Lastly, we evaluated the predictive performance of different polygenic prediction methods for dichotomous traits. We used schizophrenia as an example, for which large-scale EUR and EAS GWAS along with multiple individual-level cohorts are available (Supplementary Table 16). Specifically, we used GWAS summary statistics derived from the Psychiatric Genomics Consortium (PGC) wave 2 EUR samples (33,640 cases and 43,456 controls)34 and 10 PGC EAS cohorts15 (7,856 cases and 11,562 controls) as the discovery dataset. For the additional 7 EAS cohorts which we had access to individual-level data, we set aside one cohort (KOR1; 687 cases and 492 controls) as the validation dataset (for hyper-parameter tuning), and applied a leave-one-out approach to the remaining 6 cohorts. More specifically, we in turn used one of the 6 cohorts as the testing dataset, and meta-analyzed the remaining 5 cohorts with the 10 PGC EAS cohorts using an inverse-variance-weighted meta-analysis to generate the discovery GWAS summary statistics for the EAS population. The prediction accuracy of different PRS construction methods was then evaluated in the left-out (testing) cohort, adjusting for sex and top 20 PCs.
