A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease

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.

Figure 1

Spectrum of minor allele frequencies (MAF) and median imputation quality (MEDIAN INFO) showing the number (N) of variants in each bin (a) shows the distribution for the 9.4M 1000 Genomes phase1v3 variants (b) shows the distribution for 2.5M HapMap2 SNPs. Imputation quality was calculated as the median of the respective values in up to 48 contributing studies; the imputation quality for genotyped variants was set equal to 1.0. The 1000 Genomes training set included more low frequency variants, many of which have imputation qualities > 0.9.

Figure 2

A circular Manhattan plot summarizing the 1000 Genomes CAD association results. The meta-analysis statistics have been adjusted for over-dispersion (before double genomic control, lambda = 1.18); over-dispersion is predicted to be a regular feature in GWAS under the polygenic inheritance model. The association statistics have been capped to P = 1 × 10⁻²⁰. Genome-wide significant variants (P < 5 × 10⁻⁸) are indicated by red triangles. Novel CAD loci are named in red (Table 1). Previously reported loci showing genome-wide significance are shown in black and those showing nominal significance (P < 0.05) in our meta-analysis in blue (Supplementary Table 2). The inner track (see inset) shows the imputation quality score of the lead variants of the novel loci. The middle track shows numbered chromosome ideograms with the centromeres indicated by pink bars.

Figure 3

Imputation quality and effect size of lead variants at 46 genome-wide significant loci. (a) Imputation quality and minor allele frequency (MAF) for lead variants at 46 genome-wide significant susceptibility loci. Blue circles indicate novel additive loci, red squares - novel recessive loci, black triangles - previously mapped additive loci, black diamonds - key SNPs in LPA and APOE. Imputation quality and MAF were calculated as the median of the respective values in up to 48 contributing studies; the imputation quality for studies with genotype data was fixed at 1.0. (b) Odds ratios and effect allele frequency (EAF) for lead variants at 46 genome-wide significant loci. Blue circles indicate novel additive loci; red squares - novel recessive loci, black triangles - previously mapped additive loci. SNPs rs55730499 and rs2891168 are lead variants in the LPA and chromosome 9p21 susceptibility loci. EAF was calculated as the median value in up to 48 contributing studies.

Figure 4

Regional association plots of the eight additive (a–h) and two recessive (i–j) novel CAD loci. The association statistics have been adjusted for over-dispersion following meta-analysis (genomic control parameter 1.18 for the additive and 1.05 for the recessive models). Linkage disequilibrium (r²) calculations were based on the combined 1000 Genomes phase 1 v3 training dataset. Genomic coordinates in mega-base pairs (Mb) refer to the hg19 sequence assembly.