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Linkage Analysis Followed by Association Show NRG1 Associated with Cannabis Dependence in African Americans

  • Shizhong Han
    Affiliations
    Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut

    Veterans Affairs Connecticut Healthcare Center, West Haven, Connecticut
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  • Bao-Zhu Yang
    Affiliations
    Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut

    Veterans Affairs Connecticut Healthcare Center, West Haven, Connecticut
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  • Henry R. Kranzler
    Affiliations
    Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania

    Veterans Integrated Service Network 4 Mental Illness Research, Education and Clinical Center, Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania
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  • David Oslin
    Affiliations
    Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania

    Veterans Integrated Service Network 4 Mental Illness Research, Education and Clinical Center, Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania
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  • Raymond Anton
    Affiliations
    Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina
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  • Lindsay A. Farrer
    Affiliations
    Departments of Medicine (Biomedical Genetics), Neurology, Ophthalmology, Genetics & Genomics, Biostatistics, and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
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  • Joel Gelernter
    Correspondence
    Address correspondence to Joel Gelernter, M.D., Yale University School of Medicine, Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, 950 Campbell Avenue, 116A2, West Haven, CT, 06516
    Affiliations
    Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut

    Veterans Affairs Connecticut Healthcare Center, West Haven, Connecticut

    Departments of Genetics and Neurobiology, Yale University School of Medicine, New Haven, Connecticut
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      Background

      A genetic contribution to cannabis dependence (CaD) has been established but susceptibility genes for CaD remain largely unknown.

      Methods

      We employed a multistage design to identify genetic variants underlying CaD. We first performed a genome-wide linkage scan for CaD in 384 African American (AA) and 354 European American families ascertained for genetic studies of cocaine and opioid dependence. We then conducted association analysis under the linkage peak, first using data from a genome-wide association study from the Study of Addiction: Genetics and Environment, followed by replication studies of prioritized single nucleotide polymorphisms (SNPs) in independent samples.

      Results

      We identified the strongest linkage evidence with CaD (logarithm of odds = 2.9) on chromosome 8p21.1 in AAs. In the association analysis of the Study of Addiction: Genetics and Environment sample under the linkage peak, we identified one SNP (rs17664708) associated with CaD in both AAs (odds ratio [OR] = 2.93, p = .0022) and European Americans (OR = 1.38, p = .02). This SNP, located at NRG1, a susceptibility gene for schizophrenia, was prioritized for further study. We replicated the association of rs17664708 with CaD in an independent AAs sample (OR = 2.81, p = .0068). The joint analysis of the two AA samples demonstrated highly significant association between rs17664708 and CaD with adjustment for either global (p = .00044) or local ancestry (p = .00075).

      Conclusions

      Our study shows that NRG1 is probably a susceptibility gene for CaD, based on convergent evidence of linkage and replicated associations in two independent AA samples.

      Key Words

      Cannabis is the most commonly used illicit substance in the world with 143 million to 190 million people in 2007 having used the drug at least once worldwide (
      United Nations Office on Drugs and Crime
      World Drug Report 2009.
      ). An estimated 14.4 million Americans aged 12 or older reported cannabis use over the past month (
      Substance Abuse and Mental Health Services Administration
      Results from the 2007 National Survey on Drug Use and Health: National Findings.
      ). Of these individuals, ∼7% develop cannabis dependence (CaD) defined by DSM-IV criteria (
      • Agrawal A.
      • Lynskey M.T.
      Does gender contribute to heterogeneity in criteria for cannabis abuse and dependence? Results from the national epidemiological survey on alcohol and related conditions.
      ). With the expansion of legalization in the United States (generally for “medical” use), availability and use of cannabis are rising. Cannabis use is often accompanied by dependence on alcohol and other drugs (
      • Stinson F.S.
      • Ruan W.J.
      • Pickering R.
      • Grant B.F.
      Cannabis use disorders in the USA: Prevalence, correlates and co-morbidity.
      ) and is associated with serious consequences, including cognitive and psychomotor impairments (
      • Ramaekers J.G.
      • Moeller M.R.
      • van Ruitenbeek P.
      • Theunissen E.L.
      • Schneider E.
      • Kauert G.
      Cognition and motor control as a function of Delta9-THC concentration in serum and oral fluid: Limits of impairment.
      ,
      • Wadsworth E.J.
      • Moss S.C.
      • Simpson S.A.
      • Smith A.P.
      Cannabis use, cognitive performance and mood in a sample of workers.
      ). The use of cannabis is associated with roughly twofold increased risk of schizophrenia; there is interindividual variability in susceptibility to cannabis-induced psychosis that could be, in part, genetic in origin (
      • Casadio P.
      • Fernandes C.
      • Murray R.M.
      • Di Forti M.
      Cannabis use in young people: The risk for schizophrenia.
      ,
      • Malone D.T.
      • Hill M.N.
      • Rubino T.
      Adolescent cannabis use and psychosis: Epidemiology and neurodevelopmental models.
      ). Thus, it is important to identify factors that influence individual vulnerability to the development of CaD.
      Family and twin studies have shown that CaD has an important genetic component. The heritability of cannabis abuse or dependence was estimated to be 45% to 78% (
      • Agrawal A.
      • Lynskey M.T.
      The genetic epidemiology of cannabis use, abuse and dependence.
      ). Genome-wide linkage studies and candidate gene association studies have identified a list of possible chromosomal risk regions and candidate genes for cannabis use disorders (
      • Agrawal A.
      • Lynskey M.T.
      The genetic epidemiology of cannabis use, abuse and dependence.
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      A genome-wide scan for loci influencing adolescent cannabis dependence symptoms: Evidence for linkage on chromosomes 3 and 9.
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      • et al.
      Linkage scan for quantitative traits identifies new regions of interest for substance dependence in the Collaborative Study on the Genetics of Alcoholism (COGA) sample.
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      • et al.
      Autosomal linkage analysis for cannabis use behaviors in Australian adults.
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      • et al.
      An autosomal linkage scan for cannabis use disorders in the nicotine addiction genetics project.
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      ,
      • Ehlers C.L.
      • Gilder D.A.
      • Gizer I.R.
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      Heritability and a genome-wide linkage analysis of a Type II/B cluster construct for cannabis dependence in an American Indian community.
      ,
      • Ehlers C.L.
      • Gizer I.R.
      • Vieten C.
      • Wilhelmsen K.C.
      Linkage analyses of cannabis dependence, craving, and withdrawal in the San Francisco family study.
      ). For example, linkage studies have identified genomic regions harboring candidate genes with biological relevance, such as the monoacylglycerol lipase gene (MGLL) on chromosome 3 and the gamma-aminobutyric acid receptor subunit alpha-2 gene (GABRA2) on chromosome 4 (
      • Agrawal A.
      • Lynskey M.T.
      Candidate genes for cannabis use disorders: Findings, challenges and directions.
      ). The cannabinoid receptor gene (CNR1) and several other genes (CRN2, FAAH, and MGLL), which are specific to the endogenous cannabinoid system, have been selected for candidate gene association studies, although the results were largely inconclusive (
      • Agrawal A.
      • Lynskey M.T.
      Candidate genes for cannabis use disorders: Findings, challenges and directions.
      ). Recently, a genome-wide association study (GWAS) for CaD was conducted in 708 individuals with DSM-IV CaD and 2346 cannabis-exposed nondependent control subjects, using a GWAS dataset from the Study of Addiction: Genetics and Environment (SAGE) (
      • Agrawal A.
      • Lynskey M.T.
      • Hinrichs A.
      • Grucza R.
      • Saccone S.F.
      • Krueger R.
      • et al.
      A genome-wide association study of DSM-IV cannabis dependence.
      ). However, no results achieved genome-wide significance in this study. Despite the effort that has been made in gene mapping for CaD, genetic factors underlying CaD susceptibility remain largely unknown.
      With the growing evidence for the role of rare variants and copy number variation in psychiatric disorders (
      • Manolio T.A.
      • Collins F.S.
      • Cox N.J.
      • Goldstein D.B.
      • Hindorff L.A.
      • Hunter D.J.
      • et al.
      Finding the missing heritability of complex diseases.
      ,
      • Eichler E.E.
      • Flint J.
      • Gibson G.
      • Kong A.
      • Leal S.M.
      • Moore J.H.
      • Nadeau J.H.
      Missing heritability and strategies for finding the underlying causes of complex disease.
      ,
      • Xie P.
      • Kranzler H.R.
      • Krauthammer M.
      • Cosgrove K.P.
      • Oslin D.
      • Anton R.F.
      • et al.
      Rare nonsynonymous variants in alpha-4 nicotinic acetylcholine receptor gene protect against nicotine dependence.
      ), linkage analysis remains a useful approach to gene discovery. An adequately powered linkage study can detect diverse kinds of genetic polymorphism that segregate in families, including common variants, multiple rare variants within one locus, and inherited copy number variations. The apparent failure to identify association under linkage peaks could, in part, be attributable to the fact that often only common variants are examined under the linkage peak, whereas the linkage signal could be caused by multiple rare variants with higher penetrance (
      • Weiss L.A.
      • Arking D.E.
      • Daly M.J.
      • Chakravarti A.
      Gene Discovery Project of Johns Hopkins & the Autism Consortium
      A genome-wide linkage and association scan reveals novel loci for autism.
      ,
      • Ho M.K.
      • Goldman D.
      • Heinz A.
      • Kaprio J.
      • Kreek M.J.
      • Li M.D.
      • et al.
      Breaking barriers in the genomics and pharmacogenetics of drug addiction.
      ,
      • Ott J.
      • Kamatani Y.
      • Lathrop M.
      Family-based designs for genome-wide association studies.
      ).
      The current study employed a multistage design using a linkage scan, a GWAS dataset, and replication in independent samples to identify genetic variants associated with CaD. Specifically, the objectives of the current study were to: 1) conduct a genome-wide linkage scan to detect genetic loci influencing CaD risk in African Americans (AAs) and European Americans (EAs); 2) assess the genetic association between CaD and single nucleotide polymorphisms (SNPs) under the strongest linkage peak using the GWAS dataset from SAGE; and 3) replicate prioritized SNPs in independent AAs and EAs from our samples.

      Methods and Materials

      Study Samples

      The basic demographic information for the three samples involved in each stage of the study is summarized in Table 1. The following section provides details of the sample recruitment and characteristics for each sample set. Written informed consent was obtained from all subjects; the Institutional Review Board at each recruitment site approved the study; and National Institute on Alcohol Abuse and Alcoholism and National Institute on Drug Abuse issued certificates of confidentiality for the work.
      Table 1Basic Demographic Information for the Three Samples Included in the Current Study
      African AmericansEuropean Americans
      Linkage Sample
       Number of families384355
       Number of genotyped individuals1022874
       Number of pedigrees with 1 CaD134136
       Number of pedigrees with ≥ 2 CaD4054
       Age (years ± SD) (CaD)38.6 ± 6.533.5 ± 9.6
       Males (%) (CaD)57.860.4
      SAGE Sample
       CaD275422
        Males (%)66.567.3
        Age (years ± SD)38.7 ± 7.834.8 ± 8.5
       Control4011049
        Males (%)38.233.1
        Age (years ± SD)39.8 ± 7.539.1 ± 9.7
      Replication Sample
       CaD758568
        Males (%)67.770.4
        Age (years ± SD)40.1 ± 8.734.7 ± 10.8
       Control280318
        Males (%)26.440.3
        Age (years ± SD)37.3 ± 13.238.7 ± 13.9
      CaD, cannabis dependence; SAGE, Study of Addiction: Genetics and Environment; SD, standard deviation.

      Linkage Scan Sample

      Subjects were originally ascertained for genetic studies of cocaine dependence (CD) and opioid dependence (OD) using the affected sibling pair linkage approach (
      • Gelernter J.
      • Panhuysen C.
      • Weiss R.
      • Brady K.
      • Hesselbrock V.
      • Rounsaville B.
      • et al.
      Genomewide linkage scan for cocaine dependence and related traits: Significant linkages for a cocaine-related trait and cocaine-induced paranoia.
      ,
      • Gelernter J.
      • Panhuysen C.
      • Wilcox M.
      • Hesselbrock V.
      • Rounsaville B.
      • Poling J.
      • et al.
      Genomewide linkage scan for opioid dependence and related traits.
      ). The recruitment procedure has been previously described in detail (
      • Gelernter J.
      • Kranzler H.R.
      • Panhuysen C.
      • Weiss R.D.
      • Brady K.
      • Poling J.
      • Farrer L.
      Dense genomewide linkage scan for alcohol dependence in African Americans: Significant linkage on chromosome 10.
      ,
      • Panhuysen C.I.
      • Kranzler H.R.
      • Yu Y.
      • Weiss R.D.
      • Brady K.
      • Poling J.
      • et al.
      Confirmation and generalization of an alcohol-dependence locus on chromosome 10q.
      ,
      • Yang B.Z.
      • Han S.
      • Kranzler H.R.
      • Farrer L.
      • Gelernter J.
      A genomewide linkage scan of cocaine dependence and major depressive episode in two populations.
      ). Briefly, there were four recruitment sites: University of Connecticut Health Center, Yale University School of Medicine, Medical University of South Carolina, and McLean Hospital. Families were selected on the basis of having at least two siblings affected with either cocaine and/or opioid dependence. The distribution of family numbers recruited at each site is presented in Table S1 in Supplement 1. Cannabis use played no role in proband selection or pedigree extension. We evaluated these subjects with the Semi-Structured Assessment for Drug Dependence and Alcoholism (
      • Pierucci-Lagha A.
      • Gelernter J.
      • Chan G.
      • Arias A.
      • Cubells J.F.
      • Farrer L.
      • Kranzler H.R.
      Reliability of DSM-IV diagnostic criteria using the semi-structured assessment for drug dependence and alcoholism (SSADDA).
      ,
      • Pierucci-Lagha A.
      • Gelernter J.
      • Feinn R.
      • Cubells J.F.
      • Pearson D.
      • Pollastri A.
      • et al.
      Diagnostic reliability of the Semi-structured Assessment for Drug Dependence and Alcoholism (SSADDA).
      ), a polydiagnostic instrument that assesses a range of psychiatric diagnoses, including DSM-IV CaD. Probands with an Axis I clinical diagnosis of a major psychotic disorder such as schizophrenia or schizoaffective disorder were excluded from participation. Subjects were classified as AA or EA on the basis of a Bayesian model-based clustering method with ancestry informative genetic markers using STRUCTURE (
      • Pritchard J.K.
      • Stephens M.
      • Donnelly P.
      Inference of population structure using multilocus genotype data.
      ,
      • Falush D.
      • Stephens M.
      • Pritchard J.K.
      Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies.
      ), as described previously (
      • Yang B.Z.
      • Han S.
      • Kranzler H.R.
      • Farrer L.
      • Gelernter J.
      A genomewide linkage scan of cocaine dependence and major depressive episode in two populations.
      ).

      Study of Addiction: Genetics and Environment

      We obtained the individual level SAGE GWAS dataset from the database of Genotypes and Phenotypes. Study of Addiction: Genetics and Environment aims to identify genetic risk factors and the interplay of genes and environmental factors for addiction. Cases and control subjects were selected from three large, complementary cohorts: the Collaborative Study on the Genetics of Alcoholism, the Family Study of Cocaine Dependence, and the Collaborative Genetic Study of Nicotine Dependence, all of which have been previously described (
      • Begleiter H.
      • Reich T.
      • Hesselbrock V.
      • Porjesz B.
      • Li T.K.
      • Schuckit M.A.
      • et al.
      The Collaborative Study on the Genetics of Alcoholism.
      ,
      • Reich T.
      • Edenberg H.J.
      • Goate A.
      • Williams J.T.
      • Rice J.P.
      • Van Eerdewegh P.
      • et al.
      Genomewide search for genes affecting the risk for alcohol dependence.
      ,
      • Bierut L.J.
      • Strickland J.R.
      • Thompson J.R.
      • Afful S.E.
      • Cottler L.B.
      Drug use and dependence in cocaine dependent subjects, community-based individuals, and their siblings.
      ,
      • Luo Z.
      • Alvarado G.F.
      • Hatsukami D.K.
      • Johnson E.O.
      • Bierut L.J.
      • Breslau N.
      Race differences in nicotine dependence in the Collaborative Genetic study of Nicotine Dependence (COGEND).
      ). The current study included 4036 unrelated self-reported AA subjects (1297 in total, including 275 CaD and 422 healthy control subjects) or EA subjects (2740 in total, including 401 CaD and 1049 healthy control subjects). Lifetime CaD was defined in accordance with the DSM-IV diagnosis. Control subjects used for association analysis in the current study were defined as subjects without dependence on any substances, including cannabis, alcohol, cocaine, opioid, nicotine, and other substances.

      Replication Sample

      Subjects were recruited for participation in studies of the genetics of CD, OD, and alcohol dependence (AD) from the communities around four sites listed above for linkage scan sample plus the University of Pennsylvania. The number of samples recruited at each site for the current study is presented in Table S1 in Supplement 1. Part of the set of samples genotyped at the replication stage overlapped with the samples used for linkage analysis. To obtain an independent sample set for replication study, the overlapping samples (72 CaD cases) were excluded from analysis in the replication stage. Subjects were interviewed with the Semi-Structured Assessment for Drug Dependence and Alcoholism and the diagnosis of CaD was derived based on DSM-IV diagnostic criteria. Most control subjects were recruited at the same recruitment sites (excluding McLean) and were screened to exclude those with a diagnosis of DSM-IV substance abuse or dependence and major Axis I psychiatric disorders. All subjects included in the current study were self-reported AA or EA. Subjects were reclassified as AA or EA on the basis of 41 ancestry informative genetic markers (AIMs) using STRUCTURE , as described previously (
      • Xie P.
      • Kranzler H.R.
      • Krauthammer M.
      • Cosgrove K.P.
      • Oslin D.
      • Anton R.F.
      • et al.
      Rare nonsynonymous variants in alpha-4 nicotinic acetylcholine receptor gene protect against nicotine dependence.
      ,
      • Xie P.
      • Kranzler H.R.
      • Poling J.
      • Stein M.B.
      • Anton R.F.
      • Farrer L.A.
      • et al.
      Interaction of FKBP5 with childhood adversity on risk for post-traumatic stress disorder.
      ). Among the subjects included in the current study, 3% of the subjects reporting to be of AA descent clustered in the EA group, and 2% of subjects reporting to be EA clustered in the AA group.

      Genotyping and Quality Control

      Linkage Analysis

      Genotyping and quality control (QC) for linkage analysis have been previously described in detail (
      • Yang B.Z.
      • Han S.
      • Kranzler H.R.
      • Farrer L.
      • Gelernter J.
      A genomewide linkage scan of cocaine dependence and major depressive episode in two populations.
      ). Briefly, 1630 subjects were genotyped at the Center for Inherited Disease Research (CIDR) for the 6008 SNP Illumina Human Linkage IVb Marker Panel. An additional 266 subjects were genotyped at the Yale Keck Center with the 6090 SNP Illumina Infinium-12 Human Linkage Marker Panel. We limited our analyses to 4518 autosomal SNPs available in both panels. After QC (genotyping rate ≥95%, minor allele frequency [MAF] ≥.1, and Hardy-Weinberg equilibrium [HWE] p ≥ .01), 4133 and 4395 autosomal SNPs were retained for analysis in AAs and EAs, respectively. Mendelian inconsistencies and potential genotyping errors were identified and set as missing data using PedCheck (
      • O'Connell J.R.
      • Weeks D.E.
      PedCheck: A program for identification of genotype incompatibilities in linkage analysis.
      ) and Merlin (
      • Abecasis G.R.
      • Cherny S.S.
      • Cookson W.O.
      • Cardon L.R.
      Merlin–rapid analysis of dense genetic maps using sparse gene flow trees.
      ) programs. We used the Pedigree Relationship Statistical Test (
      • McPeek M.S.
      • Sun L.
      Statistical tests for detection of misspecified relationships by use of genome-screen data.
      ) to verify family relationships, which showed pedigree errors in two AA families and five EA families. Of these, the relationships in one AA family and five EA families were corrected based on the shared identical by descent patterns and the reassigned family relationships were verified by Pedigree Relationship Statistical Test. One AA family relationship could not be resolved and the family was excluded from further analysis.

      SAGE Dataset

      Study of Addiction: Genetics and Environment samples were genotyped on the ILLUMINA Human 1M platform at CIDR. We included 4036 unrelated self-reported AA (1297) or EA (2740) subjects (60 duplicate genotype samples were excluded from analysis). We used PLINK software to perform basic data cleaning steps before analysis (
      • Purcell S.
      • Neale B.
      • Todd-Brown K.
      • Thomas L.
      • Ferreira M.A.
      • Bender D.
      • et al.
      PLINK: A tool set for whole-genome association and population-based linkage analyses.
      ). After QC (sample call rate ≥97%; SNP call rate ≥95%; MAF ≥.005 in control subjects; HWE p ≥ .00001 in control subjects), a total of 1297 (2740) unrelated subjects and 953,258 (888,092) autosomal SNPs for AAs (EAs) were available for further analysis. To obtain a more genetically homogeneous sample and correct for population stratification in the association analysis, we computed principal components (PC) using the EIGENSOFT package (
      • Price A.L.
      • Patterson N.J.
      • Plenge R.M.
      • Weinblatt M.E.
      • Shadick N.A.
      • Reich D.
      Principal components analysis corrects for stratification in genome-wide association studies.
      ). Specifically, 172,891 pruned SNPs common to AA and EA samples and in low linkage disequilibrium (LD) (genotypic correlation <.5) with one another were fed into EIGENSOFT. The top two PCs of AA and EA samples and with the Phase II HapMap CEU (Utah residents with Northern and Western European ancestry from the CEPH collection) and YRI (Yoruba in Ibadan, Nigeria) samples are shown in Figure S1 in Supplement 1. Outliers were defined as subjects whose ancestry was at least three standard deviations from the mean of the two largest PCs. This step removed 33 AAs and 127 EAs, retaining 1264 AAs and 2613 EAs in the final cleaned dataset.

      Replication Study

      The prioritized SNP, rs17664708, was genotyped for a replication study in 2543 AAs and 2042 EAs (ethnicity verified or reclassified by STRUCTURE using 41 mostly short tandem repeat AIMs) (
      • Xie P.
      • Kranzler H.R.
      • Krauthammer M.
      • Cosgrove K.P.
      • Oslin D.
      • Anton R.F.
      • et al.
      Rare nonsynonymous variants in alpha-4 nicotinic acetylcholine receptor gene protect against nicotine dependence.
      ,
      • Xie P.
      • Kranzler H.R.
      • Poling J.
      • Stein M.B.
      • Anton R.F.
      • Farrer L.A.
      • et al.
      Interaction of FKBP5 with childhood adversity on risk for post-traumatic stress disorder.
      ). Genotyping was performed with a fluorogenic 5' nuclease assay method (TaqMan technique), using the ABI 7900HT real time polymerase chain reaction system (ABI, Foster City, California). The missing rate for genotyping was .03 in the replication sample. For genotyping quality control, 8% of samples were re-genotyped with 100% concordance.
      A subset of the replication sample (n = 931, including 672 cases and 259 control subjects) of AAs were also genotyped by the Illumina Omni1-Quad platform at CIDR (690 subjects) or the Yale Keck Center (241 subjects) for our ongoing GWAS study of alcohol, cocaine, opioid, and nicotine dependence. Genotype information from these subjects were used to control for the potential population stratification in association test in AAs (the prioritized SNP rs17664708 was not included on the Illumina Omni1-Quad chip). After QC (SNP call rate ≥95%, sample call rate ≥97%, MAF ≥.005 in control subjects, HWE p value ≥ .00001 in control subjects), 2745 AIMs across the whole genome and 188 SNPs located at NRG1 were extracted to estimate the global and local ancestry in this subset of AA samples, respectively.

      Statistical Analysis

      Linkage Analysis

      We used Merlin (
      • Abecasis G.R.
      • Cherny S.S.
      • Cookson W.O.
      • Cardon L.R.
      Merlin–rapid analysis of dense genetic maps using sparse gene flow trees.
      ) to perform the linkage scan using a nonparametric allele-sharing model. Allele frequencies were estimated by counting all genotyped individuals. The Kong and Cox (
      • Kong A.
      • Cox N.J.
      Allele-sharing models: LOD scores and accurate linkage tests.
      ) linear allele-sharing model was used to estimate the logarithm of odds (LOD) score. To minimize the inflation of linkage signals caused by marker-marker LD, we grouped SNPs by LD into clusters using the Merlin “--rsq” option (
      • Abecasis G.R.
      • Wigginton J.E.
      Handling marker-marker linkage disequilibrium: Pedigree analysis with clustered markers.
      ). Analyses were repeated with R2 thresholds of .05, .2, and .3 to evaluate the robustness of the linkage results. We assessed the thresholds for autosomal genome-wide suggestive and significant linkages and the autosomal genome-wide empirical significance of an observed LOD score by 1000 computer simulations, as described previously (
      • Yang B.Z.
      • Han S.
      • Kranzler H.R.
      • Farrer L.
      • Gelernter J.
      A genomewide linkage scan of cocaine dependence and major depressive episode in two populations.
      ).

      Global Ancestry Estimation in AAs

      Spurious association between a marker and a phenotype can arise from population stratification, especially in admixed populations such as AAs. To account for the effect of population stratification in AAs, we estimated the individual global ancestry using the STRUCTURE program and included it as a covariate in the association analysis. To obtain a more consistent estimate of individual global ancestry for SAGE and our replication AA samples, we selected 2475 AIMs that were genotyped in both the SAGE AA sample and a subset of our replication sample (931 subjects). These AIMs were common to a reported AIMs panel for AAs based on a subset of SNPs on the ILLUMINA Human 1M platform (
      • Tandon A.
      • Patterson N.
      • Reich D.
      Ancestry informative marker panels for African Americans based on subsets of commercially available SNP arrays.
      ). The log likelihood of each analysis at varying numbers of assumed population groups (k) was estimated from the average of three independent runs (5000 burn in and 5000 iterations). As expected, the results favored a two-ancestry population model. The average proportion of European ancestry was .186 in the SAGE AA sample and .166 in our replication AA sample.

      Local Ancestry Estimation in AAs

      Because the global ancestry information obtained across the whole genome may not reflect the variation of ancestry at the tested genomic locus, methods that adjust the global ancestry to control population stratification may be insufficient. However, methods that conditioned on local ancestry at the tested locus more fully account for the confounding effect of hidden population structure (
      • Wang X.
      • Zhu X.
      • Qin H.
      • Cooper R.S.
      • Ewens W.J.
      • Li C.
      • Li M.
      Adjustment for local ancestry in genetic association analysis of admixed populations.
      ). Therefore, we estimated the local ancestry at the NRG1 locus using the HAPMIX program (
      • Price A.L.
      • Tandon A.
      • Patterson N.
      • Barnes K.C.
      • Rafaels N.
      • Ruczinski I.
      • et al.
      Sensitive detection of chromosomal segments of distinct ancestry in admixed populations.
      ), and the overall ancestry across SNPs at the NRG1 locus was included as a covariate in the association analysis to control for the local population stratification (i.e., the estimated ancestry of the specific genomic region under consideration). Briefly, we downloaded the phased YRI and CEU data from HapMap Phase II as the parental reference haplotype input for HAPMIX. After QC and filtering the monomorphic SNPs in the phased YRI and CEU data, 188 SNPs that were located at the NRG1 gene and were genotyped in both the SAGE AA sample and in the subset of the replication AA sample (931) were used to estimate local ancestry at the NRG1 locus. The estimated average European ancestries at the NRG1 locus were .194 and 0.171 for the SAGE and the replication AA samples, respectively, which approximated the values of the global European ancestry proportion in each sample.

      Association Analysis

      The association between each SNP and the binary trait was estimated in a multivariate logistic regression framework under a log-additive genetic model. We used PLINK for the SNP-trait association test in the region of the linkage peak in the SAGE sample, with sex, age, and the top 10 PCs as covariates. For the replication analysis and joint data analysis stages for rs17664708, we included sex, age, and global or local ancestry estimates as covariates where appropriate, and analysis was performed using the R package “SNPassoc'” (
      • González J.R.
      • Armengol L.
      • Solé X.
      • Guinó E.
      • Mercader J.M.
      • Estivill X.
      • Moreno V.
      SNPassoc: An R package to perform whole genome association studies.
      ).

      Results

      Genome-wide Linkage Analysis for CaD

      Empirical genome-wide thresholds for suggestive and significant linkage for nonparametric linkage analyses in our family dataset were determined based on 1000 simulations. The thresholds for genome-wide suggestive and significant linkage in AAs (EAs) were 1.79 (1.76) and 3.23 (3.22), respectively.
      The genome-wide nonparametric linkage results for AAs and EAs are presented in Figure 1. The strongest linkage signal was identified on chromosome 8p21.1 at 54.9 cM (LOD = 2.9, pointwise p = .00013, empirical genome-wide p = .097) in AAs, and weak linkage evidence was detected at the same location in EAs (LOD = .62, pointwise p = .05). We identified a second genome-wide suggestive linkage peak in AAs on chromosome 14 with a peak LOD of 2.26 at 89.9 cM, though no linkage evidence was detected at this peak region in EAs. In EAs, only one region showed genome-wide suggestive linkage on chromosome 7 at 107 cM (LOD = 1.85), where weak linkage signal was observed in AAs (LOD = .19). The locations and values of these suggestive linkage peaks did not change when analyses were repeated using different R2 values to group SNPs into clusters.
      Figure thumbnail gr1
      Figure 1Genome-wide nonparametric linkage results in African Americans and European Americans. The black and red virtue lines represent the empirical genome-wide suggestive (significant) linkage thresholds for African Americans and European Americans, respectively. lod, logarithm of odds.

      Association Analysis Under the Linkage Peak Using SAGE GWAS Dataset

      Utilizing the SAGE GWAS dataset, we examined the genetic association between CaD and 4853 SNPs under the strongest linkage peak from our family sample (LOD >2, from 48.9 cM to 65.2 cM) on chromosome 8. All of these SNPs passed QC and were tested for genetic association with adjustment for sex, age, and the top 10 PCs in AAs and EAs separately. Eleven SNPs were nominally significantly associated with CaD in both AAs and EAs (Table 2). One SNP (rs17664708), which is relatively rare in AA control subjects (MAF = .02) but common in EA control subjects (MAF = .096), showed consistent evidence for association in both AAs (odds ratio [OR] = 2.93, 95% confidence interval [CI] = 1.47–5.85, p = 0.0022) and EAs (OR = 1.38, 95% CI = 1.05–1.81, p = .02). This SNP is located at NRG1, which has been previously implicated in the risk for schizophrenia. Considering the high degree of commorbidity between cannabis use and schizophrenia (
      • Casadio P.
      • Fernandes C.
      • Murray R.M.
      • Di Forti M.
      Cannabis use in young people: The risk for schizophrenia.
      ,
      • Malone D.T.
      • Hill M.N.
      • Rubino T.
      Adolescent cannabis use and psychosis: Epidemiology and neurodevelopmental models.
      ,
      • Green B.
      • Young R.
      • Kavanagh D.
      Cannabis use and misuse prevalence among people with psychosis.
      ,
      • Compton W.M.
      • Grant B.F.
      • Colliver J.D.
      Prevalence of marijuana use disorders in the United States: 1991–1992 and 2001–2002.
      ,
      • Smit F.
      • Bolier L.
      • Cuijpers P.
      Cannabis use and the risk of later schizophrenia: A review.
      ,
      • Moore T.H.
      • Zammit S.
      • Lingford-Hughes A.
      • Barnes T.R.
      • Jones P.B.
      • Burke M.
      • Lewis G.
      Cannabis use and risk of psychotic or affective mental health outcomes: A systematic review.
      ,
      • Hall W.
      • Degenhardt L.
      Cannabis use and the risk of developing a psychotic disorder.
      ), this SNP (rs17664708) was prioritized for further study.
      Table 2Summary of Association Analysis Results Under the Linkage Peak for SNPs Nominally Significantly Associated with CaD in Both AAs and EAs Using SAGE GWAS Dataset
      SNPPosition
      Coordinate based on Genome Build 36.3.
      FunctionGeneAfrican Americans (275 Cases/401 Control Subjects)European Americans (422 Cases/1049 Control Subjects)
      MAMAF (Cases)MAF (Control Subjects)OR95% CIpMAMAF (Cases)MAF (Control Subjects)OR95% CIp
      rs93187425301315IntronicDOCK5T.19.24.74.56–.99.043T.38.361.201.00–1.43.05
      rs655804928148960IntergenicA.23.181.411.05–1.89.021A.026.045.54.33–.90.017
      rs701536328159947IntergenicG.23.181.361.02–1.81.037G.026.046.53.32–.88.013
      rs701166030405716IntronicRBPMSA.19.23.74.56–.98.038A.19.22.77.62–.95.016
      rs783322930542430IntronicRBPMSA.024.042.45.22–.90.025A.11.14.74.57–.97.031
      rs17664708
      Prioritized SNP (rs17664708) for follow-up study.
      32556559IntronicNRG1T.049.0202.931.47–5.85.0022T.13.0961.381.05–1.81.020
      rs1215597034064572IntergenicC.065.0342.201.26–3.86.0057C.093.0621.511.09–2.09.012
      rs259309538587762IntergenicT.54.481.271.00–1.61.049T.33.39.77.64–.93.0056
      rs698904240301460IntergenicG.30.231.321.01–1.73.041G.23.201.251.01–1.55.037
      rs782664540924370IntergenicA.13.16.68.49–.96.026A.16.111.501.17–1.93.0016
      rs1254760941091390IntergenicC.15.21.68.50–.93.015C.21.171.311.05–1.63.018
      Association tests are adjusted by sex, age, and the top 10 principal components.
      AA, African Americans; CaD, cannabis dependence; CI, confidence interval; EA, European Americans; GWAS, genome-wide association study; MA, minor allele; MAF, minor allele frequency; OR, odds ratio; SAGE, Study of Addiction: Genetics and Environment; SNP, single nucleotide polymorphism.
      a Coordinate based on Genome Build 36.3.
      b Prioritized SNP (rs17664708) for follow-up study.

      Replication of the Prioritized SNP in Independent AAs and EAs

      We tested the association between rs17664708 and CaD in independent AAs (758 CaD and 280 healthy control subjects) and EAs (568 CaD and 318 healthy control subjects) from our own sample. The genotype distribution of this SNP was in HWE in both cases and control subjects for AAs and EAs (in AAs, p = 1 for both cases and control subjects; in EAs, p = .17 and p = .80 for cases and control subjects, respectively). We replicated the association of rs17664708 with CaD in AAs after adjusting for sex and age (OR = 2.81, 95% CI = 1.23–6.45, p = .0068) but not in EAs (OR = 1.04, 95% CI = .77–1.40, p = .82). The MAF of rs17664708 in the replication AA sample was .013 and .038 in control subjects and cases, respectively, which approximated the values from the SAGE AA sample.

      Joint Analysis in AAs

      Considering that the strongest linkage signal on chromosome 8 was observed only in AAs, we performed a joint analysis for the association of rs17664708 to CaD in AAs including sex, age, and global or local ancestry as covariates (Table 3). To permit comparison, analysis results with adjustment for global or local ancestry in each AA sample are also listed in Table 3. The association results remained significant after adjusting for either global (p = .00044) or local ancestry (p = .00075), which argues against the possibility that the significant association was caused by population stratification in AAs.
      Table 3Association Analysis for rs17664708 with CaD in the Joint Samples of AAs
      Datasets (Cases/Control Subjects)MAF (Cases)MAF (Control Subjects)Association Test Adjusted by
      Sex and AgeSex, Age, and Global AncestrySex, Age, and Local Ancestry
      OR95% CIpOR95% CIpOR95% CIp
      SAGE (275/401).049.0202.601.37–4.92.00282.711.38–5.32.00312.731.35–5.51.0043
      Replication (672/259)
      To estimate the local ancestry at NRG1 locus and the global ancestry in the replication AA sample, only a subset of individuals (931) who were genotyped by Illumina Omni1-Quad platform were included.
      .038.0132.701.17–6.24.0102.15.92–5.03.0592.17.91–5.16.063
      Joint (947/660).042.0182.451.49–4.02.000172.341.42–3.85.000442.331.39–3.91.00075
      AA, African Americans; CaD, cannabis dependence; CI, confidence interval; MAF, minor allele frequency; OR, odds ratio; SAGE, Study of Addiction: Genetics and Environment.
      a To estimate the local ancestry at NRG1 locus and the global ancestry in the replication AA sample, only a subset of individuals (931) who were genotyped by Illumina Omni1-Quad platform were included.

      Discussion

      The current study demonstrated how linkage analysis could inform genetic association studies and lead to discovery of a rare variant in NRG1 associated with CaD in AAs. We first conducted a dense genome-wide linkage study for CaD in small nuclear families recruited on the basis of affected sibling pairs with either CD and/or OD. The strongest evidence for linkage in our family sample was observed on 8p21.1 (LOD = 2.9) in AAs. To our knowledge, this region has not been previously reported by linkage studies of any substance dependence disorder. However, chromosome 8p22-p21, which overlaps our linkage signal, has been repeatedly implicated in several neuropsychiatric disorders, including schizophrenia, bipolar affective disorder, and major depression (
      • Tabarés-Seisdedos R.
      • Rubenstein J.L.
      Chromosome 8p as a potential hub for developmental neuropsychiatric disorders: Implications for schizophrenia, autism and cancer.
      ). Using the SAGE GWAS dataset, we found that rs17664708, located at NRG1 under the linkage peak, was associated with CaD in both AAs and EAs. The association was further replicated in an independent AA sample. Our rigorous QC and statistical analysis adjusting for both global and local ancestry argue against the possibility that the significant association between the rs17664708 and CaD is from the effect of population stratification in AAs.
      None of the SNPs that were nominally significant (p ≥ .05) in both AAs and EAs can survive multiple testing correction in the discovery stage of association analysis under the linkage peak using the SAGE dataset. However, our prioritization strategy, designed to order SNPs for follow-up studies, relies on both statistical evidence and prior knowledge. Accordingly, the SNP rs17664708 located at NRG1 was prioritized for further studies. NRG1 is a well-established susceptibility gene for schizophrenia in many populations, supported by genetic linkage, association studies, and meta-analysis (
      • Munafò M.R.
      • Thiselton D.L.
      • Clark T.G.
      • Flint J.
      Association of the NRG1 gene and schizophrenia: A meta-analysis.
      ,
      • Li D.
      • Collier D.A.
      • He L.
      Meta-analysis shows strong positive association of the neuregulin 1 (NRG1) gene with schizophrenia.
      ,
      • Ng M.Y.
      • Levinson D.F.
      • Faraone S.V.
      • Suarez B.K.
      • DeLisi L.E.
      • Arinami T.
      • et al.
      Meta-analysis of 32 genome-wide linkage studies of schizophrenia.
      ). There are at least three lines of prior knowledge that prompt us to consider NRG1 as a candidate gene for CaD as well. First, elevated rates of cannabis use have repeatedly been reported among individuals with schizophrenia (
      • Green B.
      • Young R.
      • Kavanagh D.
      Cannabis use and misuse prevalence among people with psychosis.
      ,
      • Compton W.M.
      • Grant B.F.
      • Colliver J.D.
      Prevalence of marijuana use disorders in the United States: 1991–1992 and 2001–2002.
      ), and epidemiologic studies suggest that frequent cannabis use is associated with about twofold increased risk for developing schizophrenia and related disorders (
      • Casadio P.
      • Fernandes C.
      • Murray R.M.
      • Di Forti M.
      Cannabis use in young people: The risk for schizophrenia.
      ,
      • Malone D.T.
      • Hill M.N.
      • Rubino T.
      Adolescent cannabis use and psychosis: Epidemiology and neurodevelopmental models.
      ,
      • Smit F.
      • Bolier L.
      • Cuijpers P.
      Cannabis use and the risk of later schizophrenia: A review.
      ,
      • Moore T.H.
      • Zammit S.
      • Lingford-Hughes A.
      • Barnes T.R.
      • Jones P.B.
      • Burke M.
      • Lewis G.
      Cannabis use and risk of psychotic or affective mental health outcomes: A systematic review.
      ,
      • Hall W.
      • Degenhardt L.
      Cannabis use and the risk of developing a psychotic disorder.
      ). The significant commorbidity between cannabis use and schizophrenia may be attributable, in part, to a shared underlying genetic component for CaD and schizophrenia. Second, the neurobiology of CaD and schizophrenia overlap; for example, the mesolimbic pathway is heavily implicated in the neurobiology of CaD (and other substance dependence) and schizophrenia (
      • Volkow N.D.
      • Wang G.J.
      • Fowler J.S.
      • Tomasi D.
      • Telang F.
      Addiction: Beyond dopamine reward circuitry.
      ,
      • Laviolette S.R.
      Dopamine modulation of emotional processing in cortical and subcortical neural circuits: evidence for a final common pathway in schizophrenia?.
      ). Third, animal studies have provided evidence supporting the role of NRG1 in CaD. NRG1 heterozygous mice have increased sensitivity to the acute neurobehavioral effects of cannabinoids (
      • Boucher A.A.
      • Arnold J.C.
      • Duffy L.
      • Schofield P.R.
      • Micheau J.
      • Karl T.
      Heterozygous neuregulin 1 mice are more sensitive to the behavioural effects of Delta9-tetrahydrocannabinol.
      ,
      • Boucher A.A.
      • Hunt G.E.
      • Karl T.
      • Micheau J.
      • McGregor I.S.
      • Arnold J.C.
      Heterozygous neuregulin 1 mice display greater baseline and Delta(9)-tetrahydrocannabinol-induced c-Fos expression.
      ); NRG1 modulates the development of tolerance to cannabinoids in mice (
      • Boucher A.A.
      • Hunt G.E.
      • Micheau J.
      • Huang X.
      • McGregor I.S.
      • Karl T.
      • Arnold J.C.
      The schizophrenia susceptibility gene neuregulin 1 modulates tolerance to the effects of cannabinoids.
      ).
      The gene NRG1 encodes neuregulin 1, a pleiotropic growth factor that is important in nervous system development and function. It has been implicated in the modulation of many processes of neural development, including neuronal migration, synapse formation, synaptic plasticity, and neuronal survival (
      • Mei L.
      • Xiong W.C.
      Neuregulin 1 in neural development, synaptic plasticity and schizophrenia.
      ). The SNP rs17664708 we reported here is located at the intron region of NRG1. We did not find any evidence supporting the functional role of this SNP involved in CaD. As it is the case for most genetic association studies, the associated SNP may not be the causal SNP but could represent a tag SNP that is in linkage disequilibrium with the surrounding causal variants. Further studies using a higher density SNP panel and deep sequencing technology are needed to fully characterize the genetic architecture of NRG1 and pinpoint the functional variants that could be involved in CaD.
      Cannabinoids have been shown to produce greater behavioral effects in female than in male rats (
      • Tseng A.H.
      • Craft R.M.
      Sex differences in antinociceptive and motoric effects of cannabinoids.
      ,
      • Tseng A.H.
      • Harding J.W.
      • Craft R.M.
      Pharmacokinetic factors in sex differences in [Delta]9-tetrahydrocannabinol-induced behavioral effects in rats.
      ). There is also evidence showing that male NRG1 heterozygous mice, being more sensitive to the acute effects of the psychotropic cannabis, constitute ΔA9-tetrahydrocannabinol, which is not observed in female mice (
      • Long L.E.
      • Chesworth R.
      • Arnold J.C.
      • Karl T.
      A follow-up study: Acute behavioural effects of Delta(9)-THC in female heterozygous neuregulin 1 transmembrane domain mutant mice.
      ). We investigated whether there is a sex-specific effect for the rs17664708 in CaD susceptibility by sex-stratified analysis. We observed a stronger effect for rs17664708 in females (OR = 3.08, 95% CI = 1.56–6.07, p = .0007) than in males (OR = 1.88, 95% CI = .95–3.74, p = .055).The stronger effect of rs17664708 in female animals provides further evidence for the interactions between NRG1 and sex in CaD susceptibility.
      Samples included in the current study were ascertained for DSM-IV cocaine or opioid dependence (linkage sample), AD (SAGE), and CD, OD, or AD (in the replication sample). Therefore, the linkage and association signals detected for CaD in AAs could be attributable to other substance dependence (SD). We examined this alternate explanation for the findings in several ways. First, we examined the linkage signals across other SD traits (AD, CD, OD, and nicotine dependence [ND]) in the same family dataset (Figure S2 in Supplement 1). The only linkage signal that we found with a LOD greater than 1 in the chromosome 8 region reported above for CaD was a modest signal (LOD = 1.4) for ND at 41 cM (i.e., ∼14 cM away from the CaD linkage peak). Second, we tested the association of rs17664708 with other SD in each AA sample and the joint datasets (Table S2 in Supplement 1). In the joint analysis, we observed highly significant associations between rs17664708 and CD (p = .0023), AD (p = .0076), ND (p = .0026), and OD (p = .00018). However, significant associations disappeared when subjects affected with CaD were excluded from AD (p = .3), ND (p = .069), and CD (p = .053). Nonetheless, the association remained highly significant for OD when the CaD cases were excluded (p = .0013), which suggests that rs17664708 could also be a risk variant for OD in AAs independent of CaD. Finally, to remove the confounding effect of OD, we tested the association between rs17664708 and CaD by either controlling for OD status in the regression model or excluding participants who were OD. The results remain significant in the joint datasets by OD status adjusted analysis (OR = 2.03, 95% CI = 1.17–3.55, p = .011) or after excluding OD participants (OR = 2.47, 95% CI = 1.48–4.11, p = .0003), arguing against the possibility that the significant associations between rs17664708 and CaD arise solely from OD. The association results from other SD might indicate that the association is not specific for CaD and could reflect a shared liability between different substances. The role of NRG1 in other SD remains to be further investigated.
      In summary, our study shows that NRG1 is probably a susceptibility gene for CaD, based on convergent evidence of linkage and replicated associations in two independent samples of AAs. Further studies using a high-density SNP panel or deep sequencing are necessary to confirm the role of NRG1 in CaD.
      This study was supported by National Institutes of Health (NIH) Grants R01 DA12690 , R01 DA12849 , RC2 DA028909 , R01 DA18432 , R01 AA11330 , R01 AA017535 , RO1 DA030976 , K01 DA24758 , and the Veterans Affairs (VA) Connecticut Reserve Educational Assistance Program center, a VA Merit Grant, VA National Center for Posttraumatic Stress Disorder Research, and the VA Connecticut and Veterans Integrated Service Network 4 Mental Illness Research, Education and Clinical Center Centers. It was also partially supported by the Alcoholic Beverage Medical Research Foundation Grant (SH).
      We are grateful to the volunteer families and individuals who participated in this research study. We gratefully acknowledge the assistance in recruitment and assessment provided at McLean Hospital by Roger Weiss, M.D., and at the Medical University of South Carolina by Kathleen Brady, M.D., Ph.D. Genotyping services of linkage analysis and our GWAS study were provided by the Center for Inherited Disease Research and Yale University (Keck Center). The Center for Inherited Disease Research is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University (contract number N01-HG-65403). We are grateful to Ann Marie Lacobelle, Michelle Cucinelli, Christa Robinson, and Greg Kay for their excellent technical assistance, to the Semi-Structured Assessment for Drug Dependence and Alcoholism interviewers who devoted substantial time and effort to phenotype the study sample, and to John Farrell for database management assistance.
      The datasets used for the analyses described in this manuscript were obtained from the database of Genotypes and Phenotypes at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000092.v1.p1 through database of Genotypes and Phenotypes accession number phs000092.v1.p. Funding support for the Study of Addiction: Genetics and Environment was provided through the NIH Genes, Environment and Health Initiative ( U01 HG004422 ). The Study of Addiction: Genetics and Environment is one of the genome-wide association studies funded as part of the Gene Environment Association Studies under the Genes, Environment and Health Initiative. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the Gene Environment Association Studies Coordinating Center ( 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 the Collaborative Study on the Genetics of Alcoholism ( U10 AA008401 ), the Collaborative Genetic Study of Nicotine Dependence ( P01 CA089392 ), and the Family Study of Cocaine Dependence ( R01 DA013423 ). Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by the NIH Genes, Environment and Health Initiative ( 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 ).
      Dr. Kranzler has been a paid consultant for Alkermes, GlaxoSmithKline, Gilead, Eli Lilly, Lundbeck, and Roche. Dr. Anton has received honoraria or grant support from Eli Lilly , Alkermes , GlaxoSmithKline , Merck , Lundbeck , and Roche and is a shareholder in Alcomed. Drs. Kranzler and Anton also report associations with Eli Lilly, Janssen, Schering Plough, Lundbeck, Alkermes, GlaxoSmithKline, Abbott, and Johnson & Johnson, as these companies provide support to the American College of Neuropsychopharmacology Alcohol Clinical Trials Initiative, and both receive support from the Alcohol Clinical Trials Initiative. The other authors reported no biomedical financial interests or potential conflicts of interest.

      Supplementary data

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