Given the modest effect sizes observed for the validated variants associated with height (Table 1; average = 0.4 cm per additional allele), it is not surprising that the quantile-quantile plots for the individual GWA studies are essentially indistinguishable from the null expectation (Fig. 1). Indeed, we calculate that a study of 3,000 unrelated individuals has 1% power to detect a variant (minor allele frequency 10%) that increases height by 0.4 cm at a statistical threshold of P = 1 × 10-5. In comparison, a study of 16,000 individuals has 72% power to identify the same variant (in fact, there is a slight loss in power when using meta-analytic methods to combine results). Our discovery of valid associations by combining individual studies with nearly null P-value distributions highlights the importance of using large datasets to find common variants with small effects. When we remove the 12 validated height variants (and nearby correlated SNPs) from the meta-analysis results, the number of low P values still exceeds the null expectation (Fig. 2a, filled squares). Furthermore, the 10,000 SNPs with the best P values
