Molecular Genetic Influences on Normative and Problematic Alcohol Use in a Population-Based Sample of College Students

Abstract

Background: Genetic factors impact alcohol use behaviors and these factors may become increasingly evident during emerging adulthood. Examination of the effects of individual variants as well as aggregate genetic variation can clarify mechanisms underlying risk. Methods: We conducted genome-wide association studies (GWAS) in an ethnically diverse sample of college students for three quantitative outcomes including typical monthly alcohol consumption, alcohol problems, and maximum number of drinks in 24 h. Heritability based on common genetic variants (h²_SNP) was assessed. We also evaluated whether risk variants in aggregate were associated with alcohol use outcomes in an independent sample of young adults. Results: Two genome-wide significant markers were observed: rs11201929 in GRID1 for maximum drinks in 24 h, with supportive evidence across all ancestry groups; and rs73317305 in SAMD12 (alcohol problems), tested only in the African ancestry group. The h²_SNP estimate was 0.19 (SE = 0.11) for consumption, and was non-significant for other outcomes. Genome-wide polygenic scores were significantly associated with alcohol outcomes in an independent sample. Conclusions: These results robustly identify genetic risk for alcohol use outcomes at the variant level and in aggregate. We confirm prior evidence that genetic variation in GRID1 impacts alcohol use, and identify novel loci of interest for multiple alcohol outcomes in emerging adults. These findings indicate that genetic variation influencing normative and problematic alcohol use is, to some extent, convergent across ancestry groups. Studying college populations represents a promising avenue by which to obtain large, diverse samples for gene identification.

Keywords: GWAS; alcohol consumption; alcohol problems; genetic ancestry; genome-wide polygenic score; heritability.

Figure 1

Regional association plot for GRID1 and 200 kb flanking regions, implemented using LocusZoom (Pruim et al., 2010). The most significant marker is in purple (rs11201929, p = 4.11e-09, q = 0.06 for Maxdrinks). Linkage disequilibrium information is based on the 1000 Genomes AFR super-population. The size of the points representing plotted SNPs corresponds to the meta-analysis sample size.

Figure 2

Regional association plot for SAMD12 and 200 kb flanking regions. The most significant marker is in purple (rs73317305, p = 9.02 × 10⁻⁹, q = 0.11 for Problems). Linkage disequilibrium information is based on the 1000 Genomes AFR super-population, as the minor allele was rare in other subgroups. The size of the points representing plotted SNPs corresponds to the meta-analysis sample size.