We then assessed whether multi-discovery methods can improve cross-population polygenic prediction. Specifically, we used different multi-discovery methods to combine GWAS summary statistics from 100K EUR samples and 20K non-EUR (EAS or AFR) samples as the discovery dataset, and evaluated their predictive performance in independent target samples (Fig. 2; Supplementary Table 1). Figure 2 shows that, in general, multi-discovery methods improved prediction accuracy over their single-discovery counterparts (i.e., PT-meta or PT-mult vs. PT; LDpred2-mult vs. LDpred2; PRS-CS-mult vs. PRS-CS), reflecting the increase in discovery sample size. When the target population was EUR, the improvement of PRS-CSx and PRS-CS-mult over PRS-CS was marginal, suggesting that the benefits of adding a small non-EUR GWAS to the discovery dataset can be limited in this case. However, when predicting into non-EUR populations, multi-discovery methods clearly outperformed single-discovery methods, with Bayesian methods (LDpred2-mult, PRS-CS-mult and PRS-CSx) demonstrating a larger advantage over PT-based methods. PRS-CSx provided an additional increase of 10.6% and 16.4% in R2 over PRS-CS-mult when the target population was EAS and AFR, respectively, demonstrating that joint modeling of the genetic architecture across populations using the coupled continuous shrinkage prior improves polygenic prediction in non-EUR populations.
