Shared additive genetic influences on DSM-IV criteria for alcohol dependence in subjects of European ancestry

• Type of Material: Article
• Authors: Rohan H. Palmer, Emory University; John E. Mcgeary, Rhode Island Hospital; Andrew C. Heath, Washington University, St. Louis; Matthew C. Keller, University of Colorado at Boulder; Leslie Brick, Emory University; Valerie S. Knopik, Rhode Island Hospital
• Language: English
• Date: 2015-01-01
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2015 John Wiley & Sons, Ltd.
• Final Published Version (URL): http://dx.doi.org/10.1111/add.13070
• Title of Journal or Parent Work: Addiction
• ISSN: 0965-2140
• Volume: 110
• Issue: 12
• Start Page: 1922
• End Page: 1931
• Grant/Funding Information: Funding support for the Study of Addiction: Genetics and Environment (SAGE) was provided through the NIH Genes, Environment and Health Initiative [GEI] (U01HG004422).; Support for collection of datasets and samples was provided by the Collaborative Study on the Genetics of Alcoholism (COGA; U10 AA008401); the Collaborative Genetic Study of Nicotine Dependence (COGEND; P01 CA089392); and the Family Study of Cocaine Dependence (FSCD; R01 DA013423).; Funding for this study was provided by AA021113 (Palmer); AA11998 (Heath); NIMH K01 MH085812 & NIMH 1R01MH100141 (Keller); and DA023134 (Knopik).; Assistance with data cleaning was provided by the National Center for Biotechnology Information.; Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GENEVA Coordinating Center (U01 HG004446).; SAGE is one of the genome-wide as association studies funded as part of the Gene Environment Association Studies (GENEVA) under GEI.; Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by the NIH GEI (U01HG004438); the National Institute on Alcohol Abuse and Alcoholism; the National Institute on Drug Abuse; and the NIH contract "High throughput genotyping for studying the gene tic contributions to human disease" (HHSN268200782096C).
• Keywords: Diagnostic and Statistical Manual of Mental Disorders; Recurrence; Female; Factor Analysis, Statistical; Genotype; Polymorphism, Single Nucleotide; Adult; genetics; Genetic Variation; Humans; diagnostic criteria; European Continental Ancestry Group; Alcoholism; alcoholism; DSM-IV; Alcohol dependence; Alcohol Abstinence; GCTA
• Research Categories: Health Sciences, Mental Health; Biology, Genetics; Health Sciences, Toxicology

Abstract

Background and Aims: Genetic studies of alcohol dependence (AD) have identified several candidate loci and genes, but most observed effects are small and difficult to reproduce. A plausible explanation for inconsistent findings may be a violation of the assumption that genetic factors contributing to each of the seven DSM-IV criteria point to a single underlying dimension of risk. Given that recent twin studies suggest that the genetic architecture of AD is complex and probably involves multiple discrete genetic factors, the current study employed common single nucleotide polymorphisms in two multivariate genetic models to examine the assumption that the genetic risk underlying DSM-IV AD is unitary. Design, Setting, Participants, Measurements: AD symptoms and genome-wide single nucleotide polymorphism (SNP) data from 2596 individuals of European descent from the Study of Addiction: Genetics and Environment were analyzed using genomic-relatedness-matrix restricted maximum likelihood. DSM-IV AD symptom covariance was described using two multivariate genetic factor models. Findings: Common SNPs explained 30% (standard error = 0.136, P = 0.012) of the variance in AD diagnosis. Additive genetic effects varied across AD symptoms. The common pathway model approach suggested that symptoms could be described by a single latent variable that had a SNP heritability of 31% (0.130, P = 0.008). Similarly, the exploratory genetic factor model approach suggested that the genetic variance/covariance across symptoms could be represented by a single genetic factor that accounted for at least 60% of the genetic variance in any one symptom. Conclusion: Additive genetic effects on DSM-IV alcohol dependence criteria overlap. The assumption of common genetic effects across alcohol dependence symptoms appears to be a valid assumption.