Genome-wide association study in people of European and African ancestry and multi-trait analysis of opioid use disorder identify 19 independent significant genome-wide risk loci

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  • Hedegaard, M, Miniño, A, & Warner, M NCHS Data Sheet: January 2020: Drug Overdose Deaths in the United States, 1999-2018. 2020; Retrieved from https://stacks.cdc.gov/view/cdc/84647-h.pdf.

  • American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. 2013; Arlington, VA: American Psychiatric Association.

  • Gelernter J, Kranzler HR, Sherva R, Koesterer R, Almasy L, Zhao H, et al. Genome-wide association study in opioid dependence: multiple associations mapped to calcium and potassium pathways. Biol Psychiatrist. 2014;76:66–74.

    CAS Google Scholar Article

  • Nelson EC, Agrawal A, Heath AC, Bogdan R, Sherva R, Zhang B, et al. Evidence for the involvement of CNIH3 in opiate addiction. Mol Psychiatry. 2016;21:608–14.

    CAS Google Scholar Article

  • Cheng Z, Zhou H, Sherva R, Farrer LA, Kranzler HR, Gelernter J. A genome-wide association study identifies a regulatory variant of RGMA associated with opioid dependence in European Americans. Biol Psychiatrist. 2018;84:762–70.

    CAS Google Scholar Article

  • Polimanti R, Walters RK, Johnson EC, Mcclintick JN, Adkins AE, Adkins DE, et al. Leveraging genome-wide data to study differences between opioid use and opioid addiction in 41,176 people from the Psychiatric Genomics Consortium. Mol Psychiatrist. 2020;25:1673–87.

    Google Scholar article

  • Zhou H, Rentsch CT, Cheng Z, Kember RL, Nunez YZ, Sherva RM, et al. Association of functional coding variant OPRM1 with opioid use disorder. JAMA Psychiatr. 2020;77:1072.

    Google Scholar article

  • Deak JD, Johnson EC. Genetics of substance use disorders: a review. Psychological Medicine 2021;51:2189–220.

  • Gelernter J, Polimanti R. Genetics of substance use disorders in the age of big data. Nat Rev Genet. 2021;22:712–29.

    CAS Google Scholar Article

  • Kranzler HR, Zhou H, Kember RL, Vickers Smith R, Justice AC, Damrauer S, et al. Genome-wide association study of alcohol use and use disorders in 274,424 people from multiple populations. Nat.Common. 2019; 10. https://doi.org/10.1038/s41467-019-09480-8.

  • Zhou H, Sealock JM, Sanchez-Roige S, Clarke TK, Levey DF, Cheng Z, et al. A genome-wide meta-analysis of problematic alcohol use in 435,563 people provides insight into the biology and relationships to other traits. Nat Neurosci. 2020;23:809–18.

    CAS Google Scholar Article

  • Johnson EC, Demontis D, Thorgeirsson TE, Walters RK, Polimanti R, Hatoum AS, et al. A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry. 2020;7:1032–45.

    Google Scholar article

  • Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet. 2018;50:229–37.

    CAS Google Scholar Article

  • Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, et al. Association studies involving up to 1.2 million individuals provide new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019;51:237–44.

    CAS Google Scholar Article

  • Song W, Kossowsky J, Torous J, Chen CY, Huang H, Mukamal KJ, et al. Genome-wide association analysis of opioid use disorder: a new approach using clinical data. Alcohol addiction. 2020;217:108276.

    CAS Google Scholar Article

  • Finngen, R5 release documentation. https://finngen.gitbook.io/documentation/. 2021; 2021-05-11.

  • Pedersen CB, Bybjerg-Grauholm J, Pedersen MG, Grove J, Agerbo E, Bækvad-Hansen M, et al. The iPSYCH2012 case cohort sample: new directions for unraveling the genetic and environmental architectures of severe mental disorders. Mol Psychiatry. 2018;23:6–14.

    CAS Google Scholar Article

  • Roden D, Pulley J, Basford M, Bernard G, Clayton E, Balser J, et al. Development of a large-scale anonymized DNA biobank to enable personalized medicine. Clin Pharmacol Therapeutics. 2008;84:362–9.

    CAS Google Scholar Article

  • Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genome-wide association scans. Bioinformatics. 2010;26:2190–1.

    CAS Google Scholar Article

  • Auton A, Abecasis GR, Altshuler DM, Durbin RM, Abecasis GR, Bentley DR, et al. A worldwide reference for human genetic variation. Nature. 2015;526:68–74.

    Google Scholar article

  • Yang J, Ferreira T, Morris AP, Medland SE, Madden PAF, Heath AC, et al. Conditional and conjoint analysis of multiple SNPs of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. 2012;44:369–75.

    CAS Google Scholar Article

  • Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.

    CAS Google Scholar Article

  • International HapMap Consortium 3. Integration of common and rare genetic variations in diverse human populations. Nature. 2010;467:52–58.

    Google Scholar article

  • Ge T, Chen CY, Ni Y, Feng Y-CA, Smoller JW Polygenic prediction via Bayesian regression and continuous shrinkage prior. NatCommon. 2019; 10. https://doi.org/10.1038/s41467-019-09718-5.

  • Grant BF, Saha TD, Ruan WJ, Goldstein RB, Chou SP, Jung J, et al. DSM-5 epidemiology of drug use disorder. JAMA Psychiatry. 2016;73:39.

    Google Scholar article

  • Thomas G. Furin at the forefront: From protein trafficking to embryogenesis and disease. Nat Rev Mol Cell Biol. 2002;3:753–66.

    CAS Google Scholar Article

  • Schizophrenia Task Force of the Psychiatric Genomics Consortium. Biological insights into 108 genetic loci associated with schizophrenia. Nature. 2014;511:421–7.

    Google Scholar article

  • Pardiñas AF, Holmans P, Pocklington AJ, Escot-Price V, Ripke S, Carrera N, et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions subject to strong background selection. Nat Genet. 2018;50:381–9.

    Google Scholar article

  • Ruderfer DM, Fanous AH, Ripke S, Mcquillin A, Amdur RL, Gejman PV, et al. Polygenic dissection of the diagnosis and clinical dimensions of bipolar disorder and schizophrenia. Mol Psychiatrist. 2014;19:1017–24.

    CAS Google Scholar Article

  • Ruderfer DM, Ripke S, Mcquillin A, Boocock J, Stahl EA, Pavlides JMW, et al. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell. 2018;173:1705–.e16.

    CAS Google Scholar Article

  • Nelson CP, Goel A, Butterworth AS, Kanoni S, Webb TR, Marouli E, et al. Association analyzes based on the false discovery rate implicate novel loci for coronary artery disease. Nat Genet. 2017;49:1385–91.

    CAS Google Scholar Article

  • Zhao G, Yang W, Wu J, Chen B, Yang X, Chen J, et al. Influence of a genetic variant associated with coronary artery disease on furin expression and effect of furin on macrophage behavior. Arteriosclerosis, Thrombosis, Vasc Biol. 2018;38:1837–44.

    CAS Google Scholar Article

  • Pilling LC, Kuo CL, Sicinski K, Tamosauskaite J, Kuchel GA, Harries LW, et al. Human longevity: 25 genetic loci associated with 389,166 UK biobank participants. Aging. 2017;9:2504–20.

    CAS Google Scholar Article

  • Gaddis N, Mathur R, Marks J, Zhou L, Quach B, Waldrop A, et al. Genome-wide multi-trait association study of opioid dependence: OPRM1 and beyond. medRxiv 2021; https://doi.org/10.1101/2021.09.13.21263503.

  • Zhang H, Luo X, Kranzler HR, Lappalainen J, Yang BZ, Krupitsky E, et al. Association between two haplotype blocks of the µ-opioid receptor gene (OPRM1) and drug or alcohol dependence. Hum Mol Genet. 2006;15:807–19.

    CAS Google Scholar Article

  • Kesler RC. The epidemiology of dual diagnosis. Biol Psychiatrist. 2004;56:730–7.

    Google Scholar article

  • Brady JE, Giglio R, Keyes KM, Dimaggio C, Li G Risk markers for fatal and non-fatal prescription drug overdose: a meta-analysis. Injury epidemiology. 2017; 4. https://doi.org/10.1186/s40621-017-0118-7.

  • Dahlman D, Ohlsson H, Edwards AC, Sundquist J, Håkansson A, Sundquist K. Socioeconomic correlates of incident and fatal opioid overdose among Swedes with opioid use disorder. Treating Previous Addiction and Policy 2021; 16. https://doi.org/10.1186/s13011-021-00409-3.

  • Kendler KS, Myers J, Prescott CA. Specificity of genetic and environmental risk factors for symptoms of cannabis, cocaine, alcohol, caffeine and nicotine dependence. Arch Gen Psychiatrist. 2007;64:1313.

    Google Scholar article

  • Kendler KS, Prescott CA, Myers J, Neale MC. The structure of genetic and environmental risk factors for common psychiatric disorders and substance use disorders in men and women. Arch Gen Psychiatrist. 2003;60:929.

    Google Scholar article

  • Tsuang MT, Lyons MJ, Meyer JM, Doyle T, Eisen SA, Goldberg J. Co-occurrence of the abuse of different drugs in men. Arch Gen Psychiatrist. 1998;55:967.

    CAS Google Scholar Article

  • Hatoum AS, Johnson EC, Colbert SMC, Polimanti R, Zhou H, Walters RK, et al. The addiction risk factor: A unitary genetic vulnerability characterizes substance use disorders and their associations with common correlates. Neuropsychopharmacol. 2021; https://doi.org/10.1038/s41386-021-01209-w.

  • Hatoum AS, Colbert SMC, Johnson EC, Huggett SB, Deak JD, Pathak GA Genome-wide multivariate association meta-analysis of over 1 million subjects identifies loci underlying brain disorders multiple substance use. medRxiv 2022; https://doi.org/10.1101/2022.01.06.22268753.

  • Sanchez-Roige S, Fontanillas P, Jennings MV, Bianchi SB, Huang Y, Hatoum AS Genome-wide association study of problematic prescription opioid use in 132,113 participants in 23andMe ancestry research European. Mol Psychiatrist. 2021. https://doi.org/10.1038/s41380-021-01335-3.

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