Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders.
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Public
- Authors
- Hatoum, Alexander S; Colbert, Sarah M C; Johnson, Emma C; Huggett, Spencer B; Deak, Joseph D; Pathak, Gita; Jennings, Mariela V; Paul, Sarah E; Karcher, Nicole R; Hansen, Isabella; Baranger, David A A; Edwards, Alexis; Grotzinger, Andrew; Substance Use Disorder Working Group of the Psychiatric Genomics Consortium; Tucker-Drob, Elliot M; Kranzler, Henry R; Davis, Lea K; Sanchez-Roige, Sandra; Polimanti, Renato; Gelernter, Joel; Edenberg, Howard J; Bogdan, Ryan; Agrawal, Arpana
- Year
- 2023
- Journal
- Nature. Mental health
- PMID
- 37250466
- DOI
- 10.1038/s44220-023-00034-y
- PMCID
- PMC10217792
Genetic liability to substance use disorders can be parsed into loci that confer general or substance-specific addiction risk. We report a multivariate genome-wide association meta-analysis that disaggregates general and substance-specific loci for published summary statistics of problematic alcohol use, problematic tobacco use, cannabis use disorder, and opioid use disorder in a sample of individuals of European descent and African descent. Nineteen independent SNPs were genome-wide significant ( < 5e-8) for the general addiction risk factor (), which showed high polygenicity. Across ancestries, was significant (among other genes), suggesting dopamine regulation as a cross-substance vulnerability. An polygenic risk score was associated with substance use disorders, psychopathologies, somatic conditions, and environments associated with the onset of addictions. Substance-specific loci (9 for alcohol, 32 for tobacco, 5 for cannabis, 1 for opioids) included metabolic and receptor genes. These findings provide insight into genetic risk loci for substance use disorders that could be leveraged as treatment targets.
Manhattan Plot of Addiction-rf Genome-Wide Significant Results.The dotted line represents genome-wide significance at 5e-8. Each SNP peak is annotated with the closest mapped gene from FUMA (Table 1). We have not included all SNPs in the credible set in Table 1, but they are shown in Supplementary Table 4. Significance is set at genome-wide significance Bonferroni correction in a two-sided test (P < 5e-8).
LLM interpretation
This is a Manhattan plot showing genome-wide significant results for addiction risk across 22 chromosomes. The x-axis represents the chromosomes and the y-axis represents the $-\log_{10}(P\text{-value})$, with a dotted horizontal line indicating the genome-wide significance threshold of $5 \times 10^{-8}$. Several significant SNP peaks are highlighted with yellow dots and labeled with their closest mapped genes, including *BANK1*, *DRD2*, *FTO*, and *GCKR*.
Manhattan Plot of TWAS Results for Addiction-rf.A Transcriptome-Wide Association Study (TWAS) of the addiction-rf, plotted as a Manhattan plot. In Panel A, Analyses were conducted in S-MultiXcan with GTeX v8 data. In Panel B, the analysis was run using S-PredixCan with weights trained from PsychEncode. The y-axis is presented as −log10(p), the color of the data point represents the tissue in which correlation between gene expression and outcome was the highest. The dotted black line represents Bonferroni corrected TWAS significance of a two-sided test (Plot A has 9,944 genes, Bonferroni = 5×10^-6 and the line is at 5.3, Plot B has 13,850 genes, Bonferroni = 3.6×10^-6, line is at 5.4)
LLM interpretation
This figure consists of two Manhattan plots (Panels A and B) showing Transcriptome-Wide Association Study (TWAS) results for addiction-rf, with the y-axis representing $-\log_{10}(p)$ and the x-axis representing genes. Panel A uses S-MultiXcan with GTeX v8 data, and Panel B uses S-PredixCan with PsychEncode weights, with data points colored by the tissue showing the highest correlation. Both panels include a dotted black line indicating the Bonferroni corrected significance threshold (5.3 for A, 5.4 for B), with several labeled genes exceeding these thresholds.
PheWAS of Genetic Correlations using MASSIVE.Genetic correlations between 1,547 traits and the addiction-rf, calculated in MASSIVE, mapped by their statistical significance (-log10(p) on the y-axis), and broad category. The top 20 correlations are annotated. The black dashed line represents Bonferroni significance for association of a two-sided test (pbon= .05/1,574 = 3.232e-05).
LLM interpretation
This is a Manhattan-style scatter plot showing genetic correlations between 1,547 traits and "addiction-rf," with the y-axis representing $-\log_{10}(p) \times \text{Direction of effect}$. Data points are color-coded by broad category (e.g., behavior, psychiatric disease, substance use), with the top 20 most significant correlations annotated by trait name. A black dashed line indicates the Bonferroni significance threshold, with several traits—most notably "Smoking status: Current" and "Smoking status: Never"—showing high statistical significance.
Polygenic Risk Score Prediction in Yale-Penn.(A) Polygenic risk score (PRS) of the addiction-rf predicts lifetime alcohol (AUD), cannabis (CUD), opioid (OUD), tobacco (TD), and cocaine (CoUD) use disorders, and variables representing more than one lifetime substance use disorder diagnosis vs no SUDs diagnosis (Polysubstance Use Disorder, 2 level), more than one lifetime diagnosis vs. one lifetime diagnosis (polysubstance vs. unitary), as well as any substance use disorder diagnosis (Any Addiction) in an independent sample (Yale Penn 3; N=1,986 individuals of European genetic ancestry). (B) The addiction-rf PRS was associated with a comparable phenotypic substance use disorders (SUD) common factor in the Yale-Penn sample. Controlling for age, sex and 10 genetic principal components of ancestry, all path estimates are fully standardized. Estimates were significant at p < .001 of a two-sided test (LAVAAN does not report P-values lower than .001).
LLM interpretation
Figure A is a horizontal bar chart showing the Nagelkerke's $R^2$ values for the addiction-rf PRS in predicting various substance use disorders (SUDs) and polysubstance categories, with the highest predictive value seen for "Polysubstance, 2 level." Figure B is a structural equation model diagram illustrating the standardized path estimates between the addiction-rf PRS, a common SUD factor, and five specific disorders (AUD, CUD, OUD, TD, CoUD). All paths in Figure B are marked with asterisks, indicating statistical significance at $p < .001$.
| # | Section | Preview |
|---|---|---|
| 40 | METHODS — Post-hoc analyses of European ancestry GWAS results — Drug Repurposing | Our signature matching technique used data from the Library of Integrated Network-based Cellular… |
| 41 | METHODS — Post-hoc analyses of European ancestry GWAS results — Drug Repurposing | In vitro medication signatures were matched with addiction-rf signatures from the transcriptome-wide… |
| 42 | METHODS — Post-hoc analyses of European ancestry GWAS results — Polygenic Risk Score Analyses in Yale-Penn — Yale-Penn. | The Yale-Penn16,66 sample includes 11,332 genotyped and phenotyped individuals recruited across… |
| 43 | METHODS — Post-hoc analyses of European ancestry GWAS results — Polygenic Risk Score Analyses in Yale-Penn — Yale-Penn. | For the present analysis, only Yale-Penn 3 EUR subjects (N=1,986) were included. DSM-IV29 substance… |
| 44 | METHODS — Post-hoc analyses of European ancestry GWAS results — Polygenic Risk Score Analyses in Yale-Penn — Yale-Penn. | We calculated the addiction-rf PRS using the PRS-cs auto approach70. This method assumes a general… |
| 45 | METHODS — Genetic Correlations and Latent Causal Variable modeling | To examine phenotypes that were genetically correlated with the addiction-rf, we calculated genetic… |
| 46 | METHODS — Phenome-wide Association Studies (PheWAS) — PheWAS in adult samples. | As MASSIVE includes a fairly sparse set of diagnoses (not all ICD codes are available) for genetic… |
| 47 | METHODS — Phenome-wide Association Studies (PheWAS) — ABCD PheWAS of phenotypes collected in childhood. | To identify phenotypes that were associated with the addiction-rf before the onset of regular… |
| 48 | METHODS — Phenome-wide Association Studies (PheWAS) — ABCD PheWAS of phenotypes collected in childhood. | PRS were generated using the PRS-cs software package70 consistent with our other (i.e., Yale-Penn 4,… |
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| Epigenetic and genetic profiling of comorbidity patterns among substance dependence diagnoses. | Pathak GA et al. | — | 2025 | → |
| Exploration of Genetic Liability to Insomnia and Substance Use Disorders in Patients With Bipolar Disorder. | Melhuish Beaupre LM et al. | — | 2025 | → |
| Externalizing as a common genetic influence for a broad spectrum of substance use and behavioral conditions: A developmental perspective from the Avon Longitudinal Study of Parents and Children. | Deng WQ et al. | — | 2025 | → |
| Genetic influences for distinct impulsivity domains are differentially associated with early substance use initiation: Results from the ABCD Study. | Kinstler E et al. | — | 2025 | → |
| Genetic risk for alcohol use disorder in relation to individual symptom criteria: Do polygenic indices provide unique information for understanding severity and heterogeneity? | Sarles YM et al. | — | 2025 | → |
| Genome-Wide Association Studies of Delay Discounting and Impulsive Personality Traits in Children From the Adolescent Behavior and Cognitive Development Study. | Deng WQ et al. | — | 2025 | → |
| Genomic structural equation study reveals links between anorexia nervosa and delay discounting and lack of perseverance but not other facets of impulsivity. | Bianchi SB et al. | — | 2025 | → |
| Identification of risk variants and cross-disorder pleiotropy through multi-ancestry genome-wide analysis of alcohol use disorder. | Icick R et al. | — | 2025 | → |
| Identifying novel protein biomarkers with cross-psychiatric disorders effects and potential intervention targets: Evidence from proteomic-Mendelian randomization. | Zhang R et al. | — | 2025 | → |
| Impulsivity facets and substance use involvement: insights from genomic structural equation modeling. | Vilar-Ribó L et al. | — | 2025 | → |
| Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models. | Castaneda EU et al. | — | 2025 | → |
| Integrating HiTOP and RDoC frameworks part II: shared and distinct biological mechanisms of externalizing and internalizing psychopathology. | Davis CN et al. | — | 2025 | → |
| Leveraging Genomic Data to Examine the Causal Impact of Alcohol, Tobacco, Cannabis, and Opioid Use on Biological and Cognitive Ageing. | Balbona JV et al. | — | 2025 | → |
| Leveraging pleiotropy for the improved treatment of psychiatric disorders. | Woodward DJ et al. | — | 2025 | → |
| Longitudinal characterization of impulsivity phenotypes boosts signal for genomic correlates and heritability. | Deng WQ et al. | — | 2025 | → |
| Mapping medically relevant RNA isoform diversity in the aged human frontal cortex with deep long-read RNA-seq. | Aguzzoli Heberle B et al. | — | 2025 | → |
| Measures of General Intelligence and Risk for Alcohol Use Disorder. | Capusan AJ et al. | — | 2025 | → |
| Measuring the Associations Between Brain Morphometry and Polygenic Risk Scores for Substance use Disorders in Drug-Naive Adolescents. | Kramer S et al. | — | 2025 | → |
| Modern pharmacotherapy guidance for treating alcohol use disorders. | Soyka M et al. | — | 2025 | → |
| Multi-ancestral genome-wide association study of clinically defined nicotine dependence reveals strong genetic correlations with other substance use disorders and health-related traits. | Johnson EC et al. | — | 2025 | → |
| Multiomic Network Analysis Identifies Dysregulated Neurobiological Pathways in Opioid Addiction. | Sullivan KA et al. | — | 2025 | → |
| Pleiotropic Effects of <i>Grm7</i>/<i>GRM7</i> in Shaping Neurodevelopmental Pathways and the Neural Substrate of Complex Behaviors and Disorders. | Gyetvai BM et al. | — | 2025 | → |
| Polygenic overlap of substance use behaviors and disorders with externalizing and internalizing problems independent of genetic correlations. | Al-Soufi L et al. | — | 2025 | → |
| Polygenic Risk Score Analysis of Antidepressant Treatment Outcomes: A CAN-BIND-1 Study Report: Analyse des résultats du traitement antidépresseur à l'aide des scores de risque polygéniques : Rapport sur l'étude CAN-BIND-1. | Magarbeh L et al. | — | 2025 | → |
| Polygenic Scores and Mood Disorder Onsets in the Context of Family History and Early Psychopathology. | Freeman K et al. | — | 2025 | → |
| Post-traumatic stress and genetic interactions affect tobacco and alcohol use after trauma: findings from a multi-ancestry cohort. | Garrison-Desany HM et al. | — | 2025 | → |
| Precision medicine in substance use disorders: Integrating behavioral, environmental, and biological insights. | Guerrin CGJ et al. | — | 2025 | → |
| Predictive performance for alcohol use disorder of polygenic scores based on the general addiction risk factor and problematic alcohol use. | Pérez-Gutiérrez AM et al. | — | 2025 | → |
| Prospective Predictors of Adolescent Screen Time and Problematic Screen Use. | Grund BA et al. | — | 2025 | → |
| Psychiatric genome-wide association study enrichment shows promise for future psychopharmaceutical discoveries. | Hatoum AS et al. | — | 2025 | → |
| Reevaluating the brain disease model of addiction. | Blithikioti C et al. | — | 2025 | → |
| Risk of substance use disorders in the adult children of parents with severe alcohol use disorder: a nationwide cohort study. | Kane K et al. | — | 2025 | → |
| Sex-specific differences in brain activity dynamics of youth with a family history of substance use disorder. | Schilling L et al. | — | 2025 | → |
| Shared genetics and causal relationship between sociability and the brain's default mode network. | Fanelli G et al. | — | 2025 | → |
| Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease. | Castaneda E et al. | — | 2025 | → |
| Targeting pro-dopaminergic agonism to attenuate depression in patients displaying genetic/epigenetic predisposition to hypodopaminergia. | Lewandrowski KU et al. | — | 2025 | → |
| The biology of addiction. | Nestler EJ | — | 2025 | → |
| The Genetic Architecture of the Human Corpus Callosum and its Subregions. | Bhatt RR et al. | — | 2025 | → |
| The mesocorticolimbic system in stimulant use disorder. | London ED et al. | — | 2025 | → |
| The Psychiatric Genomics Consortium: discoveries and directions. | Agrawal A et al. | — | 2025 | → |
| Transcriptional response to chronic long-access fentanyl self-administration in rat habenula and amygdala | Magnard R et al. | — | 2025 | — |
| Whole Genome Sequencing of Pedigrees With High Density of Substance Use and Psychiatric Disorders: A Meeting Report. | Hill SY et al. | — | 2025 | → |
| A Bivariate Twin Study of Lifetime cannabis Initiation and Lifetime Regular Tobacco Smoking Across Three Different Countries. | Zellers S et al. | — | 2024 | → |
| A Complex Relationship Among the Circadian Rhythm, Reward Circuit and Substance Use Disorder (SUD). | Samanta S et al. | — | 2024 | → |
| An emerging multi-omic understanding of the genetics of opioid addiction. | Johnson EO et al. | — | 2024 | → |
| An evolutionary perspective on complex neuropsychiatric disease. | McClellan JM et al. | — | 2024 | → |
| Associations of cannabis use, use frequency, and cannabis use disorder with violent behavior among young adults in the United States. | Volkow ND et al. | — | 2024 | → |
| Biospecimens in the HEALthy Brain and Child Development (HBCD) Study: Rationale and protocol. | Sullivan EL et al. | — | 2024 | → |
| Causation and Common Liability in the Progression of the U.S. Opioid Crisis. | Dash GF et al. | — | 2024 | → |
| Cell-type brain-region specific changes in prefrontal cortex of a mouse model of alcohol dependence. | Salem NA et al. | — | 2024 | → |
| Circuit-Wide Gene Network Analysis Reveals Sex-Specific Roles for Phosphodiesterase 1b in Cocaine Addiction. | Teague CD et al. | — | 2024 | → |
| Clinical associations with treatment resistance in depression: An electronic health record study. | Coombes BJ et al. | — | 2024 | → |
| Cognitive Decline in Ageing and Disease: Risk factors, Genetics and Treatments. | Colita E et al. | — | 2024 | → |
| Considerations for the application of polygenic scores to clinical care of individuals with substance use disorders. | Kember RL et al. | — | 2024 | → |
| Disentangling heterogeneity in substance use disorder: Insights from genome-wide polygenic scores. | Vilar-Ribó L et al. | — | 2024 | → |
| Dissecting the polygenic contribution of attention-deficit/hyperactivity disorder and autism spectrum disorder on school performance by their relationship with educational attainment. | Cabana-Domínguez J et al. | — | 2024 | → |
| Drugs and Addiction Science: NIDA Celebrates 50 Years of Research and Looks to the Future. | Volkow ND | — | 2024 | → |
| Epigenetic alterations identify a confluence of genetic vulnerabilities tied to opioid overdose. | Hoang AT et al. | — | 2024 | → |
| Evidence Based Clinical Analytics Supporting the Genetic Addiction Risk Severity (GARS) Assessment to Early Identify Probands in Preaddiction. | Blum K et al. | — | 2024 | → |
| Examining the association between the FTO gene and neuroticism reveals indirect effects on subjective well-being and problematic alcohol use. | Cai W et al. | — | 2024 | → |
| Explaining the causes and motivations for multiple substance use from the perspectives of users and therapists: A qualitative study. | Mokhtarpoor H et al. | — | 2024 | → |
| Exploring perceptions of genetic risk and the transmission of substance use disorders. | Keller A et al. | — | 2024 | → |
| GABAergic mechanisms in alcohol dependence. | Uusi-Oukari M et al. | — | 2024 | → |
| Generalized genetic liability to substance use disorders. | Miller AP et al. | — | 2024 | → |
| Genetic contribution to the comorbidity between attention-deficit/hyperactivity disorder and substance use disorders. | Koller D et al. | — | 2024 | → |
| GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis. | Ball RL et al. | — | 2024 | → |
| Human pluripotent stem cells as a translational toolkit in psychedelic research <i>in vitro</i>. | Salerno JA et al. | — | 2024 | → |
| Identification of distinct clinical profiles and trajectories in individuals at high risk of developing psychosis: A latent profile analysis of the north American prodrome longitudinal study consortium-3 dataset. | Bergé D et al. | — | 2024 | → |
| Is There a Natural, Non-addictive, and Non-anti-reward, Safe, Gene-based Solution to Treat Reward Deficiency Syndrome? KB220 Variants vs GLP-1 Analogs. | Modestino EJ et al. | — | 2024 | → |
| Male and female behavioral variability and morphine response in C57BL/6J, DBA/2J, and their BXD progeny following chronic stress exposure. | Morel C et al. | — | 2024 | → |
| Morphine-Driven m6A Epitranscriptomic Neuroadaptations in Primary Cortical Cultures. | Dabrowski KR et al. | — | 2024 | → |
| Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes. | Toikumo S et al. | — | 2024 | → |
| Neuroendocrine mechanisms in the links between early life stress, affect, and youth substance use: A conceptual model for the study of sex and gender differences. | Donovan A et al. | — | 2024 | → |
| Neurogenetic and multi-omic sources of overlap among sensation seeking, alcohol consumption, and alcohol use disorder. | Miller AP et al. | — | 2024 | → |
| Opioid trail: Tracking contributions to opioid use disorder from host genetics to the gut microbiome. | Duffy EP et al. | — | 2024 | → |
| Partitioning the Genomic Components of Behavioral Disinhibition and Substance Use (Disorder) Using Genomic Structural Equation Modeling. | Horwitz TB et al. | — | 2024 | → |
| PDE4B Missense Variant Increases Susceptibility to Post-traumatic Stress Disorder-Relevant Phenotypes in Mice. | Lipina TV et al. | — | 2024 | → |
| PET imaging in rat brain shows opposite effects of acute and chronic alcohol exposure on phosphodiesterase-4B, an indirect biomarker of cAMP activity. | Tang S et al. | — | 2024 | → |
| Pharmacogenetics of Biochemically Verified Abstinence in an Opioid Agonist Therapy Randomized Clinical Trial of Methadone and Buprenorphine/Naloxone. | Kazi I et al. | — | 2024 | → |
| Pleiotropy and genetically inferred causality linking multisite chronic pain to substance use disorders. | Koller D et al. | — | 2024 | → |
| Rare and common variants associated with alcohol consumption identify a conserved molecular network. | Leger BS et al. | — | 2024 | → |
| Role of Glial Cells in Neuronal Function, Mood Disorders, and Drug Addiction. | Tizabi Y et al. | — | 2024 | → |
| The genetic architecture of substance use and its diverse correlations with mental health traits. | Wormington B et al. | — | 2024 | → |
| The genetic landscape of substance use disorders. | Gerring ZF et al. | — | 2024 | → |
| Tobacco use disorder in patients with other mental disorders: a dual disorder perspective from clinical neuroscience. | Szerman N et al. | — | 2024 | → |
| What Risks Do Offspring of Parents With Alcohol Use Disorder Face? | Edenberg HJ | — | 2024 | → |
| A Comparative Event-Related Potentials Study between Alcohol Use Disorder, Gambling Disorder and Healthy Control Subjects through a Contextual Go/NoGo Task. | Dubuson M et al. | — | 2023 | → |
| Alcohol use and grey matter structure: Disentangling predispositional and causal contributions in human studies. | Baranger DAA et al. | — | 2023 | → |
| A multivariate approach to understanding the genetic overlap between externalizing phenotypes and substance use disorders. | Poore HE et al. | — | 2023 | → |
| Are We Getting High Cause the Thrill is Gone? | Blum K et al. | — | 2023 | → |
| A whole exome sequencing study to identify rare variants in multiplex families with alcohol use disorder. | Hill SY et al. | — | 2023 | → |
| Circular RNAs in the human brain are tailored to neuron identity and neuropsychiatric disease. | Dong X et al. | — | 2023 | → |
| Dopaminergic dysfunction: Role for genetic & epigenetic testing in the new psychiatry. | Blum K et al. | — | 2023 | → |
| Evidence for the <i>DRD2</i> Gene as a Determinant of Reward Deficiency Syndrome (RDS). | Blum K et al. | — | 2023 | → |
| High intensity interval training exercise increases dopamine D2 levels and modulates brain dopamine signaling. | Tyler J et al. | — | 2023 | → |
| Identifying genetic loci and phenomic associations of substance use traits: A multi-trait analysis of GWAS (MTAG) study. | Xu H et al. | — | 2023 | → |
| Identifying potential risk genes and pathways for neuropsychiatric and substance use disorders using intermediate molecular mediator information. | Gedik H et al. | — | 2023 | → |
| Leveraging related health phenotypes for polygenic prediction of impulsive choice, impulsive action, and impulsive personality traits in 1534 European ancestry community adults. | Deng WQ et al. | — | 2023 | → |
| Multi-omics cannot replace sample size in genome-wide association studies. | Baranger DAA et al. | — | 2023 | → |
| Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program. | Cheng Y et al. | — | 2023 | → |
| Multivariate Genome-Wide Association Study of Concentrations of Seven Elements in Seeds Reveals Four New Loci in Russian Wheat Lines. | Potapova NA et al. | — | 2023 | → |
| Pleiotropic loci for cannabis use disorder severity in multi-ancestry high-risk populations. | Peng Q et al. | — | 2023 | → |
| Predictors of treatment retention and survival among methadone-maintained patients: A possible role for a functional delta opioid receptor gene variant. | Peles E et al. | — | 2023 | → |
| Sex Differences in Cocaine Sensitization Vary by Mouse Strain. | Chapp AD et al. | — | 2023 | → |
| Shared genetic liability for alcohol consumption, alcohol problems, and suicide attempt: Evaluating the role of impulsivity. | Stephenson M et al. | — | 2023 | → |
| Single nucleus transcriptomics of ventral midbrain identifies glial activation associated with chronic opioid use disorder. | Wei J et al. | — | 2023 | → |
| The First Exploratory Personalized Medicine Approach to Improve Bariatric Surgery Outcomes Utilizing Psychosocial and Genetic Risk Assessments: Encouraging Clinical Research. | Thanos PK et al. | — | 2023 | → |
| The Genetically Informed Neurobiology of Addiction (GINA) model. | Bogdan R et al. | — | 2023 | → |
| The Interrelationship of the Genetic Risks for Different Forms of Substance Use Disorder in a Swedish National Sample: A Top-Down Genetic Analysis. | Kendler KS et al. | — | 2023 | → |
| "TO BE OR NOT TO BE" GWAS Ends the Controversy about the DRD2 Gene as a Determinant of Reward Deficiency Syndrome (RDS). | Blum K et al. | — | 2023 | → |
| Why do we focus on the exception and not the rule? Examining the prevalence of mono- versus polysubstance use in the general population. | Bailey AJ et al. | — | 2023 | → |
| Characterisation of the genetic relationship between the domains of sleep and circadian-related behaviours with substance use phenotypes. | Hatoum AS et al. | — | 2022 | → |
| Genome-wide association study in individuals of European and African ancestry and multi-trait analysis of opioid use disorder identifies 19 independent genome-wide significant risk loci. | Deak JD et al. | — | 2022 | → |
| Substance use and common contributors to morbidity: A genetics perspective. | Sanchez-Roige S et al. | — | 2022 | → |