Next, we evaluated the predictive performance of different polygenic prediction methods using 33 anthropometric or blood panel traits from UKBB28 (N=314,916–360,388) and BBJ30 (N=71,221–165,419; Supplementary Table 10). All the 33 traits, with two exceptions (Basophil and Eosinophil), had moderate to high cross-population genetic-effect correlations estimated by POPCORN16 (range 0.37–0.85; Supplementary Table 10). We applied single-discovery methods to UKBB or BBJ summary statistics, and used multi-discovery methods to combine UKBB and BBJ GWAS. All target samples are unrelated UKBB individuals that are also unrelated with the UKBB discovery samples. We assigned each target sample to one of the five 1KG super-populations [AFR, AMR (Admixed American), EAS, EUR, SAS (South Asian)] (Methods), and assessed the prediction accuracy in each target population separately, adjusting for age, sex and top 20 principal components (PCs) of the genotypes. For each population, the target dataset was randomly and evenly split into a validation dataset and a testing dataset. The prediction accuracy, measured by variance explained (R2) in linear regression after adjusting for covariates, was averaged across 100 random splits.
