BBJ GWAS, suggesting that PRS-CSx can leverage large-scale EUR GWAS to improve the prediction in non-EUR populations. PRS-CSx also had a median improvement of 10.5% (two-sided Wilcoxon signed-rank test Pwilcoxon=3.90E-4) and 8.3% (Pwilcoxon=2.84E-6) relative to LDpred2-mult and PRS-CS-mult, respectively, demonstrating the benefits of jointly modeling summary statistics from multiple populations in trans-ancestry prediction (Fig. 3a, middle panel; Supplementary Table 11). When the target population did not match any of the discovery samples, PRS-CSx was still able to increase the prediction accuracy. For example, when predicting into the AFR population, the median improvements of PRS-CSx relative to LDpred2 and PRS-CS trained on UKBB GWAS were 45.1% and 16.9%, respectively, and the median improvements relative to LDpred2-mult and PRS-CS-mult were 22.2% (Pwilcoxon=2.38E-5) and 7.1% (Pwilcoxon=2.99E-5), respectively (Fig. 3a, right panel; Supplementary Table 11).
