Genetics of substance use disorders in the era of big data.
paper
Cited
Public
- Authors
- Gelernter, Joel; Polimanti, Renato
- Year
- 2021
- Journal
- Nature reviews. Genetics
- PMID
- 34211176
- DOI
- 10.1038/s41576-021-00377-1
- PMCID
- PMC9210391
Substance use disorders (SUDs) are conditions in which the use of legal or illegal substances, such as nicotine, alcohol or opioids, results in clinical and functional impairment. SUDs and, more generally, substance use are genetically complex traits that are enormously costly on an individual and societal basis. The past few years have seen remarkable progress in our understanding of the genetics, and therefore the biology, of substance use and abuse. Various studies - including of well-defined phenotypes in deeply phenotyped samples, as well as broadly defined phenotypes in meta-analysis and biobank samples - have revealed multiple risk loci for these common traits. A key emerging insight from this work establishes a biological and genetic distinction between quantity and/or frequency measures of substance use (which may involve low levels of use without dependence), versus symptoms related to physical dependence.
From epidemiology and gene discovery to biology of substance use disorders.Genome-wide association datasets can be used as the basis for multiple analytical approaches to disentangle the biology of the traits investigated (e.g., cellular processes and molecular functions) and to understand the mechanisms underlying the associations observed in epidemiological studies.
LLM interpretation
This figure is a conceptual diagram illustrating various analytical workflows derived from genome-wide association study (GWAS) data. The central Manhattan plot shows $-\log_{10}(P\text{ value})$ across chromosomes, which feeds into downstream applications including enrichment analysis (bar chart), polygenic risk scoring (density plot of controls vs. cases), Mendelian randomization (causal inference flow chart), transcriptome-wide association studies (co-localization diagram), structural equation modelling, and genetic correlation (heatmap). The overall layout maps the progression from initial gene discovery to functional annotation, prediction, and causal inference.
Genetic correlation among SUD traits and other phenotypes.Genetic correlation of problematic alcohol use, cannabis use disorder, cocaine dependence, opioid use disorder, and nicotine dependence with psychiatric disorders, behavioural traits, and other complex phenotypes. The 95% confidence interval of the genetic correlation estimates were obtained from previous studies13, 37, 44, 48, 185 that applied the linkage disequilibrium score regression method. The traits included are those tested with respect to at least four out of the five addictions considered. There are some patterns of genetic correlation that are consistent across the five addictions presented. The major differences among them are related to the substance-specific genetic correlations, i.e. nicotine addiction vs. cigaretters per day, alcohol addiction vs. drinks per week, and cannabis addiction vs. cannabis use.
LLM interpretation
This forest plot displays the genetic correlations between five substance use disorder (SUD) traits (Alcohol, Cannabis, Cocaine, Opioids, and Tobacco) and various psychiatric and behavioral phenotypes. The x-axis represents the genetic correlation coefficient with 95% confidence intervals, with a vertical dashed line at zero indicating no correlation. Positive correlations are observed across most SUDs for traits such as depressive symptoms, neuroticism, and the Townsend deprivation index, while negative correlations are generally seen for childhood IQ and college completion.
Twin-based versus SNP-based heritabilities of alcohol, cannabis, cocaine, opioid, and tobacco addictions.Twin-based heritabilities (weighted means and ranges) were previously estimated in US surveys of addictive agents in adult twin pairs186. SNP-based heritabilities were previously estimated by genome-wide association studies using linkage disequilibrium score regression13, 37, 44, 48, 185. Bubble size represents the relative risk of addiction for each substance187. Vertical bars represent 95% confidence intervals for the SNP-based estimate, and horizontal bars represent the twin-based range. Family-based and SNP-based heritabilities of complex traits previously calculated using UK Biobank data188 are also plotted. The differences between twin-based and SNP-based heritabilities (i.e., the βmissing heritabilityβ) for four of the addictions shown is in line with those observed among other human traits and diseases. The only exception appears to be cocaine addiction that presents a large 96% confidence interval due to the small sample size of the largest genome-wide association study available for this trait to date.
LLM interpretation
This bubble plot compares twin-based heritability (x-axis) and SNP-based heritability (y-axis) for five types of addiction and several other complex traits. For most addictions (alcohol, cannabis, opioids, and tobacco), SNP-based heritabilities are lower than twin-based heritabilities, with bubble size indicating the relative risk of addiction. Cocaine addiction is an outlier with a significantly larger 95% confidence interval (vertical bar) and twin-based range (horizontal bar) compared to the other plotted traits.
| # | Section | Preview |
|---|---|---|
| 60 | Human and animal research in SUD genetics | Animal studies provide very important contributions to understanding the molecular basis of human⦠|
| 61 | Conclusions and perspectives | There has been enormous progress in SUD genetics research towards the goal of understanding the⦠|
| 62 | Conclusions and perspectives | are generally more interested in lifetime diagnoses than the research participantβsβ¦ |
| 63 | Conclusions and perspectives | Until quite recently the only risk genes that were well established for SUDs acted⦠|
| 64 | Conclusions and perspectives | located in regions with low linkage disequilibrium183, 184 that cannot be ascertained by genotyping⦠|
β Prev
61β65 of 65
| Name | Type |
|---|---|
| 220 EA women local | cohort |
| 23andMe | cohort |
| 48 heavy cannabis users local | cohort |
| A118G SNP | variant |
| AA | cohort |
| acamprosate | drug |
| acetaldehyde | drug |
| acute withdrawal | phenotype |
| addiction | phenotype |
| addictive behaviours local | phenotype |
| ADH1B | gene |
| ADH1BArg370Cys local | variant |
| ADH1B Arg48His | variant |
| ADHD | phenotype |
| adolescents | cohort |
| African American | cohort |
| African-Americans | cohort |
| African descent | cohort |
| age at first cannabis use | phenotype |
| age at smoking initiation | phenotype |
| Age-matched controls local | cohort |
| AHRR local | gene |
| airflow obstruction | phenotype |
| alcohol | phenotype |
| alcohol abuse | phenotype |
| alcohol dependence | phenotype |
| Alcohol-drinking local | phenotype |
| alcoholic liver disease | phenotype |
| alcohol intoxication | phenotype |
| alcoholism | phenotype |
| Alcohol-related cancers local | phenotype |
| Alcohol Use | phenotype |
| Alcohol Use Disorder | phenotype |
| alcohol withdrawal | phenotype |
| ALDH2 | gene |
| ALDH2 Glu504Lys local | variant |
| ALDH2Glu504Lys local | variant |
| AllOfUs local | cohort |
| All of Us Research Program | cohort |
| altered glutamate signaling local | phenotype |
| altered mitochondrial signaling local | phenotype |
| altered synaptic signaling local | phenotype |
| Alzheimer's disease | phenotype |
| amphetamine | drug |
| ancestral population local | cohort |
| anhedonia | phenotype |
| antisocial personality disorder | phenotype |
| anxiety | phenotype |
| Asian | cohort |
| attention deficit hyperactivity disorder | phenotype |
| AUD | phenotype |
| AUDIT | phenotype |
| AUDIT-C | phenotype |
| AUDIT-P | phenotype |
| autism | phenotype |
| Aversion-resistant alcohol seeking local | phenotype |
| Bdnf | gene |
| behavioural traits local | phenotype |
| biobanks | cohort |
| bipolar disorder | phenotype |
| Blood-based transcriptome-wide study local | cohort |
| body mass index | phenotype |
| brain | anatomy |
| bronchitis | phenotype |
| buprenorphine-naloxone local | drug |
| Cadm2 | gene |
| cancer | phenotype |
| cannabis initiation | phenotype |
| cannabis-naΓ―ve participants local | cohort |
| cannabis use | phenotype |
| cannabis use disorder | phenotype |
| cardiovascular disease | phenotype |
| cerebellum | anatomy |
| children's aggressive behaviour local | phenotype |
| Chinese | cohort |
| Chinese individuals local | cohort |
| CHRNA2 | gene |
| Chrna3 | gene |
| CHRNA5 | gene |
| CHRNA5Asp398Asn local | variant |
| Chrnb4 | gene |
| chronic obstructive pulmonary disease | phenotype |
| cigarettes | phenotype |
| Cnih3 | gene |
| CNNM2 local | gene |
| cocaine | phenotype |
| cocaine use disorder | phenotype |
| cognition | phenotype |
| cognitive ability | phenotype |
| complex traits | phenotype |
| controls | cohort |
| cortex | anatomy |
| cotinine | drug |
| CREB1 | gene |
| CRHR1 | gene |
| CUD | phenotype |
| CUL3 local | gene |
| current smoking | phenotype |
| CYP1A1 local | gene |
| CYP1B1 | gene |
| CYP2A6 | gene |
| Cytl1 | gene |
| DBH | gene |
| Deceased subjects | cohort |
| dependence | phenotype |
| dependence-based traits local | phenotype |
| Dependence Trait local | phenotype |
| dependence traits local | phenotype |
| depression | phenotype |
| depressive symptoms | phenotype |
| Dnmt3b | gene |
| dorsolateral prefrontal cortex | anatomy |
| DRD2 | gene |
| Drinking behaviours local | phenotype |
| drinks per week | phenotype |
| DSM-5 AUD | phenotype |
| East Asian | cohort |
| ecstasy | drug |
| educational attainment | phenotype |
| EHR data local | cohort |
| Endogenous Substances local | drug |
| Epigenetic modifications | phenotype |
| EUR | cohort |
| European ancestry | cohort |
| European population | cohort |
| EUR populations local | cohort |
| Exogenous Substances local | drug |
| Exposed controls local | cohort |
| expression quantitative trait loci local | variant |
| extraversion | phenotype |
| F2RL3 local | gene |
| facial flushing | phenotype |
| FAM53B | gene |
| fetal brain | anatomy |
| forebrain | anatomy |
| former smokers | phenotype |
| Foxp2 | gene |
| fracture | phenotype |
| frequency of alcohol use | phenotype |
| FTND GWAS (58,000 smokers) local | cohort |
| Gabra1 | gene |
| Gabrd | gene |
| Gabrg2 | gene |
| Gastrointestinal tract cancers local | phenotype |
| GBP5 local | gene |
| genetic variants | cohort |
| GSCAN | cohort |
| GSCAN28 local | cohort |
| GWAS of neuroticism local | cohort |
| GWAS of risk-tolerance local | cohort |
| hallucinogens | drug |
| healthy controls | cohort |
| Healthy female twins local | cohort |
| heavy cannabis use | phenotype |
| Hydroxycotinine local | drug |
| hyperphagia | phenotype |
| hypothalamus | anatomy |
| Illegal substance dependencies local | phenotype |
| illegal SUDs local | phenotype |
| Illegal SUDs local | phenotype |
| Increased pulse rate local | phenotype |
| Increased skin temperature local | phenotype |
| individuals with cocaine use disorder local | cohort |
| initiation | phenotype |
| insomnia | phenotype |
| interpeduncular nucleus | anatomy |
| iPSC-derived human neural cells local | cohort |
| IRF1 | gene |
| Japanese | cohort |
| Japanese individuals local | cohort |
| JCAD | gene |
| Kcnc1 | gene |
| Klb | gene |
| knockout mice | cohort |
| large biobanks and consortia local | cohort |
| Latinx local | cohort |
| Legal substance dependencies local | phenotype |
| legal SUDs local | phenotype |
| lifetime cannabis use | phenotype |
| lifetime smoking | phenotype |
| lifetime tobacco smoking local | phenotype |
| lipid metabolism | phenotype |
| living subjects | cohort |
| lung cancer | phenotype |
| major depressive disorder | phenotype |
| MaxAlc study local | cohort |
| Maximum habitual alcohol use local | phenotype |
| MBP | gene |
| MDMA | drug |
| medial habenula | anatomy |
| medial prefrontal cortex | anatomy |
| meta-analysis of 38,602 smokers local | cohort |
| Methadone | drug |
| methamphetamine | drug |
| microRNA down-regulation local | phenotype |
| Million Veteran Program | cohort |
| missing heritability | phenotype |
| model organisms | cohort |
| mood disorders | phenotype |
| morphine | drug |
| multiple substances | drug |
| MVP | cohort |
| MVP GWAS local | cohort |
| naloxone | drug |
| NEGR1 | gene |
| Neonatal opioid withdrawal syndrome local | phenotype |
| Neonatal umbilical cord blood local | cohort |
| neuronal transcriptional response dampening local | phenotype |
| neuropsychological deficits | phenotype |
| neuroticism | phenotype |
| never smokers | phenotype |
| nicotine | drug |
| nicotine addiction | phenotype |
| nicotine aversion local | phenotype |
| nicotine aversive effects local | phenotype |
| nicotine dependence | phenotype |
| Nicotine metabolic ratio local | phenotype |
| nicotine use | phenotype |
| nicotine use trajectories local | phenotype |
| NIDA | cohort |
| NIMH | cohort |
| novelty seeking | phenotype |
| NPY2R | gene |
| nucleus accumbens | anatomy |
| OD | phenotype |
| OE local | phenotype |
| opioid | drug |
| opioid dependence | phenotype |
| opioid-exposed local | phenotype |
| Opioid-exposed local | phenotype |
| Opioid exposure local | phenotype |
| opioid overdose | phenotype |
| OPRM1 | cohort |
| other psychiatric disorders | phenotype |
| OUD | phenotype |
| OUD risk local | phenotype |
| overnight-abstinent smokers local | cohort |
| PAU | phenotype |
| PCSK9 | gene |
| Pde4b | gene |
| Pdyn | gene |
| PGC | cohort |
| PGC-SUD workgroup local | cohort |
| phenotype | phenotype |
| physical health | phenotype |
| physiological dependence | phenotype |
| PLP1 | gene |
| posttraumatic stress disorder | phenotype |
| potassium chloride | drug |
| PPFIA2 local | gene |
| Prenatal opioid exposure local | drug |
| problematic alcohol use | phenotype |
| Protective effect on alcohol intake local | phenotype |
| psychiatric and behavioral traits local | phenotype |
| psychiatric disorders | phenotype |
| Psychiatric Genomics Consortium | cohort |
| psychiatric traits | phenotype |
| Psychosocial stress local | phenotype |
| putamen | anatomy |
| quantity | phenotype |
| quantity/frequency-of-use traits local | phenotype |
| Quantity/Frequency Trait local | phenotype |
| quantity of use | phenotype |
| reduced pulmonary function local | phenotype |
| Reward and impulsivity processes local | phenotype |
| RGMA | gene |
| risk allele | cohort |
| risk factor | phenotype |
| risk-taking behavior | phenotype |
| risk-tolerance local | phenotype |
| rs1229984 | variant |
| rs2066702 | variant |
| Saliva samples local | cohort |
| schizophrenia | phenotype |
| SDCCAG8 local | gene |
| self-harm behaviours local | phenotype |
| Severely dependent patients local | cohort |
| SIX3 | gene |
| SLC39A13 | gene |
| smoking | phenotype |
| smoking cessation | phenotype |
| smoking dependence | phenotype |
| smoking heaviness | phenotype |
| smoking initiation | phenotype |
| smoking phenotypes | phenotype |
| smoking severity | phenotype |
| SPDEF local | gene |
| stroke | phenotype |
| subjective well-being | phenotype |
| substance abuse | phenotype |
| substance self-administration local | phenotype |
| substance use | phenotype |
| Substance use disorder biology local | phenotype |
| Substance Use Disorder risk local | phenotype |
| substance-use traits local | phenotype |
| SUD | phenotype |
| SUD genetic liability local | phenotype |
| SUD-related traits local | phenotype |
| SUD traits local | phenotype |
| suicide | phenotype |
| TENM2 local | gene |
| Tetrahydrocannabinol | drug |
| Thai | cohort |
| THSD7B | gene |
| time to first cigarette | phenotype |
| TMEM51 local | gene |
| tobacco dependence | phenotype |
| Tobacco-smoking local | phenotype |
| Tobacco smoking behaviours local | phenotype |
| tobacco use | phenotype |
| TOPMed local | cohort |
| Trans-Omics for Precision Medicine local | cohort |
| UK Biobank | cohort |
| unexposed controls | cohort |
| use-based traits local | phenotype |
| varenicline | drug |
| Ventilation local | phenotype |
| voluntary ethanol consumption | phenotype |
| VRK2 | gene |
| wild-type mice | cohort |
| withdrawal severity | phenotype |
| YaleβPenn | cohort |
| YaleβPenn cohort | cohort |
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In this knowledge base
External
| Title | Authors | Journal | Year | Link |
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| Concerns about genetic risk testing for opioid use disorder. | Hatoum AS et al. | β | 2025 | β |
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| Genetic influences and causal pathways shared between cannabis use disorder and other substance use traits. | Galimberti M et al. | β | 2024 | β |
| Genome-wide association studies of coffee intake in UK/US participants of European ancestry uncover cohort-specific genetic associations. | Thorpe HHA et al. | β | 2024 | β |
| Heavy alcohol consumption but not smoking predicts mortality in patients with acute coronary syndrome. | Andersen A et al. | β | 2024 | β |
| Human genetics and epigenetics of alcohol use disorder. | Zhou H et al. | β | 2024 | β |
| Interactive effects of genetic liability and combat exposure on risk of alcohol use disorder among US service members. | Campbell-Sills L et al. | β | 2024 | β |
| Objective Assessments of Smoking and Drinking Outperform Clinical Phenotypes in Predicting Variance in Epigenetic Aging. | Philibert R et al. | β | 2024 | β |
| Opioid trail: Tracking contributions to opioid use disorder from host genetics to the gut microbiome. | Duffy EP et al. | β | 2024 | β |
| Partitioning the Genomic Components of Behavioral Disinhibition and Substance Use (Disorder) Using Genomic Structural Equation Modeling. | Horwitz TB et al. | β | 2024 | β |
| Pleiotropy and genetically inferred causality linking multisite chronic pain to substance use disorders. | Koller D et al. | β | 2024 | β |
| Results of an observational intervention trial: A promising harm reduction approach for persons with mental health and substance use disorders | McGirr K et al. | β | 2024 | β |
| Substance-Induced Psychiatric Disorders, Epigenetic and Microbiome Alterations, and Potential for Therapeutic Interventions. | Nohesara S et al. | β | 2024 | β |
| The <i>HINT1</i> Gene rs2526303 Polymorphism and Its Association with Personality Traits in Cigarette Smokers. | Suchanecka A et al. | β | 2024 | β |
| The impact of assortative mating, participation bias and socioeconomic status on the polygenic risk of behavioural and psychiatric traits. | Cabrera-Mendoza B et al. | β | 2024 | β |
| Two Clinically Implementable Digital PCR Assessments of DNA Methylation for Diagnosing Heavy Alcohol Consumption. | Philibert R et al. | β | 2024 | β |
| What Are the Genetic Building Blocks of Alcohol-Related Behaviors? | Gelernter J et al. | β | 2024 | β |
| 5. Collaborative Study on the Genetics of Alcoholism: Functional genomics. | Gameiro-Ros I et al. | β | 2023 | β |
| Analysis of the brain transcriptome for substance-associated genes: An update on large-scale genome-wide association studies. | Zhao Y et al. | β | 2023 | β |
| Autophosphorylation of Ξ±CaMKII regulates alcohol consumption by controlling sedative effects of alcohol and alcohol-induced loss of excitatory synapses. | CaΕy A et al. | β | 2023 | β |
| Conflicting theories on addiction aetiology and the strengths and limitations of substance use disorder disease modelling. | Greener MR et al. | β | 2023 | β |
| Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses. | Als TD et al. | β | 2023 | β |
| Dissecting the epigenomic differences between smoking and nicotine dependence in a veteran cohort. | Nagamatsu ST et al. | β | 2023 | β |
| Genetic and Cultural Transmission of Alcohol Use Disorder in Swedish Twin Pedigrees. | Maes HH et al. | β | 2023 | β |
| How has the brain disease model of addiction contributed to tobacco control? | Hall W et al. | β | 2023 | β |
| Is DNA methylation in the brain a mechanism of alcohol use disorder? | Jarczak J et al. | β | 2023 | β |
| Multi-ancestry genome-wide association study of cannabis use disorder yields insight into disease biology and public health implications. | Levey DF et al. | β | 2023 | β |
| Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals. | Zhou H et al. | β | 2023 | β |
| Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders. | Hatoum AS et al. | β | 2023 | β |
| New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications. | Andreassen OA et al. | β | 2023 | β |
| Polysubstance addiction patterns among 7,989 individuals with cocaine use disorder. | Stiltner B et al. | β | 2023 | β |
| Positive personality traits moderate persistent high alcohol consumption, determined by polygenic risk in U.S. military veterans: results from a 10-year, population-based, observational cohort study. | Na P et al. | β | 2023 | β |
| Predicting Heterogeneity in Patient Response to Morphine Treatment for Neonatal Opioid Withdrawal Syndrome. | Smolyak D et al. | β | 2023 | β |
| The evolution of Big Data in neuroscience and neurology. | Dipietro L et al. | β | 2023 | β |
| The Genetically Informed Neurobiology of Addiction (GINA) model. | Bogdan R et al. | β | 2023 | β |
| The pleiotropic contribution of genes in dopaminergic and serotonergic pathways to addiction and related behavioral traits. | AntΓ³n-Galindo E et al. | β | 2023 | β |
| The Reversion of the Epigenetic Signature of Coronary Heart Disease in Response to Smoking Cessation. | Philibert R et al. | β | 2023 | β |
| Alcohol consumption and telomere length: Mendelian randomization clarifies alcohol's effects. | Topiwala A et al. | β | 2022 | β |
| A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment. | Bel-Bahar TS et al. | β | 2022 | β |
| Back-translating GWAS findings to animal models reveals a role for Hgfac and Slc39a8 in alcohol and nicotine consumption. | Banna FKE et al. | β | 2022 | β |
| Considering gender differences in the study and treatment of internet gaming disorder. | Dong GH et al. | β | 2022 | β |
| Digital methylation assessments of alcohol and cigarette consumption account for common variance in accelerated epigenetic ageing. | Lei MK et al. | β | 2022 | β |
| Epigenetic and Proteomic Biomarkers of Elevated Alcohol Use Predict Epigenetic Aging and Cell-Type variation Better Than Self-Report. | Beach SRH et al. | β | 2022 | β |
| Genome-wide association study in a rat model of temperament identifies multiple loci for exploratory locomotion and anxiety-like traits. | Chitre AS et al. | β | 2022 | β |
| 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. | β | 2022 | β |
| Genome-Wide Investigation of Maximum Habitual Alcohol Intake in US Veterans in Relation to Alcohol Consumption Traits and Alcohol Use Disorder. | Deak JD et al. | β | 2022 | β |
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| Understanding Anhedonia from a Genomic Perspective. | Bondy E et al. | β | 2022 | β |
| Using Local and Global Genetic Correlation Approaches to Help Elucidate the Shared Genetic Etiology of Psychiatric and Substance Use Traits. | Deak JD | β | 2022 | β |
| Alcohol Use Intensity Decreases in Response to Successful Smoking Cessation Therapy. | Philibert R et al. | β | 2021 | β |