We have presented PRS-CSx, a Bayesian polygenic prediction method that integrates GWAS summary statistics from multiple populations to improve the prediction accuracy of PRS in ancestrally diverse samples. PRS-CSx leverages the correlation of genetic effects and LD diversity across populations to more accurately localize association signals and increase the effective sample size of the discovery dataset, while accounting for population-specific allele frequency and LD patterns. We have shown, via simulation studies, that PRS-CSx robustly improves cross-population prediction over existing methods across traits with varying genetic architectures, genetic overlaps between populations, and discovery GWAS sample sizes. Using quantitative traits from multiple biobanks as well as schizophrenia cohort studies of European and East Asian ancestries, we have further demonstrated the PRS-CSx can leverage large-scale European GWAS to boost the accuracy of polygenic prediction in non-European populations, for which ancestry-matched discovery GWAS may be orders of magnitude smaller in sample size.
