Rationale: Coronary artery disease (CAD) is a complex phenotype driven by genetic and environmental factors. Ninety-seven genetic risk loci have been identified to date, but the identification of additional susceptibility loci might be important to enhance our understanding of the genetic architecture of CAD.
Objective: To expand the number of genome-wide significant loci, catalog functional insights, and enhance our understanding of the genetic architecture of CAD.
Methods and results: We performed a genome-wide association study in 34 541 CAD cases and 261 984 controls of UK Biobank resource followed by replication in 88 192 cases and 162 544 controls from CARDIoGRAMplusC4D. We identified 75 loci that replicated and were genome-wide significant (P<5×10-8) in meta-analysis, 13 of which had not been reported previously. Next, to further identify novel loci, we identified all promising (P<0.0001) loci in the CARDIoGRAMplusC4D data and performed reciprocal replication and meta-analyses with UK Biobank. This led to the identification of 21 additional novel loci reaching genome-wide significance (P<5×10-8) in meta-analysis. Finally, we performed a genome-wide meta-analysis of all available data revealing 30 additional novel loci (P<5×10-8) without further replication. The increase in sample size by UK Biobank raised the number of reconstituted gene sets from 4.2% to 13.9% of all gene sets to be involved in CAD. For the 64 novel loci, 155 candidate causal genes were prioritized, many without an obvious connection to CAD. Fine mapping of the 161 CAD loci generated lists of credible sets of single causal variants and genes for functional follow-up. Genetic risk variants of CAD were linked to development of atrial fibrillation, heart failure, and death.
Conclusions: We identified 64 novel genetic risk loci for CAD and performed fine mapping of all 161 risk loci to obtain a credible set of causal variants. The large expansion of reconstituted gene sets argues in favor of an expanded omnigenic model view on the genetic architecture of CAD.
Keywords: computational biology; coronary artery disease; genetics; genome-wide association study; sample size.
© 2017 The Authors.