Measuring alcohol consumption for genomic meta-analyses of alcohol intake: opportunities and challenges.
- Authors
- Agrawal, Arpana; Freedman, Neal D; Cheng, Yu-Ching; Lin, Peng; Shaffer, John R; Sun, Qi; Taylor, Kira; Yaspan, Brian; Cole, John W; Cornelis, Marilyn C; DeSensi, Rebecca S; Fitzpatrick, Annette; Heiss, Gerardo; Kang, Jae H; O'Connell, Jeffrey; Bennett, Siiri; Bookman, Ebony; Bucholz, Kathleen K; Caporaso, Neil; Crout, Richard; Dick, Danielle M; Edenberg, Howard J; Goate, Alison; Hesselbrock, Victor; Kittner, Steven; Kramer, John; Nurnberger, John I; Qi, Lu; Rice, John P; Schuckit, Marc; van Dam, Rob M; Boerwinkle, Eric; Hu, Frank; Levy, Steven; Marazita, Mary; Mitchell, Braxton D; Pasquale, Louis R; Bierut, Laura J; GENEVA Consortium
- Year
- 2012
- Journal
- The American journal of clinical nutrition
- PMID
- 22301922
- DOI
- 10.3945/ajcn.111.015545
- PMCID
- PMC3278237
Whereas moderate drinking may have health benefits, excessive alcohol consumption causes many important acute and chronic diseases and is the third leading contributor to preventable death in the United States. Twin studies suggest that alcohol-consumption patterns are heritable (50%); however, multiple genetic variants of modest effect size are likely to contribute to this heritable variation. Genome-wide association studies provide a tool for discovering genetic loci that contribute to variations in alcohol consumption. Opportunities exist to identify susceptibility loci with modest effect by meta-analyzing together multiple studies. However, existing studies assessed many different aspects of alcohol use, such as typical compared with heavy drinking, and these different assessments can be difficult to reconcile. In addition, many studies lack the ability to distinguish between lifetime and recent abstention or to assess the pattern of drinking during the week, and a variety of such concerns surround the appropriateness of developing a common summary measure of alcohol intake. Combining such measures of alcohol intake can cause heterogeneity and exposure misclassification, cause a reduction in power, and affect the magnitude of genetic association signals. In this review, we discuss the challenges associated with harmonizing alcohol-consumption data from studies with widely different assessment instruments, with a particular focus on large-scale genetic studies.
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