Genomic SEM is a Two-Stage Structural Equation Modeling approach.12–14 In Stage 1, the empirical genetic covariance matrix and its associated sampling covariance matrix are estimated. The diagonal elements of the sampling covariance matrix are squared standard errors (SEs). The off-diagonal elements index the extent to which sampling errors of the estimates are associated, as may be the case when there is sample overlap across GWASs. In Stage 2, a SEM is specified and parameters are estimated by minimizing the discrepancy between the model-implied genetic covariance matrix and the empirical covariance matrix obtained in the previous stage. We evaluate fit with the standardized root mean square residual (SRMR), model χ2, Akaike Information Criteria (AIC), and Comparative Fit Index (CFI; Method).13,15 In a set of simulations we verify key properties of Genomic SEM (Method). We find that Genomic SEM produces unbiased parameter estimates when the correct structural model is specified, and that model fit indices consistently favor the correct model over alternative models. In a second set of simulations, we demonstrate that the inclusion of data from overlapping samples does not bias Genomic SEM parameter estimates or their standard errors.
