PRS-CSx is designed to flexibly model GWAS summary statistics from multiple populations where SNP effect sizes and/or LD patterns differ. For two or more GWAS conducted in independent samples from the same population where effect sizes and LD patterns are expected to be highly concordant, a fixed-effect meta-analysis is probably the optimal approach to combine the GWAS and maximize statistical power. However, we do not recommend meta-analyzing summary statistics across populations and applying single-discovery methods (e.g., LDpred2 or PRS-CS) to the meta-GWAS for two reasons: (1) The LD pattern of a cross-ancestry meta-analyzed GWAS is a mixture of population-specific LD, which is difficult to appropriately model. Rather, accurately modeling LD patterns is often crucial to the performance of Bayesian polygenic prediction methods. (2) The predictive performance of these “meta” methods heavily depends on whether the assumption of the fixed-effect meta-analysis (i.e., consistent SNP effects across populations) is accurate. These methods are thus less adaptive to a wide range of cross-population genetic architectures compared with PRS-CSx or the “mult” methods. That said, many existing studies have only released summary statistics from
