Abstract
Alcohol problems represent a classic example of a complex behavioral outcome that is likely influenced by many genes of small effect. A polygenic approach, which examines aggregate measured genetic effects, can have predictive power in cases where individual genes or genetic variants do not. In the current study, we first tested whether polygenic risk for alcohol problems—derived from genome-wide association estimates of an alcohol problems factor score from the age 18 assessment of the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 4304 individuals of European descent; 57% female)—predicted alcohol problems earlier in development (age 14) in an independent sample (FinnTwin12; n = 1162; 53% female). We then tested whether environmental factors (parental knowledge and peer deviance) moderated polygenic risk to predict alcohol problems in the FinnTwin12 sample. We found evidence for both polygenic association and for additive polygene-environment interaction. Higher polygenic scores predicted a greater number of alcohol problems (range of Pearson partial correlations 0.07–0.08, all p-values ≤ 0.01). Moreover, genetic influences were significantly more pronounced under conditions of low parental knowledge or high peer deviance (unstandardized regression coefficients (b), p-values (p), and percent of variance (R2) accounted for by interaction terms: b = 1.54, p = 0.02, R2 = 0.33%; b = 0.94, p = 0.04, R2 = 0.30%, respectively). Supplementary set-based analyses indicated that the individual top single nucleotide polymorphisms (SNPs) contributing to the polygenic scores were not individually enriched for gene-environment interaction. Although the magnitude of the observed effects are small, this study illustrates the usefulness of polygenic approaches for understanding the pathways by which measured genetic predispositions come together with environmental factors to predict complex behavioral outcomes.
Highlights
Alcohol consumption and related problems are classic examples of complex behavioral outcomes that likely involve many genes of small effect [1]
Lower parental knowledge and higher peer deviance were associated with higher levels of alcohol problems [r(1113) = 0.29 and r(1114) = 0.35, both p-values < 0.01, respectively], which is consistent with previous work indicating that more permissive and deviant environments are associated with a greater amount of adolescent substance use [33,41,42]
single nucleotide polymorphisms (SNPs) contributing to our polygenic scores were themselves enriched for gene-environment interaction. We examined this question using the set of top SNPs (p ≤ 0.0001) from the Avon Longitudinal Study of Parents and Children (ALSPAC) genome-wide association studies (GWAS)
Summary
Alcohol consumption and related problems are classic examples of complex behavioral outcomes that likely involve many genes of small effect [1]. In contrast to the consistent evidence for the heritability of alcohol use and problems, no robust associations have been detected in genome-wide association studies (GWAS) to date This is the case, in part, because the small samples typically used in alcohol research are underpowered to detect the very modest individual effect sizes that are generally observed in GWAS of complex behavioral outcomes. In the absence of success in identifying individual genes that account for a substantial proportion of the variance in alcohol outcomes, and lack of expectation that such genes will be found in the near future, polygenic approaches have emerged as one paradigm for examining aggregate measured genetic effects that can have predictive power when individual genes cannot [19] This approach typically uses results from a genome-wide association study in a discovery sample. We tested the hypotheses that: (1) polygenic risk for alcohol problems—derived from GWAS estimates in one population-based sample—would predict alcohol problems in adolescence in a second, independent, population-based sample; and (2) parenting and peer factors in adolescence would moderate polygenic risk to predict alcohol problems in the independent sample
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