Clinical implementation of new prediction models requires evaluation of their validity and utility in a broad range of intended use populations. Here we outline the development and validation of multiple ancestry-specific Polygenic Risk Scores (PRSs) for Coronary Artery Disease (CAD) using a dataset comprising 29,389 individuals from multiple cohorts and diverse genetic ancestry groups. We leverage summary statistics from multiple genome-wide association studies comprising over 850,000 individuals to develop calibrated CAD PRSs with an average Odds Ratio per Standard Deviation (ORxSD) of 1.57 (SD = 0.14). Relative to competing scores, across major genetic ancestry groups these PRSs identify between 26 and 184 additional high risk individuals for every 1,000 people screened. We infer ancestry- specific high risk PRS thresholds and apply these to independent test datasets to identify between 12% and 24% of individuals who are at greater than twice the polygenic risk of CAD compared to the rest of the population. Using these PRSs to reclassify borderline or intermediate 10 year Atherosclerotic Cardiovascular Disease (ASCVD) risk in a cohort of 9,691 individuals improved the classification of those at increased risk of both CAD (Net Reclassification Improvement (NRI) = 13.14% (95%CI 9.23-17.06%)) and ASCVD (NRI = 10.70 (95%CI 7.35-14.05)). Our analyses demonstrate that using PRSs as Risk Enhancers can improve clinical 10 year ASCVD risk assessments and provide an approach for utilizing polygenic information to guide ASCVD prevention efforts.