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. 2015 Mar;110(3):461-70.
doi: 10.1111/add.12822. Epub 2015 Jan 20.

Genomic Influences on Alcohol Problems in a Population-Based Sample of Young Adults

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Genomic Influences on Alcohol Problems in a Population-Based Sample of Young Adults

Alexis C Edwards et al. Addiction. .
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Abstract

Aims: Alcohol problems (AP) contribute substantially to the global disease burden. Twin and family studies suggest that AP are genetically influenced, although few studies have identified variants or genes that are robustly associated with risk. This study identifies genetic and genomic influences on AP during young adulthood, which is often when drinking habits are established.

Design: We conducted a genome-wide association study of AP. We further conducted gene-based tests, gene ontology analyses and functional genomic enrichment analyses to assess genomic factors beyond single variants that are relevant to AP.

Setting: The Avon Longitudinal Study of Parents and Children, a large population-based study of a UK birth cohort.

Participants: Genetic and phenotypical data were available for 4304 participants.

Measurements: The AP phenotype was a factor score derived from items from the Alcohol Use Disorders Identification Test, symptoms of DSM-IV alcohol dependence, and three additional problem-related items.

Findings: One variant met genome-wide significance criteria. Four out of 22,880 genes subjected to gene-based analyses survived a stringent significance threshold (q < 0.05); none of these have been implicated previously in alcohol-related phenotypes. Several biologically plausible gene ontologies were statistically over-represented among implicated single nucleotide polymorphisms (SNPs). SNPs on the Illumina 550 K SNP chip accounted for ~5% of the phenotypical variance in AP.

Conclusions: Genetic and genomic factors appear to play a role in alcohol problems in young adults. Genes involved in nervous system-related processes, such as signal transduction and neurogenesis, potentially contribute to liability to alcohol problems, as do genes expressed in non-brain tissues.

Keywords: ALSPAC; Alcohol problems; GWAS; epigentic enrichment; gene-based test; polygenic.

Conflict of interest statement

Conflicts of Interest: None to declare.

Figures

Figure 1
Figure 1
Manhattan plot of primary GWAS results. Only SNPs with p ≤ 0.01 are depicted. The top horizontal line represents the genome-wide significance cut-off (3.06 × 10−8 for imputed data [45]). The lower horizontal line represents the threshold for suggestive SNPs (1 × 10−5).
Figure 2
Figure 2
QQ-plot of primary GWAS results. Diagonal line represents the null expectation. Shaded areas represent 95% confidence intervals.
Figure 3
Figure 3
Map of SNPs falling within 10 kilobases of NLRP12. Figure was constructed using LocusZoom (Pruim et al., 2010). rs10403709 represents the SNP with the lowest p-value. Other SNPs are color-coded according to their pairwise r2 with rs10403709 (indicated by the legend in the upper right corner). The solid blue line represents the regional recombination rate (see right-side y-axis) based on the 1000 Genomes hg19 CEU reference panel.
Figure 4
Figure 4
Enrichment scores (y-axis) for SNPs meeting increasingly stringent significance thresholds (x-axis) across all tissue types. Results are presented separately for DHS (left panel) and H3K4me3 histone marks (right panel). For ease of presentation, SNPs at each –log10 p-value cut-off are distributed horizontally slightly beyond the defined cut-off. The solid line represents the mean enrichment; shaded areas represent 95% confidence intervals.

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