GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability

An Author Correction to this article was published on 05 June 2019

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Abstract

Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health–related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.

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Fig. 1: Q–Q and Manhattan plot of the GWAS meta-analysis.
Fig. 2: Regional plots of the genome-wide significant SNPs.
Fig. 3: Q–Q and Manhattan plot of the gene-based test of association.
Fig. 4: Genetic overlap between lifetime cannabis use and other phenotypes.

Change history

  • 05 June 2019

    Several occurrences of the word ‘schizophrenia’ have been re-worded as ‘liability to schizophrenia’ or ‘schizophrenia risk’, including in the title, which should have been “GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability,” as well as in Supplementary Figures 1–10 and Supplementary Tables 7–10, to more accurately reflect the findings of the work.

References

  1. 1.

    Volkow, N. D., Compton, W. M. & Weiss, S. R. Adverse health effects of marijuana use. N. Engl. J. Med. 371, 879 (2014).

    PubMed  Google Scholar 

  2. 2.

    Moore, T. H. et al. Cannabis use and risk of psychotic or affective mental health outcomes: a systematic review. Lancet 370, 319–328 (2007).

    Article  Google Scholar 

  3. 3.

    Hall, W. & Degenhardt, L. Adverse health effects of non-medical cannabis use. Lancet 374, 1383–1391 (2009).

    CAS  Article  Google Scholar 

  4. 4.

    Verweij, K. J. H. 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 

  5. 5.

    Agrawal, A., Neale, M. C., Jacobson, K. C., Prescott, C. A. & Kendler, K. S. Illicit drug use and abuse/dependence: modeling of two-stage variables using the CCC approach. Addict. Behav. 30, 1043–1048 (2005).

    CAS  Article  Google Scholar 

  6. 6.

    Agrawal, A. & Lynskey, M. T. The genetic epidemiology of cannabis use, abuse and dependence. Addiction 101, 801–812 (2006).

    Article  Google Scholar 

  7. 7.

    Verweij, K. J. H. 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).

    CAS  Article  Google Scholar 

  8. 8.

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

    Article  Google Scholar 

  9. 9.

    Minica, 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 

  10. 10.

    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).

    CAS  Article  Google Scholar 

  11. 11.

    Demontis, D. et al. Genome-wide association study implicates CHRNA2 in cannabis use disorder. Preprint at bioRxiv https://doi.org/10.1101/237321 (2018).

  12. 12.

    Gelernter, J. et al. Haplotype spanning TTC12 and ANKK1, flanked by the DRD2 and NCAM1 loci, is strongly associated to nicotine dependence in two distinct American populations. Hum. Mol. Genet. 15, 3498–3507 (2006).

    CAS  Article  Google Scholar 

  13. 13.

    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).

    CAS  Article  Google Scholar 

  14. 14.

    Clarke, T.-K. et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N = 112 117). Mol. Psychiatry 22, 1376–1384 (2017).

    CAS  Article  Google Scholar 

  15. 15.

    Boutwell, B. et al. Replication and characterization of CADM2 and MSRA genes on human behavior. Heliyon 3, e00349 (2017).

    Article  Google Scholar 

  16. 16.

    Day, F. R. et al. Physical and neurobehavioral determinants of reproductive onset and success. Nat. Genet. 48, 617–623 (2016).

    CAS  Article  Google Scholar 

  17. 17.

    Andréasson, S., Allebeck, P., Engström, A. & Rydberg, U. Cannabis and schizophrenia. A longitudinal study of Swedish conscripts. Lancet 2, 1483–1486 (1987).

    Article  Google Scholar 

  18. 18.

    Smit, F., Bolier, L. & Cuijpers, P. Cannabis use and the risk of later schizophrenia: a review. Addiction 99, 425–430 (2004).

    Article  Google Scholar 

  19. 19.

    Volkow, N. D. et al. Effects of cannabis use on human behavior, including cognition, motivation, and psychosis: a review. JAMA Psychiatry 73, 292–297 (2016).

    Article  Google Scholar 

  20. 20.

    Verweij, K. J. et al. Short communication: Genetic association between schizophrenia and cannabis use. Drug Alcohol Depend. 171, 117–121 (2017).

    Article  Google Scholar 

  21. 21.

    Power, R. A. et al. Genetic predisposition to schizophrenia associated with increased use of cannabis. Mol. Psychiatry 19, 1201–1204 (2014).

    CAS  Article  Google Scholar 

  22. 22.

    Burgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G. & Thompson, S. G. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur. J. Epidemiol. 30, 543–552 (2015).

    Article  Google Scholar 

  23. 23.

    Vaucher, J. et al. Cannabis use and risk of schizophrenia: a Mendelian randomization study. Mol. Psychiatry 23, 1287–1292 (2018).

    CAS  Article  Google Scholar 

  24. 24.

    Gage, S. H. et al. Assessing causality in associations between cannabis use and schizophrenia risk: a two-sample Mendelian randomization study. Psychol. Med. 47, 971–980 (2017).

    CAS  Article  Google Scholar 

  25. 25.

    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  Google Scholar 

  26. 26.

    Barbeira, A.N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Preprint at bioRxiv https://doi.org/10.1101/045260 (2017).

  27. 27.

    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  Google Scholar 

  28. 28.

    Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 32, 377–389 (2017).

    Article  Google Scholar 

  29. 29.

    Ibrahim-Verbaas, C. A. et al. GWAS for executive function and processing speed suggests involvement of the CADM2 gene. Mol. Psychiatry 21, 189–197 (2016).

    CAS  Article  Google Scholar 

  30. 30.

    Walther, B., Morgenstern, M. & Hanewinkel, R. Co-occurrence of addictive behaviours: personality factors related to substance use, gambling and computer gaming. Eur. Addict. Res. 18, 167–174 (2012).

    Article  Google Scholar 

  31. 31.

    Martin, C. A. et al. Sensation seeking, puberty, and nicotine, alcohol, and marijuana use in adolescence. J. Am. Acad. Child Adolesc. Psychiatry 41, 1495–1502 (2002).

    Article  Google Scholar 

  32. 32.

    Nielsen, J., Kulahin, N. & Walmod, P. S. Extracellular protein interactions mediated by the neural cell adhesion molecule, NCAM: heterophilic interactions between NCAM and cell adhesion molecules, extracellular matrix proteins, and viruses. Adv. Exp. Med. Biol. 663, 23–53 (2010).

    CAS  Article  Google Scholar 

  33. 33.

    Rubinek, T. et al. The cell adhesion molecules N-cadherin and neural cell adhesion molecule regulate human growth hormone: a novel mechanism for regulating pituitary hormone secretion. J. Clin. Endocrinol. Metab. 88, 3724–3730 (2003).

    CAS  Article  Google Scholar 

  34. 34.

    Ducci, F. et al. TTC12-ANKK1-DRD2 and CHRNA5-CHRNA3-CHRNB4 influence different pathways leading to smoking behavior from adolescence to mid-adulthood. Biol. Psychiatry 69, 650–660 (2011).

    CAS  Article  Google Scholar 

  35. 35.

    Bidwell, L. C. et al. NCAM1-TTC12-ANKK1-DRD2 variants and smoking motives as intermediate phenotypes for nicotine dependence. Psychopharmacology (Berl.) 232, 1177–1186 (2015).

    CAS  Article  Google Scholar 

  36. 36.

    Atz, M. E., Rollins, B. & Vawter, M. P. NCAM1 association study of bipolar disorder and schizophrenia: polymorphisms and alternatively spliced isoforms lead to similarities and differences. Psychiatr. Genet. 17, 55–67 (2007).

    Article  Google Scholar 

  37. 37.

    Petrovska, J. et al. The NCAM1 gene set is linked to depressive symptoms and their brain structural correlates in healthy individuals. J. Psychiatr. Res. 91, 116–123 (2017).

    Article  Google Scholar 

  38. 38.

    Weiss, L. A. et al. Association between microdeletion and microduplication at 16p11.2 and autism. N. Engl. J. Med. 358, 667–675 (2008).

    CAS  Article  Google Scholar 

  39. 39.

    McCarthy, S. E. et al. Microduplications of 16p11.2 are associated with schizophrenia. Nat. Genet. 41, 1223–1227 (2009).

    CAS  Article  Google Scholar 

  40. 40.

    González, J. R. et al. A common 16p11.2 inversion underlies the joint susceptibility to asthma and obesity. Am. J. Hum. Genet. 94, 361–372 (2014).

    Article  Google Scholar 

  41. 41.

    Zuo, L. et al. Genome-wide significant association signals in IPO11-HTR1A region specific for alcohol and nicotine codependence. Alcohol. Clin. Exp. Res. 37, 730–739 (2013).

    CAS  Article  Google Scholar 

  42. 42.

    Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    CAS  Article  Google Scholar 

  43. 43.

    Donaldson, Z. R. et al. The functional serotonin 1a receptor promoter polymorphism, rs6295, is associated with psychiatric illness and differences in transcription. Transl. Psychiatry 6, e746 (2016).

    CAS  Article  Google Scholar 

  44. 44.

    Gatt, J. M., Burton, K. L., Williams, L. M. & Schofield, P. R. Specific and common genes implicated across major mental disorders: a review of meta-analysis studies. J. Psychiatr. Res. 60, 1–13 (2015).

    Article  Google Scholar 

  45. 45.

    Takekita, Y. et al. HTR1A polymorphisms and clinical efficacy of antipsychotic drug treatment in schizophrenia: a meta-analysis. Int. J. Neuropsychopharmacol. 19, pyv125 (2016).

    Article  Google Scholar 

  46. 46.

    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 

  47. 47.

    Patrick, M. E., Wightman, P., Schoeni, R. F. & Schulenberg, J. E. Socioeconomic status and substance use among young adults: a comparison across constructs and drugs. J. Stud. Alcohol Drugs 73, 772–782 (2012).

    Article  Google Scholar 

  48. 48.

    Müller-Vahl, K. R. & Emrich, H. M. Cannabis and schizophrenia: towards a cannabinoid hypothesis of schizophrenia. Expert Rev. Neurother. 8, 1037–1048 (2008).

    Article  Google Scholar 

  49. 49.

    Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Article  Google Scholar 

  50. 50.

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

    CAS  Article  Google Scholar 

  51. 51.

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

    Article  Google Scholar 

  52. 52.

    Winkler, T. W. & Day, F. R. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).

    Article  Google Scholar 

  53. 53.

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

    CAS  Article  Google Scholar 

  54. 54.

    Turner, S.D. qqman: an R package for visualizing GWAS results using Q-Q and Manhattan plots. Preprint at bioRxiv https://doi.org/10.1101/005165 (2014).

  55. 55.

    Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

    CAS  Article  Google Scholar 

  56. 56.

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

    CAS  Article  Google Scholar 

  57. 57.

    Delaneau, O. & Marchini, J. Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel. Nat. Commun. 5, 3934 (2014).

    CAS  Article  Google Scholar 

  58. 58.

    Carithers, L. J. et al. A novel approach to high-quality postmortem tissue procurement: the GTEx project. Biopreserv. Biobank. 13, 311–319 (2015).

    Article  Google Scholar 

  59. 59.

    Sul, J. H., Han, B., Ye, C., Choi, T. & Eskin, E. Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approaches. PLoS Genet. 9, e1003491 (2013).

    CAS  Article  Google Scholar 

  60. 60.

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

    CAS  Article  Google Scholar 

  61. 61.

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

    CAS  Article  Google Scholar 

  62. 62.

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

    CAS  Article  Google Scholar 

  63. 63.

    Hemani, G. et al. MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations. Preprint at bioRxiv https://doi.org/10.1101/078972 (2017).

  64. 64.

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

    Article  Google Scholar 

  65. 65.

    Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23(R1), R89–R98 (2014).

    CAS  Article  Google Scholar 

  66. 66.

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

    Article  Google Scholar 

  67. 67.

    Ehret, G. B. et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011).

    CAS  Article  Google Scholar 

  68. 68.

    Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    Article  Google Scholar 

  69. 69.

    Hartwig, F.P., Davey Smith, G. & Bowden, J. Robust inference in two-sample Mendelian randomisation via the zero modal pleiotropy assumption. Preprint at bioRxiv https://doi.org/10.1101/126102 (2017).

  70. 70.

    Bowden, J. et al. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I 2 statistic. Int. J. Epidemiol. 45, 1961–1974 (2016).

    Article  Google Scholar 

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Acknowledgements

We would like to thank the research participants and employees of 23andMe for making this work possible. We gratefully acknowledge the Psychiatric Genomics Consortium contributing studies and the participants in those studies without whom this effort would not have been possible. J.A.P. and J.M.V. are supported by the European Research Council (Beyond the Genetics of Addiction ERC-284167, PI J.M.V.). K.J.H.V. is supported by the Foundation Volksbond Rotterdam. N.A.G. is supported by US National Institutes of Health, National Institute on Drug Abuse R00DA023549. J.L.T. is supported by the Netherlands Organization for Scientific Research (NWO; Rubicon grant 446-16-009). S.M. is supported by an Australian Research Council Fellowship. Statistical analyses were partly carried out on the Genetic Cluster Computer (http://www.geneticcluster.org) hosted by SURFsara and financially supported by the Netherlands Organization for Scientific Research (NWO 480-05-003 PI: Posthuma) along with a supplement from the Dutch Brain Foundation and the VU University Amsterdam. M.G.N. is supported by Royal Netherlands Academy of Science Professor Award to D.I.B. (PAH/6635). Part of the computation of this project was funded by NWO Exact Sciences for the application: “Population scale Genetic Analysis” awarded to M.G.N. The genome-wide association analysis on the UK Biobank dataset has been conducted using the UK Biobank resource under application numbers 9905, 16406 and 25331.

The Substance Use Disorders Working Group of the Psychiatric Genomics Consortium (PGC-SUD) is supported by funds from NIDA and NIMH to MH109532 and, previously, with analyst support from NIAAA to U01AA008401 (COGA). J.M.’s contributions were partially supported by the Peter Boris Chair in Addictions Research. S.S.-R. was supported by the Frontiers of Innovation Scholars Program (FISP; #3-P3029), the Interdisciplinary Research Fellowship in NeuroAIDS (IRFN; MH081482) and a pilot award from DA037844. R.M. was supported by the European Union through the European Regional Development Fund (Project No. 2014-2020.4.01.15-0012) and the European Union’s Horizon 2020 Research and Innovation Programme under grant agreements No 692065 and 692145. J.K. was supported by Academy Professorship grants by the Academy of Finland (263278, 292782). M.R. is a recipient of a Miguel de Servet contract from the Instituto de Salud Carlos III, Spain (CP09/00119 and CPII15/00023).

Contributors to the 23andMe Research Team

M. Agee, B. Alipanahi, A. Auton, R. K. Bell, K. Bryc, S. L. Elson, P. Fontanillas, N. A. Furlotte, D. A. Hinds, K. E. Huber, A. Kleinman, N. K. Litterman, J. C. McCreight, M. H. McIntyre, J. L. Mountain, E. S. Noblin, C. A. M. Northover, S. J. Pitts, J. Fah Sathirapongsasuti, O. V. Sazonova, J. F. Shelton, S. Shringarpure, C. Tian, J. Y. Tung, V. Vacic and C.H. Wilson.

Contributors to the International Cannabis Consortium

S. Stringer, C. C. Minica, K. J. H. Verweij, H. Mbarek, M. Bernard, J. Derringer, K. R. van Eijk, J. D. Isen, A. Loukola, D. F. Maciejewski, E. Mihailov, P. J. van der Most, C. Sánchez-Mora, L. Roos, R. Sherva, R. Walters, J.J. Ware, A. Abdellaoui, T. B. Bigdeli, S. J. T. Branje, S. A. Brown, M. Bruinenberg, M. Casas, T. Esko, I. Garcia-Martinez, S. D. Gordon, J. M. Harris, C. A. Hartman, A. K. Henders, A. C. Heath, I. B. Hickie, M. Hickman, C. J. Hopfer, J. J. Hottenga, A. C. Huizink, D. E. Irons, R. S. Kahn, T. Korhonen, H. R. Kranzler, K. Krauter, P. A. C. van Lier, G. H. Lubke, P. A. F. Madden, R. Mägi, M. K. McGue, S. E. Medland, W. H. J. Meeus, M. B. Miller, G. W. Montgomery, M. G. Nivard, I. M. Nolte, A. J. Oldehinkel, Z. Pausova, B. Qaiser, L. Quaye, J. A. Ramos-Quiroga, V. Richarte, R. J. Rose, J. Shin, M. C. Stallings, A. I. Stiby, T. L. Wall, M. J. Wright, H. M. Koot, T. Paus, J. K. Hewitt, M. Ribasés, J. Kaprio, M. P. M. Boks, H. Snieder, T. Spector, M. R. Munafò, A. Metspalu, J. Gelernter, D. I. Boomsma, W. G. Iacono, N. G. Martin, N. A. Gillespie, E. M. Derks and J. M. Vink.

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Contributions

K.J.H.V., E.M.D. and J.M.V. were responsible for the study concept and the design of the study. I.C.C. contributed existing genome-wide summary data from the International Cannabis Consortium. The PGC-SUD group provided summary statistics of the cannabis dependence GWAS. S.S., S.S.-R., M.G.N., B.M.L.B., J.-S.O., H.F.I., M.D.v.d.Z., M.B., F.R.D., P.F., S.M., J.R.B.P., A.A.P. and D.P. performed or supervised genome-wide association analyses. J.A.P. performed the quality control and meta-analysis of genome-wide association studies, under supervision of K.J.H.V., B.M.L.B. and J.M.V. J.A.P., K.J.H.V., Z.G., J.L.T., A.A., M.R.M. and E.M.D. contributed to secondary analyses of the data. J.A.P., K.J.H.V., Z.G., N.A.G., E.M.D. and J.M.V. wrote the manuscript. J.L.D., S.J.T.B., C.A.H., A.C.H., P.A.C.v.L., P.A.F.M., R.M., W.M., G.W.M., A.J.O., Z.P., J.A.R.-Q., T.P., M.R., J.K., M.P.M.B., J.T.B., T.D.S., J.G., D.I.B. and N.G.M. contributed to data acquisition of the samples in the International Cannabis Consortium. S.L.E., H.d.W., L.K.D. and J.M.K. contributed to data acquisition and analysis for the 23andMe dataset. All authors provided critical revision of the manuscript for important intellectual content.

Corresponding author

Correspondence to Jacqueline M. Vink.

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Competing interests

P.F., S.L.E. and members of the 23andMe Research Team are employees of 23andMe Inc. J.A.R.-Q. was on the speakers’ bureau and/or acted as consultant for Eli Lilly, Janssen-Cilag, Novartis, Shire, Lundbeck, Almirall, BRAINGAZE, Sincrolab and Rubió in the last 5 years. He also received travel awards (air tickets and hotel) for taking part in psychiatric meetings from Janssen-Cilag, Rubió, Shire and Eli Lilly. The Department of Psychiatry chaired by him received unrestricted educational and research support from the following pharmaceutical companies in the last 5 years: Eli Lilly, Lundbeck, Janssen- Cilag, Actelion, Shire, Ferrer and Rubió.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10

Reporting Summary

Supplementary Table 1

All genome-wide significant SNP associations in the meta-analysis

Supplementary Table 2

Independent genome-wide significant associations with lifetime cannabis use in the UK Biobank sample

Supplementary Table 3

Description of the genome-wide significant associations in the gene-based test of association and the S-PrediXcan analysis, with a short (non-comprehensive) overview of relevant literature findings on gene–phenotype associations

Supplementary Table 4

Significant S-PrediXcan associations after correction for multiple testing

Supplementary Table 5

Summary of S-PrediXcan associations by target gene

Supplementary Table 6

Results from LD score regression analysis: genetic correlations between lifetime cannabis use and various traits of interest

Supplementary Table 7

SNPs included in the genetic instruments used for bidirectional two-sample Mendelian randomization analyses between lifetime cannabis use and schizophrenia diagnosis

Supplementary Table 8

Cochran’s heterogeneity statistic (Q) for inverse-variance-weighted (IVW) bidirectional two-sample Mendelian randomization analyses between lifetime cannabis use and schizophrenia diagnosis

Supplementary Table 9

I2 statistic for the heterogeneity between genetic variants in an instrument for the MR-Egger SIMEX analysis

Supplementary Table 10

MR-Egger SIMEX intercept, indicating degree of horizontal pleiotropy, for bidirectional two-sample Mendelian randomization analyses between lifetime cannabis use and schizophrenia diagnosis

Supplementary Table 11

Methodological details of the individual GWASs

Supplementary Table 12

quality control steps in the individual GWASs

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Pasman, J.A., Verweij, K.J.H., Gerring, Z. et al. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability. Nat Neurosci 21, 1161–1170 (2018). https://doi.org/10.1038/s41593-018-0206-1

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