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 studies of lifetime and frequency of cannabis use in 131,895 individuals

Molecular Psychiatry (2025)Cite this article

Subjects

Abstract

Cannabis is one of the most widely used drugs globally. We performed genome-wide association studies (GWASs) of lifetime (N = 131,895) and frequency (N = 73,374) of cannabis use. For lifetime cannabis use, we identified two loci, one near CADM2 (rs35827242, p = 4.63E-12) and another near GRM3 (rs12673181, p = 6.90E-09). For frequency of cannabis use, we identified one locus near CADM2 (rs4856591, p = 8.10E-09; r2 = 0.76 with rs35827242). Lifetime and frequency of cannabis use were heritable (12.88 vs. 6.63%) and genetically correlated with previous GWASs of lifetime use and cannabis use disorder (CUD), as well as other substance use and cognitive traits. Polygenic scores (PGSs) for lifetime and frequency of cannabis use predicted cannabis use phenotypes in All of Us participants. A phenome-wide association study using a PGS for lifetime cannabis use to interrogate a hospital cohort replicated prior associations with substance use and mood disorders, and uncovered novel associations with celiac and infectious diseases. This work demonstrates the utility of pre-addiction phenotypes in cannabis use genomic discovery.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

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: GWASs of lifetime and frequency of cannabis use.
Fig. 2: SNP-based heritability and genetic correlation analysis comparisons across cannabis-related traits.
Fig. 3: Comparison of genetic correlations across anthropometric (light gray), health (medium gray), and psychiatric (dark gray) traits between lifetime cannabis use (lanes 1 and 2) and frequency of cannabis use (lanes 3 and 4).
Fig. 4: Percent proportion of lifetime, daily, and problematic cannabis use variance attributable to lifetime cannabis use PGS, frequency of cannabis use PGS, or both (joint-PGS) in European and African AoU cohorts.
Fig. 5: Forest plot of FDR-significant phenome associations with unconditioned (UC) lifetime cannabis use PGS, or with adjustment for cannabis use disorder (CUD), tobacco use disorder (TUD), or both (CUD-TUD).

Similar content being viewed by others

Data availability

We provide 23andMe summary statistics for the top 10,000 independent SNPs. 23andMe GWAS summary statistics will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Please visit https://research.23andme.com/collaborate/#dataset-access/ for more information and to apply to access the data. We will share the Jupyter notebooks used for PGS analysis in AoU with registered All of Us researchers upon request.

References

  1. United Nations Office on Drug and Crime. Booklet 3 - drug market trends of cannabis and opioids. Vienna; 2022.

  2. Pacek LR, Mauro PM, Martins SS. Perceived risk of regular cannabis use in the United States from 2002 to 2012: differences by sex, age, and race/ethnicity. Drug Alcohol Depend. 2015;149:232–44.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Statistics Canada. Mental and substance use disorders in Canada. 2013. (Catalogue no.82-624-X).

  4. Rotermann M. Looking back from 2020, how cannabis use and related behaviours changed in Canada. Health Rep. 2021;32:3–14.

    PubMed  Google Scholar 

  5. Whiting PF, Wolff RF, Deshpande S, Di Nisio M, Duffy S, Hernandez AV, et al. Cannabinoids for medical use: a systematic review and meta-analysis. JAMA. 2015;313:2456–73.

    Article  PubMed  Google Scholar 

  6. Bellocchio L, Inchingolo AD, Inchingolo AM, Lorusso F, Malcangi G, Santacroce L, et al. Cannabinoids drugs and oral health-from recreational side-effects to medicinal purposes: a systematic review. Int J Mol Sci. 2021;22:8329.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Diep C, Tian C, Vachhani K, Won C, Wijeysundera DN, Clarke H, et al. Recent cannabis use and nightly sleep duration in adults: a population analysis of the NHANES from 2005 to 2018. Reg Anesth Pain Med. 2022;47:100–4.

    Article  PubMed  Google Scholar 

  8. Hasin DS, Kerridge BT, Saha TD, Huang B, Pickering R, Smith SM, et al. Prevalence and correlates of DSM-5 cannabis use disorder, 2012–2013: findings from the national epidemiologic survey on alcohol and related Conditions-III. Am J Psychiatry. 2016;173:588–99.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hayley AC, Stough C, Downey LA. DSM-5 cannabis use disorder, substance use and DSM-5 specific substance-use disorders: evaluating comorbidity in a population-based sample. Eur Neuropsychopharmacol. 2017;27:732–43.

    Article  PubMed  Google Scholar 

  10. Howard J, Osborne J. Cannabis and work: need for more research. Am J Ind Med. 2020;63:963–72.

    Article  PubMed  Google Scholar 

  11. Keen L 2nd, Turner AD, George L, Lawrence K. Cannabis use disorder severity and sleep quality among undergraduates attending a Historically Black University. Addict Behav. 2022;134:107414.

    Article  PubMed  Google Scholar 

  12. Lo JO, Hedges JC, Girardi G. Impact of cannabinoids on pregnancy, reproductive health, and offspring outcomes. Am J Obstet Gynecol. 2022;227:571–81.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Pacek LR, Herrmann ES, Smith MT, Vandrey R. Sleep continuity, architecture and quality among treatment-seeking cannabis users: an in-home, unattended polysomnographic study. Exp Clin Psychopharmacol. 2017;25:295–302.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Page RL 2nd, Allen LA, Kloner RA, Carriker CR, Martel C, et al. Medical marijuana, recreational cannabis, and cardiovascular health: a scientific statement from the American Heart Association. Circulation. 2020;142:e131–52.

    Article  PubMed  Google Scholar 

  15. Feingold D, Livne O, Rehm J, Lev-Ran S. Probability and correlates of transition from cannabis use to DSM-5 cannabis use disorder: results from a large-scale nationally representative study. Drug Alcohol Rev. 2020;39:142–51.

    Article  PubMed  Google Scholar 

  16. American Psychiatric Association. Substance-related and addictive disorders. Diagnostic and statistical manual of mental disorders, 5th edn. Washington, D.C.; 2013. https://doi.org/10.1176/appi.books.9780890425596.dsm16.

  17. Hines LA, Morley KI, Rijsdijk F, Strang J, Agrawal A, Nelson EC, et al. Overlap of heritable influences between cannabis use disorder, frequency of use and opportunity to use cannabis: trivariate twin modelling and implications for genetic design. Psychol Med. 2018;48:2786–93.

    Article  PubMed  Google Scholar 

  18. Kendler KS, Ohlsson H, Maes HH, Sundquist K, Lichtenstein P, Sundquist J. A population-based Swedish Twin and Sibling Study of cannabis, stimulant and sedative abuse in men. Drug Alcohol Depend. 2015;149:49–54.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Verweij KJ, Zietsch BP, Lynskey MT, Medland SE, Neale MC, Martin NG, et al. Genetic and environmental influences on cannabis use initiation and problematic use: a meta-analysis of twin studies. Addiction. 2010;105:417–30.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Demontis D, Rajagopal VM, Thorgeirsson TE, Als TD, Grove J, Leppala K, et al. Genome-wide association study implicates CHRNA2 in cannabis use disorder. Nat Neurosci. 2019;22:1066–74.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Johnson EC, Demontis D, Thorgeirsson TE, Walters RK, Polimanti R, Hatoum AS, et al. A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry. 2020;7:1032–45.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Levey DF, Galimberti M, Deak JD, Wendt FR, Bhattacharya A, Koller D, et al. Multi-ancestry genome-wide association study of cannabis use disorder yields insight into disease biology and public health implications. Nat Genet. 2023;55:2094–103.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Xu H, Toikumo S, Crist RC, Glogowska K, Jinwala Z, Deak JD, et al. Identifying genetic loci and phenomic associations of substance use traits: a multi-trait analysis of GWAS (MTAG) study. Addiction. 2023;118:1942–52.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sanchez-Roige S, Palmer AA, Clarke TK. Recent efforts to dissect the genetic basis of alcohol use and abuse. Biol Psychiatry. 2020;87:609–18.

    Article  PubMed  Google Scholar 

  25. McLellan AT, Koob GF, Volkow ND. Preaddiction-A missing concept for treating substance use disorders. JAMA Psychiatry. 2022;79:749–51.

    Article  PubMed  Google Scholar 

  26. Agrawal A, Madden PA, Bucholz KK, Heath AC, Lynskey MT. Initial reactions to tobacco and cannabis smoking: a Twin study. Addiction. 2014;109:663–71.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Degenhardt L, Coffey C, Carlin JB, Swift W, Moore E, Patton GC. Outcomes of occasional cannabis use in adolescence: 10-year follow-up study in Victoria, Australia. Br J Psychiatry. 2010;196:290–5.

    Article  PubMed  Google Scholar 

  28. Scherrer JF, Grant JD, Duncan AE, Sartor CE, Haber JR, Jacob T, et al. Subjective effects to cannabis are associated with use, abuse and dependence after adjusting for genetic and environmental influences. Drug Alcohol Depend. 2009;105:76–82.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Swift W, Coffey C, Carlin JB, Degenhardt L, Patton GC. Adolescent cannabis users at 24 years: trajectories to regular weekly use and dependence in young adulthood. Addiction. 2008;103:1361–70.

    Article  PubMed  Google Scholar 

  30. Windle M, Wiesner M. Trajectories of marijuana use from adolescence to young adulthood: predictors and outcomes. Dev Psychopathol. 2004;16:1007–27.

    Article  PubMed  Google Scholar 

  31. Lyons MJ, Toomey R, Meyer JM, Green AI, Eisen SA, Goldberg J, et al. How do genes influence marijuana use? The role of subjective effects. Addiction. 1997;92:409–17.

    Article  PubMed  Google Scholar 

  32. Leung J, Chan GCK, Hides L, Hall WD. What is the prevalence and risk of cannabis use disorders among people who use cannabis? A systematic review and meta-analysis. Addict Behav. 2020;109:106479.

    Article  PubMed  Google Scholar 

  33. Haberstick BC, Zeiger JS, Corley RP, Hopfer CJ, Stallings MC, Rhee SH, et al. Common and drug-specific genetic influences on subjective effects to alcohol, tobacco and marijuana use. Addiction. 2011;106:215–24.

    Article  PubMed  Google Scholar 

  34. Pasman JA, Verweij KJH, Gerring Z, Stringer S, Sanchez-Roige S, Treur JL, et al. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nat Neurosci. 2018;21:1161–70.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Stringer S, Minica CC, Verweij KJ, Mbarek H, Bernard M, Derringer J, 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. 2016;6:e769.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Sanchez-Roige S, Palmer AA. Emerging phenotyping strategies will advance our understanding of psychiatric genetics. Nat Neurosci. 2020;23:475–80.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Thorpe HHA, Talhat MA, Khokhar JY. High genes: genetic underpinnings of cannabis use phenotypes. Prog Neuropsychopharmacol Biol Psychiatry. 2021;106:110164.

    Article  PubMed  Google Scholar 

  38. McBain RK, Wong EC, Breslau J, Shearer AL, Cefalu MS, Roth E, et al. State medical marijuana laws, cannabis use and cannabis use disorder among adults with elevated psychological distress. Drug Alcohol Depend. 2020;215:108191.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hughes JR, Naud S, Budney AJ, Fingar JR, Callas PW. Attempts to stop or reduce daily cannabis use: an Intensive Natural History study. Psychol Addict Behav. 2016;30:389–97.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Mallard TT, Sanchez-Roige S. Dimensional phenotypes in psychiatric genetics: lessons from genome-wide association studies of alcohol use phenotypes. Complex Psychiatry. 2021;7:45–8.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Toikumo S, Jennings MV, Pham BK, Lee H, Mallard TT, Bianchi SB, et al. Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes. Nat Human Behav. 2024;8:1177–93.

  42. Sanchez-Roige S, Jennings MV, Thorpe HHA, Mallari JE, van der Werf LC, Bianchi SB, et al. CADM2 is implicated in impulsive personality and numerous other traits by genome- and phenome-wide association studies in humans and mice. Transl Psychiatry. 2023;13:167.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Bryc K, Durand EY, Macpherson JM, Reich D, Mountain JL. The genetic ancestry of African Americans, Latinos, and European Americans across the United States. Am J Hum Genet. 2015;96:37–53.

    Article  PubMed  PubMed Central  Google Scholar 

  44. National Academies of Sciences, Engineering, and Medicine Health and Medicine Division, Division of Behavioral and Social Sciences and Education, Board on Health Sciences Policy, Committee on population, committee on the use of race, ethnicity, and ancestry as population descriptors in genomics research. Using population descriptors in genetics and genomics research: a new framework for an evolving field. Washington (DC): National Academies Press (US); 2023.

  45. Durand EY, Do CB, Mountain JL, Macpherson JM Ancestry composition: a novel, efficient pipeline for ancestry deconvolution. bioRxiv [Preprint]. 2014.

  46. Hatoum AS, Colbert SMC, Johnson EC, Huggett SB, Deak JD, Pathak G, et al. Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders. Nat Ment Health. 2023;1:210–23.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Yin B, Wang X, Huang T, Jia J. Shared genetics and causality between decaffeinated coffee consumption and neuropsychiatric diseases: a large-scale genome-wide cross-trait analysis and mendelian randomization analysis. Front Psychiatry. 2022;13:910432.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Agrawal A, Chou YL, Carey CE, Baranger DAA, Zhang B, Sherva R, et al. Genome-wide association study identifies a novel locus for cannabis dependence. Mol Psychiatry. 2018;23:1293–302.

    Article  PubMed  Google Scholar 

  49. Sherva R, Wang Q, Kranzler H, Zhao H, Koesterer R, Herman A, et al. Genome-wide Association Study of cannabis dependence severity, novel risk variants, and shared genetic risks. JAMA Psychiatry. 2016;73:472–80.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Agrawal A, Lynskey MT, Hinrichs A, Grucza R, Saccone SF, Krueger R, et al. A genome-wide association study of DSM-IV cannabis dependence. Addict Biol. 2011;16:514–8.

    Article  PubMed  Google Scholar 

  51. Minica CC, Dolan CV, Hottenga JJ, Pool R, Genome of the Netherlands Consortium, Fedko IO, et al. Heritability, SNP- and gene-based analyses of cannabis use initiation and age at onset. Behav Genet. 2015;45:503–13.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Zhao Y, Han X, Zheng ZL. Analysis of the brain transcriptome for substance-associated genes: An update on large-scale genome-wide association studies. Addict Biol. 2023;28:e13332.

    Article  PubMed  Google Scholar 

  53. Greco LA, Reay WR, Dayas CV, Cairns MJ. Exploring opportunities for drug repurposing and precision medicine in cannabis use disorder using genetics. Addict Biol. 2023;28:e13313.

    Article  PubMed  Google Scholar 

  54. Carreras-Gallo N, Dwaraka VB, Caceres A, Smith R, Mendez TL, Went H, et al. Impact of tobacco, alcohol, and marijuana on genome-wide DNA methylation and its relationship with hypertension. Epigenetics. 2023;18:2214392.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Cheng W, Parker N, Karadag N, Koch E, Hindley G, Icick R, et al. The relationship between cannabis use, schizophrenia, and bipolar disorder: a genetically informed study. Lancet Psychiatry. 2023;10:441–51.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Greco LA, Reay WR, Dayas CV, Cairns MJ. Pairwise genetic meta-analyses between schizophrenia and substance dependence phenotypes reveals novel association signals with pharmacological significance. Transl Psychiatry. 2022;12:403.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Minica CC, Verweij KJH, van der Most PJ, Mbarek H, Bernard M, van Eijk KR, et al. Genome-wide association meta-analysis of age at first cannabis use. Addiction. 2018;113:2073–86.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Sey NYA, Hu B, Mah W, Fauni H, McAfee JC, Rajarajan P, et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat Neurosci. 2020;23:583–93.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Barbeira, Dickinson AN, Bonazzola SP, Zheng R, Wheeler HE J, Torres JM, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018;9:1825.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics Consortium. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.

    Article  PubMed  PubMed Central  Google Scholar 

  61. All of Us Research Program Genomics Investigators. Genomic data in the All of Us research program. Nature. 2024;627:340–6.

    Article  Google Scholar 

  62. All of Us Research Program Investigators, Denny JC, Rutter JL, Goldstein DB, Philippakis A, Smoller JW, et al. The “All of Us” research program. N Engl J Med. 2019;381:668–76.

    Article  Google Scholar 

  63. Ge T, Chen CY, Ni Y, Feng YA, Smoller JW. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun. 2019;10:1776.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Lee SH, Goddard ME, Wray NR, Visscher PM. A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012;36:214–24.

    Article  PubMed  Google Scholar 

  65. Roden DM, Pulley JM, Basford MA, Bernard GR, Clayton EW, Balser JR, et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther. 2008;84:362–9.

    Article  PubMed  Google Scholar 

  66. Dennis J, Sealock J, Levinson RT, Farber-Eger E, Franco J, Fong S, et al. Genetic risk for major depressive disorder and loneliness in sex-specific associations with coronary artery disease. Mol Psychiatry. 2021;26:4254–64.

    Article  PubMed  Google Scholar 

  67. Fogel AI, Akins MR, Krupp AJ, Stagi M, Stein V, Biederer T. SynCAMs organize synapses through heterophilic adhesion. J Neurosci. 2007;27:12516–30.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Yan X, Wang Z, Schmidt V, Gauert A, Willnow TE, Heinig M, et al. Cadm2 regulates body weight and energy homeostasis in mice. Mol Metab. 2018;8:180–8.

    Article  PubMed  Google Scholar 

  69. Tyler RE, Besheer J, Joffe ME. Advances in translating mGlu(2) and mGlu(3) receptor selective allosteric modulators as breakthrough treatments for affective disorders and alcohol use disorder. Pharmacol Biochem Behav. 2022;219:173450.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Karlsson Linner R, Mallard TT, Barr PB, Sanchez-Roige S, Madole JW, Driver MN, et al. Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. Nat Neurosci. 2021;24:1367–76.

    Article  PubMed  Google Scholar 

  71. Agrawal A, Budney AJ, Lynskey MT. The co-occurring use and misuse of cannabis and tobacco: a review. Addiction. 2012;107:1221–33.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Galimberti M, Levey DF, Deak JD, Zhou H, Stein MB, Gelernter J. Genetic influences and causal pathways shared between cannabis use disorder and other substance use traits. Mol Psychiatry. 2024.

  73. Mallard TT, Savage JE, Johnson EC, Huang Y, Edwards AC, Hottenga JJ, et al. Item-Level Genome-Wide Association Study of the alcohol use disorders identification test in three population-based cohorts. Am J Psychiatry. 2022;179:58–70.

    Article  PubMed  Google Scholar 

  74. Kranzler HR, Zhou H, Kember RL, Vickers Smith R, Justice AC, Damrauer S, et al. Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nat Commun. 2019;10:1499.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Zhou H, Kember RL, Deak JD, Xu H, Toikumo S, Yuan K, et al. Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals. Nat Med. 2023;29:3184–92.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Sanchez-Roige S, Palmer AA, Fontanillas P, Elson SL, 23andMe Research Team, Substance Use Disorder Working Group of the Psychiatric Genomics Consortium. et al. Genome-Wide Association Study meta-analysis of the alcohol use disorders identification test (AUDIT) in two population-based cohorts. Am J Psychiatry. 2019;176:107–18.

    Article  PubMed  Google Scholar 

  77. Savage JE, Barr PB, Phung T, Lee YH, Zhang Y, Ge T, et al. Genetic heterogeneity across dimensions of alcohol use behaviors. medRxiv. 2023.2012.2026.23300537. 2023.

  78. Saunders GRB, Wang X, Chen F, Jang SK, Liu M, Wang C, et al. Genetic diversity fuels gene discovery for tobacco and alcohol use. Nature. 2022;612:720–4.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Trubetskoy V, Pardinas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604:502–8.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Lam M, Chen CY, Li Z, Martin AR, Bryois J, Ma X, et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat Genet. 2019;51:1670–8.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Pardinas AF, Holmans P, Pocklington AJ, Escott-Price V, Ripke S, Carrera N, et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet. 2018;50:381–9.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Li Z, Chen J, Yu H, He L, Xu Y, Zhang D, et al. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nat Genet. 2017;49:1576–83.

    Article  PubMed  Google Scholar 

  83. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.

    Article  PubMed Central  Google Scholar 

  84. Nagel M, Watanabe K, Stringer S, Posthuma D, van der Sluis S. Item-level analyses reveal genetic heterogeneity in neuroticism. Nat Commun. 2018;9:905.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Luciano M, Hagenaars SP, Davies G, Hill WD, Clarke TK, Shirali M, et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat Genet. 2018;50:6–11.

    Article  PubMed  Google Scholar 

  86. Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet. 2018;50:1112–21.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Hysi PG, Choquet H, Khawaja AP, Wojciechowski R, Tedja MS, Yin J, et al. Meta-analysis of 542,934 subjects of European ancestry identifies new genes and mechanisms predisposing to refractive error and myopia. Nat Genet. 2020;52:401–7.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Tedja MS, Wojciechowski R, Hysi PG, Eriksson N, Furlotte NA, Verhoeven VJM, et al. Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error. Nat Genet. 2018;50:834–48.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Watanabe K, Jansen PR, Savage JE, Nandakumar P, Wang X, 23andMe Research Team. et al. Genome-wide meta-analysis of insomnia prioritizes genes associated with metabolic and psychiatric pathways. Nat Genet. 2022;54:1125–32.

    Article  PubMed  Google Scholar 

  90. Di Menna L, Joffe ME, Iacovelli L, Orlando R, Lindsley CW, Mairesse J, et al. Functional partnership between mGlu3 and mGlu5 metabotropic glutamate receptors in the central nervous system. Neuropharmacology. 2018;128:301–13.

    Article  PubMed  Google Scholar 

  91. Ibrahim KS, Abd-Elrahman KS, El Mestikawy S, Ferguson SSG. Targeting vesicular glutamate transporter machinery: implications on metabotropic glutamate receptor 5 signaling and behavior. Mol Pharmacol. 2020;98:314–27.

    Article  PubMed  Google Scholar 

  92. Jung KM, Mangieri R, Stapleton C, Kim J, Fegley D, Wallace M, et al. Stimulation of endocannabinoid formation in brain slice cultures through activation of group I metabotropic glutamate receptors. Mol Pharmacol. 2005;68:1196–202.

    Article  PubMed  Google Scholar 

  93. GTEX portal. https://www.gtexportal.org/home/. Accessed 2024.

  94. Schoeler T, Speed D, Porcu E, Pirastu N, Pingault JB, Kutalik Z. Participation bias in the UK Biobank distorts genetic associations and downstream analyses. Nat Hum Behav. 2023;7:1216–27.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Deak JD, Levey DF, Wendt FR, Zhou H, Galimberti M, Kranzler HR, et al. Genome-wide investigation of maximum habitual alcohol intake in us veterans in relation to alcohol consumption traits and alcohol use disorder. JAMA Netw Open. 2022;5:e2238880.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Pasman JA, Demange PA, Guloksuz S, Willemsen AHM, Abdellaoui A, Ten Have M, et al. Genetic risk for smoking: disentangling interplay between genes and socioeconomic status. Behav Genet. 2022;52:92–107.

    Article  PubMed  Google Scholar 

  97. Xu K, Li B, McGinnis KA, Vickers-Smith R, Dao C, Sun N, et al. Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals. Nat Commun. 2020;11:5302.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Zhou H, Sealock JM, Sanchez-Roige S, Clarke TK, Levey DF, Cheng Z, 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. 2020;23:809–18.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Cai N, Revez JA, Adams MJ, Andlauer TFM, Breen G, Byrne EM, et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nat Genet. 2020;52:437–47.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Evangelou E, Gao H, Chu C, Ntritsos G, Blakeley P, Butts AR, et al. New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders. Nat Hum Behav. 2019;3:950–61.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Zhong VW, Kuang A, Danning RD, Kraft P, van Dam RM, Chasman DI, et al. A genome-wide association study of bitter and sweet beverage consumption. Hum Mol Genet. 2019;28:2449–57.

    Article  PubMed  PubMed Central  Google Scholar 

  102. Sanchez-Roige S, Fontanillas P, Elson SL, Gray JC, de Wit H, MacKillop J, et al. Genome-wide association studies of impulsive personality traits (BIS-11 and UPPS-P) and drug experimentation in up to 22,861 adult research participants identify loci in the CACNA1I and CADM2 genes. J Neurosci. 2019;39:2562–72.

    PubMed  PubMed Central  Google Scholar 

  103. Karlsson Linner R, Biroli P, Kong E, Meddens SFW, Wedow R, Fontana MA, et al. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat Genet. 2019;51:245–57.

    Article  PubMed  Google Scholar 

  104. Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, 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. 2019;51:237–44.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Erzurumluoglu AM, Liu M, Jackson VE, Barnes DR, Datta G, Melbourne CA, et al. Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. Mol Psychiatry. 2020;25:2392–409.

    Article  PubMed  Google Scholar 

  106. Kichaev G, Bhatia G, Loh PR, Gazal S, Burch K, Freund MK, et al. Leveraging polygenic functional enrichment to improve GWAS power. Am J Hum Genet. 2019;104:65–75.

    Article  PubMed  Google Scholar 

  107. Clifton EAD, Perry JRB, Imamura F, Lotta LA, Brage S, Forouhi NG, et al. Genome-wide association study for risk taking propensity indicates shared pathways with body mass index. Commun Biol. 2018;1:36.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Clarke TK, Adams MJ, Davies G, Howard DM, Hall LS, Padmanabhan S, 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. 2017;22:1376–84.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Brazel DM, Jiang Y, Hughey JM, Turcot V, Zhan X, Gong J, et al. Exome chip meta-analysis fine maps causal variants and elucidates the genetic architecture of rare coding variants in smoking and alcohol use. Biol Psychiatry. 2019;85:946–55.

    Article  PubMed  Google Scholar 

  110. Baselmans B, Hammerschlag AR, Noordijk S, Ip H, van der Zee M, de Geus E, et al. The genetic and neural substrates of externalizing behavior. Biol Psychiatry Glob Open Sci. 2022;2:389–99.

    Article  PubMed  Google Scholar 

  111. Strawbridge RJ, Ward J, Cullen B, Tunbridge EM, Hartz S, Bierut L, et al. Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort. Transl Psychiatry. 2018;8:39.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Mills MC, Tropf FC, Brazel DM, van Zuydam N, Vaez A, eQTLGen Consortium. et al. Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour. Nat Hum Behav. 2021;5:1717–30.

    Article  PubMed  PubMed Central  Google Scholar 

  113. Johnson EC, Sanchez-Roige S, Acion L, Adams MJ, Bucholz KK, Chan G, et al. Polygenic contributions to alcohol use and alcohol use disorders across population-based and clinically ascertained samples. Psychol Med. 2021;51:1147–56.

    Article  PubMed  Google Scholar 

  114. Sun Y, Chang S, Wang F, Sun H, Ni Z, Yue W, et al. Genome-wide association study of alcohol dependence in male Han Chinese and cross-ethnic polygenic risk score comparison. Transl Psychiatry. 2019;9:249.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Lai D, Wetherill L, Bertelsen S, Carey CE, Kamarajan C, Kapoor M, et al. Genome-wide association studies of alcohol dependence, DSM-IV criterion count and individual criteria. Genes Brain Behav. 2019;18:e12579.

    Article  PubMed  Google Scholar 

  116. Chang LH, Couvy-Duchesne B, Liu M, Medland SE, Verhulst B, Benotsch EG, et al. Association between polygenic risk for tobacco or alcohol consumption and liability to licit and illicit substance use in young Australian adults. Drug Alcohol Depend. 2019;197:271–9.

    Article  PubMed  PubMed Central  Google Scholar 

  117. Allegrini AG, Verweij KJH, Abdellaoui A, Treur JL, Hottenga JJ, Willemsen G, et al. Genetic vulnerability for smoking and cannabis use: associations with E-cigarette and water pipe use. Nicotine Tob Res. 2019;21:723–30.

    Article  PubMed  Google Scholar 

  118. Hodgson K, Coleman JRI, Hagenaars SP, Purves KL, Glanville K, Choi SW, et al. Cannabis use, depression and self-harm: phenotypic and genetic relationships. Addiction. 2020;115:482–92.

    Article  PubMed  Google Scholar 

  119. Smeland OB, Andreassen OA. Polygenic risk scores in psychiatry - large potential but still limited clinical utility. Eur Neuropsychopharmacol. 2021;51:68–70.

    Article  PubMed  Google Scholar 

  120. Kember RL, Hartwell EE, Xu H, Rotenberg J, Almasy L, Zhou H, et al. Phenome-wide association analysis of substance use disorders in a deeply phenotyped sample. Biol Psychiatry. 2023;93:536–45.

    Article  PubMed  Google Scholar 

  121. Johnson EC, Hatoum AS, Deak JD, Polimanti R, Murray RM, Edenberg HJ, et al. The relationship between cannabis and schizophrenia: a genetically informed perspective. Addiction. 2021;116:3227–34.

    Article  PubMed  PubMed Central  Google Scholar 

  122. Reginsson GW, Ingason A, Euesden J, Bjornsdottir G, Olafsson S, Sigurdsson E, et al. Polygenic risk scores for schizophrenia and bipolar disorder associate with addiction. Addict Biol. 2018;23:485–92.

    Article  PubMed  Google Scholar 

  123. Johnson EC, Austin-Zimmerman I, Thorpe HHA, Levey DF, Baranger DAA, Colbert SMC et al. Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking. Neuropsychopharmacology. 2024;49:1655–65.

  124. Hamilton I, Monaghan M. Cannabis and psychosis: are we any closer to understanding the relationship? Curr Psychiatry Rep. 2019;21:48.

    Article  PubMed  PubMed Central  Google Scholar 

  125. Di Forti M, Quattrone D, Freeman TP, Tripoli G, Gayer-Anderson C, Quigley H, et al. The contribution of cannabis use to variation in the incidence of psychotic disorder across Europe (EU-GEI): a multicentre case-control study. Lancet Psychiatry. 2019;6:427–36.

    Article  PubMed  PubMed Central  Google Scholar 

  126. Oluwoye O, Monroe-DeVita M, Burduli E, Chwastiak L, McPherson S, McClellan JM, et al. Impact of tobacco, alcohol and cannabis use on treatment outcomes among patients experiencing first episode psychosis: data from the national RAISE-ETP study. Early Interv Psychiatry. 2019;13:142–6.

    Article  PubMed  Google Scholar 

  127. Schoeler T, Monk A, Sami MB, Klamerus E, Foglia E, Brown R, et al. Continued versus discontinued cannabis use in patients with psychosis: a systematic review and meta-analysis. Lancet Psychiatry. 2016;3:215–25.

    Article  PubMed  Google Scholar 

  128. Durvasula A, Price AL Distinct explanations underlie gene-environment interactions in the UK Biobank. medRxiv 2023.

  129. Abdellaoui A, Dolan CV, Verweij KJH, Nivard MG. Gene-environment correlations across geographic regions affect genome-wide association studies. Nat Genet. 2022;54:1345–54.

    Article  PubMed  PubMed Central  Google Scholar 

  130. Thorpe HHA, Fontanillas P, Pham BK, Meredith JJ, Jennings MV, Courchesne-Krak NS, et al. Genome-wide association studies of coffee intake in UK/US participants of European ancestry uncover cohort-specific genetic associations. Neuropsychopharmacology. 2024;49:1609–18.

  131. Jang SK, Saunders G, Liu M, andMe Research T, Jiang Y, Liu DJ, et al. Genetic correlation, pleiotropy, and causal associations between substance use and psychiatric disorder. Psychol Med. 2022;52:968–78.

    Article  PubMed  Google Scholar 

  132. Chang LH, Ong JS, An J, Verweij KJH, Vink JM, Pasman J, et al. Investigating the genetic and causal relationship between initiation or use of alcohol, caffeine, cannabis and nicotine. Drug Alcohol Depend. 2020;210:107966.

    Article  PubMed  Google Scholar 

  133. Abdellaoui A, Smit DJA, van den Brink W, Denys D, Verweij KJH. Genomic relationships across psychiatric disorders including substance use disorders. Drug Alcohol Depend. 2021;220:108535.

    Article  PubMed  Google Scholar 

  134. Waldman ID, Poore HE, Luningham JM, Yang J. Testing structural models of psychopathology at the genomic level. World Psychiatry. 2020;19:350–9.

    Article  PubMed  PubMed Central  Google Scholar 

  135. Poore HE, Hatoum A, Mallard TT, Sanchez-Roige S, Waldman ID, Palmer AA, et al. A multivariate approach to understanding the genetic overlap between externalizing phenotypes and substance use disorders. Addict Biol. 2023;28:e13319.

    Article  PubMed  Google Scholar 

  136. Kayir H, Ruffolo J, McCunn P, Khokhar JY. The relationship between cannabis, cognition, and schizophrenia: it’s complicated. Curr Top Behav Neurosci. 2023;63:437–61.

    Article  PubMed  Google Scholar 

  137. Farrelly KN, Wardell JD, Marsden E, Scarfe ML, Najdzionek P, Turna J, et al. The impact of recreational cannabis legalization on cannabis use and associated outcomes: a systematic review. Subst Abuse. 2023;17:11782218231172054.

    PubMed  PubMed Central  Google Scholar 

  138. Fergusson DM, Boden JM, Horwood LJ. Psychosocial sequelae of cannabis use and implications for policy: findings from the Christchurch Health and Development Study. Soc Psychiatry Psychiatr Epidemiol. 2015;50:1317–26.

    Article  PubMed  Google Scholar 

  139. van der Pol P, Liebregts N, de Graaf R, Korf DJ, van den Brink W, van Laar M. Predicting the transition from frequent cannabis use to cannabis dependence: a three-year prospective study. Drug Alcohol Depend. 2013;133:352–9.

    Article  PubMed  Google Scholar 

  140. Walden N, Earleywine M. How high: quantity as a predictor of cannabis-related problems. Harm Reduct J. 2008;5:20.

    Article  PubMed  PubMed Central  Google Scholar 

  141. Zeisser C, Thompson K, Stockwell T, Duff C, Chow C, Vallance K, et al. A ‘standard joint’? The role of quantity in predicting cannabis-related problems. Addiction Research & Theory. 2012;20:82–92.

    Article  Google Scholar 

  142. Substance Abuse and Mental Health Services Administration. Key substance use and mental health indicators in the United States: Results from the 2022 National Survey on Drug Use and Health. Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration; 2022.

    Google Scholar 

  143. Althubaiti A. Information bias in health research: definition, pitfalls, and adjustment methods. J Multidiscip Healthc. 2016;9:211–7.

    Article  PubMed  PubMed Central  Google Scholar 

  144. Jeffers AM, Glantz S, Byers A, Keyhani S. Sociodemographic characteristics associated with and prevalence and frequency of cannabis use among adults in the US. JAMA Netw Open. 2021;4:e2136571.

    Article  PubMed  PubMed Central  Google Scholar 

  145. Karriker-Jaffe KJ. Neighborhood socioeconomic status and substance use by U.S. adults. Drug Alcohol Depend. 2013;133:212–21.

    Article  PubMed  PubMed Central  Google Scholar 

  146. Atkinson EG, Bianchi SB, Ye GY, Martinez-Magana JJ, Tietz GE, Montalvo-Ortiz JL, et al. Cross-ancestry genomic research: time to close the gap. Neuropsychopharmacology. 2022;47:1737–8.

    Article  PubMed  PubMed Central  Google Scholar 

  147. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51:584–91.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

HHAT is funded by a Canadian Institute of Health Research (CIHR) Postdoctoral Fellowship (#491556). JYK is funded by a CIHR Canada Research Chair in Translational Neuropsychopharmacology. PF, the 23andMe Research Team, and SLE are employed by 23andMe, Inc. AAP is supported by NIDA (P50DA037844 and P30DA060810). SSR is funded through the National Institute on Drug Abuse (NIDA DP1DA054394) and the Tobacco-Related Disease Research Program (T32IR5226). We would like to thank the research participants and employees of 23andMe for making this work possible. Participants provided informed consent and volunteered to participate in the research online, under a protocol approved by the external AAHRPP-accredited Institutional Review Board (IRB), Ethical & Independent (E&I) Review Services. As of 2022, E&I Review Services is part of Salus IRB (https://www.versiticlinicaltrials.org/salusirb). The following members of the 23andMe Research Team contributed to this study: Stella Aslibekyan, Adam Auton, Elizabeth Babalola, Robert K. Bell, Jessica Bielenberg, Katarzyna Bryc, Emily Bullis, Daniella Coker, Gabriel Cuellar Partida, Devika Dhamija, Sayantan Das, Teresa Filshtein, Kipper Fletez-Brant, Will Freyman, Karl Heilbron, Pooja M. Gandhi, Karl Heilbron, Barry Hicks, David A. Hinds, Ethan M. Jewett, Yunxuan Jiang, Katelyn Kukar, Keng-Han Lin, Maya Lowe, Jey C. McCreight, Matthew H. McIntyre, Steven J. Micheletti, Meghan E. Moreno, Joanna L. Mountain, Priyanka Nandakumar, Elizabeth S. Noblin, Jared O’Connell, Aaron A. Petrakovitz, G. David Poznik, Morgan Schumacher, Anjali J. Shastri, Janie F. Shelton, Jingchunzi Shi, Suyash Shringarpure, Vinh Tran, Joyce Y. Tung, Xin Wang, Wei Wang, Catherine H. Weldon, Peter Wilton, Alejandro Hernandez, Corinna Wong, Christophe Toukam Tchakouté. We would also like to thank The Externalizing Consortium for sharing the GWAS summary statistics of externalizing. The Externalizing Consortium: Principal Investigators: Danielle M. Dick, Philipp Koellinger, K. Paige Harden, Abraham A. Palmer. Lead Analysts: Richard Karlsson Linnér, Travis T. Mallard, Peter B. Barr, Sandra Sanchez-Roige. Significant Contributors: Irwin D. Waldman. The Externalizing Consortium has been supported by the National Institute on Alcohol Abuse and Alcoholism (R01AA015416 -administrative supplement), and the National Institute on Drug Abuse (R01DA050721). Additional funding for investigator effort has been provided by K02AA018755, U10AA008401, P50AA022537, as well as a European Research Council Consolidator Grant (647648 EdGe to Koellinger). The content is solely the responsibility of the authors and does not necessarily represent the official views of the above funding bodies. The Externalizing Consortium would like to thank the following groups for making the research possible: 23andMe, Add Health, Vanderbilt University Medical Center’s BioVU, Collaborative Study on the Genetics of Alcoholism (COGA), the Psychiatric Genomics Consortium’s Substance Use Disorders working group, UK10K Consortium, UK Biobank, and Philadelphia Neurodevelopmental Cohort. We gratefully acknowledge All of Us participants for their contributions, without whom this research would not have been possible. We also thank the National Institutes of Health’s All of Us Research Program for making available the participant data examined in this study. The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276.

Author information

Authors and Affiliations

  1. Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada

    Hayley H. A. Thorpe & Jibran Y. Khokhar

  2. 23andMe, Inc., Sunnyvale, CA, USA

    Pierre Fontanillas & Sarah L. Elson

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

    John J. Meredith, Mariela V. Jennings, Renata B. Cupertino, Shreya R. Pakala, Abraham A. Palmer & Sandra Sanchez-Roige

  4. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

    Lea K. Davis

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

    Lea K. Davis & Sandra Sanchez-Roige

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

    Lea K. Davis

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

    Emma C. Johnson

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

    Abraham A. Palmer & Sandra Sanchez-Roige

Authors
  1. Hayley H. A. Thorpe
    View author publications

    Search author on:PubMed Google Scholar

  2. Pierre Fontanillas
    View author publications

    Search author on:PubMed Google Scholar

  3. John J. Meredith
    View author publications

    Search author on:PubMed Google Scholar

  4. Mariela V. Jennings
    View author publications

    Search author on:PubMed Google Scholar

  5. Renata B. Cupertino
    View author publications

    Search author on:PubMed Google Scholar

  6. Shreya R. Pakala
    View author publications

    Search author on:PubMed Google Scholar

  7. Sarah L. Elson
    View author publications

    Search author on:PubMed Google Scholar

  8. Jibran Y. Khokhar
    View author publications

    Search author on:PubMed Google Scholar

  9. Lea K. Davis
    View author publications

    Search author on:PubMed Google Scholar

  10. Emma C. Johnson
    View author publications

    Search author on:PubMed Google Scholar

  11. Abraham A. Palmer
    View author publications

    Search author on:PubMed Google Scholar

  12. Sandra Sanchez-Roige
    View author publications

    Search author on:PubMed Google Scholar

Consortia

on behalf of the 23andMe Research Team

  • Pierre Fontanillas
  •  & Sarah L. Elson

Contributions

SSR and AAP conceived the idea. PF and SLE contributed formal analyses and curation of 23andMe data. HHAT contributed to formal analyses, investigation, and data visualization. contributed to formal data analysis and data visualization. JJM, MVJ, RBC, and SP contributed to formal analyses. HHAT and SSR wrote the manuscript. HHAT, PF, JJM, MVJ, RBC, SP, SLE, JYK, LKD, ECJ, AAP and SSR reviewed and edited the manuscript.

Corresponding author

Correspondence to Sandra Sanchez-Roige.

Ethics declarations

Competing interests

PF, the 23andMe Research Team, and SLE were employed by 23andMe, Inc. PF and SLE hold stock or stock options in 23andMe, Inc. The remaining authors have nothing to disclose.

Ethics statement

All methods were performed in accordance with the relevant guidelines and regulations. Participants provided informed consent and volunteered to participate in research online under a protocol approved by the external AAHRPP-accredited Institutional Review Board (IRB), Ethical & Independent (E&I) Review Services.

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

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thorpe, H.H.A., Fontanillas, P., Meredith, J.J. et al. Genome-wide association studies of lifetime and frequency of cannabis use in 131,895 individuals. Mol Psychiatry (2025). https://doi.org/10.1038/s41380-025-03219-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41380-025-03219-2

Search

Advanced search

Quick links