PRS-CSx extends PRS-CS19, a recently developed Bayesian polygenic modeling and prediction framework, to improve cross-population polygenic prediction by integrating GWAS summary statistics from multiple ancestry groups (Methods). PRS-CSx uses a shared continuous shrinkage prior to couple SNP effects across populations, which enables more accurate effect size estimation by sharing information between summary statistics and leveraging LD diversity across discovery samples. The shared prior allows for correlated but varying effect size estimates across populations, retaining the flexibility of the modeling framework. In addition, PRS-CSx explicitly models population-specific allele frequencies and LD patterns, and inherits from PRS-CS the computational advantages of continuous shrinkage priors, and the efficient and robust posterior inference algorithm (Gibbs sampling). Given GWAS summary statistics and ancestry-matched LD reference panels, PRS-CSx calculates one polygenic score for each discovery sample, and integrates them by learning an optimal linear combination to produce the final PRS (Fig. 1).
