Consistent with previous observations, PRS trained on EAS GWAS were more predictive in EAS cohorts than those trained on PGC EUR summary statistics15, despite the larger sample size for the EUR GWAS (Fig. 4a; Supplementary Table 17). Among single-discovery methods examined, LDpred2 and PRS-CS performed substantially better than PT, highlighting the importance of modeling LD patterns for highly polygenic traits. By integrating EUR and EAS summary statistics, Bayesian multi-discovery methods dramatically increased the prediction accuracy relative to single-discovery methods. Compared with LDpred2, the best-performing single-discovery method in this analysis, PRS-CSx increased the median R2 on the liability scale (assuming 1% of disease prevalence) from 0.043 (LDpred2 trained on EAS GWAS) and 0.031 (LDpred2 trained on EUR GWAS) to 0.063, a relative increase of 45.4% and 104.9%, respectively. PRS-CSx also approximately doubled the prediction accuracy of PT-meta and PT-mult, with a relative increase of 135.9% (from 0.027 to 0.063) and 95.3% (from 0.032 to 0.063) in the median liability R2, respectively. In addition, PRS-CSx provided consistent, although relatively small, improvement over LDpred2-mult (relative increase in median R2: 8.7%) and PRS-CS-mult (relative
