Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization.

Abstract

Background: Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRS_CHD) for 5 genetic ancestry groups.

Methods: We derived ancestry-specific and multi-ancestry PRS_CHD based on pruning and thresholding (PRS_PT) and ancestry-based continuous shrinkage priors (PRS_CSx) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRS_CHD in 176,988 individuals across 9 diverse cohorts.

Results: Multi-ancestry PRS_PT and PRS_CSx outperformed ancestry-specific PRS_PT and PRS_CSx across a range of tuning values. Two best-performing multi-ancestry PRS_CHD (ie, PRS_PTmult and PRS_CSxmult) and 1 ancestry-specific (PRS_CSxEUR) were taken forward for validation. PRS_PTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian ancestry (1.56 [1.50-1.61]), Hispanic/Latino ancestry (1.38 [1.24-1.54]), and African ancestry (1.16 [1.11-1.21]). PRS_CSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38-3.00]) and European ancestry (1.65 [1.59-1.71]), lower in East Asian ancestry (1.59 [1.54-1.64]), Hispanic/Latino ancestry (1.51 [1.35-1.69]), and the lowest in African ancestry (1.20 [1.15-1.26]).

Conclusions: The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRS_CHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRS_CHD.