From genome-wide association studies to functional genomics: new insights into cardiovascular disease

Abstract

Genome-wide association studies (GWASs) for coronary artery disease (CAD) have identified more than 30 variants robustly associated with CAD risk. The majority are not associated with conventional risk factors but highlight novel pathways, including cellular proliferation. Although some risk variants are nonsynonymous coding variants resulting in an amino acid change in the encoded protein, the majority are in noncoding regions of the genome and may encompass multiple signals of variable effect. The use of genetic data for development of new therapies requires the identification of causative genetic variants and elucidation of the molecular mechanisms by which they predispose to CAD. The computational and laboratory approaches for the interpretation of GWAS data are discussed with a particular focus on noncoding variants, including the study of regulatory elements, the evaluation of nonsynonymous coding variants, and expression quantitative trait locus analysis for the integration of GWAS data with genome-wide messenger RNA expression data.