We used GCTA-COJO analysis (Online Methods) to select the top associated SNPs at a range of stringent significance levels (5×10−3, 5×10−4, 5×10−5, …, 5×10−8) for estimation and prediction analyses. We then quantified the variance explained by those selected SNPs using a three-stage analysis, i.e. within-family prediction, GCTA-GREML analysis and population based prediction, in five validation studies (B-PROOF, FRAM, QIMR, TwinGene and WTCCC-T2D). To avoid sample overlap, we repeated the main GWAS meta-analysis and the multiple-SNP analysis five times, each time excluding one of the five validation studies. This approach ensured complete independence between data used to discover SNPs, and data used to estimate how much variance in height these SNPs explained and how well they predicted height. For the within-family prediction analyses, we selected 1,622, 2,758 and 1,597 pairs of full sibs from the QIMR, TwinGene and FRAM cohorts, respectively, with one sib pair per family. For the whole-genome estimation and prediction analyses, we used GCTA-GRM8 to estimate the genetic relatedness between individuals and selected unrelated individuals with pairwise genetic relatedness <0.025 in each of the five studies, i.e. B-PROOF (n = 2,555), FRAM (n = 1,145), QIMR (n = 3,627), TwinGene (n = 5,668) and WTCCC-T2D (n = 1,914).
