Genome-wide association study of phenotypes measuring progression from first cocaine or opioid use to dependence reveals novel risk genes.
paper
Primary
Public
- Authors
- Sherva, Richard; Zhu, Congcong; Wetherill, Leah; Edenberg, Howard J; Johnson, Emma; Degenhardt, Louisa; Agrawal, Arpana; Martin, Nicholas G; Nelson, Elliot; Kranzler, Henry R; Gelernter, Joel; Farrer, Lindsay A
- Year
- 2021
- Journal
- Exploration of medicine
- PMID
- 34124712
- DOI
- 10.37349/emed.2021.00032
- PMCID
- PMC8192073
AIM: Substance use disorders (SUD) result in substantial morbidity and mortality worldwide. Opioids, and to a lesser extent cocaine, contribute to a large percentage of this health burden. Despite their high heritability, few genetic risk loci have been identified for either opioid or cocaine dependence (OD or CD, respectively). A genome-wide association study of OD and CD related phenotypes reflecting the time between first self-reported use of these substances and a first DSM-IV dependence diagnosis was conducted. METHODS: Cox proportional hazards regression in a discovery sample of 6,188 African-Americans (AAs) and 6,835 European-Americans (EAs) participants in a genetic study of multiple substance dependence phenotypes were used to test for association between genetic variants and these outcomes. The top findings were tested for replication in two independent cohorts. RESULTS: In the discovery sample, three independent regions containing variants associated with time to dependence at < 5 x 10 were identified, one (rs61835088 = 1.03 x 10) for cocaine in the combined EA-AA meta-analysis in the gene on chromosome 1, and two for opioids in the AA portion of the sample in intergenic regions of chromosomes 4 (rs4860439, = 1.37 x 10) and 9 (rs7032521, = 3.30 x 10). After meta-analysis with data from the replication cohorts, the signal at rs61835088 improved (HR = 0.87, = 3.71 x 10 and an intergenic SNP on chromosome 21 (rs2825295, HR = 1.14, = 2.57 x 10) that missed the significance threshold in the AA discovery sample became genome-wide significant (GWS) for CD. CONCLUSIONS: Although the two GWS variants are not in genes with obvious links to SUD biology and have modest effect sizes, they are statistically robust and show evidence for association in independent samples. These results may point to novel pathways contributing to disease progression and highlight the utility of related phenotypes to better understand the genetics of SUDs.
Regional Manhattan plots for the FAM78B region on chromosome 1 (A) and the intergenic region on chromosome 21 (B) for time to CD in AAs in the Yale-Penn cohort. SNPs are color coded according to the correlation coefficient (r2) in the 1,000 Genomes African reference panel with the top-ranked SNP. Light blue line indicates the observed recombination rate (right-side y-axis)
← Prev
21–32 of 32
No entities extracted from this document yet.
No uploaded files.
Not in any collection.
| Citation | PMID | DOI | Status |
|---|---|---|---|
| AnthonyJ, WarnerL, KesslerR. Comparative epidemiology of dependence on tobacco, alcohol, controlled substances, and inhalants: basic findings from the National Comorbidity Survey. Exp Clin Psychopharmacol. 1994;2:244–68. | — | — | — |
| Associations of nonmedical pain reliever use and initiation of heroin use in the United States [Internet]. Rockville: Substance Abuse and Mental Health Services Administration; c2013 [cited 2020 Dec 1]. Available from: https://www.samhsa.gov/data/sites/default/files/DR006/DR006/nonmedical-pain-reliever-use-2013.htm | — | — | — |
| BayerKU, De KoninckP, LeonardAS, HellJW, SchulmanH. Interaction with the NMDA receptor locks CaMKII in an active conformation. Nature. 2001;411:801–5.1145905910.1038/35081080 | — | — | — |
| BucholzKK, CadoretR, CloningerCR, DinwiddieSH, HesselbrockVM, NurnbergerJIJr, A new, semi-structured psychiatric interview for use in genetic linkage studies: a report on the reliability of the SSAGA. J Stud Alcohol. 1994;55:149–58.818973510.15288/jsa.1994.55.149 | — | — | — |
| Cabana-DominguezJ, ShivalikanjliA, Fernandez-CastilloN, CormandB. Genome-wide association meta-analysis of cocaine dependence: shared genetics with comorbid conditions. Prog Neuropsychopharmacol Biol Psychiatry. 2019;94:109667.3121201010.1016/j.pnpbp.2019.109667 | — | — | — |
| CaseA, DeatonA. Mortality and morbidity in the 21st century. Brookings Pap Econ Act. 2017;2017: 397–476.2903346010.1353/eca.2017.0005PMC5640267 | — | — | — |
| ChanB, KondoK, FreemanM, AyersC, MontgomeryJ, KansagaraD. Pharmacotherapy for cocaine use disorder-a systematic review and meta-analysis. J Gen Intern Med. 2019;34:2858–73.3118368510.1007/s11606-019-05074-8PMC6854210 | — | — | — |
| 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;84:762–70.2947869810.1016/j.biopsych.2017.12.016PMC6041180 | — | — | — |
| ClarkCB, ZyamboCM, LiY, CropseyKL. The impact of non-concordant self-report of substance use in clinical trials research. Addict Behav. 2016;58:74–9.2692172110.1016/j.addbeh.2016.02.023PMC4808339 | — | — | — |
| ClaussnitzerM, DankelSN, KimKH, QuonG, MeulemanW, HaugenC, FTO obesity variant circuitry and adipocyte browning in humans. N Engl J Med. 2015;373:895–907.2628774610.1056/NEJMoa1502214PMC4959911 | — | — | — |
| ComptonWM, ThomasYF, StinsonFS, GrantBF. Prevalence, correlates, disability, and comorbidity of DSM-IV drug abuse and dependence in the United States: results from the national epidemiologic survey on alcohol and related conditions. Arch Gen Psychiatry. 2007;64:566–76.1748560810.1001/archpsyc.64.5.566 | — | — | — |
| CoxDR. Regression models and life-tables. J R Stat Soc Series B Stat Methodol. 1972;34:187–220. | — | — | — |
| DasS, ForerL, SchönherrS, SidoreC, LockeAE, KwongA, Next-generation genotype imputation service and methods. Nat Genet. 2016;48:1284–7.2757126310.1038/ng.3656PMC5157836 | — | — | — |
| DegenhardtL, GrebelyJ, StoneJ, HickmanM, VickermanP, MarshallBDL, Global patterns of opioid use and dependence: harms to populations, interventions, and future action. Lancet. 2019;394:1560–79.3165773210.1016/S0140-6736(19)32229-9PMC7068135 | — | — | — |
| DegenhardtL, SingletonJ, CalabriaB, McLarenJ, KerrT, MehtaS, Mortality among cocaine users: a systematic review of cohort studies. Drug Alcohol Depend. 2011;113:88–95.2082894210.1016/j.drugalcdep.2010.07.026 | — | — | — |
| DelaneauO, MarchiniJ, ZaguryJF. A linear complexity phasing method for thousands of genomes. Nat Methods. 2011;9:179–81.2213882110.1038/nmeth.1785 | — | — | — |
| EdenbergHJ. The collaborative study on the genetics of alcoholism: an update. Alcohol Res Health. 2002;26:214–8.12875050PMC6683843 | — | — | — |
| FagerbergL, HallstromBM, OksvoldP, KampfC, DjureinovicD, OdebergJ, Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol Cell Proteomics. 2014;13:397–406.10.1074/mcp.M113.035600PMC391664224309898 | — | — | — |
| FanN, AnL, ZhangM, HeH, ZhouY, OuY. GRIN2B gene polymorphism in chronic ketamine users. Am J Addict. 2020;29:105–10.3195710610.1111/ajad.12984 | — | — | — |
| FraylingTM, TimpsonNJ, WeedonMN, ZegginiE, FreathyRM, LindgrenCM, A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316:889–94.1743486910.1126/science.1141634PMC2646098 | — | — | — |
| GBD 2016 Alcohol and Drug Use Collaborators. The global burden of disease attributable to alcohol and drug use in 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Psychiatry. 2018;5:987–1012.3039273110.1016/S2215-0366(18)30337-7PMC6251968 | — | — | — |
| GelernterJ, KranzlerHR, ShervaR, KoestererR, AlmasyL, ZhaoH, Genome-wide association study of opioid dependence: multiple associations mapped to calcium and potassium pathways. Biol Psychiatry. 2014;76:66–74.2414388210.1016/j.biopsych.2013.08.034PMC3992201 | — | — | — |
| GelernterJ, PanhuysenC, WeissR, BradyK, HesselbrockV, RounsavilleB, Genomewide linkage scan for cocaine dependence and related traits: significant linkages for a cocaine-related trait and cocaine-induced paranoia. Am J Med Genet B Neuropsychiatr Genet. 2005;136B:45–52.1590929410.1002/ajmg.b.30189 | — | — | — |
| GelernterJ, ShervaR, KoestererR, AlmasyL, ZhaoH, KranzlerHR, Genome-wide association study of cocaine dependence and related traits: FAM53B identified as a risk gene. Mol Psychiatry. 2014;19:717–23.2395896210.1038/mp.2013.99PMC3865158 | — | — | — |
| GoodyearK, LeeMR, SchwandtML, HodgkinsonCA, LeggioL. Hepatic, lipid and genetic factors associated with obesity: crosstalk with alcohol dependence? World J Biol Psychiatry. 2017;18:120–8.2790521310.1080/15622975.2016.1249952PMC5382351 | — | — | — |
| GramageE, Perez-GarciaC, Vicente-RodriguezM, BollenS, RojoL, HerradonG. Regulation of extinction of cocaine-induced place preference by midkine is related to a differential phosphorylation of peroxiredoxin 6 in dorsal striatum. Behav Brain Res. 2013;253:223–31.2389192910.1016/j.bbr.2013.07.026 | — | — | — |
| GramageE, PutelliA, PolancoMJ, Gonzalez-MartinC, EzquerraL, AlguacilLF, The neurotrophic factor pleiotrophin modulates amphetamine-seeking behaviour and amphetamine-induced neurotoxic effects: evidence from pleiotrophin knockout mice. Addict Biol. 2010;15:403–12.2019294510.1111/j.1369-1600.2009.00202.x | — | — | — |
| GramageE, Vicente-RodriguezM, HerradonG. Pleiotrophin modulates morphine withdrawal but has no effects on morphine-conditioned place preference. Neurosci Lett. 2015;604:75–9.2622225710.1016/j.neulet.2015.07.022 | — | — | — |
| HesselbrockM, EastonC, BucholzKK, SchuckitM, HesselbrockV. A validity study of the SSAGA--a comparison with the SCAN. Addiction. 1999;94:1361–70.1061572110.1046/j.1360-0443.1999.94913618.x | — | — | — |
| HowieB, FuchsbergerC, StephensM, MarchiniJ, AbecasisGR. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet. 2012;44:955–9.2282051210.1038/ng.2354PMC3696580 | — | — | — |
| HuttlinEL, BrucknerRJ, PauloJA, CannonJR, TingL, BaltierK, Architecture of the human interactome defines protein communities and disease networks. Nature. 2017;545:505–9.2851444210.1038/nature22366PMC5531611 | — | — | — |
| JonesCM. Heroin use and heroin use risk behaviors among nonmedical users of prescription opioid pain relievers-United States, 2002-2004 and 2008-2010. Drug Alcohol Depend. 2013;132:95–100.2341061710.1016/j.drugalcdep.2013.01.007 | — | — | — |
| KendlerKS, KarkowskiLM, NealeMC, PrescottCA. Illicit psychoactive substance use, heavy use, abuse, and dependence in a US population-based sample of male twins. Arch Gen Psychiatry. 2000;57:261–9.1071191210.1001/archpsyc.57.3.261 | — | — | — |
| KendlerKS, PrescottCA. Cocaine use, abuse and dependence in a population-based sample of female twins. Br J Psychiatry. 1998;173:345–50.992604110.1192/bjp.173.4.345 | — | — | — |
| MalisonRT, KalayasiriR, SanichwankulK, SughondhabiromA, MutiranguraA, PittmanB, Inter-rater reliability and concurrent validity of DSM-IV opioid dependence in a Hmong isolate using the Thai version of the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA). Addict Behav. 2011;36:156–60.2088869910.1016/j.addbeh.2010.08.031PMC2981662 | — | — | — |
| MistryCJ, BaworM, DesaiD, MarshDC, SamaanZ. Genetics of opioid dependence: a review of the genetic contribution to opioid dependence. Curr Psychiatry Rev. 2014;10:156–67.2524290810.2174/1573400510666140320000928PMC4155832 | — | — | — |
| NelsonEC, AgrawalA, HeathAC, BogdanR, ShervaR, ZhangB, Evidence of CNIH3 involvement in opioid dependence. Mol Psychiatry. 2016;21:608–14.2623928910.1038/mp.2015.102PMC4740268 | — | — | — |
| NelsonEC, LynskeyMT, HeathAC, WrayN, AgrawalA, ShandFL, ANKK1, TTC12, and NCAM1 polymorphisms and heroin dependence: importance of considering drug exposure. JAMA Psychiatry. 2013;70:325–33.2330348210.1001/jamapsychiatry.2013.282PMC3789525 | — | — | — |
| NelsonEC, LynskeyMT, HeathAC, WrayN, AgrawalA, ShandFL, Association of OPRD1 polymorphisms with heroin dependence in a large case-control series. Addict Biol. 2014;19:111–21.2250094210.1111/j.1369-1600.2012.00445.xPMC3867542 | — | — | — |
| Pierucci-LaghaA, GelernterJ, ChanG, AriasA, CubellsJF, FarrerL, Reliability of DSM-IV diagnostic criteria using the semi-structured assessment for drug dependence and alcoholism (SSADDA). Drug Alcohol Depend. 2007;91:85–90.1759053610.1016/j.drugalcdep.2007.04.014PMC2039919 | — | — | — |
| PolimantiR, WaltersRK, JohnsonEC, McClintickJN, AdkinsAE, AdkinsDE, Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium. Mol Psychiatry. 2020;25:1673–87.3209909810.1038/s41380-020-0677-9PMC7392789 | — | — | — |
| PolimantiR, ZhangH, SmithAH, ZhaoH, FarrerLA, KranzlerHR, Genome-wide association study of body mass index in subjects with alcohol dependence. Addict Biol. 2017;22:535–49.2645873410.1111/adb.12317PMC5102811 | — | — | — |
| PruimRJ, WelchRP, SannaS, TeslovichTM, ChinesPS, GliedtTP, LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26:2336–7.2063420410.1093/bioinformatics/btq419PMC2935401 | — | — | — |
| ReboussinBA, AnthonyJC. Is there epidemiological evidence to support the idea that a cocaine dependence syndrome emerges soon after onset of cocaine use? Neuropsychopharmacology. 2006;31:2055–64.1648208910.1038/sj.npp.1301037PMC2575802 | — | — | — |
| ReichT, EdenbergHJ, GoateA, WilliamsJT, RiceJP, Van EerdeweghP, Genome-wide search for genes affecting the risk for alcohol dependence. Am J Med Genet. 1998;81:207–15.9603606 | — | — | — |
| RoyA Characteristics of cocaine dependent patients who attempt suicide. Arch Suicide Res. 2009;13: 46–51.1912310810.1080/13811110802572130 | — | — | — |
| RummansTA, BurtonMC, DawsonNL. How good intentions contributed to bad outcomes: the opioid crisis. Mayo Clin Proc. 2018;93:344–50.2950256410.1016/j.mayocp.2017.12.020 | — | — | — |
| SartorCE, KranzlerHR, GelernterJ. Rate of progression from first use to dependence on cocaine or opioids: a cross-substance examination of associated demographic, psychiatric, and childhood risk factors. Addict Behav. 2014;39:473–9.2423878210.1016/j.addbeh.2013.10.021PMC3855905 | — | — | — |
| SevgiM, RigouxL, KuhnAB, MauerJ, SchilbachL, HessME, An obesity-predisposing variant of the FTO gene regulates D2R-dependent reward learning. J Neurosci. 2015;35:12584–92.2635492310.1523/JNEUROSCI.1589-15.2015PMC6605390 | — | — | — |
| ShervaR, WangQ, KranzlerH, ZhaoH, KoestererR, HermanA, Genome-wide association study of cannabis dependence severity, novel risk variants, and shared genetic risks. JAMA Psychiatry. 2016;73:472–80.2702816010.1001/jamapsychiatry.2016.0036PMC4974817 | — | — | — |
| SmemoS, TenaJJ, KimKH, GamazonER, SakabeNJ, Gomez-MarinC, Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature. 2014;507:371–5.2464699910.1038/nature13138PMC4113484 | — | — | — |
| SmithAH, JensenKP, LiJ, NunezY, FarrerLA, HakonarsonH, Genome-wide association study of therapeutic opioid dosing identifies a novel locus upstream of OPRM1. Mol Psychiatry. 2017;22:346–52.2811573910.1038/mp.2016.257PMC5407902 | — | — | — |
| Sobczyk-KopciolA, BrodaG, WojnarM, KurjataP, JakubczykA, KlimkiewiczA, Inverse association of the obesity predisposing FTO rs9939609 genotype with alcohol consumption and risk for alcohol dependence. Addiction. 2011;106:739–48.2118255410.1111/j.1360-0443.2010.03248.x | — | — | — |
| SzklarczykD, GableAL, LyonD, JungeA, WyderS, Huerta-CepasJ, STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13.3047624310.1093/nar/gky1131PMC6323986 | — | — | — |
| WatanabeK, TaskesenE, van BochovenA, PosthumaD. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8:1826.2918405610.1038/s41467-017-01261-5PMC5705698 | — | — | — |
| WetherillL, LaiD, JohnsonEC, AnokhinA, BauerL, BucholzKK, Genome-wide association study identifies loci associated with liability to alcohol and drug dependence that is associated with variability in reward-related ventral striatum activity in African- and European-Americans. Genes Brain Behav. 2019;18:e12580.3109917510.1111/gbb.12580PMC6726116 | — | — | — |
| WillerCJ, LiY, AbecasisGR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.2061638210.1093/bioinformatics/btq340PMC2922887 | — | — | — |
| XieP, KranzlerHR, KrystalJH, FarrerLA, ZhaoH, GelernterJ. Deep resequencing of 17 glutamate system genes identifies rare variants in DISC1 and GRIN2B affecting risk of opioid dependence. Addict Biol. 2014;19:955–64.2385540310.1111/adb.12072PMC3815683 | — | — | — |
| ZhengZ, HuangD, WangJ, ZhaoK, ZhouY, GuoZ, QTLbase: an integrative resource for quantitative trait loci across multiple human molecular phenotypes. Nucleic Acids Research. 2019;48:D983–91.10.1093/nar/gkz888PMC694307331598699 | — | — | — |
| ZhouH, RentschCT, ChengZ, KemberRL, NunezYZ, ShervaRM, Association of OPRM1 functional coding variant with opioid use disorder: a genome-wide association study. JAMA Psychiatry. 2020;77:1072–80.3249209510.1001/jamapsychiatry.2020.1206PMC7270886 | — | — | — |
In this knowledge base
| Title | Year | PMID |
|---|---|---|
| Genome-wide association study of problematic opioid prescription use in 132,113 23andMe research participants of European ancestry. | 2021 | 34728798 |
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Exploring the early drivers of pain in Parkinson's disease. | Liu S et al. | — | 2025 | → |
| Relationship between Transposable Elements and behavioral traits: insights from six genetic isolates from North-Eastern Italy | Modenini G 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 | → |
| Variability of transposable elements in six genetic isolates from North-Eastern Italy and their relationship with alcohol consumption, tobacco use and BMI. | Modenini G et al. | — | 2025 | → |
| Considerations for the application of polygenic scores to clinical care of individuals with substance use disorders. | Kember RL et al. | — | 2024 | → |
| Decoding cocaine-induced proteomic adaptations in the mouse nucleus accumbens. | Mews P et al. | — | 2024 | → |
| G protein-coupled receptor (GPCR) pharmacogenomics. | Thompson MD et al. | — | 2024 | → |
| Associations Between Cannabis Use, Polygenic Liability for Schizophrenia, and Cannabis-related Experiences in a Sample of Cannabis Users. | Johnson EC et al. | — | 2023 | → |
| Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice. | Khan AH et al. | — | 2023 | → |
| The Collaborative Study on the Genetics of Alcoholism: Overview. | Agrawal A et al. | — | 2023 | → |
| Molecular genetics of cocaine use disorders in humans. | Fernàndez-Castillo N et al. | — | 2022 | → |
| The collaborative cross strains and their founders vary widely in cocaine-induced behavioral sensitization. | Schoenrock SA et al. | — | 2022 | → |
| Genome-wide association study of problematic opioid prescription use in 132,113 23andMe research participants of European ancestry. | Sanchez-Roige S et al. | — | 2021 | → |