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Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders

Abstract

Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest genome-wide association study to date of DSM-IV-diagnosed AD. Genome-wide data on 14,904 individuals with AD and 37,944 controls from 28 case–control and family-based studies were meta-analyzed, stratified by genetic ancestry (European, n = 46,568; African, n = 6,280). Independent, genome-wide significant effects of different ADH1B variants were identified in European (rs1229984; P = 9.8 × 10–13) and African ancestries (rs2066702; P = 2.2 × 10–9). Significant genetic correlations were observed with 17 phenotypes, including schizophrenia, attention deficit–hyperactivity disorder, depression, and use of cigarettes and cannabis. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and nonpathological drinking behaviors.

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Fig. 1: Manhattan plot of discovery transancestral meta-analysis showing strong evidence for rs1229984 in ADH1B.
Fig. 2: Regional plots for the ADH1B locus (rs1229984) in the transancestral discovery, AA, and EU meta-analyses.
Fig. 3: Genetic correlations between 45 traits and alcohol dependence in Europeans.

Data availability

Summary statistics from the genome-wide meta-analyses are available on the Psychiatric Genomics Consortium’s downloads page (http://www.med.unc.edu/pgc/results-and-downloads), including the source data for Figs. 1 and 2. Individual-level data from the genotyped cohorts and cohort-level summary statistics will be made available to researchers following an approved analysis proposal through the PGC Substance Use Disorder group with agreement of the cohort PIs; contact the corresponding authors for details. Cohort data are also available from dbGaP except where prohibited by IRB or European Union data restrictions. Expression data used to evaluate variants in ADH1B is available from GTEx (https://gtexportal.org/home/). Hi-C data used to evaluate the chromosome 3 variant can be queried with HUGIn (https://yunliweb.its.unc.edu/hugin/). Publicly available genome-wide summary statistics used for testing genetic correlations are accessible through LD Hub (http://ldsc.broadinstitute.org/) or from the Psychiatric Genomics Consortium (http://www.med.unc.edu/pgc/results-and-downloads), the Social Science Genetic Association Consortium (SSGAC; https://www.thessgac.org/data), Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA; http://enigma.ini.usc.edu/research/download-enigma-gwas-results/), and the Neale Lab (http://www.nealelab.is/uk-biobank); for availability of summary statistics from other studies, contact the respective authors. The source data for Fig. 3 is included in Supplementary Table 6.

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Acknowledgements

We thank A. Morris for providing code implementing the MANTRA Bayesian model. We thank A. Martin, A. Bloemendal, and H. Finucane for helpful conversations about the analysis protocol for admixed cohorts. We thank D. Hughes for her assistance with editorial contributions to the manuscript. We thank R. Spanagel for significant contributions as a founding member of the German Study of the Genetics of Alcoholism (GESGA). The PGC-SUD Working Group receives support from the National Institute on Drug Abuse and the National Institute of Mental Health via MH109532. We gratefully acknowledge prior support from the National Institute on Alcohol Abuse and Alcoholism. Statistical analyses for the PGC were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org) hosted by SURFsara and financially supported by the Netherlands Scientific Organization (NWO 480-05-003), along with a supplement from the Dutch Brain Foundation and the VU University Amsterdam. We thank the 23andMe research participants and employees for making this work possible. A.P. is supported by the Academy of Finland, Juselius Foundation; D.E.A. acknowledges 1K01MH093731; R.R. acknowledges AA-12502, AA-09203, AA-00145; M.K. is supported by AA009367, DA005147; M.M. is supported by AA009367, MH066140; R.A.G. is supported by AA017444; K.R. is supported by Academy of Finland; A.C.H. is supported by NIH AA07535, AA07729, AA13320, AA13321, and AA11998; A.R.D. is supported by NIH 1K01MH109765-01; A.E.A. is supported by NIH AA011408 and AA017828; A.G.W. is supported by NIH 3T32AA7464-38 S1; B.M.N. is supported by NIH U01 MH109539 and R01 MH107649; B.P.R. is supported by NIH AA011408, AA017828, and AA022537; B.S.M. is supported by NIH R01DA036525 and R01DA039408; B.T.W. is supported by NIH AA011408, AA017828, and AA022537; C. Hodgkinson. is supported by NIH and National Institute on Alcohol Abuse and Alcoholism (NIAAA) Intramural program; C.J.H. is supported by NIH DA032555, DA035804, DA011015, and DA042755; D.-S.C. is supported by NIH P20AA017830 and AA018779; W.I. is supported by NIH DA005147, DA013240, DA024417, and DA036216; S.A.B. is supported by NIH DA021905; H.d.W. is supported by NIH DA02812; M.C.S. is supported by NIH DA035804; S.V. is supported by NIH DA042755, DA037904, DA040177, and HG008983; R.P. is supported by NIH DA12690; J.G. is supported by NIH DA12690; D.B.H. is supported by NIH R01DA036583; D.G. is supported by the NIAAA Intramural program; D.M.D. is supported by NIH R01AA015416, K02AA018755, U10AA008401, and P50AA0022537; E.J.C. is supported by NIH DA023026, DA011301, and DA024413; E.O.J. is supported by NIH R01 DA044014; N.S. is supported by German Government BMBF #01EB0130; K.M. is supported by German Government BMBF #01EB0410; G.W.M. is supported by an NHMRC fellowship by GNT1078399; N.W. is supported by D.F.G. and B.M.B.F.; N. Dahmen is supported by D.F.G. and B.M.B.F.; J.D.B. is supported by NIH R01HD060726; M.A.K. is supported by Health Research Council of New Zealand 11/792, 16/600; J.M. Boden is supported by Health Research Council of New Zealand 11/792, 16/600; J.H. is supported by Health Research Council of New Zealand 11/792, 16/600; J.F.P. is supported by Health Research Council of New Zealand 11/792, 16/600; H.H.M. is supported by NIH DA025109, DA024413, and DA016977; J.E.S. is supported by NIH K01 AA024152; J.A.K. is supported by Academy of Finland 265240, 263278, and Sigrid Juselius Foundation; J.K.H. is supported by NIH DA011015; J.L.M. is supported by NIH K01DA037914; J.M. Biernaka is supported by NIH P20AA017830 and AA25214; K.K. is supported by NIH DA011015; K.S.K. is supported by NIH P50AA0022537; L.-S.C. is supported by NIH DA038076; L.J.B. is supported by NIH R01DA036583; L.M.H. is supported by NIH AA011408 and AA017828; L.D. is supported by an Australian NHMRC Principal Research Fellowship; M.A.F. is supported by NIH/NIAAA P20AA017830; M.D.R. is supported by CSAT/SAMHSA 1H79T1026423, 1H79T1026446, AHRQ 1R18HS024208, and NIH R01DA036628; N.A.G. is supported by NIH R00DA023549; P.-H.S. is supported by NIAAA Intramural Research Program; N. Diazgranados is supported by NIAAA Intramural Research Program; M. Schwandt is supported by NIAAA Intramural Research Program; R.W. is supported by NSF GRFP DGE 1144083; P.A.F.M. is supported by NIH DA012854 and R25DA027995; J.M. is supported by Peter Boris Chair in Addictions Research; K.M.H is supported by NIH R01 HD060726, R01 HD073342, and P01 HD031921; M.B.M. is supported by NIH R01HD060726; R.K.W. is supported by NIH U01 MH094432; R.C.C. is supported by NIH R01DA036583; R.E.P. is supported by NIH K01MH113848; R.E.T. is supported by NIH R21DA043735; S.S.-R. is supported by the Frontiers of Innovation Scholars Program (FISP) and the Interdisciplinary Research Fellowship in NeuroAIDS (IRFN) and by NIH P50DA037844; S.-A.B. is supported by NIH AA011408, AA017828, AA022537, and AA022717; S.E.M. is supported by NHMRC 1103623; S.M.H. is supported by NIH R21AA024888 and K08DA032680; T.B.B. is supported by NIH MH100549; M.M.N. is supported by The BMBF-funded e:Med consortium IntegraMent 01ZX1314A, SysMedAlcoholism 01ZX1311A, and DFG-funded Excellence-Cluster ImmunoSensation; S.C. is supported by The Integrated Network IntegraMent 01ZX1314A; M. Rietschel is supported by The Integrated Network IntegraMent 01ZX1314G and SysMedAlcoholism 01ZX1311A; T.L.W. is supported by NIH R01 DA021905 and R01 DA035804; A.M.G. is supported by NIH U10 AA08401; M.L.-P. is supported by University of Helsinki, Academy of Finland; J.L. is supported by University of Helsinki, Academy of Finland; V.M.K. is supported by NIH P20AA017830 and AA25214; W.E.C. is supported by NIH R01HD093651, R01DA036523, and P30DA023026; T.-K.C. is supported by Wellcome Trust (STRADL) 104036/Z/14/Z; M.J.A. is supported by Wellcome Trust (STRADL) 104036/Z/14/Z; A.M.M. is supported by Wellcome Trust (STRADL) 104036/Z/14/Z. A.A. is supported by NIH K02DA32573. Funding support for the Comorbidity and Trauma Study (dbGAP accession number: phs000277.v1.p1) was provided by the National Institute on Drug Abuse (R01 DA17305); GWAS genotyping services at the CIDR at The Johns Hopkins University were supported by the National Institutes of Health (contract N01-HG-65403). Funding support for the Center for Education and Drug Abuse Research (dbGAP accession number: phs001649.v1.p1) was provided by the National Institute on Drug Abuse (P50 DA005605). The Christchurch Health and Development Study (dbGAP in progress) has been supported by funding from the Health Research Council of New Zealand, the National Child Health Research Foundation (Cure Kids), the Canterbury Medical Research Foundation, the New Zealand Lottery Grants Board, the University of Otago, the Carney Centre for Pharmacogenomics, the James Hume Bequest Fund, US NIH grant MH077874, and NIDA grant “A developmental model of gene-environment interplay in SUDs” (R01DA024413) 2007–2012. COGA is supported by NIH Grant U10AA008401 from the NIAAA and the National Institute on Drug Abuse (NIDA). Funding support for this GWAS genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by the National Institute on Alcohol Abuse and Alcoholism, the NIH GEI (U01HG004438) and the NIH contract “High throughput genotyping for studying the genetic contributions to human disease” (HHSN268200782096C). COGA Principal Investigators: B. Porjesz, V. Hesselbrock, H. Edenberg, L. Bierut; includes eleven different centers: University of Connecticut (V. Hesselbrock); Indiana University (H.J. Edenberg, J. Nurnberger Jr., T. Foroud); University of Iowa (S. Kuperman, J. Kramer); SUNY Downstate (B. Porjesz); Washington University in St. Louis (L. Bierut, J. Rice, K. Bucholz, A. Agrawal); University of California at San Diego (M. Schuckit); Rutgers University (J. Tischfield, A. Brooks); Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA (L. Almasy), Virginia Commonwealth University (D. Dick), Icahn School of Medicine at Mount Sinai (A. Goate), and Howard University (R. Taylor). Other COGA collaborators include: L. Bauer (University of Connecticut); J. McClintick, L. Wetherill, X. Xuei, Y. Liu, D. Lai, S. O’Connor, M. Plawecki, S. Lourens (Indiana University); G. Chan (University of Iowa; University of Connecticut); J. Meyers, D. Chorlian, C. Kamarajan, A. Pandey, J. Zhang (SUNY Downstate); J.-C. Wang, M. Kapoor, S. Bertelsen (Icahn School of Medicine at Mount Sinai); A. Anokhin, V. McCutcheon, S. Saccone (Washington University); J. Salvatore, F. Aliev, B. Cho (Virginia Commonwealth University); and Mark Kos (University of Texas Rio Grande Valley). A. Parsian is an NIAAA Staff Collaborator. M. Reilly was an NIAAA staff collaborator. We continue to be inspired by our memories of Henri Begleiter and Theodore Reich, founding PI and Co-PI of COGA, and also owe a debt of gratitude to other past organizers of COGA, including T. –K. Li, currently a consultant with COGA, as well as P. M. Conneally, R. Crowe, and W. Reich, for their critical contributions. The authors thank K. Doheny and E. Pugh from CIDR and J. Paschall from the NCBI dbGaP staff for valuable assistance with genotyping and quality control in developing the dataset available at dbGaP (phs000125.v1.p1, phs000763.v1.p1, and phs000976.v1.p1). Support for the Study of Addiction: Genetics and Environment (SAGE) was provided through the NIH Genes, Environment and Health Initiative (GEI; U01 HG004422; dbGaP study accession phs000092.v1.p1). SAGE is one of the GWAS funded as part of the Gene Environment Association Studies (GENEVA) under GEI. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GENEVA 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 COGA (U10 AA008401), the Collaborative Genetic Study of Nicotine Dependence (COGEND; P01 CA089392; see also phs000404.v1.p1), and the Family Study of Cocaine Dependence (R01 DA013423, R01 DA019963). Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR), was provided by the NIH GEI (U01HG004438), the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse, and the NIH contract “High throughput genotyping for studying the genetic contributions to human disease” (HHSN268200782096C). For GESGA, M.R. and M.M.N. were supported by the German Federal Ministry of Education and Research (BMBF) through grants BMBF 01ZX1311A (to M.R. and M.M.N.), and through grants 01ZX1314A (to M.M.N.) and 01ZX1314G (to M.R.) within the e:Med research program. The GSMS project (phs000852.v1.p1) was supported by the National Institute on Drug Abuse (U01DA024413 and R01DA11301), the National Institute of Mental Health (R01MH063970, R01MH063671, R01MH048085, K01MH093731, and K23MH080230), NARSAD, and the William T. Grant Foundation. We are grateful to all the GSMS and CCC study participants who contributed to this work. The following grants supported data collection and analysis of the Center on Antisocial Drug Dependence (CADD; dbGAP in progress): DA011015, DA012845, DA021913, DA021905, DA032555, and DA035804. Funding support for Spit for Science (dbGAP in progress) has been provided by Virginia Commonwealth University, P20 AA017828, R37AA011408, K02AA018755, and P50 AA022537 from the National Institute on Alcohol Abuse and Alcoholism, and UL1RR031990 from the National Center for Research Resources and National Institutes of Health Roadmap for Medical Research. We would like to thank the Spit for Science participants for making this study a success, as well as the many University faculty, students, and staff who contributed to the design and implementation of the project. In particular we acknowledge the contributions of the many individuals who have played a critical role data collection, generation, and cleaning, including A. Adkins, F. Aliev, E. Caraway, S. B. Cho, J. Clifford, M. Cooke, E. Do, A. Edwards, N. Goyal, L. Halberstadt, S. Hawn, R. Holloway, J. Lent, M. Lind, E. Long, J. Meyers, J. Myers, A. Moore, A. Moscati, Z. Neale, J. Opalesky, C. Overstreet, K. Pedersen, L. Rappa, B. Riley, J. Salvatore, J. Savage, C. Sun, N. Thomas, B. Webb, and J. Yan. The NIAAA data (https://btris.nih.gov/) were supported by the NIAAA Intramural Research Program (IRP). Data collection, genotyping, and analysis of the Mayo Clinic Center for Individualized Treatment of Addiction (CITA) data was supported by grants from the National Institute on Alcohol Abuse and Alcoholism (P20 AA017830, and R21 AA25214), as well as the Mayo CTSA Grant Number UL1TR000135 and SC Johnson Genomics of Addiction Program. The Mayo Clinic Biobank, which served as the source of controls for the CITA cases, was supported by Mayo Clinic Center for Individualized Medicine. Alcohol Dependence in African Americans was funded by NIH grant R01 AA017444. Brisbane Longitudinal Twin Study was supported by the United States National Institute on Drug Abuse (R00DA023549) and by the Australian Research Council (DP0343921, DP0664638, 464914, 619667, and FT110100548). Gene-Environment-Development Initiative (GEDI) – Virginia Commonwealth University (VTSABD; dbGAP in progress) was supported by the National Institute on Drug Abuse (U01DA024413 and R01DA025109), the National Institute of Mental Health (R01MH045268, R01MH055557, and R01MH068521), and the Virginia Tobacco Settlement Foundation grant 8520012. We are grateful to all the VTSABD-YAFU-TSA study participants who contributed to this work. Minnesota Center for Twin and Family Research (phs000620.v1.p1) support contributing to this publication was supported by the National Institutes of Health under award numbers DA005147, DA013240, DA024417, DA036216, AA009367, and MH066140. Funding for TwinGene is a substudy of the Swedish Twin Registry, managed by Karolinska Institutet and supported by the Swedish Research Council under grant no 2017-00641. Additional funding was provided by the Swedish Research Council (M-2005-1112), GenomEUtwin (EU/QLRT-2001-01254 and QLG2-CT-2002-01254), National Institutes of Health U01-DK 066134, the Swedish Foundation for Strategic Research (SSF), and the Heart and Lung Foundation (20070481). Substance Use Disorder Liability: Candidate System Genes study was supported by R01 DA019157 and P50 DA005605. Yale–Penn (phs000425.v1.p1 and phs000952.v1.p1) was supported by National Institutes of Health Grants RC2 DA028909, R01 DA12690, R01 DA12849, R01 DA18432, R01 AA11330, and R01 AA017535 and by the Veterans Affairs Connecticut and Philadelphia Veterans Affairs Mental Illness Research, Educational, and Clinical Centers. Australian Alcohol and Nicotine studies (phs000181.v1.p1) were supported by National Institutes of Health Grants AA07535,AA07728, AA13320, AA13321, AA14041, AA11998, AA17688,DA012854, and DA019951; by grants from the Australian National Health and Medical Research Council (241944, 339462, 389927,389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485, and 552498); by grants from the Australian Research Council(A7960034, A79906588, A79801419, DP0770096, DP0212016, and DP0343921); and by the 5th Framework Programme (FP-5) GenomEUtwin Project (QLG2-CT-2002-01254). GWAS genotyping at Center for Inherited Disease Research was supported by a grant to the late Richard Todd, former Principal Investigator of grant AA13320. Irish Affected Sib-Pair Study of Alcohol Dependence GWAS data collection and analysis was supported by National Institute on Alcohol Abuse and Alcoholism grants P20-AA-017828 and P50-AA-022537. Sample collection was supported by R01-AA-011408. Control genotyping was supported by National Institute of Mental Health grant R01-MH-083094 and Wellcome Trust Case Control Consortium 2 grant WTCCC-084710. Netherland Twin Register (NTR) and Netherlands Study of Depression and Anxiety funding was obtained from multiple grants from the Netherlands Organization for Scientific Research (NWO) and MagW/ZonMW, including NWO-480-15-001/674: Netherlands Twin Registry Repository: researching the interplay between genome and environment; Geestkracht program of the Netherlands Organization for Health Research and Development (Zon-MW 10-000-1002), Genetic determinants of risk behavior in relation to alcohol use and alcohol use disorder (ZonMW-Addiction-31160008); Biobanking and Biomolecular Resources Research Infrastructure (BBMRI –NL, 184.021.007), Amsterdam Public Health research institute (APH) and Neuroscience Campus Amsterdam (NCA); and the European Science Council (ERC-230374 and ERC-284167). Part of the genotyping was funded by NWO/SPI 56-464-14192; the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health, Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the Avera Institute for Human Genetics, Sioux Falls, South Dakota (USA), and the National Institutes of Health (NIH R01 HD042157-01A1, MH081802, and Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995). The Finnish Twin Cohort/Nicotine Addiction Genetics-Finland study was supported by Academy of Finland (grants # 213506 and 129680 to J.K.), NIH DA12854 (PAFM), Global Research Award for Nicotine Dependence / Pfizer Inc. (J.K.), Wellcome Trust Sanger Institute, UK and the European Community’s Seventh Framework Programme ENGAGE Consortium (HEALTH-F4-2007- 201413). In Finntwin12, support for data collection and genotyping has come from National Institute of Alcohol Abuse and Alcoholism (grants AA-12502, AA-00145, and AA-09203 to R.J.R. and AA15416 and K02AA018755 to D.M.D.), the Academy of Finland (grants 100499, 205585, 118555, 141054, and 264146 to J.K.), and Wellcome Trust Sanger Institute, UK. This research uses data from Add Health, a program project directed by K. Mullan Harris and designed by J. R. Udry, P. S. Bearman, and K. Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis. The Helsinki Birth Cohort Study thanks all study participants as well as everybody involved in the Helsinki Birth Cohort Study. Helsinki Birth Cohort Study has been supported by grants from the Academy of Finland, the Finnish Diabetes Research Society, Folkhälsan Research Foundation, Novo Nordisk Foundation, Finska Läkaresällskapet, Juho Vainio Foundation, Signe and Ane Gyllenberg Foundation, University of Helsinki, Ministry of Education, Ahokas Foundation, and Emil Aaltonen Foundation. The Avon Longitudinal Study of Parents and Children (ALSPAC) is extremely grateful to all the families who took part in the ALSPAC study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. Funding/support was from The UK Medical Research Council and Wellcome (grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and A.C.E. will serve as guarantor for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). This research was specifically funded by NIH grants AA021399. GWAS data were generated by Sample Logistics and Genotyping Facilities at Wellcome Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe. Generation Scotland is grateful to the families who took part in GS, the GPs and Scottish School of Primary Care for their help in recruiting them, and the whole GS team that includes academic researchers, clinic staff, laboratory technicians, clerical workers, IT staff, statisticians, and research managers. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping of the GS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland, and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award ‘STratifying Resilience and Depression Longitudinally’ (STRADL) Reference 104036/Z/14/Z). LD Hub (http://ldsc.broadinstitute.org/) is grateful to the following GWAS studies, databases and consortia who have kindly made their summary data available: ADIPOGen (Adiponectin genetics consortium), C4D (Coronary Artery Disease Genetics Consortium), CARDIoGRAM (Coronary ARtery DIsease Genome wide Replication and Meta-analysis), CKDGen (Chronic Kidney Disease Genetics consortium), dbGAP (database of Genotypes and Phenotypes), DIAGRAM (DIAbetes Genetics Replication And Meta-analysis), ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis), EAGLE (EArly Genetics & Lifecourse Epidemiology Consortium, excluding 23andMe), EGG (Early Growth Genetics Consortium), GABRIEL (A Multidisciplinary Study to Identify the Genetic and Environmental Causes of Asthma in the European Community), GCAN (Genetic Consortium for Anorexia Nervosa), GEFOS (GEnetic Factors for OSteoporosis Consortium), GIANT (Genetic Investigation of ANthropometric Traits), GIS (Genetics of Iron Status consortium), GLGC (Global Lipids Genetics Consortium), GPC (Genetics of Personality Consortium), GUGC (Global Urate and Gout consortium), HaemGen (hematological and platelet traits genetics consortium), HRgene (Heart Rate consortium), IIBDGC (International Inflammatory Bowel Disease Genetics Consortium), ILCCO (International Lung Cancer Consortium), IMSGC (International Multiple Sclerosis Genetic Consortium), MAGIC (Meta-Analyses of Glucose and Insulin-related traits Consortium), MESA (Multi-Ethnic Study of Atherosclerosis), PGC (Psychiatric Genomics Consortium), Project MinE consortium, ReproGen (Reproductive Genetics Consortium), SSGAC (Social Science Genetics Association Consortium), TAG (Tobacco and Genetics Consortium), TRICL (Transdisciplinary Research in Cancer of the Lung consortium), and UK Biobank. We additionally thank the groups who directly shared GWAS results. We would like to acknowledge all participating groups of the International Cannabis Consortium, and in particular the members of the working group including S. Stringer, C. Minica, K. Verweij, H. Mbarek, E. Derks, N. Gillespie, and J. Vink. Thanks also to the ENIGMA consortium for providing GWAS results on subcortical brain volumes (available from http://enigma.ini.usc.edu/research/download-enigma-gwas-results/). Finally, we acknowledge the valuable contribution of groups who have publicly released summary statistics from their respective GWAS. Specifically, thanks to researchers from Schumann et al.16 including the CHARGE+ and AlcGen consortia (results available at https://grasp.nhlbi.nih.gov/FullResults.aspx) and to all members of Psychiatric Genomics Consortium (PGC; results available for download at http://www.med.unc.edu/pgc/results-and-downloads), in particular the working groups on attention deficit/hyperactivity disorder (ADHD; chaired by S. Faraone), autism spectrum disorder (ASD; chaired by M. Daly and B. Devlin), and eating disorders (ED; chaired by C. Bulik and G. Breen). Similar thanks to all of the participating groups in the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) consortium for their participation in the ADHD and ASD meta-analyses.

23andMe Research Team:

Michelle Agee17, Babak Alipanahi17, Adam Auton17, Robert K. Bell17, Katarzyna Bryc17, Sarah L. Elson17, Pierre Fontanillas17, Nicholas A. Furlotte17, David A. Hinds17, Karen E. Huber17, Aaron Kleinman17, Nadia K. Litterman17, Jennifer C. McCreight17, Matthew H. McIntyre17, Joanna L. Mountain17, Elizabeth S. Noblin17, Carrie A. M. Northover17, Steven J. Pitts17, J. Fah Sathirapongsasuti17, Olga V. Sazonova17, Janie F. Shelton17, Suyash Shringarpure17, Chao Tian17, Joyce Y. Tung17, Vladimir Vacic17, and Catherine H. Wilson17.

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Contributions

R.K.W., H.J.E., J.G., and A.A. conceived the analyses, wrote the first draft and prepared all drafts for submission; R.K.W. conducted primary analyses; R.P., E.C.J., and J.N.M. conducted additional analyses. H.J.E., J.G., A.A., and B.N. supervised all analyses. M.J.A., A.E.A., F.A., S.-A.B., A.B., S.B., J.M. Biernaka, T.B.B., L.-S.C., T.-K.C., Y.-L.C., F.D., A.R.D., A.C.E., P.F., J.C.F., L.F., J.F., I.G., S.G., L.M.H., A.M.H., S.M.H., S.H.-H., S.H., C. Hodgkinson, P.H. J.J.H., M.A.K., M.A.-K., B.K., J.L., M.L.-P., D.L., L.L., A.L., B.S.M., H.M., A.M.M., M.B.M., J.L.M., Y.M., T.P., J.F.P., R.E.P., S.R., E.R., N.L.S., J.E.S., S.S.-R., M. Schwandt, R.S., F.S., J. Strohmaier, N.T., J.-C.W., B.T.W., R.W., L.W. and A.G.W. prepared individual datasets and, in some cases, conducted analyses and provided summary statistics or results. The 23andMe Research Team, J.D.B., D.C., D.-S.C., W.E.C., R.C.C., N. Dahmen, L.D., B.W.D., S.L.E., M.A.F., W.G., C. Hayward, M.I., M.K., F.K., J.K., S.K., S.L., M.T.L., W.M., K.M., S.M., B.M.-M., A.D.M., J.I.N., A.P., U.P., K.R., M.D.R., M. Ridinger, N.S., M.A.S., M. Soyka, J.T., S.W., N.W., and P.Z. provided critical support regarding phenotypes and data in individual datasets; D.E.A., J.M. Boden, D.I.B., L.J.B., S.A.B., K.K.B., S.C., E.J.C., H.d.W., N. Diazgranados, D.M.D., J.G.E., L.A.F., T.M.F., N.A.G., A.M.G., D.G., R.A.G., D.B.H., K.M.H., A.C.H., V.H., J.K.H., C.J.H., J.H., W.I., E.O.J., J.A.K., V.M.K., K.S.K., H.R.K., K.K., P.L., P.A.L., M.M., J.M., P.A.F.M., H.H.M., P.M., N.G.M., S.E.M., G.W.M., E.C.N., M.M.N., A.A.P., N.L.P., B.W.J.H.P., B.P., J.P.R., M. Rietschel, B.P.R., R.R., D.R., P.-H.S., J. Silberg, M.C.S., R.E.T., M.M.V., S.V., T.L.W., J.B.W., and H.Z., as well as H.J.E. and J.G., facilitated data collection and provided critical phenotypic and analytic feedback for individual studies. B.M.N. and A.A. provided resource support. All authors reviewed the manuscript and approved it for submission.

Corresponding authors

Correspondence to Joel Gelernter, Howard J. Edenberg or Arpana Agrawal.

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Competing interests

L.J.B., A.M.G., J.P.R., J.-C.W. and the spouse of N.L.S. are listed as inventors on Issued US Patent 8080,371, “Markers for Addiction” covering the use of certain SNPs in determining the diagnosis, prognosis, and treatment of addiction. N.W. has received funding from the German Research Foundation (DFG) and Federal Ministry of Education and Research Germany (BMBF); he has received speaker’s honoraria and travel funds from Janssen-Cilag and Indivior. He took part in industry-sponsored multicenter randomized trials by D&A Pharma and Lundbeck. M. Ridinger received compensation from Lundbeck Switzerland and Lundbeck institute for advisory boards and expert meetings, and from Lundbeck and Lilly Suisse for workshops and presentations. K.M. received honoraria from Lundbeck, Pfizer, Novartis, and AbbVie. K.M. also received Honoraria (Advisory Board) from Lundbeck and Pfizer and speaker fees from Janssen Cilag. H.K. has been an advisory board member, consultant, or continuing medical education speaker for Indivior, Lundbeck, and Otsuka. He is a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was sponsored in the past three years by AbbVie, Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Ethypharm, Indivior, Lilly, Lundbeck, Otsuka, and Pfizer. H.K. and J.G. are named as inventors on PCT patent application #15/878,640, entitled “Genotype-guided dosing of opioid agonists,” filed 24 January 2018. P.F., S.L.E., and members of the 23andMe Research Team are employees of 23andMe. M.A.F. has received grant support from Assurex Health, Mayo Foundation, Myriad, NIAAA, National Institute of Mental Health (NIMH), and Pfizer; he has been a consultant for Intra-Cellular Therapies, Inc., Janssen, Mitsubishi Tanabe Pharma Corporation, Myriad, Neuralstem Inc., Otsuka American Pharmaceutical, Sunovion, and Teva Pharmaceuticals. H.d.W. has received support from Insys Therapeutics and Indivior for studies unrelated to this project, and she has consulted for Marinus and Jazz Pharmaceuticals, also unrelated to this project. T.L.W. has previously received funds from ABMRF. J.N. is an investigator for Janssen and Assurex. M.M.N. has received honoraria from the Lundbeck Foundation and the Robert Bosch Stiftung for membership on advisory boards. M. Ridinger has received honoraria from Lundbeck Switzerland and the Lundbeck Institute for membership of advisory boards and participation in expert meetings, and from Lundbeck and Lilly Suisse for workshops and presentations. N.S. has received honoraria from Abbvie, Sanofi-Aventis, Reckitt Benckiser, Indivior, Lundbeck, and Janssen-Cilag for advisory board membership and the preparation of lectures, manuscripts, and educational materials. Since 2013, N.S. has also participated in clinical trials financed by Reckitt Benckiser and Indivior. N.W. received speaker’s honoraria and travel expenses from Janssen-Cilag and Indivior; has also participated in industry-sponsored multicenter randomized trials conducted by D&A Pharma and Lundbeck. W.G. has received symposia support from Janssen-Cilag GmbH, Neuss, Lilly Deutschland GmbH, Bad Homburg, and Servier, Munich, and is a member of the Faculty of the Lundbeck International Neuroscience Foundation (LINF), Denmark. J.A.K. has provided consultations on nicotine dependence for Pfizer (Finland) 2012–2015. In the past three years, L.D. has received investigator-initiated untied educational grants for studies of opioid medications in Australia from Indivior, Mundipharma, and Seqirus. B.M.N. is a member of the scientific advisory board for Deep Genomics and has consulted for Camp4 Therapeutics Corporation, Merck & Co., and Avanir Pharmaceuticals, Inc. A.A. previously received peer-reviewed funding and travel reimbursement from ABMRF for unrelated research.

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Integrated supplementary information

Supplementary Figure 1 Results for ADH locus from transancestral modeling of unrelated individuals.

Regional association plot for the ADH gene region in trans-ancestral meta-analysis of unrelated genotyped individuals from AA and EU cohorts (ncase=11,476, ncontrol=23,080). Association with AD across ancestries was evaluated using (A) an inverse-variance weighted fixed-effects model, (B) the modified random-effects model92, and (C) the Bayesian trans-ancestral model93. The Bayesian model is fit with default priors and Metropolis-Hastings Markov chain Monte Carlo algorithm in MANTRA. Due to LD differences across ancestral groups combined in the trans-ancestral models LD is not indicated. The fixed-effects and random-effects models report conventional p-values, while the Bayesian model reports the Bayes factor for comparison of the null and alternative hypotheses. A log10 Bayes factor > 6.1 roughly corresponds the P < 5 × 10–8 significance threshold94. Plots generated with LocusZoom170 (http://locuszoom.sph.umich.edu/). See Supplementary Information for references.

Supplementary Figure 2 LD structure of ADH locus.

Linkage disequilibrium (LD) among top variants in the ADH gene region in (A) European ancestry and (B) African ancestry populations from the 1000 Genomes Project. Within each plot, the upper triangle displays Pearson correlation (r2) for each pair of markers, and the lower triangle displays D’. Missing values are indicated in gray. LD is reported for variants in the region with two-tailed P < 1 × 107 in the full discovery meta-analysis weighted by effective sample size (14,904 individuals with AD, 37,944 controls). Variants that are effectively perfectly correlated (r > 0.995) in both European and African populations are thinned to improve legibility. Plot generated with the assistance of LD Link97 (https://ldlink.nci.nih.gov/). See Supplementary Information for references.

Supplementary Figure 3 Locus plots of conditional analysis of ADH1B region.

Regional association plot for the ADH gene region in inverse-variance weighted fixed effects meta-analysis of logistic regression of unrelated genotyped individuals within each cohort conditional on genotype for top ADH1B associations. AA cohorts were conditioned on rs2066702 and, where possible, rs1229984; EU cohorts were conditioned on rs1229984. Results are shown for (A) meta-analysis across EU and AA cohorts (ncase=11,476, ncontrol=23,080), (B) meta-analysis of the AA cohorts only (ncase=2,991, ncontrol=2,808), and (C) meta-analysis of the EU cohorts (ncase=8,485, ncontrol=20,272) only. The red reference line indicates the two-tailed P < 5 × 108 threshold for genome-wide significance within each analysis. Colored points indicate LD to the index variant in individuals of (B) African ancestry or (C) European ancestry, respectively, from the 1000 Genomes Project reference data69. LD information is not shown for the trans-ancestral results (A) because the results reflect a combination of European and African ancestries and thus don’t have a well-defined population LD reference panel. Plot generated with LocusZoom170 (http://locuszoom.sph.umich.edu/). See Supplementary Information for references.

Supplementary Figure 4 Locus plots of chromosome 3 locus (rs7644567).

Regional association plot for rs7644567 in (A) the discovery meta-analysis of AA and EU cohorts (ncase = 14,904; ncontrol=37,944) and (B) the AA discovery meta-analysis (ncase=3,335; ncontrol=2,945) under a fixed effects model with effective sample size weighting. Red reference line indicates the genome-wide significance threshold of two-tailed P-value < 5 × 10-8. Colored points in panel B indicate LD to the index variant in individuals of African ancestry in the 1000 Genomes Project reference data69. LD information is not plotted in panel A due to the lack of the population LD reference panel for the combined European and African ancestries. Plot generated with LocusZoom170 (http://locuszoom.sph.umich.edu/). See Supplementary Information for references.

Supplementary Figure 5 Regions of chromatin contact for rs7644567.

HUGIn101 results identifying chromatin contacts with the region containing rs7644567 on chromosome 3. Blue lines reflect –log(p-value) for the one-tailed test, using Fit-Hi-C100, of whether observed Hi-C counts (black lines) were greater than the expected number of Hi-C counts (solid red lines) in previously reported Hi-C data99. Note differences in scale of the plots for the 3 tissue types. Tissue-specific False Discovery Rate (FDR, dashed red) and Bonferroni (dashed purple) corrections are shown for each tissue type. See Supplementary Information for references.

Supplementary Figure 6 QQ plot for omnibus test of heterogeneity across all cohorts.

QQ plot of p-values for the omnibus (34 degree of freedom) test of heterogeneity across all AA and EU cohorts in the discovery meta-analysis (ncase=14,904; ncontrol=37,944). Heterogeneity is tested with respect to a fixed effects model for meta-analysis of p-values with effective sample size-based weights. Little deviation is observed from the expected null distribution, suggesting limited heterogeneity across cohorts.

Supplementary Figure 7 QQ plots for tests of heterogeneity within and between ancestries.

QQ plot of p-values for (A) the omnibus (7 degree of freedom) test of heterogeneity across all AA cohorts in the discovery meta-analysis (ncase=3,335; ncontrol=2,945); (B) the omnibus (26 degree of freedom) test of heterogeneity across all EU cohorts in the discovery meta-analysis (ncase=11,569; ncontrol=34,999); and (C) the 1 degree of freedom test of heterogeneity between EU cohorts (ncase=11,569, ncontrol=34,999) and AA cohorts (ncase=3,335, ncontrol=2,945). Heterogeneity is tested with respect to a fixed effects model for meta-analysis of p-values with effective sample size-based weights. Little deviation is observed from the expected null distribution, suggesting limited overall heterogeneity within or between ancestry. The exception is one variant (rs4673609) with genome-wide significant heterogeneity (P = 8.78 × 1010) among AA cohorts.

Supplementary Figure 8 QQ plots for tests of heterogeneity between study designs.

QQ plot of p-values for (A) the 1 degree of freedom test of heterogeneity between simple family-based EU cohorts tested using the GEE model (ncase=2,107, ncontrol=12,353) and complex family-based EU cohorts tested using the logistic mixed model (ncase=2,897, ncontrol=5,565); (B) the 1 degree of freedom test of heterogeneity between genotyped family-based EU cohorts (ncase=5,004, ncontrol=17,918) and genotyped unrelated case/control EU cohorts (ncase=4,844, ncontrol=8,873); and (C) the 1 degree of freedom test of heterogeneity between genotyped EU cohorts (ncase=9,848, ncontrol=26,791) and EU cohorts included with summary statistics only (ncase=1,721, ncontrol=8,208). Heterogeneity is tested with respect to a fixed effects model for meta-analysis of p-values with effective sample size-based weights. Little deviation is observed from the expected null distribution, suggesting limited heterogeneity between study designs.

Supplementary Figure 9 QQ plot for discovery meta-analysis of AD.

QQ plot of two-tailed p-values for association with AD in the discovery meta-analysis of AA and EU cohorts (ncase=14,904, ncontrol=37,944). Meta-analysis is performed using effective sample size-based weights in a fixed effects model. Moderate deviation from the expected null distribution is observed, but this inflation is restricted to the upper tail of results (lambda=0.962). LD score regression within each ancestry suggests that true polygenic effects for AD are the primary source of deviations from the null distribution within both the EU (lambda=1.053, intercept=1.018, ratio=0.298) and AA (lambda=1.007, intercept=0.991-0.997; see Supplementary Information) ancestry analyses.

Supplementary Figure 10 PRS prediction using weights derived from alcohol dependence GWAS of unrelated EU and AA individuals.

Variance in alcohol phenotypes explained by polygenic risk scores (PRS) derived from the alcohol dependence GWAS meta-analysis of unrelated EU (panels A, B and D; ncase=8,485, ncontrol=20,272) and AA (panel C; ncase=2,991, ncontrol=2,808) individuals. The y-axis is pseudo-R2 for ordinal traits or R2 for continuous traits, reported as a percentage; note that the scale of the y-axis differs between plots. Panel A shows the association between EU alcohol dependence PRS and alcohol use disorder (AUD) diagnosis (dark gray) and symptom count (light gray) in the Avon Longitudinal Study of Parents and Children (ALSPAC; ncase=337, ncontrols=2,386); Panel B shows the association of CAGE alcohol screener scores with EU alcohol dependence PRS (solid bar), as well as PRS derived for alcohol consumption (striped bar), in Generation Scotland (GS; n=6,906); Panel C shows the prediction of DSM-IV alcohol dependence in the COGA AAfGWAS cohort (n=2,828) by PRS derived from the AA GWAS of alcohol dependence conducted in this study; Panel D shows the prediction of DSM-IV alcohol dependence in the COGA AAfGWAS cohort by PRS derived from the EU GWAS of alcohol dependence conducted in this study. Results are uncorrected for multiple testing.

Supplementary Figure 11 Power analysis for current meta-analysis.

Analysis of power to detect variants associated with AD at thresholds of (A) P < 5 × 10–8 and (B) P < 1 × 106 in the current study, conditional on allele frequency and effect size (odds ratio) using CaTS108. Power calculated based on an effective sample sizes of n=31,844 for the trans-ancestral discovery meta-analysis, n=26,853 for the EU meta-analysis, and n=4,991 for the AA meta-analysis. See Supplementary Information for references.

Supplementary Figure 12 PCA comparison to 1000 Genomes Project populations.

PCA of (A) AA (ncase=2,991; ncontrol=2,808) and (B) EU (ncase=8,485; ncontrol=20,272) unrelated genotyped individuals merged across cohorts and with 1000 Genomes Project69 population reference samples (AFR: African; AMR: Admixed American; EAS: East Asian; EUR: European). Orientation of the ancestry axes differs between the two plots due to the inclusion of the AA and EU samples in the respective PCA calculations. Results confirm that the EU and AA cohorts are consistent with the expected population ancestries, and that the admixed AA samples have the expected cline of admixture of African and European ancestry. See Supplementary Information for references.

Supplementary Figure 13 Example of PCA results tagging local ancestry in ADAA cohort.

Association of the 8th principle component (PC) in the ADAA cohort (n=1,813) from linear regression with (A) each SNP genome-wide and (B) estimated proportion of African ancestry on each chromosome conditional on genome-wide ancestry proportions. Panel A reports two-tailed p-values for each SNP, with the dashed blue reference line indicating the P < 5 × 108 genome-wide significance threshold, and illustrates the characteristic pattern of PCs associated with localized regions of the genome that is observed in multiple AA cohorts. Bars in Panel B reflect the t statistic for the two-sided test of association with the 8th PC in linear regression, colored according to the sign of the effect and with bar widths proportional to the size of the chromosome. The dashed blue reference line in panel B indicates Bonferroni-adjusted significance (P < 2.27 × 103 = .05/22 autosomal chromosomes); results for chromosome 6 are omitted due to computational complexity. Comparison of Panel A and Panel B suggests that the localized association with the PC strongly corresponds to differences in local ancestry across chromosomes.

Supplementary Figure 14 Comparison of AA meta-analysis results by number of PCA covariates.

QQ plots for association with AD in effective sample size weighted meta-analysis of all AA cohorts (ncase=3,335, ncontrol=2,945) (A) controlling for a full 5 principle components (PCs) in each cohort based on sample size, or (B) controlling for 1-5 PCs in each cohort, restricting to PCs that are associated with variants genome-wide rather than specific genomic regions. Compared to the basic analysis in Panel A, Panel B shows little evidence that the reduced number of PC covariates yields inflation from population stratification. The meta-analysis of AA cohorts reflected in Panel B is used as the primary analysis for the current paper.

Supplementary information

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Supplementary Tables 1–8

Supplementary Methods and Supplementary Note

Supplementary Methods and Supplementary Note

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Walters, R.K., Polimanti, R., Johnson, E.C. et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat Neurosci 21, 1656–1669 (2018). https://doi.org/10.1038/s41593-018-0275-1

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