A quantitative-trait genome-wide association study of alcoholism risk in the community: Findings and implications

Andrew C. Heath, John B. Whitfield, Nicholas G. Martin, Michele L. Pergadia, Alison M. Goate, Penelope A. Lind, Brian P. McEvoy, Andrew J. Schrage, Julia D. Grant, Yi Ling Chou, Rachel Zhu, Anjali K. Henders, Sarah E. Medland, Scott D. Gordon, Elliot C. Nelson, Arpana Agrawal, Dale R. Nyholt, Kathleen K. Bucholz, Pamela A.F. Madden, Grant W. Montgomery

Research output: Contribution to journalArticlepeer-review

147 Scopus citations

Abstract

Background: Given moderately strong genetic contributions to variation in alcoholism and heaviness of drinking (50% to 60% heritability) with high correlation of genetic influences, we have conducted a quantitative trait genome-wide association study (GWAS) for phenotypes related to alcohol use and dependence. Methods: Diagnostic interview and blood/buccal samples were obtained from sibships ascertained through the Australian Twin Registry. Genome-wide single nucleotide polymorphism (SNP) genotyping was performed with 8754 individuals (2062 alcohol-dependent cases) selected for informativeness for alcohol use disorder and associated quantitative traits. Family-based association tests were performed for alcohol dependence, dependence factor score, and heaviness of drinking factor score, with confirmatory case-population control comparisons using an unassessed population control series of 3393 Australians with genome-wide SNP data. Results: No findings reached genome-wide significance (p = 8.4 × 10 -8 for this study), with lowest p value for primary phenotypes of 1.2 × 10 -7. Convergent findings for quantitative consumption and diagnostic and quantitative dependence measures suggest possible roles for a transmembrane protein gene (TMEM108) and for ANKS1A. The major finding, however, was small effect sizes estimated for individual SNPs, suggesting that hundreds of genetic variants make modest contributions (1/4% of variance or less) to alcohol dependence risk. Conclusions: We conclude that 1) meta-analyses of consumption data may contribute usefully to gene discovery; 2) translation of human alcoholism GWAS results to drug discovery or clinically useful prediction of risk will be challenging; and 3) through accumulation across studies, GWAS data may become valuable for improved genetic risk differentiation in research in biological psychiatry (e.g., prospective high-risk or resilience studies).

Original languageEnglish
Pages (from-to)513-518
Number of pages6
JournalBiological Psychiatry
Volume70
Issue number6
DOIs
StatePublished - Sep 15 2011

Keywords

  • Alcoholism
  • genome-wide association
  • nonreplication
  • quantitative trait

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