Genome-wide association study implicates CHRNA2 in cannabis use disorder.
- Authors
- Demontis, Ditte; Rajagopal, Veera Manikandan; Thorgeirsson, Thorgeir E; Als, Thomas D; Grove, Jakob; LeppÀlÀ, Kalle; Gudbjartsson, Daniel F; Pallesen, Jonatan; Hjorthøj, Carsten; Reginsson, Gunnar W; Tyrfingsson, Thorarinn; Runarsdottir, Valgerdur; Qvist, Per; Christensen, Jane Hvarregaard; Bybjerg-Grauholm, Jonas; Bækvad-Hansen, Marie; Huckins, Laura M; Stahl, Eli A; Timmermann, Allan; Agerbo, Esben; Hougaard, David M; Werge, Thomas; Mors, Ole; Mortensen, Preben Bo; Nordentoft, Merete; Daly, Mark J; Stefansson, Hreinn; Stefansson, Kari; Nyegaard, Mette; Børglum, Anders D
- Year
- 2019
- Journal
- Nature neuroscience
- PMID
- 31209380
- DOI
- 10.1038/s41593-019-0416-1
- PMCID
- PMC7596896
Cannabis is the most frequently used illicit psychoactive substance worldwide; around one in ten users become dependent. The risk for cannabis use disorder (CUD) has a strong genetic component, with twin heritability estimates ranging from 51 to 70%. Here we performed a genome-wide association study of CUD in 2,387 cases and 48,985 controls, followed by replication in 5,501 cases and 301,041 controls. We report a genome-wide significant risk locus for CUD (Pβ=β9.31βΓβ10) that replicates in an independent population (Pβ=β3.27βΓβ10, Pβ=β9.09βΓβ10). The index variant (rs56372821) is a strong expression quantitative trait locus for cholinergic receptor nicotinic Ξ±2 subunit (CHRNA2); analyses of the genetically regulated gene expression identified a significant association of CHRNA2 expression with CUD in brain tissue. At the polygenic level, analyses revealed a significant decrease in the risk of CUD with increased load of variants associated with cognitive performance. The results provide biological insights and inform on the genetic architecture of CUD.
Genome-wide association results from the CUD GWAS resultsA) Manhattan plot of the results from the GWAS of CUD (2,387 individuals with CUD and 48,985 controls). Results are from logistic regression and P-values are two-sided. The red horizontal line indicates the threshold for genome-wide significance (P=5x10β8). The index variant is highlighted as a green diamond and SNPs in high LD with the index SNP are marked in green. B) Quantile-quantile plot of the expected and observed P-values from GWAS of CUD (2,387 individuals with CUD and 48,985 controls). The blue line indicates the distribution under the null hypothesis and the shaded area indicates the 95% confidence band.
Association results for the geneomic region with the CUD risk locusRegional association plot of the local association results for the risk locus at chromosome 8 based analysis of 2,387 individuals with CUD and 48,985 controls. Results are from logistic regression and P-values are two-sided. The index variant (rs56372821) and additional three correlated genome-wide significant variants (LD with index variant: 0.2 < r2 < 0.7) are marked with letters (a-d), the triangle represents the two-sided P-value from meta-analysis (inverse variance weighted fixed effects model) with the replication cohort from deCODE (5,501 individuals with CUD and 301,041 controls). The location and orientation of the genes in the region and the local estimates of recombination rate is shown. The association P-value (p), odds ratio (or), minor allele frequency (maf) and imputation info-score (info) is presented in upper right corner. The horizontal green line represents the threshold for genome-wide significant (P=5x10β8).
Association of CHRNA2 expression with CUDAssociation of the imputed genetically regulated expression of CHRNA2 with CUD in three brain tissues with a valid model (cerebellar hemisphere, dorsolateret prefrontal contex and cerebellum). The two-side P-value from logistic regression for the association of gene expression with CUD (βlog10(PrediXcan:P-value) and two-side P-value from logistic regression from the CUD GWAS (βlog10(GWAS:P-value)) is given on the y-axis, with a red dotted line indicating statistical significance. Both analyses include 2,387 individuals with CUD and 48,985 controls. Chromosome position is given on the x-axis and the light grey lines indicate which SNPs that are included in the models used to predict gene expression. The thin red lines indicate genetic predictors that are genome-wide significantly associated with CUD.
Association of PRS with CUDPRSs was generated for phenotypes related to cognition, personality, psychiatric disorders, reproduction and smoking behavior based on summary statistics from 22 published GWASs. The variance explained by the scores (Nagelkerke-R2) is given on the x-axis and the P-value from logistic regression for association of the PRS with CUD on the y-axis (based on analyses of 2,387 individuals with CUD and 48,985 controls). The vertical blue line indicate statistical significance (P=0.0023; correcting for 22 test). In PRS analyses of psychiatric disorders, individuals having a diagnosis of the respective psychiatric disorder being analysed (ADHD, schizophrenia, depressive symptoms and major depressive disorder) were excluded in the CUD target sample.
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| A large-scale genome-wide association study meta-analysis of cannabis use disorder. | 2020 | 33096046 |
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| Attention deficit hyperactivity disorder symptoms and cannabis use after one year among students of the i-Share cohort. | Jean FAM et al. | β | 2022 | β |
| Chronic adolescent exposure to cannabis in mice leads to sex-biased changes in gene expression networks across brain regions. | Zuo Y et al. | β | 2022 | β |
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| Consensus paper of the WFSBP task force on cannabis, cannabinoids and psychosis. | D'Souza DC et al. | β | 2022 | β |
| Genomic-Analysis-Oriented Drug Repurposing in the Search for Novel Antidepressants. | Lesmana MHS et al. | β | 2022 | β |
| Identifying risk-thresholds for the association between frequency of cannabis use and development of cannabis use disorder: A systematic review and meta-analysis. | Robinson T et al. | β | 2022 | β |
| Lower-Risk Cannabis Use Guidelines (LRCUG) for reducing health harms from non-medical cannabis use: A comprehensive evidence and recommendations update. | Fischer B et al. | β | 2022 | β |
| Neurobiological Mechanisms of Nicotine Reward and Aversion. | Wills L et al. | β | 2022 | β |
| Prevalence and correlates of cannabis use disorder among Australians using cannabis products to treat a medical condition. | Mills L et al. | β | 2022 | β |
| Social Determinants of Inter-Individual Variability and Vulnerability: The Role of Dopamine. | Faure P et al. | β | 2022 | β |
| Strong and weak cross-inheritance of substance use disorders in a nationally representative sample. | Zhang H et al. | β | 2022 | β |
| The genetic aetiology of cannabis use: from twin models to genome-wide association studies and beyond. | Verweij KJH et al. | β | 2022 | β |
| The systems medicine of cannabinoids in pediatrics: the case for more pediatric studies. | O'Dell CP et al. | β | 2022 | β |
| What genes are differentially expressed in individuals with schizophrenia? A systematic review. | Merikangas AK et al. | β | 2022 | β |
| An exploration of the genetic epidemiology of non-suicidal self-harm and suicide attempt. | Russell AE et al. | β | 2021 | β |
| An integrative systems-based analysis of substance use: eQTL-informed gene-based tests, gene networks, and biological mechanisms. | Gerring ZF et al. | β | 2021 | β |
| Association Between Genetic Risk for Psychiatric Disorders and the Probability of Living in Urban Settings. | Maxwell JM et al. | β | 2021 | β |
| Associations Between Prenatal Cannabis Exposure and Childhood Outcomes: Results From the ABCD Study. | Paul SE et al. | β | 2021 | β |
| Cannabis use does not impact on type 2 diabetes: A two-sample Mendelian randomization study. | Baumeister SE et al. | β | 2021 | β |
| Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations. | Panyard DJ et al. | β | 2021 | β |
| Clinical Trials of Cannabidiol for Substance Use Disorders: Outcome Measures, Surrogate Endpoints, and Biomarkers. | Morel A et al. | β | 2021 | β |
| Genetic basis of cannabis use: a systematic review. | Hillmer A et al. | β | 2021 | β |
| Genetic contributions to bipolar disorder: current status and future directions. | O'Connell KS et al. | β | 2021 | β |
| Genetic Modulation of Initial Sensitivity to Ξ9-Tetrahydrocannabinol (THC) Among the BXD Family of Mice. | Parks C et al. | β | 2021 | β |
| Genetics of substance use disorders: a review. | Deak JD et al. | β | 2021 | β |
| Genetics of substance use disorders in the era of big data. | Gelernter J et al. | β | 2021 | β |
| Genomic and Personalized Medicine Approaches for Substance Use Disorders (SUDs) Looking at Genome-Wide Association Studies. | Cozzoli D et al. | β | 2021 | β |
| Genomic relationships across psychiatric disorders including substance use disorders. | Abdellaoui A et al. | β | 2021 | β |
| Identifying risk factors involved in the common versus specific liabilities to substance use: A genetically informed approach. | Iob E et al. | β | 2021 | β |
| No evidence of associations between genetic liability for schizophrenia and development of cannabis use disorder. | HjorthΓΈj C et al. | β | 2021 | β |
| Polygenic Risk and the Course of Attention-Deficit/Hyperactivity Disorder From Childhood to Young Adulthood: Findings From a Nationally Representative Cohort. | Agnew-Blais JC 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 |
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| The Gut Microbiome and Substance Use Disorder. | Russell JT et al. | β | 2021 | β |
| Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): II. Externalizing superspectrum. | Krueger RF et al. | β | 2021 | β |
| A large-scale genome-wide association study meta-analysis of cannabis use disorder. | Johnson EC 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 |
| Deconstructing the neurobiology of cannabis use disorder. | Ferland JN et al. | β | 2020 | β |
| Do AKT1, COMT and FAAH influence reports of acute cannabis intoxication experiences in patients with first episode psychosis, controls and young adult cannabis users? | Hindocha C 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 |
| Exploring Phenotypic and Genetic Overlap Between Cannabis Use and Schizotypy. | Vaissiere J et al. | β | 2020 | β |
| Genetic Control of Collective Behavior in Zebrafish. | Tang W et al. | β | 2020 | β |
| Genetic feedback for psychiatric conditions: Where are we now and where are we going. | Driver MN et al. | β | 2020 | β |
| Illicit drug use and the genetic overlap with Cannabis use. | Vink JM et al. | β | 2020 | β |
| Neuronal nicotinic acetylcholine receptors mediate β<sup>9</sup> -THC dependence: Mouse and human studies. | Donvito G et al. | β | 2020 | β |
| Nicotinic Receptors in Sleep-Related Hypermotor Epilepsy: Pathophysiology and Pharmacology. | Becchetti A 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 |
| Systems Biology Analysis of the Antagonizing Effects of HIV-1 Tat Expression in the Brain over Transcriptional Changes Caused by Methamphetamine Sensitization. | Basova LV et al. | β | 2020 | β |
| The Genetics of Externalizing Problems. | Barr PB et al. | β | 2020 | β |
| The nicotinic receptor alpha5 coding polymorphism rs16969968 as a major target in disease: Functional dissection and remaining challenges. | Maskos U | β | 2020 | β |
| A regulatory variant of CHRM3 is associated with cannabis-induced hallucinations in European Americans. | Cheng Z et al. | 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 |
| Genetic Predisposition vs Individual-Specific Processes in the Association Between Psychotic-like Experiences and Cannabis Use. | Karcher NR et al. | β | 2019 | β |
| Prenatal cannabis exposure and childhood outcomes: Results from the ABCD study | Paul SE 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 |
| GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. | Pasman JA et al. | β | 2018 | β |