We have broadly classified polygenic prediction methods into two categories: single-discovery methods, which train PRS using GWAS summary statistics from a single discovery sample; and multi-discovery methods, which combine GWAS summary statistics from multiple discovery samples for PRS construction. In this work, we assess and compare within- and cross-population predictive performance of three representative single-discovery methods: (i) LD-informed pruning and p-value thresholding (PT)35; (ii) LDpred220; (iii) PRS-CS19; and four multi-discovery methods in addition to PRS-CSx: (i) PT-meta; (ii) PT-mult26; (iii) LDpred2-mult; and (iv) PRS-CS-mult. PT-meta applies PT to the meta-analyzed discovery GWAS summary statistics. The three “mult” methods respectively apply PT, LDpred2 and PRS-CS to each discovery GWAS separately, and linearly combine the resulting PRS. PT-mult has been demonstrated to improve the prediction in recently admixed populations26. Here we have extended the idea of PT-mult to LDpred2-mult and PRS-CS-mult, creating two new methods to quantify the benefits of jointly modeling multiple GWAS summary statistics via the coupled shrinkage prior. The workflow for each PRS construction method is shown in Fig. 1. In all the PRS analyses, we use the discovery
