The Sτ covariance matrices for the causal annotations were then used as input for Genomic SEM models. The two types of population generating models—a common factor and correlated factors model—were run for each annotation. For all causal annotations, Genomic SEM estimates closely matched the parameters specified in the generating population (Supplementary Table 8 and Supplementary Fig. 11). In addition, the ratio of the mean model SEs over the empirical SEs was near 1. Model fit statistics (CFI, AIC, and model χ2) also generally favored the generating model for a particular annotation (Supplementary Table 9). This was least true for the H3K27ac annotation. This is unsurprising as the population-generating model for the H3K27ac annotation—a correlated factors model with a factor correlation of 0.7—most closely matched the competing common factor model. Collectively, these results indicate that Stratified Genomic SEM produces unbiased parameter estimates and standard errors for S0 and Sτ, that Sτ shows specificity to the causal annotations of interest, and that model fit indices generally favor the appropriate model.
