Using this procedure, we performed 100 runs of Genomic SEM on raw individual-level genotype data for which we simulated multivariate phenotypic data to conform to a single genetic factor model (a latent trait that partially causes 5 observed outcomes). Across the 100 simulations, Genomic SEM estimates closely matched the parameters specified in the generating population (Supplementary Figure 36). Model SEs also closely matched the standard deviations of parameter estimates. We also compared fit statistics (CFI, AIC, and model χ2) for the correctly specified common factor model and two deliberately misspecified models: (i) a model in which all indicators were constrained to have the same factor loading, and (ii) a model for which the loading of the third indicator was set to 0. As expected, results indicated that the common factor model matching the generating population was favored ≥ 99% of the time across model fit indices (Supplementary Figure 37).
