We performed a meta-analysis of 14 genome-wide association studies of coronary artery disease (CAD) comprising 22,233 cases and 64,762 controls of European descent, followed by genotyping of top association signals in 60,738 additional individuals. This genomic analysis identified 13 novel loci harboring one or more SNPs that were associated with CAD at P<5×10−8 and confirmed the association of 10 of 12 previously reported CAD loci. The 13 novel loci displayed risk allele frequencies ranging from 0.13 to 0.91 and were associated with a 6 to 17 percent increase in the risk of CAD per allele. Notably, only three of the novel loci displayed significant association with traditional CAD risk factors, while the majority lie in gene regions not previously implicated in the pathogenesis of CAD. Finally, five of the novel CAD risk loci appear to have pleiotropic effects, showing strong association with various other human diseases or traits.

Analysis of single nucleotide polymorphisms (SNPs) has been and will be increasingly utilized in various genetic disciplines, particularly in studying genetic determinants of complex diseases. Such studies will be facilitated by rapid, simple, low cost and high throughput methodologies for SNP genotyping. One such method is reported here, named tetra-primer ARMS-PCR, which employs two primer pairs to amplify, respectively, the two different alleles of a SNP in a single PCR reaction. A computer program for designing primers was developed. Tetra-primer ARMS-PCR was combined with microplate array diagonal gel electrophoresis, gaining the advantage of high throughput for gel-based resolution of tetra-primer ARMS-PCR products. The technique was applied to analyse a number of SNPs and the results were completely consistent with those from an independent method, restriction fragment length polymorphism analysis.

BackgroundCoronary artery disease (CAD) has substantial heritability and a polygenic architecture. However, the potential of genomic risk scores to help predict CAD outcomes has not been evaluated comprehensively, because available studies have involved limited genomic scope and limited sample sizes.ObjectivesThis study sought to construct a genomic risk score for CAD and to estimate its potential as a screening tool for primary prevention.MethodsUsing a meta-analytic approach to combine large-scale, genome-wide, and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS) consisting of 1.7 million genetic variants. We externally tested metaGRS, both by itself and in combination with available data on conventional risk factors, in 22,242 CAD cases and 460,387 noncases from the UK Biobank.ResultsThe hazard ratio (HR) for CAD was 1.71 (95% confidence interval [CI]: 1.68 to 1.73) per SD increase in metaGRS, an association larger than any other externally tested genetic risk score previously published. The metaGRS stratified individuals into significantly different life course trajectories of CAD risk, with those in the top 20% of metaGRS distribution having an HR of 4.17 (95% CI: 3.97 to 4.38) compared with those in the bottom 20%. The corresponding HR was 2.83 (95% CI: 2.61 to 3.07) among individuals on lipid-lowering or antihypertensive medications. The metaGRS had a higher C-index (C = 0.623; 95% CI: 0.615 to 0.631) for incident CAD than any of 6 conventional factors (smoking, diabetes, hypertension, body mass index, self-reported high cholesterol, and family history). For men in the top 20% of metaGRS with >2 conventional factors, 10% cumulative risk of CAD was reached by 48 years of age.ConclusionsThe genomic score developed and evaluated here substantially advances the concept of using genomic information to stratify individuals with different trajectories of CAD risk and highlights the potential for genomic screening in early life to complement conventional risk prediction.