Coronary artery disease is the leading cause of death worldwide. Recently, hundreds of genomic loci have been shown to increase risk for the disease, however, the molecular mechanisms underlying signals from risk loci remain largely unclear. Here, we integrate the latest statistics of coronary artery disease genetics from over one million individuals with epigenetic data from 45 cell types to identify genes and transcription factors whose regulation is affected by variants. Applying two statistical approaches, we identify 1580 candidate disease genes, including 23.5% non-coding RNA genes. Enrichment analysis and phenome-wide association studies link the candidate genes to disease-specific pathways and risk factors. We conduct a proof-of-concept biological validation for the non-coding RNA gene IQCH-AS1 via knockout in a human preadipocyte strain. Our study not only pinpoints CAD candidate genes in a cell type-specific manner but also highlights the roles of an understudied ncRNA gene in CAD genetics.
Hecker et al. (Tue,) studied this question.