In almost all simulation scenarios, SNP selection by ADM training dataset and weights calculated by ADM (regardless of SNP selection approaches) performed the worst. Only in the simulation scenario in which there were two causal SNPs with one being polymorphic only in YRI, computation of weights in the ADM training data is sometimes advantageous over EA weights. However, this was true only when ADM12, 0.2, was the training dataset, but not when ADM12,0.4, was the training dataset. Other than that, both EA and META SNP selections and weights constructions usually performed similarly, with a few more settings in which META weights outperformed EA weights. We do note that all types of PRS suffered from outlying scenarios: specific combinations of causal SNP(s) in which one PRS produced extremely large RMSPEs. However, on average we see better performance of PRSs based on the META and EA GWAS relative to the other two selection approaches when the causal SNPs’ effect sizes are the same across populations.
