood 2009; 113: 5298303.
  • 10
    Hefler L, Jirecek S, Heim K, Grimm C, Antensteiner G, Zeillinger R, Husslein P, Tempfer C. Genetic polymorphisms associated with thrombophilia and vascular disease in women with unexplained late intrauterine fetal death: a multicenter study. J Soc Gynecol Investig 2004; 11: 424.
  • 11
    Van Der Meer FJ, Koster T, Vandenbroucke JP, Briet E, Rosendaal FR. The Leiden Thrombophilia Study (LETS). Thromb Haemost 1997; 78: 6315.
  • 12
    Blom JW, Doggen CJ, Osanto S, Rosendaal FR. Malignancies, prothrombotic mutations, and the risk of venous thrombosis. JAMA 2005; 293: 71522.
  • 13
    Germer S, Holland MJ, Higuchi R. High-throughput SNP allele-frequency determination in pooled DNA samples by kinetic PCR. Genome Res 2000; 10: 25866.
  • 14
    International HapMap Consortium. A haplotype map of the human genome. Nature 2005; 437: 1299320.
  • 15
    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: 121723.
  • 16
    Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005; 21: 2635.
  • 17
    Weir BS. Genetic Data Analysis II. Sunderland: Sinauer Associates Inc., 1996.
  • 18
    Abecasis GR, Cookson WO. GOLD – graphical overview of linkage disequilibrium. Bioinformatics 2000; 16: 1823.
  • 19
    Bonferroni C. Teoria statistica delle classi e calcolo delle probabilita. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze. 1936; 8: 362.
  • 20
    Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA. Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am J Hum Genet 2002; 70: 42534.

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Author contributions
  8. Acknowledgements
  9. Disclosure of Conflict of Interests
  10. References
  11. Supporting Information

Table S1. One hundred and three SNPs tested in the 4q35.2 locus.

Table S2. Fifty-four SNPs associated with DVT in the combined study of LETS and MEGA-1.

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JTH_3544_sm_tables.doc208KSupporting info item

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.