bioRxivGeneticsPreprint
Genetic risk for schizophrenia influences substance use in emerging adulthood: An event-level polygenic prediction model
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Abstract
BackgroundEmerging 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.\n\nMethodYoung 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 two-level hierarchical linear models designed to test associations between genetic risk for schizophrenia, participant age, and four substance use phenotypes.\n\nResultsHere, 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.\n\nConclusionsThe 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.
- organism: human
- assay: GWAS summary statistics
- research focus: polygenic risk
- approach: statistical model
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