When implementing Genomic SEM, users should be aware of the limitations and assumptions of the method. First, because Genomic SEM is a method for modeling genetic covariance matrices, it relies on the same assumptions as the method used to estimate genetic covariances, and best practices for implementing such method should be followed. For example, when LDSC is used to construct the genetic covariance matrix, SNPs should not first be pruned for linkage disequilibrium, and summary statistics for different phenotypes should be obtained from ethnically homogeneous samples of similar ancestral backgrounds.4 With respect to selecting between competing models, users should take into account a variety of both absolute fit (e.g. SRMR and model χ2) and relative fit indices (e.g. AIC and χ2 difference). We provide general standards for absolute model fit in the Method section. Finally, a formal power analysis should take into account specific characteristics of the summary data, the genetic architecture of the phenotypes, and the model to be specified. This can typically be achieved with simulation. Generally speaking, we would expect power to detect SNP effects on a
