Novel Methods for Multi-ancestry Polygenic Prediction and their Evaluations in 5.1 Million Individuals of Diverse Ancestry

Polygenic risk scores are becoming increasingly predictive of complex traits, but their suboptimal performance in non-European ancestry populations raises questions about their clinical applications and impact on health inequities. We develop CT-SLEB, a powerful and scalable method to calculate PRS based on ancestry-specific GWAS summary statistics from multi-ancestry training samples by integrating multiple techniques, including clumping and thresholding, empirical Bayes and super learning. We evaluate the performance of the proposed method and nine alternatives using large-scale simulated GWAS on ∼19 million common variants and large datasets from 23andMe Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, which include up to 1.18 million individuals from four non-European populations across thirteen complex traits. Results show that the proposed method can substantially improve the performance of PRS in non-European populations relative to simple alternatives and has comparable or superior performance relative to a recent method that requires a higher order of computational time. Furthermore, our simulation studies provide novel insights to sample size requirements and the effect of SNP density on multi-ancestry risk prediction.