Proc Natl Acad Sci U S A 107(11): 5082-5087

PMC: PMC2841942

PMID: 20202923

A genome-wide association study of alcohol dependence

William Howells+31 authors

Replication Studies.

The top associated SNPs were tested for replication in two independent datasets (Table S3). The first replication sample is the family-based study from the Collaborative Study on the Genetics of Alcoholism (COGA). In our family-based association analyses, none of the SNPs demonstrated association with a P value < 0.05; however, rs1386449 and rs10224675, which in our primary analyses are associated with alcohol dependence only in African-Americans, have a P value < 0.10 in the family-based analysis with a small number of African-American families. The top results also were examined in alcohol-dependent case and community-based comparison subjects of German ancestry (24). Of the seven SNPs that were genotyped and tested in the sample, none reached a significance level of P < 0.05.

We also show association results (Table S4) from our study for the SNPs recently reported in the independent GWAS of alcohol-dependent men by Treutlein and colleagues (24). Only one SNP, rs13160562, shows modest evidence of replication [0.88; 95% confidence interval (CI) 0.78–0.99, P = 0.03]. In a meta-analysis, this SNP did not reach genome-wide significance (meta-analysis OR = 0.83, 95% CI 0.77–0.90, P = 2.74 × 10^{). None of the other SNPs reported by Treutlein and colleagues were associated with alcohol dependence (}P > 0.05).

Candidate Gene Findings.

The findings for SNPs genotyped in GABRA2 that overlap with SNPs reported by Edenberg et al. (14) are displayed in Table 3. Results for all GABRA SNPs in the entire sample and stratified by self-reported race are shown in Table S5 and Table S6, respectively. This analysis confirms the modest association of alcohol dependence with variants in GABRA2.

Table 3.

SAGE association results for GABRA2 SNPs also genotyped in the family-based COGA sample

			SAGE
				Frequency of risk allele
SNP	Position	COGA^P	Risk allele	Cases	Controls	Adjusted odds ratio (95% CI)	P
rs572227	45,946,150	3.80E-02	A	0.376	0.366	1.15 (1.04–1.27)	8.89E-03
rs548583	45,958,101	1.20E-02	T	0.404	0.385	1.14 (1.03–1.26)	1.05E-02
rs279858	46,009,350	8.70E-03	G	0.375	0.366	1.16 (1.05–1.28)	5.04E-03
rs279843	46,019,961	4.90E-02	T	0.444	0.421	1.11 (1.00–1.22)	4.24E-02
rs279841	46,035,520	3.80E-02	A	0.368	0.364	1.13 (1.02–1.25)	2.29E-02

*COGA family based association from ref. 14.

Collaborative Study on the Genetics of Alcoholism.

A case-control series of unrelated individuals was selected from more than 8,000 subjects who participated in the genetic arm of COGA. COGA systematically recruited families with multiple members affected with alcohol dependence and community-based comparison families from participating centers across the United States. COGA contributed 612 alcohol-dependent cases and 412 control subjects of European descent and 287 alcohol-dependent cases and 97 controls of African-American descent. Individuals in this case-control sample were independent from the COGA family linkage association sample that was genotyped previously (33, 34).

Family Study of Cocaine Dependence.

Cocaine-dependent subjects were recruited systematically from chemical dependency treatment units in the greater St. Louis, MO, metropolitan area (35). Community-based comparison subjects were identified through the Missouri Family Registry and matched by age, race, gender, and residential zip code. This study contributed 280 alcohol-dependent cases and 247 controls of European descent and 268 alcohol-dependent cases and 249 controls who self-identified as African-American. Because of the study design, alcohol-dependent case subjects also met criteria for cocaine dependence.

Collaborative Genetic Study of Nicotine Dependence.

COGEND was designed as a community-based study of nicotine dependence. Subjects were recruited from Detroit, MI, and St. Louis, MO. More than 53,000 subjects were screened by telephone, more than 2,800 were personally interviewed, and nearly 2,700 donated blood samples for genetic studies (36, 37). COGEND contributed 343 alcohol-dependent cases and 774 controls of European descent and 107 African-American alcohol-dependent cases and 153 African-American controls.

Source of DNA.

All subjects deposited a blood sample in the Rutgers University Cell and DNA Repository (RUCDR), a central biologic repository for the National Institute on Alcohol Abuse and Alcoholism and National Institute on Drug Abuse (http://www.rucdr.org). DNA was extracted from the blood sample, and cell lines were developed as an additional DNA source.

Assessment.

A common assessment was performed across all three studies and was based on the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) (38). This shared methodology of interview administration, question format, and queried domains allowed harmonization of phenotypic data across all studies.

Genotyping and Data Cleaning.

Samples were genotyped at the Johns Hopkins Center for Inherited Disease Research (CIDR). Data were released for 4,189 study samples. Study samples, including 49 study duplicates, were plated and genotyped together with 135 HapMap controls (86 CEU; 49 YRI). Genotyping was performed using Illumina Human1Mv1_C BeadChips and the Illumina Infinium II assay protocol (39). Allele cluster definitions for each SNP were determined using Illumina BeadStudio Genotyping Module version 3.1.14 and the combined intensity data from the samples. Strict quality-control standards were implemented, and genotypes were released by CIDR for 1,040,106 SNPs (99.15% of attempted). The mean non-Y SNP call rate and mean sample call rate was 99.7% for the released CIDR dataset. Study duplicate reproducibility was 99.98%. Further extensive cleaning was undertaken to ensure high-quality genotyping by examining batch effects, potential chromosomal anomalies, and Mendelian errors. Further details are provided in the comprehensive data cleaning report posted at dbGaP http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/document.cgi?study_id=phs000092.v1.p1&phv=22928&phd=2274&pha=&pht=116&phvf=&phdf=20&phaf=&phtf=&dssp=1&consent=&temp=1.

SNPs with an allele frequency > 1% in either the European- or African-descent populations were analyzed (948,658 SNPs). A SNP call rate of 98% was required. Hardy–Weinberg equilibrium (HWE) was tested, and SNPs that deviated from HWE (P < 10^{) were excluded. The final number of subjects included in analyses was 3,829. Individuals were dropped if there was potential sample misidentification, sample relatedness, or other misspecification (}n = 171).

Statistical Analyses.

We used the software package EIGENSTRAT (40) with all SNPs to calculate principal components reflecting continuous variation in allele frequencies representing ancestral differences in subjects. Two principal components were identified; the first distinguished African-American participants from European-American participants and the second distinguished Hispanic and non-Hispanic subjects. Each individual received scores on each principal component. These scores, representing continuous variation in race and ethnicity, can be used to control for effects of population stratification.

Genome-wide association analysis was conducted using logistic regressions in PLINK (41). Genotypes were coded log-additively (0, 1, 2 copies of the minor allele). Covariates represented sex, age [defined, using quartiles, as 34 years and younger (reference), 35–39 years, 40–44 years, and 45 years and older] and two principal components indexing continuous variation in race/ethnicity. We repeated analyses using self-reported race (European-American, African-American) as categorical variables. Similar results were seen with both analyses, and we present results using self-reported race. The false-discovery rate was calculated using the method of Storey and Tibshirani (42).

Replication Samples.

Family sample from COGA.

A set of 258 genetically informative, multiplex alcohol-dependence pedigrees in COGA have been studied previously in linkage- and association-based analyses (33, 34). Families report European and African ancestry (n = 219 European American, n = 35 African American, and n = 4 other ancestry). These pedigrees do not overlap with any of the case-control subjects used in the SAGE GWAS sample. Affected individuals were defined as those meeting criteria for DSM-IV alcohol dependence, and unaffected subjects reported no symptoms of alcohol dependence. The SNPs with the most significant evidence of association from SAGE were selected for genotyping in this family-based sample. SNPs had a genotyping call rate > 99%. All SNPs passed an HWE threshold of P > 0.05 as calculated independently in African-American and European-American samples. Pedigree errors were cleaned using PEDCHECK (43). Family-based association analyses were performed using a Family-Based Association Test (FBAT) (44), with alcohol-dependent and nondependent phenotypes adjusted for age, gender, and ethnicity. Because FBAT is robust to population stratification, analyses were not performed independently by ethnicity.

Case-control sample.

Replication analyses were performed in the GWAS sample of alcohol-dependent subjects reported by Treutlein et al. (24). Alcohol-dependent men (n = 487) were recruited from consecutive admissions to treatment facilities as part of the German Addiction Research Network (GARN; http://www.bw-suchtweb.de). Controls subjects (n = 1,358) were recruited through population-based epidemiologic studies. SNPs reported in this replication phase passed the standard quality-control measures, and analyses were performed in PLINK. See Treutlein et al. (24) for further details on the sample description, analyses, and methods.

Family sample from COGA.

Case-control sample.

Supplementary Material

Supporting Information:

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^{Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110;}

^{Center for Inherited Disease Research, Johns Hopkins Medical Center, Baltimore, MD 21224;}

^{Department of Biostatistics, University of Washington, Seattle, WA, 98195;}

^{Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, TX 78245;}

^{Department of Epidemiology, Michigan State University, East Lansing, MI 48824;}

^{Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110;}

^{Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298;}

^{Department of Biochemistry and Molecular Biology, and}

^{Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202;}

^{Tobacco Research Center, University of Minnesota, Minneapolis, MN, 55415;}

^{Department of Psychiatry, University of Connecticut, Farmington, CT 06030;}

^{Division of Health, Social and Economic Research, Research Triangle Institute International, Research Triangle Park, NC 27709;}

^{Department of Psychiatry, University of Iowa College of Medicine, Iowa City, IA 52242;}

^{Division of Child Psychiatry, University of Iowa Hospitals, Iowa City, IA 52242;}

^{Department of Addictive Behavior and Addictive Medicine, Central Institute of Mental Health, Mannheim, Germany;}

^{Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany;}

^{Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202;}

^{Department of Psychiatry, State University of New York, Brooklyn, NY 11203;}

^{Department of Psychiatry, Mental State Hospital, University of Regensburg, Regensburg, Germany;}

^{Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110;}

^{Department of Psychiatry, University of California-San Diego, La Jolla, CA 92037;}

^{Department of Genetics, Rutgers University, Piscataway, NJ, 08854; and}

^{Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Mannheim, Germany}

^{To whom correspondence should be addressed. E-mail:}ude.ltsuw.yrtaihcysp@ltureib.

Edited by Mary-Claire King, University of Washington, Seattle, WA, and approved February 2, 2010 (received for review September 25, 2009)

Author contributions: L.J.B., K.K.B., A.L.H., H.J.E., A.M.G., and J.P.R. designed research; L.J.B., K.K.B., K.F.D., S.F., N.B., D.H., V.H., E.O.J., J.K., S.K., K.M., M.M.N., J.I.N., B.P., M. Ridinger, J.A.T., and J.P.R. performed research; K.M. contributed new reagents/analytic tools; L.J.B., A.A., C.L., E.P., L.F., W.H., S.B., M. Rietschel, and J.P.R. analyzed data; and L.J.B., A.A., K.K.B., K.F.D., C.L., E.P., S.F., L.F., W.H., S.B., A.L.H., L.A., N.B., R.C.C., D.M.D., H.J.E., T.F., R.A.G., D.H., V.H., E.O.J., J.K., R.F.K., S.K., M.L., R.J.N., J.I.N., B.P., N.L.S., S.F.S., M.A.S., J.A.T., J.C.W., M. Rietschel, A.M.G., and J.P.R. wrote the paper.

Edited by Mary-Claire King, University of Washington, Seattle, WA, and approved February 2, 2010 (received for review September 25, 2009)

Freely available online through the PNAS open access option.

Abstract

Excessive alcohol consumption is one of the leading causes of preventable death in the United States. Approximately 14% of those who use alcohol meet criteria during their lifetime for alcohol dependence, which is characterized by tolerance, withdrawal, inability to stop drinking, and continued drinking despite serious psychological or physiological problems. We explored genetic influences on alcohol dependence among 1,897 European-American and African-American subjects with alcohol dependence compared with 1,932 unrelated, alcohol-exposed, nondependent controls. Constitutional DNA of each subject was genotyped using the Illumina 1M beadchip. Fifteen SNPs yielded P < 10^{, but in two independent replication series, no SNP passed a replication threshold of}P < 0.05. Candidate gene GABRA2, which encodes the GABA receptor α2 subunit, was evaluated independently. Five SNPs at GABRA2 yielded nominal (uncorrected) P < 0.05, with odds ratios between 1.11 and 1.16. Further dissection of the alcoholism phenotype, to disentangle the influence of comorbid substance-use disorders, will be a next step in identifying genetic variants associated with alcohol dependence.

Keywords: genetics, candidate genes

Abstract

Excessive alcohol use is the third leading cause of preventable death in the United States (1). Although normative alcohol use is ubiquitous, alcohol dependence is a serious medical illness (2) that is experienced by ≈14% of alcohol users (3). Alcohol dependence constitutes a substantial health and economic burden, costing an estimated $184 billion in expenditures stemming from alcohol-related morbidity, accidents, lost productivity, and incarceration (4). These challenges underscore the importance of clarifying the etiology of alcohol dependence as a key public health priority.

Liability to alcohol dependence has both genetic and environmental influences, which act independently and in concert. First-degree relatives of affected individuals are at a 2- to 8-fold increased risk for alcohol dependence (5, 6). Adoption studies and twin studies have clarified that this familial clustering of alcohol dependence is attributable largely to genetic factors (7 –11). In most recent studies, these heritable influences explain ≈50–80% of the individual differences in liability to alcohol dependence (12, 13).

In an effort to unmask specific genomic influences on alcohol dependence, scientists have brought a vast genomic toolkit to bear on this problem. Large-scale genome-wide association studies (GWAS) offer considerable promise. By genotyping a dense set of SNPs throughout the genome, investigators have the potential to identify with considerable precision genes that may lead to unknown biological pathways involved in alcohol dependence.

Candidate gene strategies frequently have identified significant associations between SNPs in the gene encoding the alpha2 subunit of the γ-aminobutyric acid A receptor (GABRA2), a major inhibitory neurotransmitter in the human nervous system that is involved in the behavioral effects of alcohol (14). Although some exceptions exist (15 –17), there are multiple positive reports of association between SNPs in GABRA2 and alcohol- and other substance-related phenotypes (14, 18–22 and reviewed in 23).

We report on a large, well-characterized sample of 1,897 Diagnostic and Statistical Manual of Mental Disorders, edition 4 (DSM-IV) alcohol-dependent cases and 1,932 alcohol-exposed, nondependent controls from the Study of Addiction: Genetics and Environment (SAGE) analyzed at 948,658 SNPs that span the genome. First, using a hypothesis-free, genome-wide association strategy, we nominate SNPs associated with vulnerability to alcohol dependence. Second, we specifically examine the important role of SNPs in GABRA2 using a targeted, hypothesis-driven approach.

AA, African-American; Chr, chromosome; EA, European-American; FDR, false discovery rate.

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Acknowledgments

Funding support for SAGE was provided through the National Institutes of Health (NIH) Genes, Environment and Health Initiative (GEI) Grant U01 HG004422. SAGE is one of the GWAS funded as part of the GENEVA under GEI. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GENEVA Coordinating Center (Grant U01 HG004446). Assistance with data cleaning was provided by the National Center for Biotechnology Information. Support for collection of datasets and samples was provided by COGA (Grant U10 AA008401), COGEND (Grant P01 CA089392), and FSCD (Grant R01 DA013423). Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by NIH GEI Grant 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 genetic contributions to human disease” (HHSN268200782096C). Funding support for the work by Treutlein et al., 2009 (24) was provided by the National Genome Research Network (NGFNplus).

Acknowledgments

Footnotes

Conflict of Interest Statement: L.J.B., J.P.R., A.M.G., S.F.S., and J.C.W. are inventors on the patent “Markers for Addiction” (US 20070258898) covering the use of certain SNPs in determining the diagnosis, prognosis, and treatment of addiction. N.L.S. is the spouse of S. F.S., who is listed as an inventor on the patent. L.J.B. served as a consultant for Pfizer Inc. in 2008.

This article is a PNAS Direct Submission.

Data deposition: Data can be obtained from dbGaP at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000092.v1.p1 through dbGaP accession number phs000092.v1.p1.

This article contains supporting information online at www.pnas.org/cgi/content/full/0911109107/DCSupplemental.

Footnotes

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