PT-mult26, LDpred2-mult and PRS-CS-mult apply PT, LDpred2 and PRS-CS to each discovery summary statistics separately. The most predictive PRS derived from each discovery sample were then used to fit a linear regression in the validation dataset: y~w1PRS1+w2PRS2+⋯+wKPRSK, where PRSk is the standardized PRS for population k, and wk is the corresponding regression coefficient. The optimal hyper-parameter for each discovery sample and the estimated regression coefficients for the linear combination of standardized PRS were used in the independent testing dataset to calculate the final PRS and its performance metrics. We screened the same grid of hyper-parameters for each method (i.e., the p-value threshold for PT; the proportion of causal variants for LDpred2; and the global shrinkage parameter for PRS-CS). The 1KG super-population samples (EUR, EAS, AFR or AMR) whose ancestry matched the discovery sample were used as the LD reference panel.
