We proceeded with confirmatory factor analysis to formally model genetic covariances with Genomic SEM, which is unbiased by sample overlap and sample-size imbalances10,19. As indicated by its model fit indices: χ2(44) = 8007.35; Akaike information criterion (AIC) = 8051.35; comparative fit index (CFI) = 0.662; standardized root-mean-square residual (SRMR) = 0.161, we found that a common factor model with 11 phenotypes did not satisfy our preregistered criteria (i.e., CFI and SRMR >0.9 and <0.08, respectively). Two more complicated specifications were tested, a correlated three-factor model (i.e., akin to the best-fitting exploratory model) and a bifactor model (Supplementary Table 7), but neither of these two models met the criteria or provided a parsimonious interpretation. Finally, we estimated a revised and less complex common factor model with the seven phenotypes (Table 1 and Figure 1A) that displayed moderate-to-large (i.e., ≥ 0.5) loadings on the single factor estimated in the first common factor model with 11 phenotypes. The revised common factor model with seven externalizing phenotypes provided the best fit across all specifications, and it closely approximated the observed genetic covariance matrix (i.e.,
