Genetic risk for schizophrenia influences substance use in emerging adulthood: An event-level polygenic prediction model

Abstract

Background: Emerging adulthood is a peak period of risk for alcohol and illicit drug use. Recent advances in psychiatric genetics suggest that the co-occurrence of substance use and psychopathology arises, in part, from a shared genetic etiology. We sought to extend this research by investigating the influence of genetic risk for schizophrenia on trajectories of four substance use behaviors as they occurred across emerging adulthood.Method: Young adult participants of non-Hispanic European descent provided DNA samples and completed daily reports of substance use for one month per year across four years (N=30,085 observations of N=342 participants). Polygenic scores for schizophrenia were included in twolevel hierarchical linear models designed to test associations between genetic risk for schizophrenia, participant age, and four substance use phenotypes.Results: Here, we interpret results at p<.05 as suggestive and results at p<.005 as significant.Accordingly, our results suggest that polygenic scores for schizophrenia were positively associated with participants' overall likelihood to engage in illicit drug use, but not alcohol-related substance use. Moreover, our results indicate that participants with a greater polygenic loading for schizophrenia experienced greater age-related increases in the likelihood of using substances across emerging adulthood. Conclusions:The present study used a novel combination of polygenic prediction and intensive longitudinal methods to characterize the influence of genetic risk for schizophrenia on patterns of age-related change in substance use across emerging adulthood. Results suggest that genetic risk for schizophrenia exerts both broad and developmentally-specific influences on substance use behaviors in a non-clinical population of young adults.

Coupled Mixed Model for Joint Genetic Analysis of Complex Disorders with Two Independently Collected Data Sets

“…A joint analysis may uncover genetic mechanisms that cannot be discovered in a single analysis (Mukherjee et al, 2013). For example, recent studies have revealed overlapping genetic factors that influence multiple psychiatric disorders (Pain et al, 2018), genetic correlations between schizophrenia, ADHD, depression, and cannabis use (Walters et al, 2018), as well as an association between schizophrenia and illicit drug use (Mallard et al, 2018). Also, co-occurrences of substance use disorders (SUDs) and psychopathology have been observed in national epidemiologic Figure 1: Illustration of the existing challenges when conducting a joint analysis on two independently collected data sets with two different phenotypes.…”