Having established that single GC correction is sufficient to identify SNPs that are likely to be truly associated with height, we next performed a series of analyses using GWAS data from five independent validation studies to quantify the fraction of phenotypic variance explained by SNPs selected from the GCTA-COJO analyses7 of the meta-analysis data, which excluded data from the validation studies, at a range of statistical thresholds, and to quantify the accuracy of predicting height using these selected SNPs (Online Methods). We first developed a new method that uses within-family prediction to partition the variance of the SNP-based predictor into components due to real SNP effects, errors in estimating SNP effects, and population stratification (Online Methods), and applied the method to data on full-sib pairs from three of the five validation studies (Online Methods). Consistently across the three studies, all 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
