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Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits

Nature Neuroscience volume 23, pages 809–818 (2020)Cite this article

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Abstract

Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. Although genome-wide association studies have identified PAU risk genes, the genetic architecture of this trait is not fully understood. We conducted a proxy-phenotype meta-analysis of PAU, combining alcohol use disorder and problematic drinking, in 435,563 European-ancestry individuals. We identified 29 independent risk variants, 19 of them novel. PAU was genetically correlated with 138 phenotypes, including substance use and psychiatric traits. Phenome-wide polygenic risk score analysis in an independent biobank sample (BioVU, n = 67,589) confirmed the genetic correlations between PAU and substance use and psychiatric disorders. Genetic heritability of PAU was enriched in brain and in conserved and regulatory genomic regions. Mendelian randomization suggested causal effects on liability to PAU of substance use, psychiatric status, risk-taking behavior and cognitive performance. In summary, this large PAU meta-analysis identified novel risk loci and revealed genetic relationships with numerous other traits.

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Fig. 1: Overview of the analysis.
Fig. 2: Association results for AUD and PAU meta-analyses.
Fig. 3: Estimated SNP-based h2.
Fig. 4: Genetic correlations with published traits.
Fig. 5: Phenome-wide associations with PAU PRS in BioVU.

Data availability

The full summary-level association data from the meta-analysis are available through dbGaP at accession no. phs001672.v3.p1.

Code availability

Kinship analysis was performed using KING (http://people.virginia.edu/~wc9c/KING/). PCAs were performed using EIGENSOFT (https://data.broadinstitute.org/alkesgroup/EIGENSOFT/). Imputation was performed using EAGLE2 (https://data.broadinstitute.org/alkesgroup/Eagle/), Minimac3 (https://genome.sph.umich.edu/wiki/Minimac3), Sanger imputation server (https://imputation.sanger.ac.uk/) or RICOPILI (https://data.broadinstitute.org/mpg/ricopili/), the choice depending on the sample. GWAS was performed using PLINK (https://www.cog-genomics.org/plink2). Meta-analyses were performed using METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation). Polygenic risk score analyses were performed using PRSice-2 (https://www.prsice.info/) or PRS-CS (https://github.com/getian107/PRScs). GCTA (https://cnsgenomics.com/software/gcta/#Overview) was used for identification of independent loci (GCTA-COJO), multi-trait conditional analysis (GCTA-mtCOJO) and MR (GCTA-GSMR). LDSC (https://github.com/bulik/ldsc) was used for heritability estimation, genetic correlation analysis (also using LD Hub (http://ldsc.broadinstitute.org/)) and heritability enrichment analyses. FUMA (https://fuma.ctglab.nl/) was used for gene association, functional enrichment and gene set enrichment analyses. Transcriptomic analyses were performed using S-PrediXcan and S-MultiXcan (https://github.com/hakyimlab/MetaXcan). PheWAS analyses were run using the PheWAS R package (https://github.com/PheWAS/PheWAS). The Mendelian Randomization R Package (https://cran.r-project.org/web/packages/MendelianRandomization/index.html) and MR–PRESSO (https://github.com/rondolab/MR-PRESSO) were used for MR analyses. MTAG (https://github.com/omeed-maghzian/mtag) was used for multiple trait analysis.

References

  1. GBD 2016 Alcohol Collaborators. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 392, 1015–1035 (2018).

  2. Walters, R. K. et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat. Neurosci. 21, 1656–1669 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Kranzler, H. R. et al. Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nat. Commun. 10, 1499 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Sanchez-Roige, S. et al. Genome-wide association study meta-analysis of the alcohol use disorders identification test (AUDIT) in two population-based cohorts. Am. J. Psychiatry 176, 107–118 (2019).

    Article  PubMed  Google Scholar 

  5. Gelernter, J. et al. Genome-wide association study of maximum habitual alcohol intake in >140,000 US European and African American veterans yields novel risk loci. Biol. Psychiatry 86, 365–376 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).

    Article  PubMed  Google Scholar 

  7. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Gelernter, J. et al. Genome-wide association study of alcohol dependence:significant findings in African- and European-Americans including novel risk loci. Mol. Psychiatry 19, 41–49 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cotto, K. C. et al. DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Res. 46, D1068–D1073 (2018).

    Article  CAS  PubMed  Google Scholar 

  12. Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Roadmap Epigenomics Consortium, et al.Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  PubMed Central  CAS  Google Scholar 

  16. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

  17. Watanabe, K. et al. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. de Leeuw, C. A. et al. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Marees, A.T. et al. Potential influence of socioeconomic status on genetic correlations between alcohol consumption measures and mental health. Psychol. Med. 50, 484–498 (2020).

  20. Grant, B. F. et al. Epidemiology of DSM-5 alcohol use disorder: results from the National Epidemiologic Survey on Alcohol and Related Conditions III. JAMA Psychiatry 72, 757–766 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Andersen, A. M. et al. Polygenic scores for major depressive disorder and risk of alcohol dependence. JAMA Psychiatry 74, 1153–1160 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zhou, H. et al. Genetic risk variants associated with comorbid alcohol dependence and major depression. JAMA Psychiatry 74, 1234–1241 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue-specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1825 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Battle, A. et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 24, 14–24 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Barbeira, A. N. et al. Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet. 15, e1007889 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Evangelou, E. et al. New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders. Nat. Hum. Behav. 3, 950–961 (2019).

  27. Bowden, J. et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat. Med. 36, 1783–1802 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Bowden, J. et al. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Verbanck, M. et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  34. Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Nagel, M. et al. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat. Genet. 50, 920–927 (2018).

    Article  CAS  PubMed  Google Scholar 

  36. Karlsson Linner, R. et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat. Genet. 51, 245–257 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. Jansen, P. R. et al. Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways. Nat. Genet. 51, 394–403 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Pasman, J. A. et al. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nat. Neurosci. 21, 1161–1170 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

  42. Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. Pilling, L. C. et al. Human longevity is influenced by many genetic variants: evidence from 75,000 UK Biobank participants. Aging (Albany NY) 8, 547–560 (2016).

    Article  CAS  Google Scholar 

  44. Loh, P. R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  47. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Galinsky, K. J. et al. Fast principal-component analysis reveals convergent evolution of ADH1B in Europe and East Asia. Am. J. Hum. Genet. 98, 456–472 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: Polygenic Risk Score software. Bioinformatics 31, 1466–1468 (2015).

    Article  CAS  PubMed  Google Scholar 

  52. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    Article  CAS  PubMed  Google Scholar 

  54. InternationalHapMap Consortium, et al.Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

    Article  CAS  Google Scholar 

  55. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

    Article  CAS  PubMed  Google Scholar 

  57. Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ge, T. et al. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–904 (2006).

    Article  CAS  PubMed  Google Scholar 

  62. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1110 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Carroll, R. J., Bastarache, L. & Denny, J. C. R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment. Bioinformatics 30, 2375–2376 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Pedersen, C. B. et al. The iPSYCH2012 case-cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders. Mol. Psychiatry 23, 6–14 (2018).

    Article  CAS  PubMed  Google Scholar 

  66. Lam, M. et al. RICOPILI: Rapid Imputation for COnsortias PIpeLIne. Bioinformatics 36, 930–933 (2020).

    Article  PubMed  CAS  Google Scholar 

  67. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Yavorska, O. O. & Burgess, S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 46, 1734–1739 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This research used data from MVP, and was supported by funding from the Department of Veterans Affairs Office of Research and Development, Million Veteran Program Grant nos. I01BX003341 and I01CX001849; and the VA Cooperative Studies Program study, no. 575B. This publication does not represent the views of the Department of Veterans Affairs or the United States Government. A list of members and affiliations of MVP appears in the Supplementary Information. Supported also by NIH (NIAAA) no. P50 AA12870 (to J.G.), a NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (to H.Z.) and NIH grants nos. 5T32GM080178 (to J.M.S.) and K02DA32573 (to A.A.), and the National Institute for Healthcare Research (NIHR) Imperial Biomedical Research Centre (BRC; to S.R.A. and M.R.T.). This research also used summary data from the PGC Substance Use Disorders (SUD) working group. PGC–SUD is supported by funds from NIDA and NIMH to MH109532 and, previously, had analyst support from NIAAA to U01AA008401 (COGA). PGC–SUD gratefully acknowledges its contributing studies and the participants in those studies, without whom this effort would not be possible. This research also used individual-level/summary data from UKB, a population-based sample of participants whose contributions we gratefully acknowledge; project ID 41910. We thank the iPSYCH-Broad Consortium for access to data on the iPSYCH cohort. The iPSYCH project is funded by the Lundbeck Foundation (nos. R102-A9118 and R155-2014-1724) and the universities and university hospitals of Aarhus and Copenhagen. Genotyping of iPSYCH samples was supported by grants from the Lundbeck Foundation and the Stanley Foundation, and The Danish National Biobank resource was supported by the Novo Nordisk Foundation. Data handling and analysis on the GenomeDK HPC facility was supported by NIMH (no. 1U01MH109514-01 to A.D.B.). High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the Centre for Integrative Sequencing, Aarhus University, Denmark (grant to A.D.B.). The UCL and STOPAH case samples were genotyped with funding from the NIHR BRC. UCL cases and controls were collected with UK Medical Research Council project grants nos. G9623693N, G0500791, G0701007 and G1000708, and with support from the NIHR. Genotyping of the UCL control sample was supported by grants from the Stanley Foundation. The UK Household Longitudinal Study (Understanding Society) is led by the Institute for Social and Economic Research at the University of Essex and funded by the Economic and Social Research Council. The survey was conducted by NatCen, and the genome-wide scan data were analyzed and deposited by the Wellcome Trust Sanger Institute. Information on how to access the data can be found on the Understanding Society website: https://www.understandingsociety.ac.uk/. A.M. is supported by the University College London Hospitals NHS Foundation Trust NIHR BRC.

Author information

Authors and Affiliations

  1. Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA

    Hang Zhou, Daniel F. Levey, Zhongshan Cheng, Renato Polimanti & Joel Gelernter

  2. Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA

    Hang Zhou, Daniel F. Levey, Zhongshan Cheng, Renato Polimanti, Amy C. Justice & Joel Gelernter

  3. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA

    Julia M. Sealock & Lea K. Davis

  4. Division of Medical Genetics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Julia M. Sealock & Lea K. Davis

  5. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

    Sandra Sanchez-Roige & Abraham A. Palmer

  6. Division of Psychiatry, University of Edinburgh, Edinburgh, UK

    Toni-Kim Clarke

  7. Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA

    Boyang Li

  8. Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Rachel L. Kember

  9. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA

    Rachel L. Kember & Henry R. Kranzler

  10. University of Louisville School of Nursing, Louisville, KY, USA

    Rachel Vickers Smith

  11. Division of Psychiatry, University College London, London, UK

    Johan H. Thygesen & Andrew McQuillin

  12. UCL Institute for Liver & Digestive Health, Division of Medicine, Royal Free Campus, University College London, London, UK

    Marsha Y. Morgan

  13. Department of Metabolism Digestion & Reproduction, Imperial College London, London, UK

    Stephen R. Atkinson & Mark R. Thursz

  14. Department of Biomedicine, Aarhus University, Aarhus, Denmark

    Mette Nyegaard, Manuel Mattheisen & Anders D. Børglum

  15. Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark

    Mette Nyegaard & Anders D. Børglum

  16. The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark

    Mette Nyegaard & Anders D. Børglum

  17. Center for Genomics and Personalized Medicine, Aarhus, Denmark

    Mette Nyegaard & Anders D. Børglum

  18. Department of Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany

    Manuel Mattheisen

  19. Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

    Manuel Mattheisen

  20. Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA

    Emma C. Johnson & Arpana Agrawal

  21. Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA

    Emma C. Johnson & Amy C. Justice

  22. Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT, USA

    Amy C. Justice

  23. Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA

    Abraham A. Palmer

  24. Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA

    Lea K. Davis

  25. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA

    Howard J. Edenberg

  26. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

    Howard J. Edenberg

  27. Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Henry R. Kranzler

  28. Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA

    Joel Gelernter

Authors
  1. Hang Zhou
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  2. Julia M. Sealock
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  7. Boyang Li
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Contributions

H.Z., J.G., H.R.K. and A.A.P. conceived the analyses. H.Z. and J.G. wrote the first draft and prepared all drafts for submission. J.G. supervised and H.Z. accomplished primary analyses. J.M.S., S.S.-R., T.-K.C., D.F.L., Z.C., B.L. and A.M. conducted additional analyses. J.G., H.R.K., A.A.P., L.K.D., H.J.E. and A.A. supervised additional analyses. J.M.S., S.S.-R., T.-K.C., A.A.P., A.M. and L.K.D. prepared individual datasets and provided summary statistics or results. R.P., R.L.K., R.V.S., J.H.T., M.Y.M., S.R.A., M.R.T., M.N., M.M., A.D.B., E.C.J., A.C.J., A.M., L.K.D. and H.R.K. provided critical support regarding phenotypes and data in individual datasets. J.G., A.C.J. and H.R.K. provided resource support. All authors reviewed the manuscript and approved it for submission.

Corresponding author

Correspondence to Joel Gelernter.

Ethics declarations

Competing interests

H.R.K. is a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was supported over the past 3 years by AbbVie, Alkermes, Ethypharm, Indivior, Lilly, Lundbeck, Otsuka, Pfizer, Arbor, and Amygdala Neurosciences. H.R.K. and J.G. are named as inventors on PCT patent application no. 15/878,640, entitled Genotype-guided dosing of opioid agonists, filed 24 January 2018.

Additional information

Peer review information Nature Neuroscience thanks Gerome Breen, Eske Derks, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

A list of members and affiliations in MVP, and Supplementary Figs. 1–6.

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Supplementary Tables 1–16.

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Zhou, H., Sealock, J.M., Sanchez-Roige, S. et al. Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nat Neurosci 23, 809–818 (2020). https://doi.org/10.1038/s41593-020-0643-5

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