A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease

TLDR

Identifying individuals at highest risk of coronary artery disease before onset remains a public health priority, and genome‑wide polygenic scores have been developed to stratify risk based on its substantial inherited component. The study develops an improved polygenic score, GPS Mult, integrating genome‑wide association data from five ancestries and ten CAD risk factors. GPS Mult was constructed by integrating GWAS data from >269,000 CAD cases and >1,178,000 controls across five ancestries and ten CAD risk factors. GPS Mult showed strong associations with prevalent (OR 2.14 per SD) and incident CAD (HR 1.73 per SD) in European UK Biobank participants, identified 20 % of the population with 3‑fold increased risk and 3 % of healthy individuals with future risk equivalent to existing disease, and outperformed all previously published CAD polygenic scores across African, European, Hispanic, and South Asian cohorts.

Abstract

Abstract Identification of individuals at highest risk of coronary artery disease (CAD)—ideally before onset—remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPS Mult , that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPS Mult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10–2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPS Mult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70–1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPS Mult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPS Mult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.