Chunk #99 — 7.0 Recommendations to Advance Endophenotype Genetics — 7.3 Adequate power to detect individual effects is crucial but almost never attained in existing endophenotype genetic association studies — 7.3.2 Power in GREML
Like genetic association studies, GREML also requires large samples. Figure 2 provides sample size estimates for univariate SNP heritability estimates (panel A) and genetic correlations between pairs of traits (panel B), based on R code made publicly available by Jian Yang, the developer of the GCTA software, which introduced the GREML approach (Yang et al., 2010). (An online power calculator is also available at http://cnsgenomics.com/shiny/gctaPower/.) Panel A plots the power for finding that SNP heritability is significant for a range of sample sizes and a range of levels of trait SNP heritability (h2 = .20, .40, and .60). To have adequate power to find a significant SNP heritability estimate requires 1,500 subjects if heritability is high (.60), and 4,450 if heritability is relatively low (.20). In panel B of Figure 2 we show power required to estimate bivariate genetic correlations. For the sake of simplicity, power estimates for genetic correlations assume that the heritability of both traits is the same, using the same range of true heritability values as in panel A. Power is plotted for phenotypic correlations between the
