A multivariate approach to understanding the genetic overlap between externalizing phenotypes and substance use disorders

Substance use disorders (SUDs) are phenotypically and genetically correlated with each other and with other psychological traits characterized by behavioural under‐control, termed externalizing phenotypes. In this study, we used genomic structural equation modelling to explore the shared genetic architecture among six externalizing phenotypes and four SUDs used in two previous multivariate genome‐wide association studies of an externalizing and an addiction risk factor, respectively. We first evaluated five confirmatory factor analytic models, including a common factor model, alternative parameterizations of two‐factor structures and a bifactor model. We next explored the genetic correlations between factors identified in these models and other relevant psychological traits. Finally, we quantified the degree of polygenic overlap between externalizing and addiction risk using MiXeR. We found that the common and two‐factor structures provided the best fit to the data, evidenced by high factor loadings, good factor reliability and no evidence of concerning model characteristics. The two‐factor models yielded high genetic correlations between factors (rgs ≥ 0.87), and between the effect sizes of genetic correlations with external traits (rg ≥ 0.95). Nevertheless, 21 of the 84 correlations with external criteria showed small, significant differences between externalizing and addiction risk factors. MiXer results showed that approximately 81% of influential externalizing variants were shared with addiction risk, whereas addiction risk shared 56% of its influential variants with externalizing. These results suggest that externalizing and addiction genetic risk are largely shared, though both constructs also retain meaningful unshared genetic variance. These results can inform future efforts to identify specific genetic influences on externalizing and SUDs. In this study, we investigated the shared genetic architecture between externalizing and addiction risks. We explored alternative structural models that capture their covariation, examined their correlations with external traits and quantified their degree of polygenic overlap. Overall, we found that externalizing and addiction risks share a substantial proportion of their genetic influences in common while also retaining meaningful unique genetic variance.