We next conducted genetic association analyses, estimating the zero-order genetic correlations between factors identified in the best-performing models and a wide range of preregistered phenotypes (https://osf.io/v8q2y/) from the domains of personality, risk taking, physical health, psychiatric traits and disorders, anthropometric traits, cognitive traits, socioeconomic status and reproductive health. These genetic association analyses served two purposes: (1) to explore the genetic relationships between our latent construct(s) and other relevant traits and (2) to compare patterns of genetic correlations in the best-performing two-factor models. This latter purpose allowed us to quantify the degree to which these factors provided meaningfully distinct information about genetic risk for externalizing phenotypes and SUDs. As a further test of this question, we fit models in which the correlations between the two factors and an individual external criterion variable were constrained to be equal and observed the resulting change in χ2. We note, however, that χ2 difference tests are very sensitive to small changes, especially in the presence of a large sample size, and interpret these results with caution. We used a Bonferroni corrected p value <0.05 to judge statistical significance.
