each discovery GWAS. PRS-CSx inherits many features from PRS-CS, including robustness to varying genetic architectures, multivariate modeling of population-specific LD patterns, and computational efficiency. In this work, we used pre-calculated 1KG Phase 3 LD reference panels43 for EUR, EAS, AFR and AMR populations, which were constructed for HapMap3 variants with MAF >1%. We recommend using 1,000*K Markov Chain Monte Carlo (MCMC) iterations with the first 500*K steps as burin-in in Gibbs sampling, where K is the number of discovery populations, reflecting the growing number of unknown parameters with the number of discovery GWAS jointly modelled. For a fixed global shrinkage parameter ϕ, PRS-CSx returns posterior SNP effect size estimates for each discovery population, which can be used to calculate K population-specific PRS in the target sample. For each ϕ value, we fitted a linear (or logistic) regression of the z-scored PRS (one for each discovery population) in the validation dataset: y~wϕ,1PRSϕ,1+wϕ,2PRSϕ,2+⋯+wϕ,KPRSϕ,K, where y is the trait of interest, PRSϕ,k is the standardized PRS for population k, and wϕ,k is the regression coefficient. We screened four different ϕ values, 10−6, 10−4, 10−2 and 1.0, in this work. The ϕ value and the corresponding regression coefficients for the linear combination of PRS
