Genomic SEM is a Two-Stage Structural Equation Modeling approach.12–14 In the first stage, the empirical genetic covariance matrix and it sampling covariance matrix are estimated. In principle, these matrices may be obtained using a variety of methods for estimating SNP heritabilities, genetic covariances, and their joint estimation errors. Here we use a novel version of LDSC that accounts for potentially unknown degrees of sample overlap by populating the off-diagonal elements of the sampling covariance matrix. The same strengths, as well as assumptions and limitations, that are known to apply to LDSC39,40 apply to its extension used here and to Genomic SEM. In Stage 2, the user specifies a multivariate system of regression and covariance associations involving the genetic components of phenotypes with one another and/or more general latent factors. These associations are represented by parameters that may be fixed or freely estimated, so long as the model is statistically identified (e.g., the number of freely estimated parameters does not exceed the number of nonredundant elements in the genetic covariance matrix being modeled). A set of parameters (θ) is estimated such
