Yang et al. (2014) explored the genetic architecture of AD in 1,719 AA AD cases and 1,156 AA controls that were included in a previous GWAS on AD (Gelernter et al., 2014a). Here, they used the GCTA method and estimated that, in the unrelated subjects (n=1,838), 22.1% (s.e. 17.7%) of the risk for AD was attributable to common SNPs. A related analysis using a common variance component to account for shared environment (Do et al. 2012), yielded a very similar estimate (i.e., 23.9%; s.e. 9.3%). The higher estimates of SNP heritability in this study than those in Vrieze et al. (2013) potentially reflect the samples’ different population composition, affection rate, and statistical power. Estimating the variance explained by top-ranking SNPs for nine different P-value thresholds, Yang et al. (2014) found that, at the least stringent threshold (P≤0.001), the top SNPs explained only 0.68% of the phenotypic variance, and that the variance explained plateaued at P≤0.01 (17.6%), suggesting that the SNP heritability signal is primarily coming from SNPs with P≤0.01. When they partitioned the genome by chromosome and by genic/intergenic regions
