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Genome-wide association study implicates CHRNA2 in cannabis use disorder

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.

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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. 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).

  2. 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).

  3. 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).

  4. 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).

  5. 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. 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).

  7. 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).

  8. 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).

  9. 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).

  10. 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).

  11. 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).

  12. 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).

  13. 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).

  14. 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).

  15. 15.

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

  16. 16.

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

  17. 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).

  18. 18.

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

  19. 19.

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

  20. 20.

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

  21. 21.

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

  22. 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).

  23. 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).

  24. 24.

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

  25. 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).

  26. 26.

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

  27. 27.

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

  28. 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).

  29. 29.

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

  30. 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).

  31. 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).

  32. 32.

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

  33. 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).

  34. 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).

  35. 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).

  36. 36.

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

  37. 37.

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

  38. 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).

  39. 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).

  40. 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).

  41. 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).

  42. 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).

  43. 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).

  44. 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).

  45. 45.

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

  46. 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).

  47. 47.

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

  48. 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).

  49. 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).

  50. 50.

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

  51. 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).

  52. 52.

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

  53. 53.

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

  54. 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).

  55. 55.

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

  56. 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).

  57. 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).

  58. 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. 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).

  60. 60.

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

  61. 61.

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

  62. 62.

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

  63. 63.

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

  64. 64.

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

  65. 65.

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

  66. 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).

  67. 67.

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

  68. 68.

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

  69. 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).

  70. 70.

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

  71. 71.

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

  72. 72.

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

  73. 73.

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

  74. 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).

  75. 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).

  76. 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).

  77. 77.

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

  78. 78.

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

  79. 79.

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

  80. 80.

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

  81. 81.

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

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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

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.

Correspondence to Ditte Demontis or Anders D. Børglum.

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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.

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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)

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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.
Supplementary Fig. 1: Null distribution for rs56372821 odds ratio for schizophrenia.
Supplementary Fig. 2: CHRNA2 expression in cerebellum among rs56372821 genotypes.
Supplementary Fig. 3: Quintile plots of Odds Ratio for CUD by PRS for cognitive performance/educational attainment.
Supplementary Fig. 4: Gene expression of CHRNA2 and CNR1 in Allan Brain Atlas.