two new methods to quantify the benefits of jointly modeling multiple GWAS summary statistics via the coupled shrinkage prior. The workflow for each PRS construction method is shown in Fig. 1. In all the PRS analyses, we use the discovery dataset to estimate the marginal effect sizes of genetic variants and generate GWAS summary statistics for each population; we use the validation dataset, with individual-level genotypes and phenotypes, to tune hyper-parameters for different polygenic prediction methods; and we use the testing dataset, with individual-level genotypes and phenotypes, to evaluate the prediction accuracy of PRS and compute performance metrics using hyper-parameters learnt in the validation dataset. The three datasets comprise non-overlapping individuals. For convenience, we use the target dataset to refer to the combination of validation and testing datasets, which have matched ancestry. For fair comparison, we use 1000 Genomes Project (1KG) Phase 336 super-population samples (European N=503; East Asian N=504; African N=661; Admixed American N=347) as the LD reference panels across different PRS construction methods throughout the paper.
