Background: Coronary Artery Disease (CAD) represents the leading cause of mortality and morbidity worldwide despite declines in the prevalence of environmental risk factors. This trend has drawn attention to the risk conferred by genetic variation. Twin and linkage studies demonstrate a profound hereditary risk for CAD, especially in young individuals. Rare genetic variants conferring high risk for extreme disease phenotypes can provide invaluable insight into novel mechanisms underlying CAD development. Methods: Whole exome sequencing was performed to characterize rare protein-altering variants in 52 early-onset CAD (EOCAD) patients encompassing the DECODE study. The enrichment of Mendelian dyslipidemias in EOCAD was assessed through interrogation of pathogenic mutations among known lipid genes. The identification of novel genetic CAD associations was conducted through case-only and case-control approaches across all protein-coding genes using rare variant burden and variance component tests. Lastly, beta coefficients for significant risk genes from the European population in the Early-onset Myocardial Infarction (EOMI) cohort (N=552) were used to construct calibrated, single-sample rare variant gene scores (RVGS) in DECODE Europeans (N=39) and a local European CAD-free cohort (N=77). Results: A 20-fold enrichment of Familial hypercholesterolemia mutation carriers was detected in EOCAD cases compared to CAD-free controls (P=0.005). Association analysis using EOMI Europeans revealed exome-wide and nominal significance for two known CAD/MI genes: CELSR2 (P=1.1x10-17) and APOA5 (P=0.001). DECODE association revealed exome-wide and nominal significance for genes involved in endothelial integrity and immune cell activity. RVGS based upon beta coefficients of significant CAD/MI risk genes were significantly increased in DECODE (z-score=1.84; p=0.03) and insignificantly decreased among CAD-free individuals (z-score=-1.61; p=0.053). Conclusion: Rare variants play a pivotal role in the development early CAD through Mendelian and polygenic mechanisms. Construction of RVGS that are calibrated against population and technical biases can facilitate discovery of single-sample and cohort-based associations beyond what is detectable using standard methods. / Thesis / Master of Science (MSc)