Consistent with simulation results, Bayesian multi-discovery methods examined here (LDpred2-mult, PRS-CS-mult and PRS-CSx) often outperformed published single-discovery methods and PT-based multi-discovery methods, suggesting the importance of integrating available GWAS summary statistics and appropriately accounting for population-specific LD patterns in cross-population prediction (Fig. 3; Supplementary Table 11). The improvement of PRS-CSx in prediction accuracy relative to LDpred2 and PRS-CS trained on UKBB summary statistics (which on average were more accurate than PRS trained on BBJ GWAS), and LDpred2-mult and PRS-CS-mult (which were often the second and third best multi-discovery method) depended on the target population.
