Using omics data and genome editing methods to decipher GWAS loci associated with coronary artery disease

Abstract

Coronary artery disease (CAD) is due to atherosclerosis, a pathophysiological process that involves several cell-types and results in the accumulation of lipid-rich plaque that disrupt the normal blood flow through the coronary arteries to the heart. Genome-wide association studies have identified 1000s of genetic variants robustly associated with CAD or its traditional risk factors (e.g. blood pressure, blood lipids, type 2 diabetes, smoking). However, gaining biological insights from these genetic discoveries remain challenging because of linkage disequilibrium and the difficulty to interpret the functions of non-coding regulatory elements in the human genome. In this review, we present different statistical methods (e.g. Mendelian randomization) and molecular datasets (e.g. expression or protein quantitative trait loci) that have helped connect CAD-associated variants with genes, biological pathways, and cell-types or tissues. We emphasize that these various strategies make predictions, which need to be validated in orthologous systems. We discuss specific examples where the integration of omics data with GWAS results has prioritized causal CAD variants and genes. Finally, we review how targeted and genome-wide genome editing experiments using the CRISPR/Cas9 toolbox have been used to characterize new CAD genes in human cells. Researchers now have the statistical and bioinformatic methods, the molecular datasets, and the experimental tools to dissect comprehensively the loci that contribute to CAD risk in humans.