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Arterioscler Thromb Vasc Biol. Author manuscript; available in PMC 2012 March 29.
Published in final edited form as:
PMCID: PMC3315048
EMSID: UKMS5156

Large Scale Association Analysis of Novel Genetic Loci for Coronary Artery Disease

Coronary Artery Disease Consortium*

Abstract

Background

Combined analysis of 2 genome-wide association studies in cases enriched for family history recently identified 7 loci (on 1p13.3, 1q41, 2q36.3, 6q25.1, 9p21, 10q11.21, and 15q22.33) that may affect risk of coronary artery disease (CAD). Apart from the 9p21 locus, the other loci await substantive replication. Furthermore, the effect of these loci on CAD risk in a broader range of individuals remains to be determined.

Methods and Results

We undertook association analysis of single nucleotide polymorphisms at each locus with CAD risk in 11 550 cases and 11 205 controls from 9 European studies. The 9p21.3 locus showed unequivocal association (rs1333049, combined odds ratio [OR]=1.20, 95% CI [1.16 to 1.25], probability value=2.81×10−21). We also confirmed association signals at 1p13.3 (rs599839, OR=1.13 [1.08 to 1.19], P=1.44×10−7), 1q41 (rs3008621, OR=1.10 [1.04 to 1.17], P=1.02×10−3), and 10q11.21 (rs501120, OR=1.11 [1.05 to 1.18], P=4.34×10−4). The associations with 6q25.1 (rs6922269, P=0.020) and 2q36.3 (rs2943634, P=0.032) were borderline and not statistically significant after correction for multiple testing. The 15q22.33 locus did not replicate. The 10q11.21 locus showed a possible sex interaction (P=0.015), with a significant effect in women (OR=1.29 [1.15 to 1.45], P=1.86×10−5) but not men (OR=1.03 [0.96 to 1.11], P=0.387). There were no other strong interactions of any of the loci with other traditional risk factors. The loci at 9p21, 1p13.3, 2q36.3, and 10q11.21 acted independently and cumulatively increased CAD risk by 15% (12% to 18%), per additional risk allele.

Conclusions

The findings provide strong evidence for association between at least 4 genetic loci and CAD risk. Cumulatively, these novel loci have a significant impact on risk of CAD at least in European populations.

Keywords: coronary artery disease, genetics, risk factors

Coronary artery disease (CAD), and its main complication, myocardial infarction (MI), have a significant genetic basis. Until recently, attempts at identifying genetic variants that affect risk of these common diseases have been hampered by poor reproducibility of associations and limited coverage of the genome.1 However, well-powered genome-wide association (GWA) studies have now identified several novel putative loci that increase risk of CAD and MI.25 Specifically, combined analysis of the Wellcome Trust Case Control Consortium (WTCCC) and the German MI Family GWA studies identified 7 chromosomal loci (on 1p13.3, 1q41, 2q36.3, 6.q25.1, 9p21.3, 10q11.21, and 15q22.33), all of which showed highly significant associations with CAD.5 The locus on chromosome 9p21.3 was also identified in 2 other GWA studies3,4 and has since been associated with CAD, stroke, as well as abdominal aortic and intracranial aneurysms in several other cohorts.68 The locus on chromosome 1p13.3 was recently shown to also be strongly associated with LDL cholesterol concentration,9-13 reinforcing the close mechanistic association between the variability in LDL levels and CAD risk. Beyond these initial studies on the loci at 9p21.3 and 1p13.3, the reproducibility of the association with CAD risk of the other loci identified by GWA studies has not yet been studied systematically.

Many of the exploratory GWA studies were carried out on patients with a high genetic burden of the disease. For example, both the WTCCC and German MI Family Study analyzed cases enriched for a positive family history of CAD.5 Here, in one of the largest molecular-genetic experiments on CAD, we report the replication analysis of the 7 principal loci for CAD identified thus far in GWA studies,2-5 in a broader group of CAD patients, explore their interactions with traditional risk factors, and assess their cumulative impact on CAD risk.

Materials and Methods

Study Populations

We investigated participants recruited into 9 separate studies in Europe with validated cases of CAD and appropriate controls: the Academic Medical Center Amsterdam Premature Atherosclerosis Study (AMC-PAS), the Etude Cas-Témoins sur l’infarctus du Myocarde Study (ECTIM), the European Prospective Investigation into Cancer and Nutrition Study (EPIC-Norfolk), the German MI Family Study (GerMIFS; only including subjects that did not overlap with the original GWA Study), the Cooperative Health Research in the Region of Augsburg Study (KORA/GOC), the Ludwigshafen Risk and Cardiovascular Health Study (LURIC), the MORGAM Study, which has harmonized data from prospective follow-up of population cohorts in several countries, the Population-based northern German cross-sectional study (PopGen), and the UKMI Study. Almost all of these participants were of white Northern European origin. Details of the recruitment process in each study and references for each study are provided in the supplemental materials (available online at http://atvb.ahajournals.org). For the chromosome 9p21.3 locus some of the study groups (GerMIFS, KORA/GOC, PopGen, and UKMI) overlap with a previous publication on this locus.6 In addition, the MORGAM Study has recently reported an analysis of the association of the novel loci with disease history and risk factors at baseline, and CAD and stroke events and death during follow-up in their prospective cohorts.14

Definition of Phenotypes

Common criteria for CAD and MI were applied across all the studies and required validated evidence for the phenotype (see supplemental materials). Cases not meeting these criteria were excluded. Similarly, uniform criteria were defined for partitioning of participants for risk factors (see supplemental materials). Those individuals where the information was unavailable or could not be assigned according to the criteria were classified as missing for the variable.

SNP Selection and Genotyping

For the 7 loci (1p13.3, 1q41, 2q36.3, 6.q25.1, 9p21.3, 10q11.21, 15q22.33), we selected the SNP showing the strongest association with CAD in the previous GWA analysis (lead SNP).5 In addition, we selected SNP rs10738610 in the 9p21.3 locus which had shown marginally stronger association in a fine mapping experiment using HapMap SNPs,6 and SNP rs2972147 in the 2q36.3 locus which is a proxy for rs2943634. Finally, linkage disequilibrium analysis of the 7 loci in HapMap identified that in 3 of them, there were subsets of highly correlated SNPs (r2>0.8) significantly associated with CAD/MI which were not in the haplotype block defined by the lead SNP. The most significant SNP in each of these secondary haplotype blocks was also selected for genotyping giving a total of 13 SNPs (supplemental Table I and supplemental Figure I).

Genotyping was carried out with the iPLEX assay (Sequenom) for all SNPs except rs599839 and rs2943634, which were assayed by TaqMan (Applied Biosystems) using standard protocols (assays details available by request from the authors). iPLEX genotyping was performed at the Wellcome Trust Sanger Institute in Cambridge, UK, for all studies apart from MORGAM which was genotyped at the National Public Health Institute, Helsinki, Finland (Sequenom assay) and INSERM Unit 525, Paris France (TaqMan assays).

Statistical Analyses

Analysis was performed in Stata (Stata Statistical Software Release 10, 2007, StataCorp LP). Each study was analyzed separately by unconditional logistic regression using an additive genetic model (ie, genotype codes 0, 1, 2) adjusting for center in studies involving multiple sites. Heterogeneity between the studies was tested using Cochran Q chi-squared test, and the size of the heterogeneity was measured using the I2 statistic. Only one nonlead SNP showed strong between-study heterogeneity (see supplemental Table III). Consequently, the odds ratios (OR) for the studies were meta-analyzed under a fixed effect model. Bonferroni correction was used to adjust for the number of SNPs tested. The analysis was performed for CAD cases and then for the subset of MI cases. The assumption of an additive model was assessed in the whole dataset by comparing the fit of that model with the fit of a 2-degree of freedom pairwise comparison in a likelihood ratio test. To assess the overall strength of the association of novel loci with CAD risk, probability values from the present analysis were combined with those from the GWA studies5 using Fisher method, both with and without adjusting the original GWAs findings for multiple comparisons using the Bonferroni method. All tests were 2-sided.

To investigate whether there was any interaction between a locus and relevant demographic characteristics or cardiovascular risk factors (age, sex, body mass index, hypertension, smoking, and diabetes) participants were divided into 2 groups on the basis of the particular covariate. The analysis for CAD was repeated on the appropriate selection of patients in the same way as for the full study, and then the results were combined into a single forest plot.

Independent effects of the associated loci were verified by including them all in a single logistic regression. Cumulative risk was assessed by forming a score based on the total number of risk alleles across 4 or 6 loci (see Results). The case/control status was then compared with the number of risk alleles in a logistic regression analysis adjusting for study and center within study. The number of risk alleles was considered both as a categorical measure and as a continuous measure in a trend test.

Power calculations were performed by simulation. Data were generated to represent studies of the same numbers of cases and controls as in the actual replication study using an additive model with a given common odds ratio and allele frequency and assuming Hardy-Weinberg equilibrium (HWE). Analysis was by logistic regression and then meta-analysis as in the main study. The number of individual studies that were significant at the 5% level was counted and whether the combined result was significant was noted. Each set of simulations was repeated 1000 times.

Results

Study Participants

The characteristics of the pooled case and control participants from the 9 European studies are shown in Table 1. Additionally, the characteristics of participants in each study individually are shown in supplemental Table II. Altogether, 22 755 participants (11 550 cases and 11 205 controls) underwent genotyping from these studies. As anticipated, the prevalence of established cardiovascular risk factors was higher in CAD cases than in controls (Table 1). Of the CAD cases, 6831 (59.1%) had a confirmed history of MI and the mean age of cases at first event was 59.5 (std. dev. 10.0) years.

Table 1
Summary Characteristics of Participants Included in the Study

Association Analysis

Genotypes in excess of 90% were obtained for all SNPs, and there was no difference in the proportion of successful genotypes between cases and controls (supplemental Table I). None of the SNPs showed significant deviation from HWE. Nominally significant association (P<0.05) with CAD was observed at 6 of the 7 chromosomal loci studied (Table 2). For all loci, except for chromosome 1q41, the lead SNP identified in the GWA studies5 showed the strongest association among the genotyped SNPs (supplemental Table III). Moreover, in all instances the same allele as in the previous study showed the increased risk with CAD. Interestingly, for 1q41, no significant association was seen for the previously reported lead SNP (rs17465637; supplemental Table III); however, a SNP (rs3008621) in an adjacent haplotype block showed a significant association (Table 2). The strength of the association ranged from an OR of 1.20 (95% CI: 1.16 to 1.25) for the 9p21 locus (rs1333049, P=1.63×10−21) to an OR of 1.05 (1.00 to 1.09) for the locus at 2q36.3 (rs2943634, P=0.03) and 1.05 (1.01 to 1.10) for the locus at 6q25.1 (rs6922269 P=0.02). The associations for the loci at 2q36.3 and 6q25.1 were not statistically significant after Bonferroni correction for the number of SNPs tested (Table 2). We found no evidence for association with the locus at 15q22.33 (Table 2). The associations in the subset of cases with MI largely paralleled those seen for CAD (Table 2).

Table 2
Pooled Association Results for the Lead SNP* at Each Locus for CAD and Separately for MI

To examine the totality of our evidence of association for each locus, we also combined the association results from the present studies with those from the 2 original GWA studies.5 The signals for the 6 loci that showed nominally significant association in the present study became stronger in a meta-analysis that included these prior studies, even after correction for multiple testing in the GWA studies (supplemental Table IV). There was no evidence of nonadditivity for any of the loci assessed (ie, better fit using a dominant or recessive model: supplemental Table V).

Heterogeneity and Interactions

There was no significant heterogeneity in the magnitude of the associations of the loci between the pooled studies (Table 2). However, as expected from the power calculations (see Methods and supplemental Table VI), associations were not individually significant in every study (findings for each study by locus are shown in supplemental Figure II).

We also investigated whether there was any interaction between the effect of the loci and a number of prespecified characteristics or risk factors, namely age, sex, BMI, hypertension, diabetes mellitus, and smoking on risk of CAD. The analyses are displayed in supplemental Figure III. Note that the analyses for the risk factors are limited because only demographic information (age and sex) was available from 3 of the control groups (supplemental Table II). The most notable finding was that the magnitude of the association of the locus on 10q11.21 with CAD was greater (P=0.015 for interaction) in women (OR=1.29 [1.15 to 1.45], P=1.86×10−5) compared with men (OR=1.03 [0.96 to 1.11], P=0.387). There was also a suggestion that the effect of the locus on 1q41 was only present in older subjects and that the effect of the chromosome 9p21 locus was stronger in women and weaker in the presence of hypertension. However, neither of these interactions was significant (P>0.05), and otherwise the association of the loci with CAD appeared largely independent of anthropometric characteristics and risk factors (supplemental Figure III).

Distribution of Risk Alleles and Cumulative Risk

The proportions of cases and controls carrying different number of copies of the risk alleles for the 4 most strongly associated loci (1p13.3, 1q41, 9p21, and 10q11.21) are shown in the Figure. There is a significant rightward shift in the number of risk alleles carried by cases (P<0.0001). Because of the high prevalence of these alleles, the majority of these European white individuals carry more than 5 out of a possible 8 alleles (Figure). To investigate the cumulative risk associated with carriage of multiple risk alleles, we estimated the ORs in individuals carrying different numbers of the risk alleles for these loci. The 4 loci act independently with a combined OR of 1.15 (1.12 to 1.18) per additional risk allele. Because of the sex-specific effect of the locus on 10q11.21, the OR per additional risk allele was higher in women (1.21 [1.15 to 1.27]) compared with men (1.12 [1.08 to 1.16]). There was no significant interaction with age (P=0.30).

Figure
Distribution of cases (dark gray) and controls (light gray) carrying different number of risk alleles, ranging from 0 to 8, for the 4 most strongly associated loci: those on 1p13.3, 1q41, 9p21, and 10q11.21. Note the rightward shift in the distribution ...

Discussion

In this study we describe a large scale evaluation of novel loci for CAD identified through previous GWA studies.5 In addition to the 9p21 locus, which has already been robustly replicated in several other studies,2,3,68 we provide compelling evidence for the association of at least 3 further loci (1p13.3, 1q41, and 10q11.21) with CAD risk. Nominal associations (P<0.05) were observed for 2 further loci, those at 2q36.3 and 6q25.1, but these became statistically nonsignificant after correction for the number of variants examined.

The increase in risk among the loci ranged from 5% to 20% per copy of the risk allele. These are less than those we found in the GWA studies (20% to 37%).5 There are perhaps two main reasons for this. First, the GWA studies were carried out in relatively young cases enriched for a genetic tendency for CAD (each case had to have at least one first degree relative affected with CAD) which may have enhanced the genetic effect. Second, primary association findings by their nature tend to often be more inflated than the true degree of association. Thus, some degree of “the winner’s curse” was to be expected.15 Our present analysis was carried out in a much wider range of individuals with CAD, better reflecting the disease spectrum with regard to age of onset as well as relevant comorbidities and thus likely to provide a more reliable estimate of the association of each locus with CAD risk in general populations. Although individually the effect of carrying each risk allele is relatively modest, their importance in terms of contribution to the development of CAD and the public health needs to also take into account the prevalence of the risk alleles which range from 26% to 87% (Table 2). Thus, most European individuals carry multiple risk alleles (Figure).

Our study emphasizes the scale of endeavor required to quantify reliably the modest effect sizes which are typically being found for loci detected using GWA approaches for complex traits. Even with a combined sample size of more than 22 000 European participants, we only had sufficient (>80%) power to detect OR >1.05 across a range of allele frequencies (supplemental Table VI); hence the evidence of association for 2 of the loci (those at 2q36.3 and 6q25.1) remains inconclusive. Furthermore, simulations showed that even under the most favorable scenario, that pertaining to the locus at 9p21 with an OR of 1.20 and an allele frequency approaching 0.5, a “true” effect would not have been expected to be observed in each of the individual studies. Indeed, for the sizes of the studies involved here, the proportion of positive studies we observed for each locus was largely consistent with what might have been expected for “true” effects (supplemental Table VI). These findings are therefore remarkable in that we were able to detect a definite association with at least four of the initial 7 loci that emerged from the GWA studies in populations based in geographically and culturally different parts of Europe. This suggests that further loci with similar effect sizes await discovery in even larger analyses. Although we cannot rule out important gene–gene or gene–environment effects, our findings suggest that the loci identified affect CAD risk under a wide range of circumstances. This is also consistent with the lack of significant interactions with demographic parameters or other cardiovascular risk factors except for the locus on chromosome 10q11.21 (see below).

Our study does not identify the precise causal variant(s) at each locus. This will require resequencing of the entire recombination interval for each locus in a large set of chromosomes enriched for the risk allele to define the full spectrum of variants followed by fine mapping of the association signal. The finding at the locus on chromosome 1q41 emphasizes the importance of this work. We confirmed an association not with the GWA lead SNP but a related SNP suggesting that both markers are in linkage disequilibrium (LD) with the causal variant(s) at this locus but the strength of pair-wise LD differs.

So what are the implications of the present findings? The role in disease prediction often dominates discussion of such findings. Our analysis shows that although, cumulatively, carriage of increasing number of risk alleles imparts substantial additional risk (eg, carriage of seven risk alleles versus 4 risk alleles increases risk on average by 52%), genetic testing for the 4 most strongly replicated loci is unlikely to be sufficiently discriminatory to identify people likely to develop CAD (Figure). This lack of discriminatory capability is very similar to that seen for genetic loci underlying other complex traits such as diabetes16 as well as with other cardiovascular risk factors (eg, plasma cholesterol level)17 and consistent with theoretical considerations.18 Potentially a more immediate and realistic clinical application of the findings could be to help identify people at increased coronary risk so that primary preventive measures, eg, cholesterol lowering, could be directed to those at highest genetic risk. This stratification could theoretically be carried out from a relatively young age, as DNA analysis can be done at any stage from birth. However, whether such testing is clinically beneficial and cost-effective requires much further investigation.

Perhaps, the greatest utility of these findings will come from understanding the mechanisms and pathways by which the loci affect CAD risk as this could provide new targets for drug development. The genes located within each locus (Table 3) have not been previously implicated in the pathogenesis of CAD. Recently, for the locus at 1p13.3, the same allele of SNP rs599839 that is associated with increased CAD risk, has been shown to be associated with higher plasma total and LDL cholesterol in multiple studies,9-13 providing a possible explanation for the effect on CAD risk, although even for it the precise mechanism by which the locus affects LDL cholesterol and the gene(s) involved awaits elucidation.19 The 9p21 locus shows a region of association coincident with a gene for a noncoding RNA, ANRIL.20 Such RNAs often play a regulatory role in gene expression or translation. There is preliminary evidence that ANRIL may affect the expression of the adjacent cyclin-dependent kinase inhibitors,20 which in turn could affect vascular remodeling processes which are important in the pathogenesis of atherosclerosis and its complications. The association signal at 1q41 lies within the melanoma inhibitory activity family, member 3 (MIA3) gene, which may play a similar role in cell growth or inhibition.21 The locus at 10q11.21 lies upstream of the CXCL12 gene which codes for stromal cell-derived factor-1 (SDF-1), a chemokine which plays a key role in stem-cell homing and tissue regeneration in ischemic cardiomyopathy22 and in promoting angiogenesis through recruitment of endothelial progenitor cells.23 Altogether, the findings open up the prospect of novel therapies for CAD, which may be broadly applicable, from a better understanding of the pathogenic mechanisms in the vascular wall affected by these loci.

Table 3
Genes Located Within or Adjacent to the Six Loci Associated With CAD

Women are less prone to CAD than men, which could partly be attributable to differences in gene–environment interactions. Interestingly, the locus on chromosome 10q11.21 showed a stronger association in women than in men. The nature of the locus with CXCL12 as the most proximate gene (Table 3) does not suggest an immediate mechanism that could explain a gender interaction and whether this finding, which was of borderline statistical significance and would not have been significant if we had adjusted for the multiple interaction analyses carried out, represents a true sex difference in effect requires further investigation. Apart from this, we did not find any other striking interactions, although it should be noted that the lack of data on some risk factors for three control populations means that our ability to detect such interactions was constrained and further investigation in a larger sample is necessary.

In summary, through a large scale replication study we provide compelling evidence for the association of at least 4 genetic loci and risk for CAD. The findings provide a strong foundation for further investigation of these loci as risk factors for CAD and their potential value in the treatment and prevention of this common condition.

Supplementary Material

Supplemental Materials

Acknowledgments

We thank the participants and staff in each of the studies who contributed to the present article. We particularly thank Siv Knaappila and Minttu Jussila for technical support in MORGAM. We thank members of the MORGAM Management Group who are not coauthors: Stefan Blankenberg, Marco Ferrario, Leena Peltonen, Markus Perola, Denis Shields, Hugh Tunstall-Pedoe, and Kjell Asplund.

Sources of Funding: Data and sample collation and genotyping were funded by the EU Integrated Project Cardiogenics and also supported by the Wellcome Trust. The GerMIFS Study was partly funded through the German Federal Ministry of Education and Research (BMBF) in the context of the German National Genome Research Network (NGFN-2 and NGFN-plus). The MORGAM study was partly funded through the European Community’s Seventh Framework Programme ENGAGE project (grant agreement HEALTH-F4-2007-201413), the Center of Excellence in Complex Disease Genetics of the Academy of Finland (CoECDG), and Finnish Foundation for Cardiovascular Research. N.J.S. holds a Chair supported by the British Heart Foundation.

Appendix

*CAD Consortium (alphabetical order)

Philippe Amouyel, Dominique Arveiler, S. Matthijs Boekholdt, Peter Braund, Petra Bruse, Suzannah J. Bumpstead, Peter Bugert, Francois Cambien, John Danesh, Panos Deloukas, Angela Doering, Pierre Ducimetière, Ruth M. Dunn, Nour-Eddine El Mokhtari, Jeanette Erdmann, Alun Evans, Phil Ewels, Jean Ferrières, Marcus Fischer, Philippe Frossard, Stephen Garner, Christian Gieger, Mohammed J.R. Gohri, Alison H. Goodall, Anika Großhennig, Alistair Hall, Rob Hardwick, Ari Haukijärvi, Christian Hengstenberg, Thomas Illig, Juha Karvanen, John Kastelein, Frank Kee, Kay-Tee Khaw, Harald Klüter, Inke R. König, Kari Kuulasmaa, Paivi Laiho, Gérald Luc, Winfried März, Ralph McGinnis, William McLaren, Christa Meisinger, Caroline Morrison, Xiodan Ou, Willem H. Ouwehand, Michael Preuss, Carole Proust, Radhi Ravindrarajah, Wilfried Renner, Kate Rice, Jean-Bernard Ruidavets, Danish Saleheen, Veikko Salomaa, Nilesh J. Samani, Manjinder S. Sandhu, Arne S. Schäfer, Michael Scholz, Stefan Schreiber, Heribert Schunkert, Kaisa Silander, Ravi Singh, Nicole Soranzo, Klaus Stark, Birgitta Stegmayr, Jonathan Stephens, John Thompson, Laurence Tiret, Mieke D. Trip, Ellen van der Schoot, Jarmo Virtamo, Nicholas J. Wareham, H-Erich Wichmann, Per-Gunnar Wiklund, Ben Wright, Andreas Ziegler, Jaap-Jan Zwaginga

Steering Committee

H. Schunkert (Cochair), N.J. Samani (Cochair), F. Cambien, J. Danesh, P. Deloukas, J. Erdmann, A. Evans, A. Hall, C. Hengstenberg, K. Kuulasmaa, R. McGinnis, W.H. Ouwehand, D. Saleheen, M. Scholz, J. Thompson, A. Ziegler

Core Writing Group

N.J. Samani (Chair), P. Deloukas, J. Erdmann, C. Hengstenberg, K. Kuulasmaa, R. McGinnis, H. Schunkert, N. Soranzo, J. Thompson, L.Tiret, A. Ziegler

Analysis Group

R. McGinnis (Cochair), J. Thompson (Cochair), A. Ziegler (Cochair), M. Fischer, C. Gieger, A. Großhennig, I.R. König, J. Karvanen, W. McLaren, M. Preuss, M. Scholz, N. Soranzo, L. Tiret, B. Wright

DNA, Genotyping, Data QC, and Informatics

J. Erdmann (Cochair), N. Soranzo (Cochair), P. Braund, P. Bruse, S.J. Bumpstead, P. Deloukas, R.M. Dunn, P. Ewels, S. Garner, R. Hardwick, A. Haukijärvi, M.J.R. Ghori, J. Karvanen, K. Kuulasmaa, P. Laiho, R. McGinnis, W. McLaren, W. März, X. Ou, W.H. Ouwehand, C. Proust, R. Ravindrarajah, K. Rice, D. Saleheen, M. Sandhu, A.S. Schäfer, M. Scholz, K. Silander, J. Stephens, L. Tiret, M.D. Trip

Primary Investigators of Each Participating Study

AMC-PAS: M.D. Trip, J. Kastelein

SANQUIN controls: E. van der Schoot, J.-J. Zwaginga

ECTIM: D. Arveiler, F. Cambien, A. Evans, F. Kee, G. Luc, C. Morrison, J.-B. Ruidavets

EPIC-Norfolk: M.S. Sandhu, N.J. Wareham, S.M. Boekholdt, K.-T. Khaw

GerMIFS: J. Erdmann, H. Schunkert, C. Hengstenberg

KORA/GOC: M. Fischer, C. Hengstenberg, K. Stark, C. Meisinger,

KORA S4 controls: T. Illig, A. Doering, H.-Erich Wichmann, C. Gieger

LURIC: W. März, W. Renner

German Blood Service (GerBS) controls: P. Bugert, H. Klüter

MORGAM: V. Salomaa, J. Virtamo, P. Amouyel, B. Stegmayr, A. Evans, K. Kuulasmaa, L. Tiret, J. Karvanen, K. Silander, P. Laiho, A. Haukijärvi, C. Proust, D. Arveiler, J. Ferrières, P. Ducimetière, P.-G. Wiklund

PopGen: A.S. Schäfer, N.-E. El Mokhtari, S. Schreiber

UKMI: N.J. Samani, P. Braund, R. Singh, A.H. Goodall

Affiliations

Pasteur Institute, Lille, France (P.A.); Department of Epidemiology and Public Health, Louis Pasteur University, Strasbourg, France (D.A.); Academic Medical Center, University of Amsterdam, The Netherlands (S.M.B., J. Kastelein, M.D.T.); Department of Cardiovascular Sciences, University of Leicester, UK (P. Braund, A.H.G., R.H., N.J.S., R.S.); Medizinische Klinik II, Universität zu Lübeck, Germany (P. Bruse, J.E., H.S.); The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK (S.J.B., P. Deloukas, R.M.D., P.E., M.J.R.G., R.M., W. McLaren, X.O., R.R., K.R., N.S.); Institute of Transfusion Medicine and Immunology, University of Heidelberg, Mannheim, Germany (P. Bugert, H.K.); INSERM UMR_S 525, UPMC Univ. Paris, France (F.C., C.P., L.T.); Department of Public Health and Primary Care, University of Cambridge, UK (J.D., K.-T.K. D.S., M.S.S.); Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany (A.D., C.G., T.I., C. Meisinger, H.-E.W.); IFR69 INSERM-Paris XI University, Paris, France (P. Ducimetière); Institut für Klinische Molekularbiologie, Christian-Albrechts Universität, Kiel, Germany (N.-E.E.M., A.S.S., S.S.); Queens University Belfast, Northern Ireland (A.E., F.K.); INSERM 558, Department of Epidemiology, Paul Sabatier-Toulouse Purpan University, Toulouse, France (J.F.); Klinik und Poliklinik für Innere Medizin II, Universität Regensburg, Germany (M.F., C.H., K. Stark); Aga Khan University, Karachi, Pakistan (P.F., D.S.); Department of Hematology, University of Cambridge, UK (S.G., W.H.O., J.S.); Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Germany (A.G., I.R.K., M.P., A.Z.); LIGHT, University of Leeds, UK (A. Hall); National Public Health Institute (KTL), Helsinki, Finland (A. Haukijärvi, J. Karvanen, K.K., P.L., V.S., K. Silander, J.V.); INSERM U508, MONICA Lille, Service d’Epidémiologie et de Santé Publique, Institut Pasteur, Lille, France (G.L.); Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University Graz, Austria (W. März, W.R.); The MONICA Project, Glasgow Royal Infirmary, Scotland, UK (C. Morrison); INSERM U518, Faculté de Médecine, MONICA Toulouse, France (J.-B.R.); Trium Analysis Online GmbH, Munich, Germany (M.S.); FIMM, Institute for Molecular Medicine Finland, Helsinki (P.L., K. Silander); Twin Genetic Epidemiology, King’s College London, UK (N.S.); Umeå University Hospital, Umeå, Sweden (B.S., P.-G.W.); Department of Health Sciences and Genetics, University of Leicester, UK (J.T., B.W.); Department of Experimental Immunohematology, Sanquin Research, Amsterdam, The Netherlands (E.v.d.S., J.J.Z.); MRC Epidemiology Unit, Cambridge, UK (N.J.W.).

Footnotes

Disclosures: None.

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