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

Holly E Poore; Alexander Hatoum; Travis T Mallard; Sandra Sanchez-Roige; Irwin D Waldman; Abraham A Palmer; K Paige Harden; Peter B Barr; Danielle M Dick

doi:10.1111/adb.13319

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

Addict Biol. 2023 Sep;28(9):e13319. doi: 10.1111/adb.13319.

Authors

Holly E Poore¹, Alexander Hatoum², Travis T Mallard³, Sandra Sanchez-Roige^{4

5}, Irwin D Waldman⁶, Abraham A Palmer^{4

7}, K Paige Harden^{8

9}, Peter B Barr^{10

11}, Danielle M Dick¹

Affiliations

¹ Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA.
² Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA.
³ Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
⁴ Department of Psychiatry, University of California, San Diego, San Diego, California, USA.
⁵ Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
⁶ Department of Psychology, Emory University, Atlanta, Georgia, USA.
⁷ Institute for Genomic Medicine, University of California, San Diego, San Diego, California, USA.
⁸ Department of Psychology, University of Texas at Austin, Austin, Texas, USA.
⁹ Population Research Center, University of Texas at Austin, Austin, Texas, USA.
¹⁰ Department of Psychiatry & Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, New York, USA.
¹¹ VA New York Harbor Healthcare System, Brooklyn, New York, USA.

Abstract

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 (r_g s ≥ 0.87), and between the effect sizes of genetic correlations with external traits (r_g ≥ 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.

Keywords: externalizing; genomic structural equation modelling; substance use disorders.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Behavior, Addictive*
Genome-Wide Association Study
Humans
Phenotype
Reproducibility of Results
Substance-Related Disorders* / genetics

Abstract

Publication types

MeSH terms

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