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A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease

This article has been updated

Abstract

Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association study (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of 185,000 CAD cases and controls, interrogating 6.7 million common (minor allele frequency (MAF) > 0.05) and 2.7 million low-frequency (0.005 < MAF < 0.05) variants. In addition to confirming most known CAD-associated loci, we identified ten new loci (eight additive and two recessive) that contain candidate causal genes newly implicating biological processes in vessel walls. We observed intralocus allelic heterogeneity but little evidence of low-frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD, showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect size.

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Figure 1: Comparing the 1000 Genomes Project and HapMap imputation training sets.
Figure 2: A circular Manhattan plot summarizing the 1000 Genomes Project CAD association results.
Figure 3: The imputation quality and effect size of lead variants at 46 genome-wide significant loci.
Figure 4: Regional association plots for newly identified loci associated with CAD.

Change history

  • 14 September 2015

    In the version of this article initially published online, there was a typographical error in the third sentence of the abstract. The corrected sentence should read: "In addition to confirming most known CAD-associated loci, we identified ten new loci (eight additive and two recessive) that contain candidate causal genes newly implicating biological processes in vessel walls." The error has been corrected for the print, PDF and HTML versions of this article.

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Acknowledgements

We sincerely thank the participants and the medical, nursing, technical and administrative staff in each of the studies who have contributed to this project. We are grateful for support from our funders; more detailed acknowledgments are included in the Supplementary Note.

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Contributions

Cohort oversight: D.A., E.B., I.B.B., E.P.B., J.E.B., J.C.C., R. Collins, L.A.C., J.D., I.D., R.E., S.E.E., T.E., M.F.F., O.H.F., M.G.F., C.B.G., D. Gu, V.G., A.S.H., A. Hamsten, T.B.H., S.L.H., C.H., A. Hofman, E.I., C.I., J.W.J., P.J.K., B.-J.K., J.S.K., I.J.K., T.L., R.J.F.L., O.M., A.M., W.M., C.N.P., M.P., T.Q., D.J.R., P.M.R., S.R., R.R., V.S., D.K.S., S.M.S., U.S., A.F.S., D.J.S., J.T., P.A.Z., C.J.O'D., M.P.R., T.L.A., J.R.T., J.E., H.W., S. Kathiresan, R.M., P.D., H.S., N.J.S. and M.F. Cohort genotyping: H.-H.W., S. Kanoni, D.S., J.C.H., Jie Huang, M.E.K., Y.L., L.-P.L., A.U., S.S.A., L.B., G.D., D. Gauguier, A.H.G., M.H., B.-G.H., S.J., L. Lind, C.M.L., M.-L.L., P.K.M., A.P.M., M.S.N., N.L.P., J.S., K.E.S., S.T., L.W., I.B.B., J.C.C., R. Collins, M.F.F., A. Hofman, E.I., J.S.K., T.L., R.R., D.K.S., A.F.S., R. Clarke, P.D. and N.J.S. Cohort phenotyping: D.S., J.C.H., A.D., M.A., K.A., Y.K.K., E.M., L.M.R., S.S.A., F.B., G.D., P.F., A.H.G., O.G., Jianfeng Huang, T. Kessler, I.R.K., L. Lannfelt, W.L., L. Lind, C.M.L., P.K.M., N.H.M., N.M., T.M., F.-ur-R.M., A.P.M., N.L.P., A.P., L.S.R., A.R., M. Samuel, S.H.S., K.S.Z., D.A., J.E.B., J.C.C., R. Collins, R.E., C.B.G., V.G., A.S.H., A. Hamsten, S.L.H., E.I., J.W.J., P.J.K., J.S.K., I.J.K., O.M., A.M., M.P., R.R., D.K.S., A.F.S., D.J.S., P.A.Z., M.P.R., R. Clarke, S. Kathiresan, H.S. and N.J.S. Cohort data analyst: M.N., A.G., H.-H.W., L.M.H., C.W., S. Kanoni, D.S., T. Kyriakou, C.P.N., J.C.H., T.R.W., L.Z., A.D., M.A., S.M.A., K.A., A.B., D.I.C., S.C., I.F., N.F., C. Gieger, C. Grace, S.G., Jie Huang, S.-J.H., Y.K.K., M.E.K., K.W.L., X.L., Y.L., L.-P.L., E.M., A.C.M., N.P., L.Q., L.M.R., E.S., R.S., M. Scholz, A.V.S., E.T., A.U., X.Y., W. Zhang, W. Zhao, M.d.A., P.S.d.V., N.R.v.Z., M.F.F., J.R.T. and M.F. Meta-analysis: M.N., A.G., H.-H.W., L.M.H., C.P.N., J.R.T. and M.F. Variant annotation: M.N., A.G., H.-H.W., T. Kyriakou, J.C.H. and T.R.W. Manuscript drafting: M.N., A.G., H.-H.W., L.M.H., T. Kyriakou, J.C.H., H.W., S. Kathiresan, R.M., H.S., N.J.S. and M.F. Project steering committee: M.N., A.G., H.-H.W., L.M.H., S. Kanoni., J.C.H., D.I.C., M.E.K., N.R.v.Z., C.N.P., R.R., C.J.O'D., M.P.R., T.L.A., J.R.T., J.E., R. Clarke, H.W., S. Kathiresan, R.M., P.D., H.S., N.J.S. and M.F. (secretariat: J.C.H. and R. Clarke). CARDIoGRAMplusC4D executive committee: J.D., D. Gu, A. Hamsten, J.S.K., R.R., H.W., S. Kathiresan, P.D., H.S. and N.J.S.

Corresponding authors

Correspondence to Hugh Watkins or Sekar Kathiresan or Ruth McPherson or Martin Farrall.

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Competing interests

The author declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Venn diagram showing case-control overlap between 1000G GWAS and Metabochip studies.

Venn diagram showing the number of cases (top) and controls (bottom) that overlap between the present 1000G GWAS meta-analysis study and the Metabochip study (Nat. Genet. 45, 25–33, 2013). There is a 57.5% overlap of our cases and a 40.1% overlap of our controls with the previously published study.

Supplementary Figure 2 A Manhattan plot summarizing the 1000 Genomes CAD additive association results.

The meta-analysis statistics have been adjusted for overdispersion (genomic control parameter = 1.18) and have been capped to P = 1 × 10−20. The genome-wide significance threshold is shown as a horizontal blue line at P < 5 × 10−8. Novel CAD loci are presented with red stacks and gene names (Table 1). Previously reported loci showing genome-wide significance are shown in brown, and those showing nominal significance (P < 0.05) in our meta-analysis are shown in blue (Supplementary Table 2).

Supplementary Figure 3 Comparing effect sizes for the MI subphenotype and the inclusive CAD phenotype.

Point estimates of effect sizes (odds ratios) are shown by open circles, with 95% confidence intervals represented by solid lines. The line of identity is shown as a dashed line. Loci showing marked differences in effect sizes are shown in blue.

Supplementary Figure 4 Heat map of the number of variants with power >90% to detect genome-wide significant association.

Heat map summarizing the number of variants that were calculated to be powered at ≥90% in the meta-analysis to detect a genome-wide significant association with an additive susceptibility variant with odds ratio (OR) = 1.3. Each cell is shaded from white to black to represent larger and smaller numbers of variants, respectively. The modal cell covers variants in the sector with 0.05 < MAF < 0.075 and imputation quality >0.95. Source data

Supplementary Figure 5 Allele frequency analysis to identify strand flipping and data formatting issues.

Allele frequency analysis to identify systematic allele mismatching in individual studies due to strand flipping and other data formatting issues. (a) Proportion of variants that align with the 1000 Genomes phase 1 v3 training set minor allele after assignment to bins on the basis of MAF. The blue plot shows a typical analysis for studies with well-matched alleles, such that there is 100% concordance for lower-frequency alleles (MAF < 0.2) that declines to 50% for more frequent alleles. The red trace for study TH (Supplementary Table 1 of studies with study code) shows a marked discordance in allele frequencies that was resolved before inclusion in the meta-analysis. (b) Surface plot of 28 studies with 2 studies showing systematic strand flipping and further studies showing more subtly different allele frequency patterns. (c) Allele frequency analysis for data submitted to the meta-analysis (i.e., after any systematic mismatching issues had been resolved). Six studies of East and South Asian, Hispanic and African-American ancestries show MAF distortions that contrast with those of the remaining 42 European-ancestry studies.

Supplementary Figure 6 Quantile-quantile plots of the double–genomic controlled CAD meta-analysis results.

Shaded areas represent 95% confidence intervals. (a,b) Plots showing all additive and recessive results. (c,d) Plots showing additive and recessive results after removing variants from known loci. Supplementary Table 15 refers to the genomic control correction of each study before the final meta-analysis.

Supplementary Figure 7 Comparison of GCTA joint association analysis with standard multiple–logistic regression analysis in four studies.

We have investigated the accuracy of the GCTA joint association analysis by comparing the approximate GCTA results with a standard multiple–logistic regression analysis in 4 studies (MIGEN, PROCARDIS, OHGS and Interheart) for the 202 FDR variants. The figure shows scatterplots of the regression coefficients (left column), standard errors (center column) and –log10 (P values) (right column) for each variant; for each scatterplot, the x axis shows the standard multiple–logistic regression result, and the y axis shows the corresponding GCTA COJO result. The regression coefficients and standard errors for the majority (95%) of the variants are very accurately approximated as their results lie close to the line of identity (y = x) shown in red. The –log10 (P values) for the two analyses were positively correlated (0.86 < ρ < 0.93).

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the CARDIoGRAMplusC4D Consortium., Nikpay, M., Goel, A. et al. A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease. Nat Genet 47, 1121–1130 (2015). https://doi.org/10.1038/ng.3396

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