the partitioned variance components increased as a less stringent significance level was used for SNP selection in the discovery sample and the error variance increased more dramatically than the genetic variance when more SNPs selected at a less significance level were included in the predictor (Fig. 2a–c). We demonstrated the partitioning of variance due to population stratification by the within-family prediction analyses with and without adjusting for principal components (PCs) (Supplementary Fig. 5). The results again confirmed that the impact of population stratification on the top associated SNPs was minor and demonstrated that the variation in the predictor due to true SNP effect, estimation error and population stratification was quantifiable. We next inferred, using these partitioned variance components from the within-family prediction analysis, how well different selected sets of SNPs would predict height in independent samples. We showed that the observed prediction accuracy (squared correlation between phenotype and predictor, R2) in five different population-based cohorts was highly consistent with the values inferred from the within-family based analyses, with prediction accuracy peaking at ~17% using the ~1,900 SNPs reaching P<5×10−5 (Fig. 2d). Finally we estimated variance explained by the selected SNPs in population-based studies using the GCTA-GREML method4,8 (Fig. 2e). The
