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:

Genome-wide association study implicates CHRNA2 in cannabis use disorder

Nature Neuroscience volume 22, pages 1066–1074 (2019)Cite this article

Subjects

Abstract

Cannabis is the most frequently used illicit psychoactive substance worldwide; around one in ten users become dependent. The risk for cannabis use disorder (CUD) has a strong genetic component, with twin heritability estimates ranging from 51 to 70%. Here we performed a genome-wide association study of CUD in 2,387 cases and 48,985 controls, followed by replication in 5,501 cases and 301,041 controls. We report a genome-wide significant risk locus for CUD (P = 9.31 × 10−12) that replicates in an independent population (Preplication = 3.27 × 10−3, Pmeta-analysis = 9.09 × 10−12). The index variant (rs56372821) is a strong expression quantitative trait locus for cholinergic receptor nicotinic α2 subunit (CHRNA2); analyses of the genetically regulated gene expression identified a significant association of CHRNA2 expression with CUD in brain tissue. At the polygenic level, analyses revealed a significant decrease in the risk of CUD with increased load of variants associated with cognitive performance. The results provide biological insights and inform on the genetic architecture of CUD.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: CUD GWAS results.
Fig. 2: Association results for the genomic region with the CUD risk locus.
Fig. 3: Association of CHRNA2 expression with CUD.
Fig. 4: Association of PRS with CUD.

Similar content being viewed by others

Data availability

Summary statistics with the results from the CUD GWAS are available on the iPSYCH website (https://ipsych.au.dk/downloads/). For access to the genotypes from the iPSYCH cohort, interested researchers should contact A.D.B.

References

  1. Compton, W. M., Han, B., Jones, C. M., Blanco, C. & Hughes, A. Marijuana use and use disorders in adults in the USA, 2002–14: analysis of annual cross-sectional surveys. Lancet Psychiatry 3, 954–964 (2016).

    Article  Google Scholar 

  2. Lopez-Quintero, C. et al. Probability and predictors of transition from first use to dependence on nicotine, alcohol, cannabis, and cocaine: results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Drug Alcohol Depend. 115, 120–130 (2011).

    Article  Google Scholar 

  3. Verweij, K. J. et al. Genetic and environmental influences on cannabis use initiation and problematic use: a meta-analysis of twin studies. Addiction 105, 417–430 (2010).

    Article  Google Scholar 

  4. Kendler, K. S. et al. A population-based Swedish Twin and Sibling Study of cannabis, stimulant and sedative abuse in men. Drug Alcohol Depend. 149, 49–54 (2015).

    Article  Google Scholar 

  5. European Monitoring Centre for Drugs and Drug Addiction. Treatment of Cannabis-related Disorders in Europe http://www.emcdda.europa.eu/system/files/publications/1014/TDXD14017ENN.pdf (EMCDDA, 2015).

  6. Arria, A. M., Caldeira, K. M., Bugbee, B. A., Vincent, K. B. & O’Grady, K. E. Marijuana use trajectories during college predict health outcomes nine years post-matriculation. Drug Alcohol Depend. 159, 158–165 (2016).

    Article  Google Scholar 

  7. Marconi, A., Di Forti, M., Lewis, C. M., Murray, R. M. & Vassos, E. Meta-analysis of the association between the level of cannabis use and risk of psychosis. Schizophr. Bull. 42, 1262–1269 (2016).

    Article  Google Scholar 

  8. Cougle, J. R., Hakes, J. K., Macatee, R. J., Chavarria, J. & Zvolensky, M. J. Quality of life and risk of psychiatric disorders among regular users of alcohol, nicotine, and cannabis: an analysis of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC). J. Psychiatr. Res. 66–67, 135–141 (2015).

    Article  Google Scholar 

  9. Kedzior, K. K. & Laeber, L. T. A positive association between anxiety disorders and cannabis use or cannabis use disorders in the general population: a meta-analysis of 31 studies. BMC Psychiatry 14, 136 (2014).

    Article  Google Scholar 

  10. Meier, M. H. et al. Persistent cannabis users show neuropsychological decline from childhood to midlife. Proc. Natl Acad. Sci. USA 109, E2657–E2664 (2012).

    Article  CAS  Google Scholar 

  11. Verweij, K. J. et al. The genetic aetiology of cannabis use initiation: a meta-analysis of genome-wide association studies and a SNP-based heritability estimation. Addict. Biol. 18, 846–850 (2013).

    Article  CAS  Google Scholar 

  12. Stringer, S. et al. Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium. Transl. Psychiatry 6, e769 (2016).

    Article  CAS  Google Scholar 

  13. Minică, C. C. et al. Heritability, SNP- and gene-based analyses of cannabis use initiation and age at onset. Behav. Genet. 45, 503–513 (2015).

    Article  Google Scholar 

  14. 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  Google Scholar 

  15. Agrawal, A. et al. A genome-wide association study of DSM-IV cannabis dependence. Addict. Biol. 16, 514–518 (2011).

    Article  Google Scholar 

  16. Agrawal, A. et al. DSM-5 cannabis use disorder: a phenotypic and genomic perspective. Drug Alcohol Depend. 134, 362–369 (2014).

    Article  Google Scholar 

  17. Sherva, R. et al. Genome-wide association study of cannabis dependence severity, novel risk variants, and shared genetic risks. JAMA Psychiatry 73, 472–480 (2016).

    Article  Google Scholar 

  18. Agrawal, A. et al. Genome-wide association study identifies a novel locus for cannabis dependence. Mol. Psychiatry 23, 1293–1302 (2018).

    Article  CAS  Google Scholar 

  19. Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Article  CAS  Google Scholar 

  20. Tyrfingsson, T. et al. Addictions and their familiality in Iceland. Ann. N. Y. Acad. Sci. 1187, 208–217 (2010).

    Article  Google Scholar 

  21. Wray, N. R. et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013).

    Article  CAS  Google Scholar 

  22. Toftdahl, N. G., Nordentoft, M. & Hjorthøj, C. Prevalence of substance use disorders in psychiatric patients: a nationwide Danish population-based study. Soc. Psychiatry Psychiatr. Epidemiol. 51, 129–140 (2016).

    Article  Google Scholar 

  23. Chavez-Noriega, L. E. et al. Pharmacological characterization of recombinant human neuronal nicotinic acetylcholine receptors hα2β2, hα2β4, hα3β2, hα3β4, hα4β2, hα4β4 and hα7 expressed in Xenopus oocytes. J. Pharmacol. Exp. Ther. 280, 346–356 (1997).

    CAS  PubMed  Google Scholar 

  24. Corley, R. P. et al. Association of candidate genes with antisocial drug dependence in adolescents. Drug Alcohol Depend. 96, 90–98 (2008).

    Article  CAS  Google Scholar 

  25. Wang, S. et al. Significant associations of CHRNA2 and CHRNA6 with nicotine dependence in European American and African American populations. Hum. Genet. 133, 575–586 (2014).

    Article  CAS  Google Scholar 

  26. Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).

    Article  Google Scholar 

  27. Furberg, H. et al. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).

    Article  CAS  Google Scholar 

  28. Agrawal, A., Budney, A. J. & Lynskey, M. T. The co-occurring use and misuse of cannabis and tobacco: a review. Addiction 107, 1221–1233 (2012).

    Article  Google Scholar 

  29. Kutlu, M. G., Parikh, V. & Gould, T. J. Nicotine addiction and psychiatric disorders. Int. Rev. Neurobiol. 124, 171–208 (2015).

    Article  CAS  Google Scholar 

  30. 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. Nature 51, 237–244 (2019).

    CAS  Google Scholar 

  31. D’Souza, M. S. & Markou, A. Schizophrenia and tobacco smoking comorbidity: nAChR agonists in the treatment of schizophrenia-associated cognitive deficits. Neuropharmacology 62, 1564–1573 (2012).

    Article  Google Scholar 

  32. Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet. 48, 1462–1472 (2016).

    Article  CAS  Google Scholar 

  33. 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  Google Scholar 

  34. Miquel, M. et al. Have we been ignoring the elephant in the room? Seven arguments for considering the cerebellum as part of addiction circuitry. Neurosci. Biobehav. Rev. 60, 1–11 (2016).

    Article  Google Scholar 

  35. Wagner, M. J., Kim, T. H., Savall, J., Schnitzer, M. J. & Luo, L. Cerebellar granule cells encode the expectation of reward. Nature 544, 96–100 (2017).

    Article  CAS  Google Scholar 

  36. Stella, N. Chronic THC intake modifies fundamental cerebellar functions. J. Clin. Invest. 123, 3208–3210 (2013).

    Article  CAS  Google Scholar 

  37. Mahgoub, M. et al. Effects of cannabidiol on the function of α7-nicotinic acetylcholine receptors. Eur. J. Pharmacol. 720, 310–319 (2013).

    Article  CAS  Google Scholar 

  38. Cachope, R. et al. Selective activation of cholinergic interneurons enhances accumbal phasic dopamine release: setting the tone for reward processing. Cell Rep. 2, 33–41 (2012).

    Article  CAS  Google Scholar 

  39. Nava, F., Carta, G., Colombo, G. & Gessa, G. L. Effects of chronic Δ9-tetrahydrocannabinol treatment on hippocampal extracellular acetylcholine concentration and alternation performance in the T-maze. Neuropharmacology 41, 392–399 (2001).

    Article  CAS  Google Scholar 

  40. Timmermann, D. B. et al. Augmentation of cognitive function by NS9283, a stoichiometry-dependent positive allosteric modulator of α2- and α4-containing nicotinic acetylcholine receptors. Br. J. Pharmacol. 167, 164–182 (2012).

    Article  CAS  Google Scholar 

  41. Papke, R. L., Thinschmidt, J. S., Moulton, B. A., Meyer, E. M. & Poirier, A. Activation and inhibition of rat neuronal nicotinic receptors by ABT-418. Br. J. Pharm. 120, 429–438 (1997).

    Article  CAS  Google Scholar 

  42. Frolich, L., Ashwood, T., Nilsson, J. & Eckerwall, G. & Sirocco, I. Effects of AZD3480 on cognition in patients with mild-to-moderate Alzheimer’s disease: a phase IIb dose-finding study. J. Alzheimers Dis. 24, 363–374 (2011).

    Article  Google Scholar 

  43. McKay, J. D. et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat. Genet. 49, 1126–1132 (2017).

    Article  CAS  Google Scholar 

  44. Stout, S. M. & Cimino, N. M. Exogenous cannabinoids as substrates, inhibitors, and inducers of human drug metabolizing enzymes: a systematic review. Drug Metab. Rev. 46, 86–95 (2014).

    Article  CAS  Google Scholar 

  45. Horwood, L. J. et al. Cannabis use and educational achievement: findings from three Australasian cohort studies. Drug Alcohol Depend. 110, 247–253 (2010).

    Article  Google Scholar 

  46. Verweij, K. J., Huizink, A. C., Agrawal, A., Martin, N. G. & Lynskey, M. T. Is the relationship between early-onset cannabis use and educational attainment causal or due to common liability? Drug Alcohol Depend. 133, 580–586 (2013).

    Article  Google Scholar 

  47. Carey, C. E. et al. Associations between polygenic risk for psychiatric disorders and substance involvement. Front. Genet. 7, 149 (2016).

    Article  Google Scholar 

  48. Kolla, N. J. et al. Adult attention deficit hyperactivity disorder symptom profiles and concurrent problems with alcohol and cannabis: sex differences in a representative, population survey. BMC Psychiatry 16, 50 (2016).

    Article  Google Scholar 

  49. Koskinen, J., Löhönen, J., Koponen, H., Isohanni, M. & Miettunen, J. Rate of cannabis use disorders in clinical samples of patients with schizophrenia: a meta-analysis. Schizophr. Bull. 36, 1115–1130 (2010).

    Article  Google Scholar 

  50. Reginsson, G. W. et al. Polygenic risk scores for schizophrenia and bipolar disorder associate with addiction. Addict. Biol. 23, 485–492 (2018).

    Article  CAS  Google Scholar 

  51. 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  Google Scholar 

  52. Mors, O., Perto, G. P. & Mortensen, P. B. The Danish Psychiatric Central Research Register. Scand. J. Public Health 39, 54–57 (2011).

    Article  Google Scholar 

  53. Pedersen, C. B. The Danish Civil Registration System. Scand. J. Public Health 39, 22–25 (2011).

    Article  Google Scholar 

  54. Nørgaard-Pedersen, B. & Hougaard, D. M. Storage policies and use of the Danish Newborn Screening Biobank. J. Inherit. Metab. Dis. 30, 530–536 (2007).

    Article  Google Scholar 

  55. Lynge, E., Sandegaard, J. L. & Rebolj, M. The Danish National Patient Register. Scand. J. Public Health 39, 30–33 (2011).

    Article  Google Scholar 

  56. Børglum, A. D. et al. Genome-wide study of association and interaction with maternal cytomegalovirus infection suggests new schizophrenia loci. Mol. Psychiatry 19, 325–333 (2014).

    Article  Google Scholar 

  57. Hollegaard, M. V. et al. Robustness of genome-wide scanning using archived dried blood spot samples as a DNA source. BMC Genet. 12, 58 (2011).

    Article  Google Scholar 

  58. Illumina GenCall Data Analysis Software. Illumina SNP Genotyping Tech. https://www.illumina.com/Documents/products/technotes/technote_gencall_data_analysis_software.pdf (Illumina, 2005).

  59. Korn, J. M. et al. Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nat. Genet. 40, 1253–1260 (2008).

    Article  CAS  Google Scholar 

  60. Goldstein, J. I. et al. zCall: a rare variant caller for array-based genotyping: genetics and population analysis. Bioinformatics 28, 2543–2545 (2012).

    Article  CAS  Google Scholar 

  61. Sudmant, P. H. et al. An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–81 (2015).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  63. Delaneau, O., Marchini, J. & Zagury, J. F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2011).

    Article  Google Scholar 

  64. Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 (Bethesda) 1, 457–470 (2011).

    Article  Google Scholar 

  65. Price, A. L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–135 (2008).

    Article  CAS  Google Scholar 

  66. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  68. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  Google Scholar 

  69. 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  Google Scholar 

  70. Gudbjartsson, D. F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015).

    Article  CAS  Google Scholar 

  71. Sveinbjornsson, G. et al. Rare mutations associating with serum creatinine and chronic kidney disease. Hum. Mol. Genet. 23, 6935–6943 (2014).

    Article  CAS  Google Scholar 

  72. 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  Google Scholar 

  73. Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    Article  Google Scholar 

  74. 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  Google Scholar 

  75. 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  Google Scholar 

  76. Goodwin, R. D. et al. Trends in daily cannabis use among cigarette smokers: United States, 2002–2014. Am. J. Public Health 108, 137–142 (2018).

    Article  Google Scholar 

  77. 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  Google Scholar 

  78. Ardlie, K. G. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  80. Huckins, L. M. et al. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nat. Genet. 51, 659–674 (2019).

    Article  CAS  Google Scholar 

  81. Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The iPSYCH project is funded by the Lundbeck Foundation (grant nos. R102-A9118 and R155-2014-1724) and the universities and university hospitals of Aarhus and Copenhagen. Genotyping of the iPSYCH samples was supported by grants from the Lundbeck, Stanley and Simons Foundations (grant no. SFARI 311789 to M.J.D.), and the National Institute of Mental Health (NIMH; grant no. 5U01MH094432-02 to M.J.D.). The study was supported by a European Commission Horizon 2020 Programme grant no. 667302 (CoCA) to A.D.B. The Danish National Biobank resource was supported by the Novo Nordisk Foundation. Data handling and analysis on the GenomeDK HPC facility was supported by the NIMH (grant no. 1U01MH109514-01 to M. O’Donovan and A.D.B.). High-performance computer capacity for the handling and statistical analysis of the iPSYCH data on the GenomeDK HPC facility was provided by the Centre for Integrative Sequencing (iSEQ) at Aarhus University, Denmark (grant to A.D.B.). Work at deCODE was supported in part by the National Institute of Health (R01DA034076). We gratefully acknowledge all the studies and databases that made the GWAS summary data available: Genetics of Personality Consortium, Psychiatric Genomics Consortium, Social Science Genetics Association Consortium (SSGAC), Tobacco and Genetics Consortium, UK Biobank and Complex Traits Genetics Lab. We gratefully acknowledge the data made available by the GTEx Project (supported by the Common Fund of the Office of the Director of the National Institutes of Health, National Cancer Institute, National Human Genome Research Institute, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, NIMH and National Institute of Neurological Disorders and Stroke). The data used for the analyses described in this manuscript were obtained from https://www.gtexportal.org/home/ on 1 November 2017.

Author information

Authors and Affiliations

  1. Department of Biomedicine—Human Genetics and Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark

    Ditte Demontis, Veera Manikandan Rajagopal, Thomas D. Als, Jakob Grove, Kalle Leppälä, Jonatan Pallesen, Per Qvist, Jane Hvarregaard Christensen, Mette Nyegaard & Anders D. Børglum

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

    Ditte Demontis, Veera Manikandan Rajagopal, Thomas D. Als, Jakob Grove, Kalle Leppälä, Jonatan Pallesen, Carsten Hjorthøj, Per Qvist, Jane Hvarregaard Christensen, Jonas Bybjerg-Grauholm, Marie Bækvad-Hansen, Esben Agerbo, David M. Hougaard, Thomas Werge, Ole Mors, Preben Bo Mortensen, Merete Nordentoft, Mette Nyegaard & Anders D. Børglum

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

    Ditte Demontis, Veera Manikandan Rajagopal, Thomas D. Als, Jakob Grove, Kalle Leppälä, Jonatan Pallesen, Per Qvist, Jane Hvarregaard Christensen & Anders D. Børglum

  4. deCODE genetics Amgen, Reykjavík, Iceland

    Thorgeir E. Thorgeirsson, Daniel F. Gudbjartsson, Gunnar W. Reginsson, Hreinn Stefansson & Kari Stefansson

  5. Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark

    Jakob Grove

  6. Copenhagen University Hospital, Mental Health Centre Copenhagen, Mental Health Services in the Capital Region of Denmark, Hellerup, Denmark

    Carsten Hjorthøj & Merete Nordentoft

  7. National Center of Addiction Medicine (SAA), Vogur Hospital, Reykjavík, Iceland

    Thorarinn Tyrfingsson & Valgerdur Runarsdottir

  8. Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark

    Jonas Bybjerg-Grauholm, Marie Bækvad-Hansen & David M. Hougaard

  9. Division of Psychiatric Genomic, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Laura M. Huckins & Eli A. Stahl

  10. Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Laura M. Huckins & Eli A. Stahl

  11. National Centre for Register-based Research, Aarhus University, Aarhus, Denmark

    Allan Timmermann, Esben Agerbo & Preben Bo Mortensen

  12. Centre for Integrated Register-Based Research, Aarhus University, Aarhus, Denmark

    Esben Agerbo & Preben Bo Mortensen

  13. Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark

    Thomas Werge

  14. Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark

    Thomas Werge

  15. Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark

    Ole Mors

  16. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Merete Nordentoft

  17. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Mark J. Daly

  18. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Mark J. Daly

  19. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Mark J. Daly

Authors
  1. Ditte Demontis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Veera Manikandan Rajagopal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thorgeir E. Thorgeirsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas D. Als
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jakob Grove
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kalle Leppälä
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Daniel F. Gudbjartsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jonatan Pallesen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Carsten Hjorthøj
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Gunnar W. Reginsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Thorarinn Tyrfingsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Valgerdur Runarsdottir
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Per Qvist
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jane Hvarregaard Christensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jonas Bybjerg-Grauholm
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Marie Bækvad-Hansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Laura M. Huckins
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Eli A. Stahl
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Allan Timmermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Esben Agerbo
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. David M. Hougaard
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Thomas Werge
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Ole Mors
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Preben Bo Mortensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Merete Nordentoft
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Mark J. Daly
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Hreinn Stefansson
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Kari Stefansson
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Mette Nyegaard
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Anders D. Børglum
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.D. and A.D.B. conceived the idea, and supervised and directed the study. T.E.T., T.T., V.R., J.B.-G., M.B.-H., A.T., E.A., D.M.H., T.W., O.M., P.B.M., M. Nordentoft, M.J.D., H.S., K.S. and A.D.B. provided samples and/or data. D.D., V.M.R., T.E.T., T.D.A., J.G., K.L., D.F.G., J.P., G.W.R., L.M.H. and E.A.S. performed the analyses. D.D., V.M.R., T.E.T., T.D.A, J.G., K.L., D.F.G., P.Q., C.H., J.H.C., H.S., M. Nyegaard and A.D.B. interpreted the results. D.D. wrote the manuscript. The core revision group consisted of D.D., T.E.T., J.G., K.L., D.F.G., H.S. and A.D.B. All authors discussed the results and approved the final version of the manuscript.

Corresponding authors

Correspondence to Ditte Demontis or Anders D. Børglum.

Ethics declarations

Competing interests

T. Werge has been a lecturer and advisor to H. Lundbeck A/S. T.E. Thorgeirsson, D.F. Gudbjartsson, G.W. Reginsson, H. Stefansson and K. Stefansson are employees of deCODE genetics/Amgen.

Additional information

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

Integrated supplementary information

Supplementary Fig. 1 Null distribution for rs56372821 odds ratio for schizophrenia.

Null distribution of the odds ratio for rs56372821 when applying 10,000 permutations removing randomly 554 schizophrenia cases and 101 individuals. The Red lines indicate the observed ln(ORrs56372821) when excluding individuals with CUD (554 SZ cases and 101 controls with CUD) mirrored around the mean ln(ORrs56372821) of the null distribution obtained by random removal of the same number of SZ cases and control.

Supplementary Fig. 2 CHRNA2 expression in cerebellum among rs56372821 genotypes.

The distribution of CHRNA2 expression over rs56372821 genotypes (Hom Ref = GG; He = AG; Homo Alt = AA), for individuals in the GTEx database (The GTEx Consortium. Science, 2015, 348, 648–660). The major allele (G) is the risk allele for CUD. (picture downloaded from: https://www.gtexportal.org/home/eqtls/bySnp?snpId=rs56372821&tissueName=All)

Supplementary Fig. 3 Quintile plots of Odds Ratio for CUD by PRS for cognitive performance/educational attainment.

Odds ratio (OR) for CUD by PRS within each quintile. The plots represent the three phenotypes related to cognitive/educational performance demonstrating significant association of PRS with CUD after correcting for multiple testing. Error bars indicate 95% confidence limits. A. OR for CUD by PRS for educational years (SSGAC) B. OR for CUD by PRS for college completion (SSGAC) C. OR for CUD by PRS for human intelligence

Supplementary Fig. 4 Gene expression of CHRNA2 and CNR1 in Allan Brain Atlas.

Gene expression of CHRNA2 and CNR1 in Allan Brain Atlas (http://www.brain-map.org/) in 26 brain regions from 6 donors. Donors are marked in six different colors in the first row, and the 26 different regions are marked by different colors in the second row. Cerebellar cortex and cerebellar nuclei are marked with arrows. The results are shown in ‘Coarse’ mode, where the brain is divided into large neuroanatomical divisions or regions and the samples within each of the regions are averaged together to provide a summary value for the partition. The colors of the heat map are normalized expression values. The color green should be interpreted as relatively low expression and red as relatively high expression within the scope of each probe. There are two probes tagging CHRNA2 expression (both shown) and 89 probes tagging CNR1. The six probes tagging CNR1 expression demonstrating strongest negative correlation with CHRNA2 expression is shown (Pearson’s correlation coefficient, rmax = −0.498). 67% of the probes tagging CNR1 correlated with CHRNA2 expression with r > −0.4. (Picture downloaded from: http://human.brain-map.org/microarray/search/show?domain=&donors=9861,10021,12876,14380,15496,15697&search_type=correlation&search_term=&seed=1058207)

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Demontis, D., Rajagopal, V.M., Thorgeirsson, T.E. et al. Genome-wide association study implicates CHRNA2 in cannabis use disorder. Nat Neurosci 22, 1066–1074 (2019). https://doi.org/10.1038/s41593-019-0416-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-019-0416-1

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