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 results showed that ~670 SNPs at P<5×10−8 and ~9,500 SNPs at P<5×10−3 captured ~16% and ~29% of phenotypic variance respectively (Table 1), which was also consistent with the estimates inferred from the within-family prediction analysis. As shown in equation [19], prediction R2 is not equal to the variance explained but a function of the variance of true SNP effects and the error variance in estimating SNP effects, in the absence of population structure. This is demonstrated in Figure 2, where at thresholds below genome-wide significance, variance explained is higher than the prediction accuracy, because the latter is deflated both by imprecise estimates of effect sizes (estimation errors) and by inclusion of SNPs that are not associated with height. The estimate of variance explained by all the HapMap3 (ref. 11) SNPs without SNP selection was ~50% (Table 1), consistent with previous estimates4,5. Thus, a group of ~9,500 SNPs (representing <1% of common SNPs) selected at P<5×10−3, explained ~29% of phenotypic variance.
