Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Cross-ancestry meta-analysis of opioid use disorder uncovers novel loci with predominant effects in brain regions associated with addiction

Nature Neuroscience volume 25, pages 1279–1287 (2022)Cite this article

Subjects

Abstract

Despite an estimated heritability of ~50%, genome-wide association studies of opioid use disorder (OUD) have revealed few genome-wide significant loci. We conducted a cross-ancestry meta-analysis of OUD in the Million Veteran Program (N = 425,944). In addition to known exonic variants in OPRM1 and FURIN, we identified intronic variants in RABEPK, FBXW4, NCAM1 and KCNN1. A meta-analysis including other datasets identified a locus in TSNARE1. In total, we identified 14 loci for OUD, 12 of which are novel. Significant genetic correlations were identified for 127 traits, including psychiatric disorders and other substance use-related traits. The only significantly enriched cell-type group was CNS, with gene expression enrichment in brain regions previously associated with substance use disorders. These findings increase our understanding of the biological basis of OUD and provide further evidence that it is a brain disease, which may help to reduce stigma and inform efforts to address the opioid epidemic.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Manhattan and quantile–quantile plot for cross-ancestry meta-analysis of opioid use disorder (Ncases = 31,480 and Ncontrols = 394,484).
Fig. 2: Enrichment of opioid use disorder in the brain.
Fig. 3: Phenotypic spectrum associated with opioid use disorder.
Fig. 4: Genomic SEM analysis of OUD with other substance use traits and psychiatric disorders.

Similar content being viewed by others

Data availability

The full summary-level association data from the meta-analysis are available through dbGaP at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001672.

Code availability

Imputation was performed using Minimac3 (https://genome.sph.umich.edu/wiki/Minimac3). GWAS was performed using PLINK2 (https://www.cog-genomics.org/plink2). Meta-analyses were performed using METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation). GCTA (https://cnsgenomics.com/software/gcta/#Overview) was used for identification of independent loci (GCTA-COJO) and gene-based analysis (GCTA-fastBAT). 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). LDSC (https://github.com/bulik/ldsc) was used for heritability estimation, genetic correlation analysis (also using the CTG-VL; https://genoma.io) and heritability enrichment analyses. Trans-ancestry genetic correlation was estimated using Popcorn (https://github.com/brielin/Popcorn). PRS analyses were performed using PRS-CS (https://github.com/getian107/PRScs). PheWAS analyses were run using the PheWAS R package (https://github.com/PheWAS/PheWAS). The MendelianRandomization R package (https://cran.r-project.org/web/packages/MendelianRandomization/index.html) was used for MR analyses. Genomic SEM was conducted using the GenomicsSEM R package (https://github.com/GenomicSEM/GenomicSEM).

References

  1. American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2013); https://doi.org/10.1176/appi.books.9780890425596

  2. Strang, J. et al. Opioid use disorder. Nat. Rev. Dis. Primers 6, 3 (2020).

    Article  PubMed  Google Scholar 

  3. Vowles, K. E. et al. Rates of opioid misuse, abuse, and addiction in chronic pain: a systematic review and data synthesis. Pain 156, 569–576 (2015).

    Article  PubMed  Google Scholar 

  4. Centers for Disease Control and Prevention Vital signs: overdoses of prescription opioid pain relievers and other drugs among women–United States, 1999–2010. MMWR Morb. Mortal. Wkly Rep. 62, 537–542 (2013).

  5. Key Substance Use and Mental Health Indicators in the United States: Results from the 2019 National Survey on Drug Use and Health (Substance Abuse and Mental Health Services Administration, 2020); https://www.samhsa.gov/data/sites/default/files/reports/rpt29393/2019NSDUHFFRPDFWHTML/2019NSDUHFFR090120.htm

  6. Wilson, N. Drug and opioid-involved overdose deaths—United States, 2017–2018. MMWR Morb. Mortal. Wkly Rep. 69, 290–297 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kendler, K. S., Jacobson, K. C., Prescott, C. A. & Neale, M. C. Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am. J. Psychiatry 160, 687–695 (2003).

    Article  PubMed  Google Scholar 

  8. Gelernter, J. et al. Genome-wide association study of opioid dependence: multiple associations mapped to calcium and potassium pathways. Biol. Psychiatry 76, 66–74 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Cheng, Z. et al. Genome-wide association study identifies a regulatory variant of rgma associated with opioid dependence in European Americans. Biol. Psychiatry 84, 762–770 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Nelson, E. C. et al. Evidence of CNIH3 involvement in opioid dependence. Mol. Psychiatry 21, 608–614 (2016).

    Article  CAS  PubMed  Google Scholar 

  11. Polimanti, R. et al. Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium. Mol. Psychiatry 25, 1673–1687 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Song, W. et al. Genome-wide association analysis of opioid use disorder: a novel approach using clinical data. Drug Alcohol Depend. 217, 108276 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhou, H. et al. Association of OPRM1 functional coding variant with opioid use disorder: a genome-wide association study. JAMA Psychiatry 77, 1072–1080 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Deak, J. D. et al. Genome-wide association study and multi-trait analysis of opioid use disorder identifies novel associations in 639,709 individuals of European and African ancestry. Mol. Psychiatry https://doi.org/10.1038/s41380-022-01709-1 (2022).

  15. Sanchez-Roige, S. et al. Genome-wide association study of problematic opioid prescription use in 132,113 23andMe research participants of European ancestry. Mol. Psychiatry https://doi.org/10.1038/s41380-021-01335-3 (2021).

  16. Gaddis, N. et al. Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond. Preprint at medRxiv https://doi.org/10.1101/2021.09.13.21263503v1 (2021).

  17. 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 

  18. The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  PubMed Central  Google Scholar 

  19. Davydov, E. V. et al. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput. Biol. 6, e1001025 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  20. 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  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  22. Qi, T. et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat. Commun. 9, 2282 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  23. 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 

  24. Cuellar-Partida, G. et al. Complex-Trait Genetics Virtual Lab: a community-driven web platform for post-GWAS analyses. Preprint at bioRxiv https://doi.org/10.1101/518027 (2019).

  25. Crist, R. C. & Berrettini, W. H. Pharmacogenetics of OPRM1. Pharmacol. Biochem. Behav. 123, 25–33 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Crist, R. C., Reiner, B. C. & Berrettini, W. H. A review of opioid addiction genetics. Curr. Opin. Psychol. 27, 31–35 (2019).

    Article  PubMed  Google Scholar 

  27. Moningka, H., Lichenstein, S. & Yip, S. W. Current understanding of the neurobiology of opioid use disorder: an overview. Curr. Behav. Neurosci. Rep. 6, 1–11 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Gelernter, J., Kranzler, H. & Cubells, J. Genetics of two mu opioid receptor gene (OPRM1) exon I polymorphisms: population studies, and allele frequencies in alcohol- and drug-dependent subjects. Mol. Psychiatry 4, 476–483 (1999).

    Article  CAS  PubMed  Google Scholar 

  29. Howe, K. L. et al. Ensembl 2021. Nucleic Acids Res. 49, D884–D891 (2021).

    Article  CAS  PubMed  Google Scholar 

  30. Zhang, H. et al. Association between two µ-opioid receptor gene (OPRM1) haplotype blocks and drug or alcohol dependence. Hum. Mol. Genet. 15, 807–819 (2006).

    Article  CAS  PubMed  Google Scholar 

  31. Breslin, M. B. et al. Differential processing of proenkephalin by prohormone convertases 1(3) and 2 and furin. J. Biol. Chem. 268, 27084–27093 (1993).

    Article  CAS  PubMed  Google Scholar 

  32. Christakoudi, S., Evangelou, E., Riboli, E. & Tsilidis, K. K. GWAS of allometric body-shape indices in UK Biobank identifies loci suggesting associations with morphogenesis, organogenesis, adrenal cell renewal and cancer. Sci. Rep. 11, 10688 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 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 

  34. Grice, D. E. et al. Transcriptional profiling of C57 and DBA strains of mice in the absence and presence of morphine. BMC Genomics 8, 76 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Yu, L. et al. Activity in projection neurons from prelimbic cortex to the PVT is necessary for retrieval of morphine withdrawal memory. Cell Rep. 35, 108958 (2021).

    Article  CAS  PubMed  Google Scholar 

  36. Fujita-Hamabe, W., Nakamoto, K. & Tokuyama, S. Involvement of NCAM and FGF receptor signaling in the development of analgesic tolerance to morphine. Eur. J. Pharmacol. 672, 77–82 (2011).

    Article  CAS  PubMed  Google Scholar 

  37. Yang, B.-Z. et al. Association of haplotypic variants in DRD2, ANKK1, TTC12 and NCAM1 to alcohol dependence in independent case control and family samples. Hum. Mol. Genet. 16, 2844–2853 (2007).

    Article  CAS  PubMed  Google Scholar 

  38. Yang, B.-Z. et al. Haplotypic variants in DRD2, ANKK1, TTC12 and NCAM1 are associated with comorbid alcohol and drug dependence. Alcohol. Clin. Exp. Res. 32, 2117–2127 (2008).

    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. Vujkovic, M. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat. Genet. 52, 680–691 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 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 

  42. Amare, A. T. et al. Bivariate genome-wide association analyses of the broad depression phenotype combined with major depressive disorder, bipolar disorder or schizophrenia reveal eight novel genetic loci for depression. Mol. Psychiatry 25, 1420–1429 (2020).

    Article  CAS  PubMed  Google Scholar 

  43. Koob, G. F. Neurobiology of opioid addiction: opponent process, hyperkatifeia and negative reinforcement. Biol. Psychiatry 87, 44–53 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Leshner, A. I. Addiction is a brain disease, and it matters. Science 278, 45–47 (1997).

    Article  CAS  PubMed  Google Scholar 

  45. Karlsson Linnér, R. et al. Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. Nat. Neurosci. 24, 1367–1376 (2021).

    Article  PubMed  Google Scholar 

  46. Vanyukov, M. M. et al. Common liability to addiction and ‘gateway hypothesis’: Theoretical, empirical and evolutionary perspective. Drug Alcohol Depend. 123, S3–S17 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Cross-Disorder Group of the Psychiatric Genomics Consortium. Genomic relationships, novel loci and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482 (2019).

  48. Howell, B. A. et al. Validity of incident opioid use disorder (OUD) diagnoses in administrative data: a chart verification study. J. Gen. Intern. Med. 36, 1264–1270 (2021).

    Article  PubMed  Google Scholar 

  49. 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 

  50. Delaneau, O., Zagury, J.-F., Robinson, M. R., Marchini, J. L. & Dermitzakis, E. T. Accurate, scalable and integrative haplotype estimation. Nat. Commun. 10, 5436 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  PubMed  Google Scholar 

  53. Fang, H. et al. Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am. J. Hum. Genet. 105, 763–772 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

  55. 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 

  56. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Altshuler, D. M. et al. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

    Article  CAS  PubMed  Google Scholar 

  58. 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 

  59. Bernstein, B. E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045–1048 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  61. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Bakshi, A. et al. Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Sci. Rep. 6, 32894 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Barbeira, A. N. et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 22, 49 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  64. 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 

  65. Aguet, F. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  Google Scholar 

  66. Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Ng, B. et al. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat. Neurosci. 20, 1418–1426 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Freshour, S. L. et al. Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 49, D1144–D1151 (2021).

    Article  CAS  PubMed  Google Scholar 

  69. Brown, B. C., Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).

  70. Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Dennis, J. K. et al. Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease. Genome Med. 13, 6 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Grotzinger, A. D. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Zhou, H. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Johnson, E. C. et al. A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry 7, 1032–1045 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

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

  76. Mullins, N. et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat. Genet. 53, 817–829 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 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 

  78. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. 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 

  80. Yu, D. et al. Interrogating the genetic determinants of Tourette’s syndrome and other tic disorders through genome-wide association studies. Am. J. Psychiatry 176, 217–227 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  81. International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS). Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis. Mol. Psychiatry 23, 1181–1188 (2018).

Download references

Acknowledgements

This work was supported by Merit Review Awards from the US Department of Veterans Affairs Biomedical Laboratory Research and Development Service (no. I01 BX003341 (to A.C.J. and H.R.K.)) and Clinical Science Research and Development Service (no. I01 CX001734 (to K.M.K.)); the VISN 4 Mental Illness Research, Education and Clinical Center (to H.R.K.); NIAAA grant K01 AA028292 (to R.L.K.); and NIDA grant DA046345 (to H.R.K.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views expressed in this article are those of the authors and do not necessarily represent the position or policy of the Department of Veterans Affairs or the US Government.

Author information

Author notes

  1. A list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA

    Rachel L. Kember, Rachel Vickers-Smith, Emily E. Hartwell, Richard C. Crist, Kyle M. Kampman & Henry R. Kranzler

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

    Rachel L. Kember, Heng Xu, Sylvanus Toikumo, Emily E. Hartwell, Richard C. Crist, Kyle M. Kampman & Henry R. Kranzler

  3. Department of Epidemiology, University of Kentucky College of Public Health, Lexington, KY, USA

    Rachel Vickers-Smith

  4. Center on Drug and Alcohol Research, Department of Behavioral Science, University of Kentucky College of Medicine, Lexington, KY, USA

    Rachel Vickers-Smith

  5. Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Maria Niarchou, Lea K. Davis & Sandra Sanchez-Roige

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

    Hang Zhou, Christopher T. Rentsch, Amy C. Justice & Joel Gelernter

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

    Hang Zhou, Christopher T. Rentsch, Amy C. Justice & Joel Gelernter

  8. Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK

    Christopher T. Rentsch

  9. Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA

    Amy C. Justice

  10. Department of Psychiatry, University of California, San Diego, San Diego, CA, USA

    Sandra Sanchez-Roige

Authors
  1. Rachel L. Kember
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rachel Vickers-Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Heng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sylvanus Toikumo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maria Niarchou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Emily E. Hartwell
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Richard C. Crist
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Christopher T. Rentsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lea K. Davis
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Amy C. Justice
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Sandra Sanchez-Roige
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Kyle M. Kampman
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Joel Gelernter
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Henry R. Kranzler
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

Million Veteran Program

Contributions

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

Corresponding author

Correspondence to Henry R. Kranzler.

Ethics declarations

Competing interests

H.R.K. is a member of advisory boards for Dicerna Pharmaceuticals, Sophrosyne Pharmaceuticals and Enthion Pharmaceuticals; a consultant to Sobrera Pharmaceuticals; the recipient of research funding and medication supplies for an investigator-initiated study from Alkermes, and a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was supported in the last three years by Alkermes, Dicerna, Ethypharm, Lundbeck, Mitsubishi and Otsuka. J.G. and H.R.K. are holders of US patent no. 10,900,082 titled: ‘Genotype-guided dosing of opioid agonists,’ issued 26 January 2021. The other authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks Ditte Demontis and Andrew McIntosh for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kember, R.L., Vickers-Smith, R., Xu, H. et al. Cross-ancestry meta-analysis of opioid use disorder uncovers novel loci with predominant effects in brain regions associated with addiction. Nat Neurosci 25, 1279–1287 (2022). https://doi.org/10.1038/s41593-022-01160-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-022-01160-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing