PROJECT SUMMARY/ABSTRACT Coronary artery disease (CAD) is a leading cause of death among adults in the United States. Its prevalence is highest in individuals of African ancestry. It has been estimated that genetic factors account for 26% to 69% of interindividual variation in CAD risk. Large-scale genome-wide association studies (GWAS) of CAD have mainly been conducted in populations of European and East-Asian ancestries and identified 207 independent loci so far. Few of the identified loci have been replicated in populations of African ancestries. Large-scale genetic study of CAD in African-ancestry populations are lacking. This proposal will efficiently leverage the existing resources of the Population Architecture using Genomics and Epidemiology Consortium, Million Veteran Program and other established cohorts to create the largest-ever sample size for a genetic study of African-ancestry populations comprehensively phenotyped for CAD and related cardiometabolic traits. We propose to address the following Specific Aims. Aim 1 will interrogate the genome using admixture mapping, univariate GWAS, multi-variate GWAS and trans-ethnic GWAS approaches to identify loci associated with CAD in African-ancestry populations. Aim 2 will use phenome-wide association studies, variant-trait hierarchical clustering and integrative genomic analyses to characterize CAD loci and gain insights into phenotypic, physiologic, and mechanistic impacts that underlie the pathophysiology of CAD. Aim 3 will explore the public health impact and clinical relevance of CAD risk variants by constructing polygenic CAD risk scores and identifying pathogenic variants in Mendelian syndromes of CAD genes that are relevant to African-ancestry populations. The construction of population- specific polygenic risk scores and identification of rare and low-frequency pathogenic variants of large effect in Mendelian syndromes of CAD genes will facilitate quantification of CAD risk in individuals of African ancestry and potentially narrow the translational gap towards clinical use of genetic information across diverse populations. The comprehensive cross-trait associations of identified CAD risk loci will facilitate the discovery of subtypes of CAD. Both improved genetic CAD risk classifications and refined CAD sub-phenotyping would help with the implementation of precision medicine in CAD. The new biological insights elucidated from novel loci identified in African-ancestry populations may also be generalized to other populations for the diagnosis, prevention, and treatment of CAD.