We performed GWASs of 5 quantitative traits and 3 common diseases with different genetic architectures in 313,284 unrelated UKB participants of EUR ancestry (Supplementary Note 5). The 5 quantitative traits are standing height (Height), body mass index (BMI), HDL and LDL cholesterol (HDL and LDL) and triglycerides (TG); and the 3 common disease (cohort prevalence >5% in each ancestry) are asthma, type 2 diabetes (T2D) and hypertension (HTN). We report in Supplementary Table 1, the numbers of quasi-independent GWS SNPs for each trait and disease (Methods). We used these GWS SNPs to create polygenic predictors of each trait and disease then evaluated their predictive performances in the validation sub-samples of the UKB as described in the Methods section. We evaluated the accuracy of PGS of diseases on the liability scale using ancestry-specific disease prevalence estimated in the UKB and the transformation proposed previously by Lee and colleagues31 (Methods). Note that using ancestry-specific prevalence from previous population studies32–35 did not change our results (Supplementary Fig. 9). We also assessed the predictive accuracy of PGS based upon sub-significant SNPs (Supplementary Note 6)
