Genetic Analysis of a Population Heavy Drinking Phenotype Identifies Risk Variants in Whites
Year: 2013
Type: article
Study design: Observational Study
Abstract: Genetic association studies thus far have used detailed diagnoses of alcoholism to identify loci associated with risk. This proof-of-concept analysis examined whether population data of lifetime heaviest alcohol consumption may be used to identify genetic loci that modulate risk. We conducted a genetic association study in European Americans between variants in approximately 2100 genes and alcohol consumption as part of the Candidate gene Association Resource project. We defined cases as individuals with a history of drinking 5 or more drinks per day almost every day of the week and controls as current light drinkers (1-5 drinks per week). We cross-validated identified single nucleotide polymorphisms in a meta-analysis of 2 cohorts of unrelated individuals--Atherosclerosis Risk in Communities (ARIC) and Cardiovascular Health Study (CHS)--and in a separate cohort of related individuals--Framingham Heart Study (FHS). The most significant variant in the meta-analysis of ARIC and CHS was rs6933598 in methylenetetrahydrofolate dehydrogenase (P = 7.46 × 10(-05)) with a P value in FHS of 0.042. The top variants in FHS were rs12249562 in cubulin (P = 3.03 × 10(-05)) and rs9839267 near cholecystokinin (P = 3.05 × 10(-05)) with a P value of 0.019 for rs9839267 in CHS. We have here shown feasibility in evaluating lifetime incidence of heavy alcohol drinking from population-based studies for the purpose of conducting genetic association analyses.
Institutions: Northwestern University, National Heart, Lung, and Blood Institute, Tougaloo College, Vanderbilt University Medical Center, SRI International, The University of Texas Health Science Center at Houston, University of Pittsburgh, Framingham Heart Study, Beth Israel Deaconess Medical Center, University of California, San Francisco, Ernest Gallo Clinic and Research Center, Jackson State University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2089901843
