Genomic SEM’s Multivariable LD score regression method (Grotzinger et al., 2019) was first used to estimate the genetic covariance matrix (S) and sampling covariance matrix (V) for the eight psychiatric traits. Quality control for this step included removing SNPs with an MAF < 1%, information scores < .9, SNPs from the MHC region, and filtering SNPs to HapMap3. All SNP effects were standardized using the sumstats function in Genomic SEM. To examine genome-wide factor structure, models using only the genetic covariance and sampling covariance matrix were fit. Genomic SEM provides indices of model fit—standardized root mean square residual (SRMR), model 2, Akaike Information Criteria (AIC), and Comparative Fit Index (CFI)—that can be used to determine how well the proposed model captures the observed data. Model fit for the common factor model in which the loadings were freely estimated was only fair, (2 (20) = 313.94, AIC = 345.9, CFI = .786, SRMR = .149), suggesting that there were nuances in the genetic architecture not fully captured by a single cross-trait index of genetic risk. An exploratory factor analysis (EFA) of
