Figure 4: Prediction ${\mathrm{R}}^2$ on UKBB validation individuals of EUR (17,457 – 19,030), AFR (7,954 – 8,598), EAS (1,752 – 1,921), and SAS (9,385 – 10,288) origin based on discovery GWAS from GLGC on EUR (${\text{N}}_{\text{GWAS}}=\mathrm{842,660}-\mathrm{930,671}$), AFR or admixed AFR (${\text{N}}_{\text{GWAS}}=\mathrm{87,760}-\mathrm{92,555}$), Hispanic/Latino (${\text{N}}_{\text{GWAS}}=\mathrm{46,040}-\mathrm{49,582}$), EAS (${\text{N}}_{\text{GWAS}}=\mathrm{82,587}-\mathrm{146,492}$), and SAS (${\text{N}}_{\text{GWAS}}=\mathrm{33,658}-\mathrm{34,135}$).

The LD reference data are from either (a) 1000 Genomes Project (498 EUR, 659 AFR, 347 AMR, 503 EAS, 487 SAS), or (b) UKBB data (PRS-CSx: default UKBB LD reference data which overlaps with our testing samples including 375,120 EUR, 7,507 AFR, 687 AMR, 2,181 EAS, and 8,412 SAS; all other methods: UKBB tuning samples including 10,000 EUR, 4,585 AFR, 1,010 EAS, and 5,427 SAS). The ancestry of UKBB individuals were determined by a genetic ancestry prediction approach (Supplementary Notes). Due to the low prediction accuracy of genetic component analysis and extremely small validation sample size of UKBB AMR, prediction ${\mathrm{R}}^2$ on UKBB AMR is unreliable and thus is not reported here. All methods were evaluated on the ~2.0 million SNPs that are available in HapMap 3 + MEGA, except for PRS-CSx which is evaluated based on the HapMap 3 SNPs only, as implemented in their software. Ancestry- and trait-specific GWAS sample sizes, number of SNPs included, and validation sample sizes are summarized in Supplementary Table 4.1. A random half of the validation individuals is used as the tuning set to tune model parameters, as well as train the SL in CT-SLEB and ME-Bayes SL or the linear combination model in weighted LDpred2, PRS-CSx, and weighted ME-Bayes. The other half of the validation set is used as the testing set to report ${\mathrm{R}}^2$ values for each ancestry, after adjusting for age, sex, and the top 10 genetic principal components. In (b), PRS-CSx and other methods do not have a fair comparison because the UKBB LD reference data provided by the PRS-CSx software (UKBB_PRS-CSx) is much larger than that for other methods, and thus the ${\mathrm{R}}^2$ of PRS-CSx PRS may be inflated due to a big overlap between UKBB_PRS-CSx and the UKBB testing sample.