Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium.
paper
Primary
Public
- Authors
- Polimanti, Renato; Walters, Raymond K; Johnson, Emma C; McClintick, Jeanette N; Adkins, Amy E; Adkins, Daniel E; Bacanu, Silviu-Alin; Bierut, Laura J; Bigdeli, Tim B; Brown, Sandra; Bucholz, Kathleen K; Copeland, William E; Costello, E Jane; Degenhardt, Louisa; Farrer, Lindsay A; Foroud, Tatiana M; Fox, Louis; Goate, Alison M; Grucza, Richard; Hack, Laura M; Hancock, Dana B; Hartz, Sarah M; Heath, Andrew C; Hewitt, John K; Hopfer, Christian J; Johnson, Eric O; Kendler, Kenneth S; Kranzler, Henry R; Krauter, Kenneth; Lai, Dongbing; Madden, Pamela A F; Martin, Nicholas G; Maes, Hermine H; Nelson, Elliot C; Peterson, Roseann E; Porjesz, Bernice; Riley, Brien P; Saccone, Nancy; Stallings, Michael; Wall, Tamara L; Webb, Bradley T; Wetherill, Leah; Psychiatric Genomics Consortium Substance Use Disorders Workgroup; Edenberg, Howard J; Agrawal, Arpana; Gelernter, Joel
- Year
- 2020
- Journal
- Molecular psychiatry
- PMID
- 32099098
- DOI
- 10.1038/s41380-020-0677-9
- PMCID
- PMC7392789
To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.
Regional Manhattan plots of the genetic association identified: rs201123820 in the African-ancestry ODunexposed GWAS meta-analysis (A); rs92911211 in the European-ancestry OEcontrols GWAS meta-analysis (B); rs12461856 in the trans-ancestry OEcontrols GWAS meta-analysis (C).
Significant tissue enrichments identified in the African-ancestry ODunexposed meta-analysis (A); the trans-ancestry ODunexposed GWAS meta-analysis (B), and the European-ancestry OEcontrols GWAS meta-analysis (C).
Manhattan plot of the phenome-wide scan conducted in the UK Biobank with respect to rs12461856, rs201123820, and rs9291211 (bottom, center, and upper panels, respectively). As indicated in the legend, the phenotypic categories are color coded. Red dashed line indicates the significance threshold accounting for the number of variants and phenotypes tested (FDR q < 0.05).
Relationship between PRS z scores (A: risk tolerance; B: neuroticism) and effective sample size across the opioid-related phenotypes tested. Each circle represents an individual cohort; the diamond represents the results from the meta-analysis with respect to the phenotypes tested.
1–20 of 40
Next →
No entities extracted from this document yet.
No uploaded files.
Not in any collection.
| Citation | PMID | DOI | Status |
|---|---|---|---|
| American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision. APA Publishing: Washington DC, USA, 2000. | — | — | — |
| Bulik-SullivanBK, LohPR, FinucaneHK, RipkeS, YangJ, Schizophrenia Working Group of the Psychiatric Genomics C LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 2015; 47(3): 291–295.2564263010.1038/ng.3211PMC4495769 | — | — | — |
| BusseJW, CraigieS, SadeghiradB, CoubanR, HongP, OparinY Management of acute musculoskeletal pain (excluding low back pain): protocol for a systematic review and network meta-analysis of randomised trials. BMJ Open 2019; 9(4): e024441.10.1136/bmjopen-2018-024441PMC650032330948570 | — | — | — |
| Centers for Disease Control Prevention. CDC grand rounds: prescription drug overdoses-a U.S. epidemic. MMWR Morb Mortal Wkly Rep 2012; 61(1): 10–13.22237030 | — | — | — |
| ChengZ, ZhouH, ShervaR, FarrerLA, KranzlerHR, GelernterJ. Genome-wide Association Study Identifies a Regulatory Variant of RGMA Associated With Opioid Dependence in European Americans. Biol Psychiatry 2018; 84(10): 762–770.2947869810.1016/j.biopsych.2017.12.016PMC6041180 | — | — | — |
| CiceroTJ, EllisMS. Nonmedical Prescription-Opioid Use and Heroin Use. N Engl J Med 2016; 374(13): 1295–1296.2702893110.1056/NEJMc1601875 | — | — | — |
| ClarkRE, BaxterJD, AwehG, O’ConnellE, FisherWH, BartonBA. Risk Factors for Relapse and Higher Costs Among Medicaid Members with Opioid Dependence or Abuse: Opioid Agonists, Comorbidities, and Treatment History. J Subst Abuse Treat 2015; 57: 75–80.2599767410.1016/j.jsat.2015.05.001PMC4560989 | — | — | — |
| ColeJB, FlorezJC, HirschhornJN. Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic loci and establishes causal relationships between educational attainment and healthy eating. bioRxiv 2019: 662239. | — | — | — |
| Consortium. GP, AutonA, BrooksLD, DurbinRM, GarrisonEP, KangHM A global reference for human genetic variation. Nature 2015; 526(7571): 68–74.2643224510.1038/nature15393PMC4750478 | — | — | — |
| ConsortiumGTEx. Genetic effects on gene expression across human tissues. Nature 2017; 550(7675): 204–213.2902259710.1038/nature24277PMC5776756 | — | — | — |
| de LeeuwCA, MooijJM, HeskesT, PosthumaD. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol 2015; 11(4): e1004219.2588571010.1371/journal.pcbi.1004219PMC4401657 | — | — | — |
| EuesdenJ, LewisCM, O’ReillyPF. PRSice: Polygenic Risk Score software. Bioinformatics 2015; 31(9): 1466–1468.2555032610.1093/bioinformatics/btu848PMC4410663 | — | — | — |
| GelernterJ, KranzlerHR, ShervaR, KoestererR, AlmasyL, ZhaoH Genome-wide association study of opioid dependence: multiple associations mapped to calcium and potassium pathways. Biol Psychiatry 2014; 76(1): 66–74.2414388210.1016/j.biopsych.2013.08.034PMC3992201 | — | — | — |
| GelernterJ, SunN, PolimantiR, PietrzakRH, LeveyD, BryoisJ Genome-wide association study of post-traumatic stress disorder reexperiencing symptoms in >165,000 US veterans. Nature Neuro 2019.10.1038/s41593-019-0447-7PMC695363331358989 | — | — | — |
| GroveJ, RipkeS, AlsTD, MattheisenM, WaltersRK, WonH Identification of common genetic risk variants for autism spectrum disorder. Nat Genet 2019; 51(3): 431–444.3080455810.1038/s41588-019-0344-8PMC6454898 | — | — | — |
| HoffmannTJ, EhretGB, NandakumarP, RanatungaD, SchaeferC, KwokPY Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation. Nat Genet 2017; 49(1): 54–64.2784187810.1038/ng.3715PMC5370207 | — | — | — |
| HowardDM, AdamsMJ, ClarkeTK, HaffertyJD, GibsonJ, ShiraliM Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci 2019; 22(3): 343–352.3071890110.1038/s41593-018-0326-7PMC6522363 | — | — | — |
| HowieBN, DonnellyP, MarchiniJ. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 2009; 5(6): e1000529.1954337310.1371/journal.pgen.1000529PMC2689936 | — | — | — |
| International HapMap Consortium, AltshulerDM, GibbsRA, PeltonenL, AltshulerDM, GibbsRA Integrating common and rare genetic variation in diverse human populations. Nature 2010; 467(7311): 52–58.2081145110.1038/nature09298PMC3173859 | — | — | — |
| JiangW, YuW. Power estimation and sample size determination for replication studies of genome-wide association studies. BMC Genomics 2016; 17 Suppl 1: 3.2681895210.1186/s12864-015-2296-4PMC4895704 | — | — | — |
| KalsiG, EuesdenJ, ColemanJR, DucciF, AlievF, NewhouseSJ Genome-Wide Association of Heroin Dependence in Han Chinese. PLoS One 2016; 11(12): e0167388.2793611210.1371/journal.pone.0167388PMC5147879 | — | — | — |
| Karlsson LinnerR, BiroliP, KongE, MeddensSFW, WedowR, FontanaMA 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(2): 245–257.3064325810.1038/s41588-018-0309-3PMC6713272 | — | — | — |
| KellyTM, DaleyDC, DouaihyAB. Treatment of substance abusing patients with comorbid psychiatric disorders. Addict Behav 2012; 37(1): 11–24.2198178810.1016/j.addbeh.2011.09.010PMC3196788 | — | — | — |
| KichaevG, BhatiaG, LohPR, GazalS, BurchK, FreundMK Leveraging Polygenic Functional Enrichment to Improve GWAS Power. Am J Hum Genet 2019; 104(1): 65–75.3059537010.1016/j.ajhg.2018.11.008PMC6323418 | — | — | — |
| KranzlerHR, ZhouH, KemberRL, Vickers SmithR, JusticeAC, DamrauerS Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nat Commun 2019; 10(1): 1499.3094081310.1038/s41467-019-09480-8PMC6445072 | — | — | — |
| KreekMJ, NielsenDA, ButelmanER, LaForgeKS. Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. Nat Neurosci 2005; 8(11): 1450–1457.1625198710.1038/nn1583 | — | — | — |
| LaiD, WetherillL, BertelsenS, CareyCE, KamarajanC, KapoorM Genome-wide association studies of alcohol dependence, DSM-IV criterion count and individual criteria. Genes Brain Behav 2019; 18(6): e12579.3109016610.1111/gbb.12579PMC6612573 | — | — | — |
| LamM, AwasthiS, WatsonHJ, GoldsteinJ, PanagiotaropoulouG, TrubetskoyV RICOPILI: Rapid Imputation for COnsortias PIpeLIne. Bioinformatics 2019.10.1093/bioinformatics/btz633PMC786804531393554 | — | — | — |
| LeeJJ, WedowR, OkbayA, KongE, MaghzianO, ZacherM Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet 2018; 50(8): 1112–1121.3003839610.1038/s41588-018-0147-3PMC6393768 | — | — | — |
| LiberzonA, SubramanianA, PinchbackR, ThorvaldsdottirH, TamayoP, MesirovJP. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011; 27(12): 1739–1740.2154639310.1093/bioinformatics/btr260PMC3106198 | — | — | — |
| LiuM, JiangY, WedowR, LiY, BrazelDM, ChenF 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(2): 237–244.3064325110.1038/s41588-018-0307-5PMC6358542 | — | — | — |
| MacArthurJ, BowlerE, CerezoM, GilL, HallP, HastingsE The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res 2017; 45(D1): D896–D901.2789967010.1093/nar/gkw1133PMC5210590 | — | — | — |
| MatsudaA, SuzukiY, HondaG, MuramatsuS, MatsuzakiO, NaganoY Large-scale identification and characterization of human genes that activate NF-kappaB and MAPK signaling pathways. Oncogene 2003; 22(21): 3307–3318.1276150110.1038/sj.onc.1206406 | — | — | — |
| NagelM, JansenPR, StringerS, WatanabeK, de LeeuwCA, BryoisJ Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat Genet 2018; 50(7): 920–927.2994208510.1038/s41588-018-0151-7 | — | — | — |
| NelsonEC, AgrawalA, HeathAC, BogdanR, ShervaR, ZhangB Evidence of CNIH3 involvement in opioid dependence. Mol Psychiatry 2016; 21(5): 608–614.2623928910.1038/mp.2015.102PMC4740268 | — | — | — |
| O’ConnellJ, GurdasaniD, DelaneauO, PirastuN, UliviS, CoccaM A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet 2014; 10(4): e1004234.2474309710.1371/journal.pgen.1004234PMC3990520 | — | — | — |
| PasmanJA, VerweijKJH, GerringZ, StringerS, Sanchez-RoigeS, TreurJL GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nat Neurosci 2018; 21(9): 1161–1170.3015066310.1038/s41593-018-0206-1PMC6386176 | — | — | — |
| PerezY, ShorerZ, Liani-LeibsonK, ChabosseauP, KadirR, VolodarskyM SLC30A9 mutation affecting intracellular zinc homeostasis causes a novel cerebro-renal syndrome. Brain 2017; 140(4): 928–939.2833485510.1093/brain/awx013PMC5837213 | — | — | — |
| PrimesG, FiederM. Real-life helping behaviours in North America: A genome-wide association approach. PLoS One 2018; 13(1): e0190950.2932485210.1371/journal.pone.0190950PMC5764334 | — | — | — |
| RamasamyA, TrabzuniD, GuelfiS, VargheseV, SmithC, WalkerR Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci 2014; 17(10): 1418–1428.2517400410.1038/nn.3801PMC4208299 | — | — | — |
| RuddRA, AleshireN, ZibbellJE, GladdenRM. Increases in Drug and Opioid Overdose Deaths--United States, 2000–2014. MMWR Morb Mortal Wkly Rep 2016; 64(50–51): 1378–1382.2672085710.15585/mmwr.mm6450a3 | — | — | — |
| Sanchez-RoigeS, PalmerAA, FontanillasP, ElsonSL, andMe Research Team tSUDWGotPGC, AdamsMJ Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts. Am J Psychiatry 2019; 176(2): 107–118.3033670110.1176/appi.ajp.2018.18040369PMC6365681 | — | — | — |
| Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014; 511(7510): 421–427.2505606110.1038/nature13595PMC4112379 | — | — | — |
| SirugoG, WilliamsSM, TishkoffSA. The Missing Diversity in Human Genetic Studies. Cell 2019; 177(1): 26–31.3090154310.1016/j.cell.2019.02.048PMC7380073 | — | — | — |
| SudlowC, GallacherJ, AllenN, BeralV, BurtonP, DaneshJ UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015; 12(3): e1001779.2582637910.1371/journal.pmed.1001779PMC4380465 | — | — | — |
| SulJH, HanB, YeC, ChoiT, EskinE. Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approaches. PLoS Genet 2013; 9(6): e1003491.2378529410.1371/journal.pgen.1003491PMC3681686 | — | — | — |
| SullivanPF, AgrawalA, BulikCM, AndreassenOA, BorglumAD, BreenG Psychiatric Genomics: An Update and an Agenda. Am J Psychiatry 2018; 175(1): 15–27.2896944210.1176/appi.ajp.2017.17030283PMC5756100 | — | — | — |
| SullivanPF, GeschwindDH. Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders. Cell 2019; 177(1): 162–183.3090153810.1016/j.cell.2019.01.015PMC6432948 | — | — | — |
| VisscherPM, WrayNR, ZhangQ, SklarP, McCarthyMI, BrownMA 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet 2017; 101(1): 5–22.2868685610.1016/j.ajhg.2017.06.005PMC5501872 | — | — | — |
| VolkowND, FriedenTR, HydePS, ChaSS. Medication-assisted therapies--tackling the opioid-overdose epidemic. N Engl J Med 2014; 370(22): 2063–2066.2475859510.1056/NEJMp1402780 | — | — | — |
| WaltersRK, PolimantiR, JohnsonEC, McClintickJN, AdamsMJ, AdkinsAE Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat Neurosci 2018; 21(12): 1656–1669.3048294810.1038/s41593-018-0275-1PMC6430207 | — | — | — |
| WatanabeK, StringerS, FreiO, Umicevic MirkovM, de LeeuwC, PoldermanTJC A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet 2019; 51(9): 1339–1348.3142778910.1038/s41588-019-0481-0 | — | — | — |
| WatanabeK, TaskesenE, van BochovenA, PosthumaD. Functional mapping and annotation of genetic associations with FUMA. Nat Commun 2017; 8(1): 1826.2918405610.1038/s41467-017-01261-5PMC5705698 | — | — | — |
| WillerCJ, LiY, AbecasisGR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010; 26(17): 2190–2191.2061638210.1093/bioinformatics/btq340PMC2922887 | — | — | — |
| WrayNR, RipkeS, MattheisenM, TrzaskowskiM, ByrneEM, AbdellaouiA Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 2018; 50(5): 668–681.2970047510.1038/s41588-018-0090-3PMC5934326 | — | — | — |
| YasukochiY, SakumaJ, TakeuchiI, KatoK, OguriM, FujimakiT Identification of CDC42BPG as a novel susceptibility locus for hyperuricemia in a Japanese population. Mol Genet Genomics 2018; 293(2): 371–379.2912444310.1007/s00438-017-1394-1PMC5854719 | — | — | — |
| ZouJ, ZhouJ, FallerS, BrownR, EskinE. Accurate modeling of replication rates in genome-wide association studies by accounting for winner’s curse and study-specific heterogeneity. bioRxiv 2019: 856898.10.1093/g3journal/jkac261PMC971338036250793 | — | — | — |
In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Genome-wide meta-analyses of cross substance use disorders in diverse populations. | Lai D et al. | — | 2026 | → |
| Large-scale behavioral characterization of oxycodone self-administration in heterogeneous stock rats reveals initial analgesic effects are associated with addiction-like behaviors. | Kallupi M et al. | — | 2026 | → |
| Optimizing Control Definitions in Opioid Use Disorder Genetic Research Using Electronic Health Records | Niarchou M et al. | — | 2026 | → |
| Addiction Susceptibility: Genetic Factors, Personality Traits, and Epigenetic Interactions with the Gut Microbiome. | Borrego-Ruiz A et al. | — | 2025 | → |
| Cell type-specific multi-omics analysis of cocaine use disorder in the human caudate nucleus. | Zillich L et al. | — | 2025 | → |
| Examining the genetic links between clusters of immune-mediated diseases and psychiatric disorders. | Breunig S et al. | — | 2025 | → |
| Genetic Associations of Persistent Opioid Use After Surgery Point to OPRM1 but Not Other Opioid-Related Loci as the Main Driver of Opioid Use Disorder. | Annis AC et al. | — | 2025 | → |
| Genome-wide association study reveals multiple loci for nociception and opioid consumption behaviors associated with heroin vulnerability in outbred rats. | Kuhn BN et al. | — | 2025 | → |
| Identifying bio-behavioural signatures of persistent opioid use risk in trauma injury patients: a protocol for a prospective cohort study. | Schmitz JM et al. | — | 2025 | → |
| Identifying compounds to treat opiate use disorder by leveraging multi-omic data integration and multiple drug repurposing databases. | Stratford JK et al. | — | 2025 | → |
| Impulsivity facets and substance use involvement: insights from genomic structural equation modeling. | Vilar-Ribó L et al. | — | 2025 | → |
| Scoping review of associations between cytochrome P450 3A4/5 single nucleotide polymorphisms and risk factors for fentanyl overdose. | Petrovitch D et al. | — | 2025 | → |
| The BioSUD Biobank as a genomic resource for substance use disorders in Italy. | Ribatti RM et al. | — | 2025 | → |
| A genome-wide Association study of the Count of Codeine prescriptions. | Song W et al. | — | 2024 | → |
| An emerging multi-omic understanding of the genetics of opioid addiction. | Johnson EO et al. | — | 2024 | → |
| Considerations for the application of polygenic scores to clinical care of individuals with substance use disorders. | Kember RL et al. | — | 2024 | → |
| Differential etiologic associations of heroin use and prescription opioid misuse with psychopathology. | Dash GF et al. | — | 2024 | → |
| DNA methylation as a possible mechanism linking childhood adversity and health: results from a 2-sample mendelian randomization study. | Schuurmans IK et al. | — | 2024 | → |
| Generalized genetic liability to substance use disorders. | Miller AP et al. | — | 2024 | → |
| Neuronal-specific methylome and hydroxymethylome analysis reveal significant loci associated with alcohol use disorder. | Andrade-Brito DE 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 | → |
| The association between the Glutathione S-transferase polymorphisms and addiction to opioids and methamphetamine in the Iranian population. | Eskandarion MR et al. | — | 2024 | → |
| The genetic landscape of substance use disorders. | Gerring ZF et al. | — | 2024 | → |
| The interaction effects of age, APOE and common environmental risk factors on human brain structure. | Chen J et al. | — | 2024 | → |
| The risk of Alzheimer's disease and cognitive impairment characteristics in eight mental disorders: A UK Biobank observational study and Mendelian randomization analysis. | Liu Y et al. | — | 2024 | → |
| A review of the genomics of neonatal abstinence syndrome. | Yen E et al. | — | 2023 | → |
| Cross-phenotype relationship between opioid use disorder and suicide attempts: new evidence from polygenic association and Mendelian randomization analyses. | Huang Y et al. | — | 2023 | → |
| Exploratory factor analysis of shared and specific genetic associations in depression and anxiety. | Li C et al. | — | 2023 | → |
| Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism. | Burstein D 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 | → |
| Induction of dopaminergic neurons for neuronal subtype-specific modeling of psychiatric disease risk. | Powell SK et al. | — | 2023 | → |
| Neuronal-specific methylome and hydroxymethylome analysis reveal replicated and novel loci associated with alcohol use disorder | Andrade-Brito DE et al. | — | 2023 | — |
| Polygenic scores for psychiatric disorders in a diverse postmortem brain tissue cohort. | Duncan L et al. | — | 2023 | → |
| Predicting Heterogeneity in Patient Response to Morphine Treatment for Neonatal Opioid Withdrawal Syndrome. | Smolyak D et al. | — | 2023 | → |
| Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition. | Zhou JL et al. | — | 2023 | → |
| Specificity in genetic and environmental risk for prescription opioid misuse and heroin use. | Dash GF et al. | — | 2023 | → |
| Testing the causal relationships of physical activity and sedentary behaviour with mental health and substance use disorders: a Mendelian randomisation study. | Iob E et al. | — | 2023 | → |
| The Collaborative Study on the Genetics of Alcoholism: Overview. | Agrawal A et al. | — | 2023 | → |
| The Concept of Resistance to Substance Use and a Research Approach: The Resist! Project. | Vanyukov MM et al. | — | 2023 | → |
| The Genetically Informed Neurobiology of Addiction (GINA) model. | Bogdan R et al. | — | 2023 | → |
| Transcriptional signatures of heroin intake and relapse throughout the brain reward circuitry in male mice. | Browne CJ et al. | — | 2023 | → |
| Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing. | Grotzinger AD et al. | — | 2023 | → |
| Accelerating Opioid Use Disorders Research by Integrating Multiple Data Modalities. | Bianchi SB et al. | — | 2022 | → |
| Association of polygenic risk scores and hair cortisol with mental health trajectories during COVID lockdown. | Ahrens KF et al. | — | 2022 | → |
| Common and distinguishing genetic factors for substance use behavior and disorder: an integrated analysis of genomic and transcriptomic studies from both human and animal studies. | Chang XW et al. | — | 2022 | → |
| Convergence of case-specific epigenetic alterations identify a confluence of genetic vulnerabilities tied to opioid overdose. | Corradin O et al. | — | 2022 | → |
| Cross-ancestry meta-analysis of opioid use disorder uncovers novel loci with predominant effects in brain regions associated with addiction. | Kember RL et al. | — | 2022 | → |
| Epigenetic Signatures of Smoking in Five Brain Regions. | Zillich L et al. | — | 2022 | → |
| Genetic overlap between mood instability and alcohol-related phenotypes suggests shared biological underpinnings. | Icick R et al. | — | 2022 | → |
| Genetics in psychiatry: Methods, clinical applications and future perspectives. | Fabbri C | — | 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 | → |
| Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data. | Jennings MV et al. | — | 2022 | → |
| Molecular rhythm alterations in prefrontal cortex and nucleus accumbens associated with opioid use disorder. | Xue X et al. | — | 2022 | → |
| Multi-omics signatures of alcohol use disorder in the dorsal and ventral striatum. | Zillich L et al. | — | 2022 | → |
| Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond. | Gaddis N et al. | — | 2022 | → |
| Pairwise genetic meta-analyses between schizophrenia and substance dependence phenotypes reveals novel association signals with pharmacological significance. | Greco LA et al. | — | 2022 | → |
| Substance abuse and the risk of severe COVID-19: Mendelian randomization confirms the causal role of opioids but hints a negative causal effect for cannabinoids. | Jabalameli MR et al. | — | 2022 | → |
| Substance use and common contributors to morbidity: A genetics perspective. | Sanchez-Roige S et al. | — | 2022 | → |
| The addiction risk factor: A unitary genetic vulnerability characterizes substance use disorders and their associations with common correlates. | Hatoum AS et al. | — | 2022 | → |
| Biobank Scale Pharmacogenomics Informs the Genetic Underpinnings of Simvastatin Use. | Wendt FR et al. | — | 2021 | → |
| Biomarkers to predict staging and treatment response in opioid dependence: A narrative review. | Vorspan F et al. | — | 2021 | → |
| Epigenome-wide study of brain DNA methylation following acute opioid intoxication. | Shu C et al. | — | 2021 | → |
| Genetics of substance use disorders: a review. | Deak JD et al. | — | 2021 | → |
| Genetics of substance use disorders in the era of big data. | Gelernter J et al. | — | 2021 | → |
| Genome-wide association study of phenotypes measuring progression from first cocaine or opioid use to dependence reveals novel risk genes. | Sherva R et al. | — | 2021 | → |
| Genome-wide association study of problematic opioid prescription use in 132,113 23andMe research participants of European ancestry. | Sanchez-Roige S et al. | — | 2021 | → |
| Genomic and Personalized Medicine Approaches for Substance Use Disorders (SUDs) Looking at Genome-Wide Association Studies. | Cozzoli D et al. | — | 2021 | → |
| Multi-environment gene interactions linked to the interplay between polysubstance dependence and suicidality. | Polimanti R et al. | — | 2021 | → |
| The Cocaine and Oxycodone Biobanks, Two Repositories from Genetically Diverse and Behaviorally Characterized Rats for the Study of Addiction. | Carrette LLG et al. | — | 2021 | → |
| The Genomics of Opioid Addiction Longitudinal Study (GOALS): study design for a prospective evaluation of genetic and non-genetic factors for development of and recovery from opioid use disorder. | Heil J et al. | — | 2021 | → |
| The phenomics and genetics of addictive and affective comorbidity in opioid use disorder. | Freda PJ et al. | — | 2021 | → |
| Transcriptional Alterations in Dorsolateral Prefrontal Cortex and Nucleus Accumbens Implicate Neuroinflammation and Synaptic Remodeling in Opioid Use Disorder. | Seney ML et al. | — | 2021 | → |
| Two data-driven approaches to identifying the spectrum of problematic opioid use: A pilot study within a chronic pain cohort. | Schirle L et al. | — | 2021 | → |
| Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): II. Externalizing superspectrum. | Krueger RF et al. | — | 2021 | → |
| Genetic Architecture and Molecular Neuropathology of Human Cocaine Addiction. | Huggett SB et al. | — | 2020 | → |
| Genetic variation regulates opioid-induced respiratory depression in mice. | Bubier JA et al. | — | 2020 | → |
| Genome-wide association analysis of opioid use disorder: A novel approach using clinical data. | Song W et al. | — | 2020 | → |
| Heterogeneity and Polygenicity in Psychiatric Disorders: A Genome-Wide Perspective. | Wendt FR et al. | — | 2020 | → |
| Molecular Psychiatry, August 2020: new impact factor, and highlights of recent advances in psychiatry, including an overview of the brain's response to stress during infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). | Licinio J et al. | — | 2020 | → |