Coronary artery disease (CAD) remains the leading cause of death in the western world despite significant advances in early detection and extensive use of lipid-lowering and anti-hypertensive drugs. To date no single drug has been developed to target the primary disease process in the vessel wall. A more complete understanding of the disease susceptibility is urgently needed to develop additional therapies. Common forms of atherosclerosis involve environmental factors, hundreds of genetic variations, and their interactions, each of which exert a relatively small effect on disease susceptibility. The most recent human GWAS identified 304 independent variants that are associated with increased risk for CAD. However, most of the underlying genes and the related mechanisms of how these loci contribute to the disease process remain unknown. This proposal outlines a comprehensive data analysis plan to predict the causal genes and pathways underlying the GWAS loci by combining publically accessible data from expression quantitative loci studies, curated genetic association databases, co-expression and Bayesian gene expression networks, and regulatory element predictions from DNAse-Seq datasets from ENCODE and Roadmap Epigeneomics studies. The overall goal of the proposed studies is to integrate systems biology and computational pipelines leading to mechanistic predictions of the gene networks that are perturbed by CAD.
Coronary artery disease is the primary cause of death in the Western world. Understanding the biologic networks that underlie the complex interactions in coronary artery disease is required for disease prevention, diagnosis, and treatment.
