Abstract
BACKGROUND: While genetic influences on alcohol dependence (AD) are substantial, progress in the identification of individual genetic variants that impact on risk has been difficult. METHODS: We performed a genome-wide association study on 3,169 alcohol consuming subjects from the population-based Molecular Genetics of Schizophrenia (MGS2) control sample. Subjects were asked 7 questions about symptoms of AD which were analyzed by confirmatory factor analysis. Genotyping was performed using the Affymetrix 6.0 array. Three sets of analyses were conducted separately for European American (EA, n = 2,357) and African-American (AA, n = 812) subjects: individual single nucleotide polymorphisms (SNPs), candidate genes and enriched pathways using gene ontology (GO) categories. RESULTS: The symptoms of AD formed a highly coherent single factor. No SNP approached genome-wide significance. In the EA sample, the most significant intragenic SNP was in KCNMA1, the human homolog of the slo-1 gene in C. Elegans. Genes with clusters of significant SNPs included AKAP9, phosphatidylinositol glycan anchor biosynthesis, class G (PIGG), and KCNMA1. In the AA sample, the most significant intragenic SNP was CEACAM6 and genes showing empirically significant SNPs included KCNQ5, SLC35B4, and MGLL. In the candidate gene based analyses, the most significant findings were with ADH1C, nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (NFKB1) and ankyrin repeat and kinase domain containing 1 (ANKK1) in the EA sample, and ADH5, POMC, and CHRM2 in the AA sample. The ALIGATOR program identified a significant excess of associated SNPs within and near genes in a substantial number of GO categories over a range of statistical stringencies in both the EA and AA sample. CONCLUSIONS: While we cannot be highly confident about any single result from these analyses, a number of findings were suggestive and worthy of follow-up. Although quite large samples will be needed to obtain requisite power, the study of AD symptoms in general population samples is a viable complement to case-control studies in identifying genetic risk variants for AD.