GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia.
- Authors
- Pasman, JoΓ«lle A; Verweij, Karin J H; Gerring, Zachary; Stringer, Sven; Sanchez-Roige, Sandra; Treur, Jorien L; Abdellaoui, Abdel; Nivard, Michel G; Baselmans, Bart M L; Ong, Jue-Sheng; Ip, Hill F; van der Zee, Matthijs D; Bartels, Meike; Day, Felix R; Fontanillas, Pierre; Elson, Sarah L; 23andMe Research Team; de Wit, Harriet; Davis, Lea K; MacKillop, James; Substance Use Disorders Working Group of the Psychiatric Genomics Consortium; International Cannabis Consortium; Derringer, Jaime L; Branje, Susan J T; Hartman, Catharina A; Heath, Andrew C; van Lier, Pol A C; Madden, Pamela A F; MΓ€gi, Reedik; Meeus, Wim; Montgomery, Grant W; Oldehinkel, A J; Pausova, Zdenka; Ramos-Quiroga, Josep A; Paus, Tomas; Ribases, Marta; Kaprio, Jaakko; Boks, Marco P M; Bell, Jordana T; Spector, Tim D; Gelernter, Joel; Boomsma, Dorret I; Martin, Nicholas G; MacGregor, Stuart; Perry, John R B; Palmer, Abraham A; Posthuma, Danielle; MunafΓ², Marcus R; Gillespie, Nathan A; Derks, Eske M; Vink, Jacqueline M
- Year
- 2018
- Journal
- Nature neuroscience
- PMID
- 30150663
- DOI
- 10.1038/s41593-018-0206-1
- PMCID
- PMC6386176
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 (Nβ=β184,765), 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.
a) QQ-plot of the distribution of the βlog 10(p-values) observed for the SNP associations with lifetime cannabis use against those expected under the null hypothesisExpected βlog 10(p-values) under the null hypothesis are indicated by the red line. Genomic inflation is indicated with Ξ» in the plot. There was no evidence for population stratification (LD score regression b0=1.00, SE=0.007).b) Manhattan plot for the SNP-based GWAS meta-analysisResults are based on N=184,764 individuals and NSNPs=11,733,371 SNPs. The SNP with the lowest p-value per independent (R2<0.1, window size 250 kb) genome-wide significant locus is annotated (red circle with rs-number). The red line represents the conventional genome-wide significance threshold of p<5e-08. The statistical test comprised linear regression; significance was tested two-sided.
Regional plots of the genome-wide significant SNPsUnderlined in yellow are the genes that were significant in the gene-based test (tested two-sided; p<2.74e-06, Bonferroni corrected p-value threshold of p<0.05 adjusted for 18,293 tests); underlined in green the genes that were identified in the S-PrediXcan analysis only (p<1.92e-07, Bonferroni corrected p-value threshold of p<0.05 adjusted for 259,825 tests). Colors of the dots indicate the level of LD (blue for low and red for high LD) with the lead SNP (purple; independent defined as R2<0.1, window size 250 kb).
a) QQ-plot of the distribution of the βlog10(p-values) for the gene-based association with lifetime cannabis use against those expected under the null hypothesisExpected βlog 10(p-values) under the null hypothesis are indicated by the red line. Genomic inflation is indicated with Ξ». The gene-based test was performed in MAGMA, which uses multiple regression (tested two-sided).b) Manhattan plot for the gene-based test of associationThe red line represents the genome-wide significance threshold of p<2.74e-06, (Bonferroni corrected p-value threshold of p<0.05 adjusted for 18,293 tests; NSNPs=5,710,956 were mapped to at least one gene). The top-gene (with the lowest p-value) per locus is annotated (red circle with gene symbol).
Genetic overlap between lifetime cannabis use and other phenotypesBlue dots represent point estimates of the genetic correlation, blue error bars represent 95% confidence intervals and red asterisks indicate significant associations after correction for multiple testing (two-sided p<0.002, Bonferroni corrected p-value threshold of p<0.05 adjusted for 25 tests).MDD=Major Depressive Disorder; ADHD=Attention Deficit Hyperactivity Disorder; BMI=Body Mass Index
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| 20 | Online Methods β Samples | a private institutional review board. The sample included 55.3% females and the age ranged betweenβ¦ |
| 21 | Online Methods β Phenotype and covariates | For all participants, self-report data were available on whether the participant had ever usedβ¦ |
| 22 | Online Methods β Genotyping and imputation | Genotyping was performed on various genotyping platforms and standard quality control checks wereβ¦ |
| 23 | Online Methods β Statistics | All statistical tests were two-sided and -unless stated otherwise- we used the conventional p-valueβ¦ |
| 24 | Online Methods β Statistics β Genome-wide association analyses and meta-analysis | We conducted the GWASs in 23andMe and UK-Biobank samples separately. Associations between the binaryβ¦ |
| 25 | Online Methods β Statistics β Genome-wide association analyses and meta-analysis | We applied genomic control to the three GWAS files prior to meta-analysis. Inflation due toβ¦ |
| 26 | Online Methods β Statistics β Genome-wide association analyses and meta-analysis | Manhattan and QQ-plots for the GWAS, meta-analysis, and gene-based test results were created usingβ¦ |
| 27 | Online Methods β Statistics β Gene-based test of association | Testing associations on the level of protein-coding genes can be more biologically meaningful and isβ¦ |
| 28 | Online Methods β Statistics β Identification of genes with differential expression levels between cannabis users and non-users | We used S-PrediXcan to integrate eQTL (expression quantitative trait loci) information with our GWASβ¦ |
| 29 | Online Methods β Statistics β Identification of genes with differential expression levels between cannabis users and non-users | We used the GTExPortal (https://www.gtexportal.org/home/; GTEx Analysis Release V7)58 to obtain geneβ¦ |
| 30 | Online Methods β Statistics β SNP-based heritability analysis | The proportion of variance in liability to cannabis use that could be explained by the aggregatedβ¦ |
| 31 | Online Methods β Statistics β Genetic correlations with other substances and mental health phenotypes | We used cross-trait LD-Score regression62 to estimate the genetic correlation between lifetimeβ¦ |
| 32 | Online Methods β Statistics β Causal association between cannabis use and schizophrenia: Two-sample Mendelian randomization | We performed two-sample Mendelian randomization analyses (MR)22 to examine whether there wasβ¦ |
| 33 | Online Methods β Statistics β Causal association between cannabis use and schizophrenia: Two-sample Mendelian randomization | MR utilizes genetic variants strongly associated with an exposure variable as an βinstrumentβ toβ¦ |
| 34 | Online Methods β Statistics β Causal association between cannabis use and schizophrenia: Two-sample Mendelian randomization | Bi-directional causal effects were tested between lifetime cannabis use and schizophrenia. We usedβ¦ |
| 35 | Online Methods β Statistics β Causal association between cannabis use and schizophrenia: Two-sample Mendelian randomization | Genetic variants were pruned (R2<0.001) and the remaining genetic variants (or proxies [R2β₯0.8]β¦ |
| 36 | Online Methods β Statistics β Causal association between cannabis use and schizophrenia: Two-sample Mendelian randomization | Evidence for both a gene-exposure and a gene-outcome association suggests a causal effect, providedβ¦ |
| 37 | Online Methods β Statistics β Causal association between cannabis use and schizophrenia: Two-sample Mendelian randomization | exposure) does not correlate with the effect the instrument has on the outcome (i.e. the InSIDEβ¦ |
| 38 | Online Methods β Statistics β Causal association between cannabis use and schizophrenia: Two-sample Mendelian randomization | variants while accounting for LD between these variants64. Because GSMR accounts for LD, geneticβ¦ |
| 39 | Online Methods β Statistics β Causal association between cannabis use and schizophrenia: Two-sample Mendelian randomization | To calculate variance explained (R2) by the instrument, first we selected a single SNP to obtain anβ¦ |
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| Resource profile and user guide of the Polygenic Index Repository. | Becker J et al. | β | 2021 | β |
| Sex differences in the genetic regulation of the blood transcriptome response to glucocorticoid receptor activation. | Moore SR et al. | β | 2021 | β |
| Shared Genetic Liability and Causal Associations Between Major Depressive Disorder and Cardiovascular Diseases. | Zhang F et al. | β | 2021 | β |
| Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies. | Munn-Chernoff MA et al. | β | 2021 | β |
| Sleep deficits and cannabis use behaviors: an analysis of shared genetics using linkage disequilibrium score regression and polygenic risk prediction. | Winiger EA et al. | β | 2021 | β |
| Smoking is significantly associated with increased risk of COVID-19 and other respiratory infections. | Rosoff DB et al. | β | 2021 | β |
| Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. | Skrivankova VW et al. | β | 2021 | β |
| Testing the association between tobacco and cannabis use and cognitive functioning: Findings from an observational and Mendelian randomization study. | Mahedy L et al. | β | 2021 | β |
| The intermediary role of adolescent temperamental and behavioral traits on the prospective associations between polygenic risk and cannabis use among young adults of European Ancestry. | Brick LA et al. | β | 2021 | β |
| The phenomics and genetics of addictive and affective comorbidity in opioid use disorder. | Freda PJ et al. | β | 2021 | β |
| The relationship between cannabis and schizophrenia: a genetically informed perspective. | Johnson EC et al. | β | 2021 | β |
| Transcriptome-wide association study of treatment-resistant depression and depression subtypes for drug repurposing. | Fabbri C et al. | β | 2021 | β |
| Understanding the genetics and neurobiological pathways behind addiction (Review). | Popescu A et al. | β | 2021 | β |
| Using Mendelian randomization analysis to better understand the relationship between mental health and substance use: a systematic review. | Treur JL et al. | β | 2021 | β |
| Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): II. Externalizing superspectrum. | Krueger RF et al. | β | 2021 | β |
| Adolescent cannabinoid exposure interacts with other risk factors in schizophrenia: A review of the evidence from animal models. | Dunn AL et al. | β | 2020 | β |
| A Family-Based Genome Wide Association Study of Externalizing Behaviors. | Barr PB et al. | β | 2020 | β |
| A large-scale genome-wide association study meta-analysis of cannabis use disorder. | Johnson EC et al. | β | 2020 | β |
| An analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in Alzheimer's disease. | Gerring ZF et al. | β | 2020 | β |
| An update on the role of common genetic variation underlying substance use disorders. | Johnson EC et al. | β | 2020 | β |
| Association between polygenic liability for schizophrenia and substance involvement: A nationwide population-based study in Taiwan. | Wang SH et al. | β | 2020 | β |
| Cannabis use and psychosis: a review of reviews. | Hasan A et al. | β | 2020 | β |
| Cannabis Use and the Risk for Psychosis and Affective Disorders. | Sideli L et al. | β | 2020 | β |
| Cannabis use, depression and self-harm: phenotypic and genetic relationships. | Hodgson K et al. | β | 2020 | β |
| Causal relationships between substance use and insomnia. | Pasman JA et al. | β | 2020 | β |
| Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism. | Matoba N et al. | β | 2020 | β |
| Deconstructing the neurobiology of cannabis use disorder. | Ferland JN et al. | β | 2020 | β |
| Does Cannabis Intake Protect Against Non-alcoholic Fatty Liver Disease? A Two-Sample Mendelian Randomization Study. | Wang X et al. | β | 2020 | β |
| Emerging phenotyping strategies will advance our understanding of psychiatric genetics. | Sanchez-Roige S et al. | β | 2020 | β |
| Exploring Phenotypic and Genetic Overlap Between Cannabis Use and Schizotypy. | Vaissiere J et al. | β | 2020 | β |
| Factors Moderating the Association Between Cannabis Use and Psychosis Risk: A Systematic Review. | van der Steur SJ et al. | β | 2020 | β |
| From apathy to addiction: Insights from neurology and psychiatry. | Kirschner M et al. | β | 2020 | β |
| Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals. | Xu K et al. | β | 2020 | β |
| Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. | Zhou H et al. | β | 2020 | β |
| Heterogeneity and Polygenicity in Psychiatric Disorders: A Genome-Wide Perspective. | Wendt FR et al. | β | 2020 | β |
| <i>Cannabis</i> Use Is Associated With Increased Levels of Soluble gp130 in Schizophrenia but Not in Bipolar Disorder. | Szabo A et al. | β | 2020 | β |
| Identification of a functional SNP rs7304782 at schizophrenia risk locus 12q24.31 and validation of its association with schiz ophrenia in Chinese populations. | Ma C et al. | β | 2020 | β |
| Identification of Functional Genetic Variants Associated With Alcohol Dependence and Related Phenotypes Using a High-Throughput Assay. | Thapa KS et al. | β | 2020 | β |
| Illicit drug use and the genetic overlap with Cannabis use. | Vink JM et al. | β | 2020 | β |
| Investigating the genetic and causal relationship between initiation or use of alcohol, caffeine, cannabis and nicotine. | Chang LH et al. | β | 2020 | β |
| Investigation of Schizophrenia with Human Induced Pluripotent Stem Cells. | Powell SK et al. | β | 2020 | β |
| Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium. | Polimanti R et al. | β | 2020 | β |
| Magnetic Resonance Spectroscopy in Adolescent Cannabis Users: Metabolites in the Anterior Cingulate Cortex Reflects Individual Differences in Personality Traits and can Affect Rehabilitation Compliance. | Zoccatelli G et al. | β | 2020 | β |
| Neural substrates underlying the negative impact of cannabinoid exposure during adolescence. | Molla HM et al. | β | 2020 | β |
| Onset of regular cannabis use and young adult insomnia: an analysis of shared genetic liability. | Winiger EA et al. | β | 2020 | β |
| Pairwise common variant meta-analyses of schizophrenia with other psychiatric disorders reveals shared and distinct gene and gene-set associations. | Reay WR et al. | β | 2020 | β |
| Possible therapeutic applications of cannabis in the neuropsychopharmacology field. | FernΓ‘ndez-Ruiz J et al. | β | 2020 | β |
| Post-GWAS analysis of six substance use traits improves the identification and functional interpretation of genetic risk loci. | Marees AT et al. | β | 2020 | β |
| Potential influence of socioeconomic status on genetic correlations between alcohol consumption measures and mental health. | Marees AT et al. | β | 2020 | β |
| Psychiatric comorbidities in Asperger syndrome are related with polygenic overlap and differ from other Autism subtypes. | GonzΓ‘lez-PeΓ±as J et al. | β | 2020 | β |
| Psychosis-Relevant Effects of Intravenous Delta-9-Tetrahydrocannabinol: A Mega Analysis of Individual Participant-Data from Human Laboratory Studies. | Ganesh S et al. | β | 2020 | β |
| Ranking evidence in substance use and addiction. | Reddon H et al. | β | 2020 | β |
| Recent Efforts to Dissect the Genetic Basis of Alcohol Use and Abuse. | Sanchez-Roige S et al. | β | 2020 | β |
| Sex and Strain Variation in Initial Sensitivity and Rapid Tolerance to Ξ9-Tetrahydrocannabinol. | Parks C et al. | β | 2020 | β |
| 'Standard THC units': a proposal to standardize dose across all cannabis products and methods of administration. | Freeman TP et al. | β | 2020 | β |
| Substance use: Interplay between polygenic risk and neighborhood environment. | Pasman JA et al. | β | 2020 | β |
| The Genetics of Externalizing Problems. | Barr PB et al. | β | 2020 | β |
| The polygenic architecture of schizophrenia - rethinking pathogenesis and nosology. | Smeland OB et al. | β | 2020 | β |
| Using genetic information to inform policy on cannabis. | Hines LA et al. | β | 2020 | β |
| Using polygenic scores for identifying individuals at increased risk of substance use disorders in clinical and population samples. | Barr PB et al. | β | 2020 | β |
| Ξ<sup>9</sup>-tetrahydrocannabinol and 2-AG decreases neurite outgrowth and differentially affects ERK1/2 and Akt signaling in hiPSC-derived cortical neurons. | Shum C et al. | β | 2020 | β |
| Association between polygenic risk for tobacco or alcohol consumption and liability to licit and illicit substance use in young Australian adults. | Chang LH et al. | β | 2019 | β |
| Associations between polygenic risk for tobacco and alcohol use and liability to tobacco and alcohol use, and psychiatric disorders in an independent sample of 13,999 Australian adults. | Chang LH et al. | β | 2019 | β |
| Cannabis and Psychosis Through the Lens of DSM-5. | Pearson NT et al. | β | 2019 | β |
| Cannabis and psychosis: triangulating the evidence. | Gage SH | β | 2019 | β |
| Cannabis legalization: Did we make a mistake? Update 2019 | Leyton M | β | 2019 | β |
| Exploring the relationship between polygenic risk for cannabis use, peer cannabis use and the longitudinal course of cannabis involvement. | Johnson EC et al. | β | 2019 | β |
| Exposure to cannabinoids can lead to persistent cognitive and psychiatric disorders. | Krebs MO et al. | β | 2019 | β |
| Genetic and environmental risk factors in the non-medical use of over-the-counter or prescribed analgesics, and their relationship to major classes of licit and illicit substance use and misuse in a population-based sample of young adult twins. | Gillespie NA et al. | β | 2019 | β |
| Genetic Variants Associated With Anxiety and Stress-Related Disorders: A Genome-Wide Association Study and Mouse-Model Study. | Meier SM et al. | β | 2019 | β |
| Genetic variation in CADM2 as a link between psychological traits and obesity. | Morris J et al. | β | 2019 | β |
| Genome-wide association study identifies loci associated with liability to alcohol and drug dependence that is associated with variability in reward-related ventral striatum activity in African- and European-Americans. | Wetherill L et al. | β | 2019 | β |
| Genome-wide association study implicates CHRNA2 in cannabis use disorder. | Demontis D et al. | β | 2019 | β |
| GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations. | Chen HH et al. | β | 2019 | β |
| High-potency cannabis and incident psychosis: correcting the causal assumption. | Gillespie NA et al. | β | 2019 | β |
| <i>De novo</i> Mutations From Whole Exome Sequencing in Neurodevelopmental and Psychiatric Disorders: From Discovery to Application. | Wang W et al. | β | 2019 | β |
| Implementing social justice in the transition from illicit to legal cannabis. | Adinoff B et al. | β | 2019 | β |
| Insights Into the Pathophysiology of Endocannabinoid Signaling in Schizophrenia. | Volk DW et al. | β | 2019 | β |
| Onset of regular cannabis use and adult sleep duration: Genetic variation and the implications of a predictive relationship. | Winiger EA et al. | β | 2019 | β |
| Psychosocial moderation of polygenic risk for cannabis involvement: the role of trauma exposure and frequency of religious service attendance. | Meyers JL et al. | β | 2019 | β |
| Systematic Review of Polygenic Gene-Environment Interaction in Tobacco, Alcohol, and Cannabis Use. | Pasman JA et al. | β | 2019 | β |
| The neuropsychopharmacology of cannabis: A review of human imaging studies. | Bloomfield MAP et al. | β | 2019 | β |
| Three-dimensional chromosome architecture and drug addiction. | Chitaman JM et al. | β | 2019 | β |
| Transcriptomic Characterization of the Human Insular Cortex and Claustrum. | Ibrahim C et al. | β | 2019 | β |
| Adiposity and Cardiometabolic Outcomes: What Can Meta-analyses of Mendelian Randomization Studies Contribute? | Wade KH et al. | β | 2018 | β |
| Relating addiction and psychiatric disorders. | Vink JM et al. | β | 2018 | β |
| Transcriptomic Characterization of the Human Habenula Highlights Drug Metabolism and the Neuroimmune System. | Le Foll B et al. | β | 2018 | β |