mutation screen of the gad2 gene and association study of alcoholism in three populations

American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 144B:183–192 (2007)

Mutation Screen of the GAD2 Gene and AssociationStudy of Alcoholism in Three PopulationsJaakko Lappalainen,1,2* Evgeny Krupitsky,3 Henry R. Kranzler,4 Xingguang Luo,1,2

Mikhail Remizov,3 Sofia Pchelina,3 Anastaisa Taraskina,3 Edwin Zvartau,3

Pirkko Rasanen,5 Taru Makikyro,5 Lucia K. Somberg,1,2 John H. Krystal,1,2

Murray B. Stein,6 and Joel Gelernter1,2

1Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut2VA Connecticut Healthcare System, West Haven, Connecticut3St. Petersburg State Pavlov Medical University, St. Petersburg, Russia4Department of Psychiatry, University of Connecticut School of Medicine, Farmington, Connecticut5Department of Psychiatry, University of Oulu, Oulu, Finland6Department of Psychiatry, University of California, San Diego, California

Synaptic actions of g-amino butyric acid (GABA)have been implicated in many facets of ethanol’seffects and risk for alcoholism. We examinedwhether variation in glutamate decarboxylase-2(GAD2), a gene encoding for a major enzyme inthe synthesis of GABA, contributes to risk ofalcohol dependence (AD). We screened GAD2 forsequence variants using dHPLC in a population of96 individuals. Several single nucleotide poly-morphisms (SNPs), including four rare non-synonymous polymorphisms, were identified.Thirteen SNPs located in the GAD2 gene weregenotyped in a sample of 113 Russian males withAD and 100 Russian male controls. These analysesrevealed a modest association between the func-tional GAD2 �243 A>G SNP (rs2236418) and AD(alleleP¼0.038, genotypeP¼ 0.008). An additionalsample of 138 Russian males with AD weregenotyped for the GAD2 �243 A>G. These ana-lyses supported an association of this polymorph-ism with AD (combined sample allele P¼0.038,genotype P¼0.0009). We extended these findingsto additional populations: a sample of 538 collegestudents assessed using the AUDIT and a sampleof European–American (EA) AD subjects (n¼235)and controls (n¼310). Analyses in these popula-tions did not support a role for GAD2 in alcohol-ism. In summary, the results of an extensivesearch for an association ofGAD2with AD suggestthat variation in GAD2 is not a major risk

factor for AD in EAs. The functional promoterGAD2�243 A>G variant may influence risk for ADin some populations, or its role may be limited tosusceptibility to severe AD. � 2006 Wiley-Liss, Inc.

KEY WORDS: alcoholism; GABA; genetics; glu-tamate decarboxylase; polymor-phism

Please cite this article as follows: Lappalainen J,Krupitsky E, Kranzler HR, Luo X, Remizov M, Pchelina S,Taraskina A, Zvartau E, Rasanen P, Makikyro T,Somberg LK, Krystal JH, Stein MB, Gelernter J. 2007.Mutation Screen of the GAD2 Gene and AssociationStudy of Alcoholism in Three Populations. Am J MedGenet Part B 144B:183–192.

INTRODUCTION

g-amino butyric acid (GABA) is the primary inhibitoryneurotransmitter in brain. Several studies in animals andhumans using different paradigms have implicated GABA inmany behavioral effects of ethanol. In rodent studies, agentsthat increaseGABAcontent in brain orGABAreceptor activityenhance acute sensitivity to ethanol and maintain ethanolpreference, whereas agents that decrease GABAergic trans-mission attenuate acute effects of alcohol and reduce alcoholpreference in animals [Buck, 1996]. In humans, several genesencoding GABAA receptor subunits have been associated withincreased risk of alcohol dependence (AD) [Dick and Foroud,2003]. The strongest evidence has been presented for theGABAA receptor a-2 subunit (GABRA2), which has beenassociated with AD in three independent studies [Covaultet al., 2004; Edenberg et al., 2004; Lappalainen et al., 2005].

In the brain, virtually all GABA is produced by decarboxyla-tion of L-glutamic acid by two enzymes: glutamic aciddecarboxylase-67 (GAD67) and glutamic acid decarboxylase-65 (GAD65), which are products of two different genes locatedon chromosomes 2 (GAD1) and 10 (GAD2), respectively.GAD67 and GAD65 enzymes differ in their subcellularlocalization, contribution to vesicular andnon-vesicularGABApools, regulation of expression, and other functions [Soghomo-nian and Martin, 1998]. Studies in animals deleted for theGAD1 or GAD2 genes suggest that the GAD67 isoformproduces most of the brain GABA. Animals deleted for GAD2maintain normal whole brain GABA levels, but show beha-vioral differences compared to wild-type animals, includingincreased anxiety behavior in open-field and zero-mazeparadigms and decreased response to benzodiazepines and

Grant sponsor: National Institutes of Health; Grant numbers:K08 AA13732, R01 AA11330, P50 AA12870, M01 RR06192, K05AA14906, K24 AA13736, R01 AA011321-04, P50-AA03510, K24MH64122; Grant sponsor: Alcoholic Beverage Medical ResearchFoundation (ABMRF); Grant sponsor: US Department of Veter-ans Affairs [VA Alcohol Research Center, VA Mental IllnessResearch, Education and Clinical Center (VA MIRECC), and VAVeterans Research Enhancement Award Program (VA REAP)];Grant sponsor: Ethel F. Donaghue Women’s Health InvestigatorProgram at Yale.

*Correspondence to: Jaakko Lappalainen, M.D., Ph.D., YaleUniversity, VA Connecticut Healthcare System, Psychiatry116A2, 950 Campbell Ave, West Haven CT 06516.E-mail: [email protected]

Received 2 January 2006; Accepted 17 May 2006

DOI 10.1002/ajmg.b.30377

� 2006 Wiley-Liss, Inc.

pentobarbital [Asada et al., 1996; Kash et al., 1999]. Thesefindings suggest that variation in GAD65 activity maypotentially alter responses to alcohol, or that variation in thegene may influence alcohol-related phenotypes, such asanxiety, and increase risk for AD indirectly.

GAD2 is located on chromosome 10p12; it spans a total ofabout 84,000 bp of genomic sequence, including 16 exonswhichencode a protein of 585 amino acids.GAD65 andGAD67 share65% amino acid identity and are derived from a commonancestor in evolution [Bu and Tobin, 1994]. Most of thesequence differences between GAD2 and GAD1 are located inexons 1–3, while in the other areas of the coding sequence,especially in the area of the cofactor pyridoxal 50 phosphate(PLP) binding site, the genes show a high degree of homology.Considering that GABA is the most ubiquitous inhibitoryneurotransmitter in brain, these genes are potential candi-dates for susceptibility to psychiatric disorders inwhichGABAdysfunction has been implicated. A report by Smoller et al.[2001] suggested that a GAD2 repeat polymorphism may beassociated with behavioral inhibition in children. Beyond thatstudy, the role of GAD2 has not been extensively studied inrelation to susceptibility to psychiatric disorders in humans.

Several studies have examined the role of GAD2 in diabetesand obesity. Autoantibodies against GAD2 enzyme areassociated with type 1 diabetes, making this gene a logicalcandidate for influencing risk for this disease [Atkinson et al.,1990; Johnson et al., 2002]. Linkage and association studieshave suggested that genetic variants of GAD2 may besusceptibility factors for obesity. Genetic linkage betweenmarkers on chromosome 10p and obesity was originallyreported in French families [Hager et al., 1998] and laterreplicated in other populations [Price et al., 2001; Boutin et al.,2003; Saar et al., 2003]. A maximum lod score was obtained atmarker D10S197, which is located within GAD2. A recentfollow-up study reported association between single nucleotidepolymorphism (SNPs) located inGAD2 and morbid obesity. Ofparticular interest was the association between the G allele ofthe GAD2 �243 A>G SNP and obesity [Boutin et al., 2003].Boutin et al. [2003] also studied the effect of the �243 A>GSNP onGAD65 expression in vitro and demonstrated that theG allele was associated with a sixfold increase in promotertranscriptional activity and increased transcription factorbinding [Boutin et al., 2003]. However, these findings havenot been replicated unambiguously [Meyre et al., 2005;Swarbrick et al., 2005].

In this study, we examined the role ofGAD2 in susceptibilityto alcoholismusing genetic association techniques.We derivedthis hypothesis from previous data implicating brain GABAfunction in the effects of alcohol and genetic variation inGABAA receptors in risk for alcoholism in humans. We firstperformed a dHPLC screen of the GAD2 coding sequence toidentify novel genetic variants. Four rare non-synonymoussubstitutions and common synonymous and non-coding var-iants were discovered in the screening sample. A total of 13SNPs were genotyped in population samples to test forassociation between alcoholism and GAD2. Our analysesrevealed that the functional GAD2 �243 A>G SNP isassociatedwithAD in two samples of Russianmales. However,follow-up studies in a large sample of US college students(assessed without respect to AD) and samples of European–American alcohol dependent and control subjects did notsupport association between GAD2 and alcoholism.

MATERIALS AND METHODS

GAD2 Mutation Screen

The population for the GAD2 mutation screen consisted of96 subjects. Sixty-four of the subjects were recruited at the

VA Connecticut Healthcare System, West Haven Campus orthrough a multicenter VA co-operative study 425 ‘‘Naltrexonein the Treatment of Alcoholism’’ [Krystal et al., 2001]. Thisscreening population was comprised of individuals diagnosedwith affective disorder, AD, and schizophrenia and 65% ofthese individuals were European–American, 16% African–American, 10% Hispanic, and 9% of unknown race. Thesestudies were approved by the Institutional Review Boards(IRB) of Yale University School of Medicine and VA Connecti-cut Healthcare System.

Thirty-two adolescents were recruited from the AdolescentInpatient Unit at the Department of Psychiatry, OuluUniversity Hospital, Oulu, Finland. Psychiatric diagnoses inthis populationwere established using theKiddie-Schedule forAffective Disorders and Schizophrenia (K-SADS). Subjectsfulfilled the criteria for either affective disorder, posttraumaticstress disorder (PTSD), anxiety disorder, schizophrenia orschizoaffective disorder. Comorbidity with alcoholism anddrug dependence was common in this population. Thesestudies were approved by the Northern Ostrobothnia HospitalDistrict’s Ethics Committee and by the IRBs of YaleUniversitySchool of Medicine and VA Connecticut Healthcare System.

A dHPLC mutation screen of the GAD2 gene was accom-plished using the Transgenomic Wave dHPLC mutationdetection system [Underhill et al., 1997]. PCR primers wereselected using Primer3 (http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi) and Transgenomic Wavemakersoftware. PCR primers were designed based on melting curveanalysis with the goal of optimizing the sensitivity of thedHPLCanalysis. GC clampswere used either in the 50 or 30 endof the amplicons, when determined to be necessary for optimaldHPLC analysis conditions. GAD2 sequence information wasobtained from the University of California, Santa CruzGenome Bioinformatics web site ‘‘Golden Path’’ (http://genome.ucsc.edu). The structure of the GAD2 gene has been reportedearlier by Bu and Tobin, and the exon and intron numbering inthe present study corresponds to that presented in that article[Bu and Tobin, 1994]. Sixteen exons of the gene containingthe coding sequence of theGAD2 protein and about 0–175 bpsof the flanking intronic sequences were PCR amplified usingthe AmpliTaq polymerase (PE Biosystems, Foster City, CA).Betaine (1M)was added to PCR reactions as needed to enhancethe specificity or yield of the amplification process. After PCR,the reaction mixture was loaded on a DNASep cartridge(Transgenomic Inc) using a linear acetonitrile gradient in atriethylamine (TEAA) buffer at a constant flow rate of 0.9 ml/min. The gradient was created by mixing the eluents A (0.1 MTEAA) and B (0.1 TEAA and 25% Acetonitrile) at concentra-tions determined by the Transgenomic Wavemaker software.Before loading, the PCR mixture was heated to 958C for 4 minand allowed to cool on a table top for 10 min. Amplicons wereanalyzed under more than one temperature if recommendedby the melting curve analysis. All samples were evaluatedby visual inspection and those with potential variants weresequenced using cycle sequencing at theYaleKeckFoundationBiotechnology Resource Laboratory (Table I).

Russian Alcohol Dependent and Control Subjects

The Russian study population was composed of 113 alcoholdependent men (mean age 39, SD¼ 10) and 100 male controlsscreened to exclude psychiatric and substance use disorders(meanage 40, SD¼ 5). This samplewill be referred to hereafteras the initial Russian alcohol dependent sample. All alcoholdependent individuals were recruited at the LeningradRegional Center of Addictions (LRCA) affiliated with St.Petersburg State Pavlov Medical University. The participantsin the study were recruited from the patient populationadmitted for detoxification in the facility during the years

184 Lappalainen et al.

2002–2003. Psychiatric interviews of the participants wereconducted during the inpatient rehabilitation phase. Adiagnosis of AD (ICD-X) was established using clinical inter-views administered by a trained psychiatrist under thesupervision of EK. Individuals with primary psychotic dis-orders were excluded. Ten percent of the participants fulfilledthe diagnostic criteria for lifetime or current drug abuse ordependence (in addition to AD). The median number of timesparticipants of this study had been hospitalized for alcoholdetoxification treatment prior to the current episode was two.Through non-structured interviews, the participants reportedhaving an average age of onset of drinking of 16 years (SD¼ 3)and an average age of onset of heavy drinking of 22 years(SD¼ 5). Family history of alcoholism in a first-degree relativewas reported by 67% of the subjects. The control populationwas composed of military personnel of the local subdivisionwho were participating in an epidemiological study focusingon cardiac disease. The participants were screened usingnon-structured clinical interviews to exclude the presenceof psychiatric and substance use disorders at the time ofrecruitment for the study.

At a later stage of the study, an additional population of 138alcohol dependent Russian males became available through asecond wave of recruitment at LRCA. This sample will bereferred to hereafter as the additional Russian alcoholdependent sample. Diagnosis of AD in the additional samplewas established as described for the initial Russian alcoholdependent cohort. In the additional sample, six percent of theparticipants fulfilled the diagnostic criteria for lifetime orcurrent drug abuse or dependence (in addition to AD). Themedian number of times participants of the additional samplehad been hospitalized for treatment of AD prior to the currentepisode was two. Through non-structured interviews, theparticipants reported having an average age of onset ofdrinking of 16 years (SD¼ 3) and an average age of onsetof heavy drinking of 23 years (SD¼ 5). Family history ofalcoholism in a first degree relative was reported by 46% of thesubjects. The informed consent form and the study protocolwere approved by the IRBs of St. Petersburg State PavlovMedical University, Yale University School of Medicine andVA Connecticut Healthcare System.

The College Student Sample

Participants (n¼ 538) were recruited from among under-graduate psychology students at San Diego State University

who had, in a group testing session (at which some ques-tionnaires were completed and demographic informationprovided), indicated their willingness to be contacted byresearch investigators to participate in future psychologicalexperiments. They each came for a scheduled appointment, atwhich a blood sample (60 ml) was drawn for genetic studies,and the subjects completed questionnaires and/or computer-ized tasks, including the Alcohol Use Disorders IdentificationTest (AUDIT). The AUDIT consists of ten questions regar-ding alcohol consumption and problems experienced withalcohol use; it was developed to help identify hazardous andharmful drinkers [Saunders et al., 1993]. A total score of 8 orhigher is the recommended cutoff for identification of hazar-dous or harmful alcohol consumption [Saunders et al., 1993].Subjects gave informed, written consent to participate inthis part of the study, which was approved by the HumanResearch Protection Programs at both San Diego StateUniversity and the University of California San Diego. Of the538 subjects, 260 subjects were European–American, 110Hispanic, 55 Filipino, 38 Asian–American, 27 African–Amer-ican, and 48 of other ethnicities or information regarding thesubject’s ethnicity wasmissing. Average age in this populationwas 19 (SD¼ 2.2).

A European–American Sample of AlcoholDependent and Control Subjects

These subjects were recruited at the University of Connecti-cut Health Center and VA Connecticut Healthcare System-West Haven Campus. Alcohol dependent subjects and controlsubjects were recruited primarily through advertisement inlocal newspapers. Approximately half of the AD subjects wererecruited through clinical treatment trials of alcoholism. Allsubjects gave informed consent before participating in thestudy, which was approved by the IRB at each of theparticipating institutions. The alcoholic subjects in this studymet criteria for a life-time diagnosis of DSM-III-R or DSM-IVAD. The diagnoses were made using the Structured ClinicalInterview for DSM-III-R, the Structured Clinical Interview forDSM-IV or the Schedule for Affective Disorders and Schizo-phrenia.Control subjectswere screened to excludemajorAxis Imental disorders, including alcohol or drug dependence,psychotic disorders (including schizophrenia or schizophre-nia-like disorders), mood disorders, and anxiety disordersusing semi-structured interview instruments or clinical inter-views. In the alcohol dependent sample, 41 subjects werefemale, 191 subjects were male and 3 subjects had missing sexinformation. In the control sample, 184 subjects were femaleand 126 were male.

GAD2 SNP Genotyping

A total of 13 SNPs were genotyped in the populations usingtheTaqManmethod onanABI 7,900 real-timePCRapparatus.The markers were chosen with the goal of encompassing theentire length of the gene with informative SNPmarkers or themarkers were chosen because they altered the amino acidstructure or expression of the gene. Seven SNP assays wereobtained through Applied Biosystem’s Assay-On-Demandservice [�1,400 C>A (rs2839670), þ5,177 T>C (rs3893253),þ6,636C>T (rs8190612),þ46,375C>T (rs1330582),þ61,949T>C (rs3781113), þ62,623 C>G (rs3781109), þ75,354 T>C(rs3781108)]. Six assays were custom designed and obtainedthrough ABI’s Assay-by-Design service. Four of the 13 SNPsgenotyped were identified through our mutation scanningefforts [þ2,404 C>A (rs2839672), þ7,812 G>A (rs2839673),þ52,326 C>T (rs# not available), and þ75,582 G>C (rs# notavailable)] and two had been described earlier in the literature[�243 A>G (rs2236418) and þ83 A>G (rs# not available)

TABLE I. List of PCR Primers Used for Amplification of theGAD2 Coding Sequence

Segment Primer, left Primer, right

Exon 1 caggcgacctgctccagt cccgaagaagtttcctgttgcExon 2 accccggactgattgattt tgagaagactgcttggacccExon 3 agtcggggtttcctggct gctccccgctgagtctttExon 4 tgtggcattttaatttcattcttt tttattattttcgatgagacaataccExon 5 ttttcactggaggcaatcct cctaaattacataaaaacacatttggcExon 6 cagaggtaaaatgtggccatta ccctaggtttacaacaaggcaExon 7 ccaaggaggactcaggtcag taatgcatttccagccatcaExon 8 gccagtggaatggatcttgt gcaggagcccatgttaaaaaExon 9 caacagtttggggttggact aacctgggagattgaggcttExon 10 aactctgctgctgcttcctc cacatgaaaaacgtgcaaggExon 11 aggcaatgacaaactgctacc aattgactgtaatttgtgtttcacagExon 12 gagcctgtcagggtggagt ccagagaaagtcaaccgcatExon 13 tttctctaagatatgcaatgtctctt gaaagcaaatcttgggtaaggaExon 14 gacagagacggcaggatgac ggttggaattgactaaactttctgExon 15 tctgaacctgctgaatacatcg taggctgtctcctaaggccaExon 16 ggagaaagtcaccatgct ctacaaatacattcacacag

GAD65 and Alcoholism 185

[Johnson et al., 2001; Boutin et al., 2003]]. Of the initialRussian alcoholic and control samples, 53% of the sampleswere run in duplicate for quality control purposes. Estimatederror rate in this sample was <0.1%. Of the college studentsample, approximately 60% of the samples were run induplicate with mismatch rate <0.1%. Of the European–American sample, approximately 90% of the samples wererun in duplicatewithmismatch rate of 1.3%. SinceGAD2�243A>G creates a natural RFLP site for DraI, a small number ofrandom samples and all Russian control samples weregenotyped using both TaqMan and RFLP with perfectconcordance of the results. The Met204Ile creates a naturallyoccurring RFLP site for HinfI, which was used to genotype thisvariant (Fig. 1).

Statistical Analysis

PHASE [Stephens et al., 2001] and POWERMARKER(www.powermarker.net) software were used to reconstructGAD2 haplotypes and estimate their population frequencies.Each PHASE run consisted of 10,000 iterations and 100,000burn-ins of thePHASEalgorithm.POWERMARKERwasusedfor comparison of allele and genotype frequencies betweencases and controls and examination of Hardy–Weinberg equi-librium (HWE). Haploview software was used for visualizationof linkage disequilibrium (LD) in GAD2 [Barrett et al., 2005].The COCAPHASE module of the statistical packageUNPHASED was used to test for association between GAD2haplotypes and alcoholism in the Russian sample [Dudbridge,2003]. Comparison of haplotype frequencies between cases andcontrols was done using a moving window across every threeconsecutive SNPs. Analyses were restricted to SNPs that werepresent at a frequency of 5% or greater and to haplotypes thatwere present at a frequency of 3% or greater.

In the college student sample, analyses were made bycomparing AUDIT scores in the three genotype groups (i.e.,homozygotes, heterozygotes, and homozygotes) for each SNP.Since the AUDIT scores in this population were not normallydistributed, they were transformed using natural logarithm(ln). ln-transformed AUDIT scores were used for all analyses.AUDIT scoreswerefirst compared between genotype classes ateach SNP locus using analysis of covariance (ANCOVA). Inthese analyses, AUDIT scores were used as a dependentvariable, genotype and sex as fixed factors and age as acovariate. All 13 SNPs were examined in this populationindividually.

Since population stratification was a potential confounderin this population, positive associations were followed up byincluding the population ancestry as a covariate in theANCOVA model. Population ancestry and ancestry propor-tions were derived for the subjects from the data obtained bygenotyping 36 short tandem repeat markers (STR) and theFY (�) polymorphism and analyzing the marker data usingthe software STRUCTURE 2.1 [Pritchard and Rosenberg,1999]. The program allocates the genetic background of each

individual probabilistically between subpopulations (theproportions allocated between populations will be referredto hereafter as the ancestry coefficient). Selection, evalua-tion, and composition of the marker panel and genotypingmethods are described elsewhere [Stein et al., 2004; Yanget al., 2005]. Briefly, the STR panel consisted of theAmpFLSTR Identifiler PCR amplification kit, which pro-vided data for 15 loci used for forensic purposes [AppliedBiosystems (ABI)]. An additional 21 markers were selectedbased on their known high d between European–Americansand African–Americans, and in some cases between Hispa-nic and Asian populations [Smith et al., 2001]. We havepreviously demonstrated that this marker set is sufficient tomake accurate population ancestry assignments for Eur-opean–Americans and African–Americans [Yang et al.,2005]. Association analyses using ancestry coefficients tocontrol for population stratification were conducted in twodifferent ways. First, ancestry coefficients derived bySTRUCTURE were included as covariates in the ANCOVAanalyses for the SNPs which showed association to AUDITscores in preliminary analyses that ignored populationstratification (i.e., all subjects were analyzed regardless oftheir ethnicity).Weused ancestry coefficients frombothK¼ 3andK¼ 4 population solutions of the STRUCTURE analysis.Second, association was tested in a subpopulation of subjectswith an estimated European–American ancestry coefficienthigher than 0.9.

In addition to single SNP analyses, we also tested GAD2haplotypes for association to AUDIT scores in the collegestudent sample. These analyses were done using the softwarehaplo.score [Schaid et al., 2002] and a moving haplotypewindow across each three consecutive SNPs. These analyseswere restricted to SNPs that were present in this population ata frequency of 5% of higher. Haplotypes that were rarer than3% were excluded from the analyses. Association analysesfocusing on allele and genotype frequencies for theGAD2�243A>G SNP in the European–American alcohol dependent andcontrol samples were conducted using the w2 statistic imple-mented in POWERMARKER.

RESULTS

Results of the GAD2 Mutation Screen

A total of ten SNPs were identified in the screening sample.Seven SNPs are located in the coding sequence ofGAD2. Fourof the coding sequence SNPs cause a change in the amino acidcomposition of theGAD65protein. Two of thenon-synonymousSNPs [þ2,404 C>A (Pro153Gln) and þ7,812 G>A (Gly232-Glu)] have been described previously (www.ncbi.nlm.nih.gov)and two are novel [þ7,729 (Met204Ile) and þ52,326(Ser313Phe)]. All non-synonymous SNPs were rare in thepopulations studied. The frequency of the Met204Ile was notdetermined in the association samples because our prelimin-ary analyses indicated it to be very rare in the US populations

Fig. 1. Schematic illustration of the GAD2 gene. Vertical bars represent exons (exons are not to the size). Arrows indicate approximate positions of theSNPs genotyped.


(Table II). Allele frequencies are presented in Tables II and III.Human GAD2 sequence was compared with that fromchimpanzee, rat, mouse, chicken, and drosophila using theClustalW program (http://www.ebi.ac.uk/clustalw) to deter-mine whether the identified substitutions change GAD65amino acid sequence at conserved sites. The glycine allele ofthe Gly232Glu substitution and the serine allele of theSer313Phe substitution were present in all six speciesexamined. The proline allele of the Pro153Gln substitutionand the methionine allele of the Met204Ile substitution werepresent in all species except fruitfly.

LD in Russian, European–American, Hispanicand African–American Populations

LD in GAD2 was evaluated in the Russian sample and inthe European–American, African–American, and Asian–American subpopulations of the college student sample usingPHASE and Haploview software. LD in the European–American sample is presented in Figure 2. In the European–American population, there were two haplotype blocks in theGAD2 gene, defined by nine common SNPs genotyped(frequency >5%; Fig. 2). In the 50 portion of the gene, ahaplotypeblockwasdetected spanning theSNPs�1,400C>A,�243 G>A, and þ83 G>A, which were in tight LD with one

another (r2> 0.98, D0 ¼ 1 between each pair). Another haplo-type block was observed spanning from SNP þ46,375 C>T toþ75,582 G>C with D0 ¼ 1 between each marker pair. The LDpattern in theGAD2 gene in the Russian andAsian–Americanpopulations was highly similar to the pattern in the EApopulation (data not shown). In both of these populations,there were two haplotype blocks in GAD2, and similar to theEA population, the blocks were demarcated by the þ6,636C>T SNP. The LD pattern in the African–American popula-tion appeared more complex, but evaluation of these data waslimited by the small sample size (n¼ 27).

Analysis of HWE in the Russian, US CollegeStudent, and European–American Samples

Significant deviations from HWE were observed in theRussian andEuropean–American populations. In the Russiancontrol sample, genotypes at SNPs �1,400 C>A, �243 A>G,andþ83 G>Awere found to deviate fromHWE (�1,400 C>AP¼ 0.02, �243 A>G P¼ 0.02, þ83 G>A P¼ 0.03). In theRussian AD sample and in the pooled Russian sample (AD andcontrols), there were no significant deviations from HWE forany of the GAD2 SNPs. In the European–American subjectsrecruited in Connecticut, there was a significant deviationfrom HWE in the AD sample (P¼ 0.0006), a trend-level

TABLE II. List of GAD2 Variants Identified Using dHPLC and Sequencing

Position Flank Type PR aa MAF

þ1,120 (exon 3) TGCGA C/T Cys-Cys 75 NDþ2,404 (exon 4) ACCAC C/A Pro-Gln 153 0.01þ7,729 (exon 6) AGGTT G/A Met-Ile 204 0.00þ7,812 (exon 6) AGGGG G/A Gly-Glu 232 0.01þ7,822 (exon 6) GGCGA C/T Gly-Gly 235 NDþ29,198 (intron 8) CACGC C/T Intronic NDþ52,326 (exon 9) ATCTG C/T Ser-Phe 313 <0.01þ56,840 (exon 11) GGGGG G/A Gly-Gly 369 NDþ69,482 (intron 12) ATAAA A/G Intronic NDþ75,582 (intron 13) ACGGT G/C Intronic 0.21

Position refers to the base pair counted from the A of the initiation codon ATG. PR, protein residue; aa, amino acid;MAF,minor allele frequency in theEAstudent population.TheMet204Ilewasnot found ina sample of 213 subjectsfrom theUS, including 61 alcohol dependent subjects and 91 healthy control subjects. TheMet204Ile was found inthe Finnish population where it is present at a frequency of about 0.5–1% (data not shown).

TABLE III. List of GAD2 SNPs Genotyped in the Population Samples

Name RU (n¼ 100) EA (n¼261) AsA (n¼38) AA (n¼ 27)

1 �1,400 C>A1 0.18 0.20 0.34 0.522 �243 A>G2 0.20 0.20 0.34 0.673 þ83 G>A 0.18 0.20 0.34 0.214 þ2,404 C>A3 0.005* 0.01 0.000 0.0005 þ5,177 T>C4 0.005 0.002 0.000 0.356 þ6,636 C>T5 0.16 0.13 0.03 0.077 þ7,812 G>A6 0.000# 0.01 0.000 0.0008 þ46,375 C>T7 0.22 0.20 0.30 0.219 þ52,326 C>T 0.000## 0.004 0.000 0.00010 þ61,949 T>C8 0.23 0.21 0.30 0.3911 þ62,623 C>G9 0.29 0.32 0.32 0.7012 þ75,354 T>C10 0.22 0.21 0.30 0.5913 þ75,582 G>C 0.21 0.21 0.30 0.60

Allele frequency in the Russian control population (RU), European–American (EA), Asian–American (AsA), andAfrican–American (AA) college students is presented. The frequency of the allelewhichwas theminor allele in theEuropean–Americans is given. NCBI dbSNP nomenclature (rs) and ABI assay number (in parenthesis):1rs2839670 (hcv1443775), 2rs2236418, 3rs2839672, 4rs3893253 (hcv8868485), 5rs8190612 (hcv11562861),6rs2839673, 7rs1330582 (hcv1443809), 8rs3781113 (hcv1443799), 9rs3781109 (hcv1443798), 10rs3781108(hcv1443791). #Gly232Glu (þ7,812 G>A) and ##Ser313Phe (þ52,326 C>T) were not found in the Russiancontrol or alcoholic subjects. *Pro153Gln (þ2404 C>A) frequency was 0.015 in the Russian alcoholic sample.


deviation in the control sample (P¼ 0.08) and a significantdeviation in the pooled sample of cases and controls(P¼ 0.0009) for the �243 A>G SNP (the only SNP genotypedin this sample). There were no significant deviations fromHWE in the European–American college student sample forany of the GAD2 SNPs.

Testing for Association Between Alcoholism and GAD2Gene in a Russian Population

Association between GAD2 SNPs and alcoholism was testedfirst inapopulationof 113Russianalcohol dependentmalesand100 male population control subjects. These analyses revealedan association between the �243 G>A SNP and AD. The twoflanking markers located within the same haplotype block alsoshoweda trend level association toalcoholism.These results arereported in Table IV. Allelewise haplotype analyses using theCOCAPHASEmodule of the software packageUNPHASEDdidnot improve the results of the association analysis. There werenothree-SNPhaplotypes thatweresignificantlyassociatedwithalcoholism in this sample (P< 0.05). A trend level associationbetween the�1,400C>A��243A>G�þ83G>Ahaplotypeand alcoholism was observed (COCAPHASE global P-value¼ 0.068). At a later stage of the study, an additionalsample of 138 alcohol dependent subjects became availablethrough a second wave of recruiting at the LRCA. Only theGAD2 �243 A>G SNP was genotyped in this sample. Thegenotype distributions in the additional Russian alcoholdependent sample were GG¼ 0.04, GA¼ 0.20, AA¼ 0.76 withallele frequencies of G¼ 0.14 and A¼ 0.86. The genotypedistributions in the initial AD sample and control sample arepresented in Table IV. Comparison of the genotype frequenciesbetween the additional Russian alcohol dependent sample andcontrol sample revealed a significant genotype difference(P¼ 0.0025), but the allele frequencies were not significantly

different (P¼ 0.13).The initialandadditionalalcoholdependentsamples were combined for further analyses (referred tohereafter as the total alcohol dependent sample). In the totalalcohol dependent sample, the GAD2 �243 A>G genotypedistribution was GG¼ 0.03, GA¼ 0.20, AA¼ 0.77, with allelefrequencies of G¼ 0.13 and A¼ 0.87. Comparisons of allele andgenotype frequencies revealed significant differences betweenthe total alcohol dependent sample and the control sample(genotype P¼ 0.0009, allele P¼ 0.038). Allele and genotypefrequencies between the initial and additional Russian ADsamples were not different.

Testing for Association Between AUDIT Scores andGAD2 in the College Student Sample

We tested for association between GAD2 and AUDIT scoresin a sample of 538 college students using ANCOVA. Analyseswere first conducted on each GAD2 SNP in the entire collegesample ignoring subjects’ ethnicities. These analyses revealeda weak association between the AUDIT score and two GAD2SNPs: �243 A>G and þ6,636 C>T SNPs (P< 0.05). Afterinclusion of ancestry coefficients as covariates in ANCOVA,these SNPs were no longer significantly associated with theAUDIT score. Likewise, association tests using ANCOVAwithGAD2 SNPs andAUDIT scores in a population of subjects withestimated European–American ancestry coefficient>0.9 werenon-significant.Haplotype association analyses ofGAD2SNPsand AUDIT scores were negative.

Testing for Association Between Alcoholism and GAD2in European–American Population

To follow up on the finding of an association between �243A>G and AD in the Russian population, we genotyped thisSNP inapopulation ofEuropean–Americanalcohol dependentsubjects (n¼ 235) and controls (n¼ 310). Distribution of GG,

Fig. 2. LD in GAD2 identified by common SNPs in the European–American population. Solid dark squares indicate absolute LD (D0 ¼ 1). Numberswithin the squares are D0 values for the respective SNP pairs (e.g., D0 ¼0.90 between SNP 1 and 5).


AG, and AA genotypes were 0.06, 0.21, and 0.72 in alcoholdependent subjects and 0.05, 0.27, and 0.69 in control subjects.Comparisons of allele and genotype frequencies betweenalcohol dependent and control subjects using w2 analysis werenot statistically significant.

DISCUSSION

Numerous preclinical and clinical studies have confirmedthat brain GABA systems are involved in susceptibility toalcoholism. In rodents, agents that increase brain GABAcontent or GABA receptor activity enhance acute sensitivity toethanol and maintain ethanol preference, whereas drugs thatdecrease GABAergic transmission attenuate acute effects ofalcohol and reduce alcohol preference in animals [Buck, 1996].Human genetic studies have suggested that variation inseveral genes encoding subunits of GABAA receptors mayinfluence risk for alcoholism [Dick and Foroud, 2003]. Thestrongest evidence thus far has been presented for GABRA2,whichhas been shown to be associatedwith alcoholism in threeindependent population studies [Covault et al., 2004; Eden-berg et al., 2004; Lappalainen et al., 2005]. We hypothesizedthat genetically influenced variation in the function of thebrain GABA system, not directly involving GABAA receptorfunction, may also alter the risk for alcoholism. An example ofsuch an effect was seen in knockout studies, in which animalsdeleted forGAD2 have diminished sensitivity to the anxiolyticproperties of diazepam and a decreased sedative response topentobarbital, suggesting that variation in GAD2 activity caninfluence responses to drugs known to exert their effects viadirect stimulation of the GABAA receptors [Asada et al., 1996;Kash et al., 1999].

We first screened the GAD2 gene for genetic variants usingdHPLC. Several coding sequence and intronic variants werediscovered in a screening population of 96 individuals withvarious neuropsychiatric illnesses and who representeddifferent populations. None of the common variants (>10%)identified were found to alter the amino acid composition ofGAD65 suggesting that the majority of genetically influencedvariation in the function of this enzyme does not result fromcommon functional SNPs in the coding sequence. Four rarenon-synonymous substitutions were discovered. Two of thenon-synonymous variants are recorded in the publicly avail-ableSNPdatabases (http://www.ncbi.nlm.nih.gov) and twoarenovel. Interestingly, all rare non-synonymous substitutionsinvolved amino acid residues conserved across several species,suggesting that they may be important for the function of theGAD65 protein and that theymay influence how efficiently theenzyme synthesizes GABA. Owing to the rarity of these

variants, they are likely to have at the population level atmost a limited impact on disorders inwhichGABAdysfunctionplays a role.However, carriers of the variants, or thosewho arehomozygous at the SNP loci, may be at a greater risk fordiseases involving GABA function in brain or periphery.Recently, a very rare syndrome involving mental retardationand spastic cerebral palsy was described in a consanguineousfamily from Pakistan [Lynex et al., 2004]. All affectedindividuals were found to be homozygous for the cys allele ofa newly discovered Ser12Cys variant inGAD1, demonstratingthe profound impact of rare functional variants influencingGABA synthesis on brain development and function.

We determined the frequency of the Pro153Gln, Gly232Glu,and Ser313Phe variants in the European–American, African–American, Russian, and Asian–American samples and foundthe variants to be rare (1% or less) or absent in thesepopulations. The rare allele of the Met204Ile was not found ina sample of 213 European–American subjects. We have foundthis variant in the Finnish population where it is present at afrequency of 0.5–1% (data not shown). As would be predictedfrom the observed allele frequencies, no homozygotes for any ofthese variants were observed. Further in vitro studies may bewarranted todeterminewhether thesevariantsare functionallyactive. Although no formal statistical testing was done toevaluate whether selection has influenced GAD2, thereappeared to be an abundance of rare GAD2 non-synonymoussubstitutions,whereasno commonnon-synonymousSNPswerediscovered inGAD2. A similar tendency towardspreservationofthe coding sequence against common functional variants wasrecently reported for the genes encoding the vesicular mono-amine transporter (SLC18A2) and the serotonin transporter(SLC6A4) [Glatt et al., 2001]. The paucity of common functionalvariants in GAD2may imply that selection has acted onGAD2to preserve the sequence of this gene.

To testwhetherGAD2 is associatedwithAD,we genotyped apanel of 13 GAD2 SNPs in 113 Russian alcohol dependentmales and 100 male controls. This panel included thefunctional GAD2 �243 A>G SNP located in the 50 promoterregion of the gene. In vitro studies have suggested that the Gallele is associated with increased transcription factor bindingand transcriptional activity of GAD2 [Boutin et al., 2003],although thiswasnot replicated in a recent study bySwarbricket al. [2005]. Our initial studies revealed a modest associationbetween the �243 A>G SNP and AD. These data suggestedthat the G allele or the AG genotype may be protective ofalcoholism. These data were also in accordance with theproposed higher transcriptional efficiency of the G allele[Boutin et al., 2003] and greater alcohol sensitivity associatedwith higher brain GABA activity [Buck, 1996]. Haplotype

TABLE IV. Comparison of Allele and Genotype Frequencies in the Initial Sample of Russian Alcohol Dependent Subjects and Controls

SNP

Alcohol dependence Controls

Allele P GT P

Alleles Genotypes Alleles Genotypes

1 2 11 12 22 1 2 11 12 22

�1,400 C>A 0.12 0.88 0.02 0.20 0.78 0.19 0.81 0.00 0.37 0.63 0.08 0.01�243 A>G 0.12 0.88 0.02 0.20 0.78 0.19 0.81 0.00 0.38 0.62 0.04 0.008þ83 G>A 0.12 0.88 0.02 0.19 0.79 0.18 0.82 0.00 0.36 0.64 0.09 0.02þ6,636 C>T 0.22 0.78 0.07 0.33 0.61 0.16 0.84 0.01 0.31 0.68 0.10 0.11þ46,375 C>T 0.17 0.83 0.03 0.27 0.70 0.22 0.78 0.02 0.39 0.58 0.19 0.22þ61,949 T>C 0.16 0.84 0.03 0.27 0.70 0.22 0.78 0.02 0.40 0.57 0.12 0.15þ62,623 C>G 0.25 0.75 0.07 0.35 0.58 0.29 0.71 0.08 0.43 0.49 0.35 0.50þ75,354 T>C 0.15 0.85 0.03 0.25 0.72 0.22 0.78 0.02 0.40 0.58 0.09 0.10þ75,582 G>C 0.15 0.85 0.03 0.24 0.73 0.21 0.79 0.02 0.39 0.59 0.09 0.08

Allele P refers to allele P-value, GT P refers to genotype P-value.


analyses did not improve the results, which was expected,considering that the twoSNPs adjacent to the�243A>Gwerein almost absolute LD with this SNP. In the later stage of thestudy, an additional sample of 138 alcohol dependent subjectsbecame available who were genotyped for the GAD2 �243A>G SNP. These analyses provided further support for anassociation betweenGAD2�243 A>G and AD in the Russianpopulation.

Considering that candidate gene analyses are sometimescapable of producing false positive associations, and also withthe ideaof delimiting theaspects of alcohol usephenotypes thatmight be related to these variants, we sought to replicate ourfindings in other populations. We first genotyped the panel of13 GAD2 SNPs in a sample of 538 college students assessedusing the AUDIT. An extensive set of analyses was conductedin this dataset, including SNP and haplotype analyses. Thesestudies did not support association between GAD2 andhazardous/harmful alcohol consumption (AD per se could notbe evaluated in this sample). Considering that the average agein this populationwas 19, it is apparent thatmany subjects hadnot passed the age of maximal risk for the development ofalcoholism. This could contribute to type 2 error (i.e., obscuringan association) unless the genotype predisposes to early onsetof the disorder. However, considering that the sample wascomposed of college students, it is possible that those whodevelop early-onsetADarenotwell represented in this sample.We also sought to replicate the association between the �243A>G SNP and AD in a population of alcohol dependentsubjects (n¼ 235) and controls (n¼ 310) recruited in theGreater Hartford and New Haven areas of Connecticut (US).No association between the 243 A>G SNP and AD was foundin this population. Together, these findings suggest that theGAD2 �243 A>G SNP does not play a major role indetermining risk for alcoholism in US populations.

The discrepancy between the observations in the RussianandUSsamplesmaybedue to several factors. It is possible thatthe association between the �243 A>G SNP and AD in theRussianpopulation is spurious given that the size of the controlpopulation was relatively small. Another factor that may havecontributed to the differences in the two samples is that theRussian sample was recruited solely from an inpatientdetoxification setting, which would have biased the sampletowards more severe AD. For example, it is unlikely that asignificant proportion of the 538 college students assessed inthis study will develop a severe form of alcoholism requiringrepeated detoxification treatments, which was characteristicof the Russian sample. In addition, the Russian sampleconsisted ofmale alcoholics andmale controlswhile the samplecollected in Connecticut consisted of males and females. If theGAD65�243 A>GSNP had a sex-specific effect inmales, thiseffect could also contribute to the discrepancy. Furthermore,the alcohol dependent subjects recruited in Connecticut mayhave had a less severe form of alcoholism compared to theRussian subjects given thatmost European–American alcoholdependent subjects in this sample were recruited throughadvertisement and clinical treatment trials of alcoholism.Further studies focusing on theGAD2�243 A>G polymorph-ism and severe AD may be warranted to elucidate thisdiscrepancy. We do not believe that the finding in the Russiansample is due to population stratification considering that allparticipants were of Russian descent. Furthermore, ourprevious findings in this population do not implicate popula-tion stratification as a confounding factor in this sample[including the observation that the FY (�) polymorphism wasvirtually monomorphic in this population; Lappalainen et al.,2005]. In addition, the GAD2 þ5,177 T>C, which has anallele frequency of 35% in AA and a frequency of <1% in EAwas virtually monomorphic in the Russian alcohol depen-dent and control samples supporting that population strati-

fication in this sample is unlikely. Our quality controlexperiments indicated low genotyping error rate, thus that isnot likely to be the source of the discrepant findings in the twosamples.

Interestingly, there were significant deviations from HWEin populations that were examined in this study. Hardy–Weinberg disequilibriumwas observed for themarkers locatedin the 50 haplotype block of the GAD2 gene in the Russiancontrol and European–American alcohol dependent andcontrol populations (the latter population was genotyped forthe�243 A>G SNP only). The most significant deviation wasobserved in the European–American alcohol dependentsample (P¼ 0.0006). The SNPs located in the more distalGAD2 haplotype block were in HWE in all populations westudied. Possible explanations for these findings are (a)genotyping error, (b) presence of an occult null allele (whichcould cause a spurious observation of homozygosity), (c)population stratification, (d) chance, or (e) association withphenotype. As discussed above, we do not believe that errors ingenotyping contributed to these findings. We also studiedwhether a common null allele in the 50 region could contributeto apparent deviation fromHWEby examining transmission oftheGAD2�243 A>G alleles in a sample of 79 Finnish parent-offspring trios. No evidence of errors in Mendelian transmis-sion were found (data not shown), suggesting that a commonnull allele is an unlikely explanation for the observed deviationfrom HWE. In addition, we recently examined whetherpopulation stratification and admixture were present in asample that contained the European–American alcoholdependent and control subjects studied here. No evidencesupporting significant admixture and was found, suggestingthat this explanation for departure from HWE is unlikely[Yang et al., 2005]. It is also possible that these findings are dueto chance, especially in the Russian sample, which iscomparatively small. Consistency of the finding of departurefrom HWE in two populations screened for the presence orabsence of alcoholism argues against this explanation. A moreinteresting possibility is that the observed deviation fromHWE is due to association of the�243 A>G SNPwith AD. Anassociation between a trait and a locus, especially if theassociation is genotypic rather than allelic, may lead to adeparture from HWE, depending on the mode of inheritance,frequency of the susceptibility allele, prevalence of the disease,and genotype relative risk [Nielsen et al., 1998; Wittke-Thompson et al., 2005]. Departures from HWE may also beobserved for risk loci in healthy control subjects if the sample isscreened to exclude individuals with the disease and if theprevalence of the disease in the population is high (as is thecase for alcoholism) [Wittke-Thompson et al., 2005]. Atpresent, however, the reason for the departure from HWE inour sample remains undetermined. Further studies in other,larger population samples that include alcohol dependentsubjects may be warranted to elucidate this issue.

In summary, we report a comprehensive examination ofGAD2 as a candidate gene for alcoholism.We found that codingsequence variants in GAD2 contribute only a small amount tothe total genetic variation at this locus. Four rare GAD2 non-synonymous SNPs were discovered that may merit furtherstudy since the variants potentially influence the function ofthe gene and could contribute to behavioral variation in rareinstances. Association analyses between AD and GAD2revealed discrepant findings. In the Russian sample, therewas an association between the functional GAD2 �243 A>GSNPand alcoholism. This findingwas not supported by studiesin two additional samples recruited in theUS. The discrepancymaybe related to chance variation owing to the relatively smallsample size or it may relate to differences in the ascertainmentof subjects (and consequently to the phenotype assessed forassociation), biasing the samples towards a different severity


of AD, or toward an as yet unidentified endophenotype ofalcoholism. Although our primary analyses do not supportassociation between AD and the �243 A>G SNP in theEuropean–American sample, the observation of a significantdeviation from HWE in the European–American alcoholdependent samplemaywarrant further investigations regard-ing the association of this SNP with AD.

ACKNOWLEDGMENTS

This study was supported by the National Institutes ofHealth (K08 AA13732, R01 AA11330, P50 AA12870, M01RR06192, K05 AA14906, K24 AA13736, R01 AA011321-04,P50-AA03510,K24MH64122), theAlcoholic BeverageMedicalResearch Foundation (ABMRF), the US Department ofVeterans Affairs [VA Alcohol Research Center, VA MentalIllness Research, Education and Clinical Center (VA MIR-ECC), and VA Veterans Research Enhancement AwardProgram (VA REAP)], and the Ethel F. Donaghue Women’sHealth Investigator Program at Yale. We also wish to thankUK Human Genome Mapping Project Resource Centerfor technical help and proving access to UNPHASED andGLUE. Technical help of Greg Dalton-Key, Ann MarieLacobelle Wantroba and Katarzyna Kanarek is also greatlyappreciated.

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mutation screen of the gad2 gene and association study of alcoholism in three populations

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