Skip to main content

Advertisement

Log in

An Update on the Role of Common Genetic Variation Underlying Substance Use Disorders

  • Neurogenetics and Psychiatric Genetics (C Cruchaga and C Karch, Section Editors)
  • Published:
Current Genetic Medicine Reports Aims and scope Submit manuscript

Abstract

Purpose of the Review

Sample size increases have resulted in novel and replicable loci for substance use disorders (SUDs). We summarize some of the latest insights into SUD genetics and discuss next steps for the field.

Recent Findings

Genome-wide association studies have substantiated the role of previously known variants (e.g., rs1229984 in ADH1B for alcohol) and identified several novel loci for alcohol, tobacco, cannabis, opioid, and cocaine use disorders. SUDs are genetically correlated with psychiatric outcomes, while liability to substance use is inconsistently associated with these outcomes and more closely associated with lifestyle factors. Specific variant associations appear to differ somewhat across populations, although similar genes and systems are implicated.

Summary

The next decade of human genetic studies of addiction should focus on expanding to non-European populations, consider pleiotropy across SUDs and with other psychiatric disorders, and leverage human and cross-species functional data to elucidate the biological mechanisms underlying SUDs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. World Health Organization Global status report on alcohol and health, 2014. World Health Organization; 2014.

  2. World Health Organization. The Global Burden of Disease: 2004 update. 2004 Updat. 2008;146.

  3. Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet. 2013;382(9904):1575–86.

  4. Organization WH. WHO report on the global tobacco epidemic, 2017: monitoring tobacco use and prevention policies. World Health Organization; 2017.

  5. Rehm J, Mathers C, Popova S, Thavorncharoensap M, Teerawattananon Y, Patra J. Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. Lancet. 2009;373(9682):2223–33.

    PubMed  Google Scholar 

  6. Degenhardt L, Whiteford HA, Ferrari AJ, Baxter AJ, Charlson FJ, Hall WD, et al. Global burden of disease attributable to illicit drug use and dependence: findings from the Global Burden of Disease Study 2010. Lancet. 2013;382(9904):1564–74.

  7. Rudd RA, Aleshire N, Zibbell JE, Gladden RM. Increases in drug and opioid overdose death–United States, 2000–2014. CDC morbidity and mortality weekly report. Jan. 1. 2016.

  8. Volkow ND, Frieden TR, Hyde PS, Cha SS. Medication-assisted therapies—tackling the opioid-overdose epidemic. N Engl J Med. 2014;370(22):2063–6.

    PubMed  Google Scholar 

  9. CDC C for DC and P. CDC grand rounds: prescription drug overdoses-a US epidemic. MMWR Morb Mortal Wkly Rep. 2012;61(1):10.

    Google Scholar 

  10. Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G, Murray CJL, et al. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med. 2013;10(11):e1001547.

  11. Hasin D, Kilcoyne B. Comorbidity of psychiatric and substance use disorders in the United States: current issues and findings from the NESARC. Curr Opin Psychiatry. 2012 May;25(3):165–71.

    PubMed  PubMed Central  Google Scholar 

  12. Koob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry. 2016;3:760–73.

  13. Koob GF, Le Moal M. Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology. 2001;24(2):97–129.

    CAS  PubMed  Google Scholar 

  14. Wise RA, Koob GF. The development and maintenance of drug addiction. Neuropsychopharmacology. 2014;

  15. Association AP. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub; 2013.

  16. Verhulst B, Neale MC, Kendler KS. The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies. Psychol Med. 2015;45(5):1061–72.

    CAS  PubMed  Google Scholar 

  17. Kendler KS, Jacobson KC, Prescott CA, Neale MC. Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am J Psychiatry. 2003;160(4):687–95.

    PubMed  Google Scholar 

  18. Tsuang MT, Bar JL, Harley RM, Lyons MJ. The Harvard twin study of substance abuse: what we have learned. Harv Rev Psychiatry. 2001;9(6):267–79.

    CAS  PubMed  Google Scholar 

  19. Kendler KS, Myers J, Prescott CA. Specificity of genetic and environmental risk factors for symptoms of cannabis, cocaine, alcohol, caffeine, and nicotine dependence. JAMA Psychiatry. 2007;64(11):1313–20.

    Google Scholar 

  20. Hicks BM, Blonigen DM, Kramer MD, Krueger RF, Patrick CJ, Iacono WG, et al. Gender differences and developmental change in externalizing disorders from late adolescence to early adulthood: a longitudinal twin study. J Abnorm Psychol. 2007;116(3):433–47.

  21. Hicks BM, Krueger RF, Iacono WG, McGue M, Patrick CJ. Family transmission and heritability of externalizing disorders: a twin-family study. JAMA Psychiatry. 2004;61(9):922–8.

    Google Scholar 

  22. Kendler KS, Prescott CA, Myers J, Neale MC. The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Arch Gen Psychiatry. 2003;60(9):929–37.

    PubMed  Google Scholar 

  23. Korhonen T, Latvala A, Dick DM, Pulkkinen L, Rose RJ, Kaprio J, et al. Genetic and environmental influences underlying externalizing behaviors, cigarette smoking and illicit drug use across adolescence. Behav Genet. 2012;42(4):614–25.

  24. Sanchez-Roige S, Palmer AA, Clarke T-K. Recent efforts to dissect the genetic basis of alcohol use and abuse. Biol Psychiatry. 2019.

  25. Edenberg HJ, Gelernter J, Agrawal A. Genetics of alcoholism. Curr Psychiatry Rep. 2019;21(4):26.

    PubMed  Google Scholar 

  26. Crist RC, Reiner BC, Berrettini WH. A review of opioid addiction genetics. Curr Opin Psychol. 2019;27:31–5.

    PubMed  Google Scholar 

  27. •• Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, et al. 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):237–44. The largest GWAS of substance use traits to date.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. • Pasman JA, Verweij KJH, Gerring Z, Stringer S, Sanchez-Roige S, Treur JL, et al. 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):1161–70. Large GWAS of cannabis use that identified risk loci, showed genetic correlations with psychiatric disorders and other complex traits of relevance.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Luciano M, Hagenaars SP, Davies G, Hill WD, Clarke T-K, Shirali M, et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat Genet. 2018;50(1):6–11.

  30. Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet. 2018;50(8):1112–21.

  31. Saunders JB, Aasland OG, Babor TF, De la Fuente JR, Grant M. Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction. 1993;88(6):791–804.

    CAS  PubMed  Google Scholar 

  32. • Edenberg HJ, McClintick JN. Alcohol dehydrogenases, aldehyde dehydrogenases, and alcohol use disorders: a critical review. Alcohol Clin Exp Res. 2018;42(12):2281–97. Detailed review of important genes for alcohol use disorder.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Lane-Donovan C, Herz J. ApoE, ApoE receptors, and the synapse in Alzheimer’s disease. Trends Endocrinol Metab. 2017;28(4):273–84.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Tzioras M, Davies C, Newman A, Jackson R, Spires-Jones T. Invited review: APOE at the interface of inflammation, neurodegeneration and pathological protein spread in Alzheimer’s disease. Neuropathol Appl Neurobiol. 2019;45(4):327–46.

    CAS  PubMed  Google Scholar 

  35. Jung Y, Montel RA, Shen P-H, Mash DC, Goldman D. Assessment of the association of D2 dopamine receptor gene and reported allele frequencies with alcohol use disorders: a systematic review and meta-analysis. JAMA Netw Open. 2019;2(11):e1914940–e1914940.

    Google Scholar 

  36. •• Kranzler HR, Zhou H, Kember RL, Vickers Smith R, Justice AC, Damrauer S, et al. Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations. Nat Commun. 2019;10(1):1499. Largest GWAS of AUD to date, contrasted patterns of genetic relationships between alcohol consumption vs. AUD with psychiatric disorders, socioeconomic-related traits, and metabolic traits.

    PubMed  PubMed Central  Google Scholar 

  37. Claussnitzer M, Dankel SN, Kim K-H, Quon G, Meuleman W, Haugen C, et al. FTO obesity variant circuitry and adipocyte browning in humans. N Engl J Med. 2015;373(10):895–907.

  38. Matschinsky FM. Glucokinase as glucose sensor and metabolic signal generator in pancreatic β-cells and hepatocytes. Diabetes. 1990;39(6):647–52.

    CAS  PubMed  Google Scholar 

  39. Matschinsky FM. Regulation of pancreatic β-cell glucokinase: from basics to therapeutics. Diabetes. 2002;51(suppl 3):S394–404.

    CAS  PubMed  Google Scholar 

  40. Clarke T-K, Adams MJ, Davies G, Howard DM, Hall LS, Padmanabhan S, et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK biobank (N=112,117). Molecular Psychiatry. The Author(s); 2017.

  41. • Sanchez-Roige S, Palmer AA, Fontanillas P, Elson SL. 23andMe Research Team, Substance Use Disorder Working Group of the Psychiatric Genomics Consortium, et al. Genome-wide association study meta-analysis of the Alcohol Use Disorders Identification Test (AUDIT) in two population-based cohorts. Am J Psychiatry. 2018; GWAS of AUDIT that showed different patterns of genetic correlation for the consumption (AUDIT-C) subscale and the problem use (AUDIT-P) subscale.

  42. Gelernter J, Sun N, Polimanti R, Pietrzak RH, Levey DF, Lu Q, et al. Genome-wide association study of maximum habitual alcohol intake in >140,000 U.S. European and African American veterans yields novel risk loci. Biol Psychiatry. 2019;86(5):365–76.

  43. • Walters RK, Polimanti R, Johnson EC, McClintick JN, Adams MJ, Adkins AE, et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat Neurosci. 2018;21(12):1656–69. Large trans-ancestral GWAS that identified different individual variants driving theADH1Bassociation in European and African ancestries.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Gelernter J, Sun N, Polimanti R, Pietrzak R, Levey DF, Bryois J, et al. Genome-wide association study of post-traumatic stress disorder reexperiencing symptoms in >165,000 US veterans. Nat Neurosci. 2019;22(9):1394–401.

  45. Heatherton TF, Kozlowski LT, Frecker RC, FAGERSTROM K. The Fagerström test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict. 1991;86(9):1119–27.

    CAS  PubMed  Google Scholar 

  46. • Hancock DB, Guo Y, Reginsson GW, et al. Genome-wide association study across European and African American ancestries identifies a SNP in DNMT3B contributing to nicotine dependence. Mol Psychiatry. 2018;23:1911–1919. Largest GWAS of nicotine dependence to date that identified significant variants in the CHRNA5-CHRNA3-CHRNB4 cluster, as well as a novel association at DNMT3B.

  47. Bierut LJ, Stitzel JA, Wang JC, Hinrichs AL, Grucza RA, Xuei X, et al. Variants in nicotinic receptors and risk for nicotine dependence. Am J Psychiatry. 2008;165(9):1163–71.

  48. Hancock DB, Reginsson GW, Gaddis NC, Chen X, Saccone NL, Lutz SM, et al. Genome-wide meta-analysis reveals common splice site acceptor variant in CHRNA4 associated with nicotine dependence. Transl Psychiatry. 2015;5(10):e651–e651.

  49. Rollema H, Coe JW, Chambers LK, Hurst RS, Stahl SM, Williams KE. Rationale, pharmacology and clinical efficacy of partial agonists of α4β2 nACh receptors for smoking cessation. Trends Pharmacol Sci. 2007;28(7):316–25.

    CAS  PubMed  Google Scholar 

  50. Chenoweth MJ, O’Loughlin J, Sylvestre M-P, Tyndale RF. CYP2A6 slow nicotine metabolism is associated with increased quitting by adolescent smokers. Pharmacogenet Genomics. 2013;23(4):232–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. • Demontis D, Rajagopal VM, Thorgeirsson TE, Als TD, Grove J, Leppälä K, et al. Genome-wide association study implicates CHRNA2 in cannabis use disorder. Nat Neurosci. 2019:1 Largest GWAS of cannabis use disorder to date, which identified a significant association withCHRNA2driven by an eQTL in brain tissue.

  52. Consortium SWG of the PG. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421.

    Google Scholar 

  53. Sherva R, Wang Q, Kranzler H, Zhao H, Koesterer R, Herman A, et al. Genome-wide association study of cannabis dependence severity, novel risk variants, and shared genetic risks. JAMA psychiatry. 2016;73(5):472–80.

  54. Gelernter J, Sherva R, Koesterer R, Almasy L, Zhao H, Kranzler HR, et al. Genome-wide association study of cocaine dependence and related traits: FAM53B identified as a risk gene. Mol Psychiatry. 2014;19(6):717–23.

  55. Gelernter J, Panhuysen C, Weiss R, Brady K, Hesselbrock V, Rounsaville B, et al. Genomewide linkage scan for cocaine dependence and related traits: significant linkages for a cocaine-related trait and cocaine-induced paranoia. Am J Med Genet Part B Neuropsychiatr Genet. 2005;136(1):45–52.

    Google Scholar 

  56. Cabana-Domínguez J, Shivalikanjli A, Fernàndez-Castillo N, Cormand B. Genome-wide association meta-analysis of cocaine dependence: shared genetics with comorbid conditions. Prog Neuro-Psychopharmacology Biol Psychiatry. 2019;94:109667.

    Google Scholar 

  57. Sekar A, Bialas AR, de Rivera H, Davis A, Hammond TR, Kamitaki N, et al. Schizophrenia risk from complex variation of complement component 4. Nature. 2016;530(7589):177–83.

  58. Smith AH, Jensen KP, Li J, Nunez Y, Farrer LA, Hakonarson H, et al. Genome-wide association study of therapeutic opioid dosing identifies a novel locus upstream of OPRM1. Mol Psychiatry. 2017;22(3):346–52.

  59. •• Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8(1):1826. Useful web-based tool that incorporates MAGMA-based analyses and data from GTEx, CommonMind, and other resources; has transformed post-GWAS annotation pipelines.

  60. • Marees AT, Smit DJA, Ong J-S, MacGregor S, An J, Denys D, et al. Potential influence of socioeconomic status on genetic correlations between alcohol consumption measures and mental health. Psychol Med. 2019:1–15. Important paper that demonstrated the impact of socioeconomic-related variables on genetic correlations between measures of alcohol consumption and psychiatric traits.

  61. Rosoff DB, Clarke T-K, Adams MJ, McIntosh AM, Davey Smith G, Jung J, et al. Educational attainment impacts drinking behaviors and risk for alcohol dependence: results from a two-sample Mendelian randomization study with ~780,000 participants. Mol Psychiatry. 2019.

  62. Adams M, Hill WD, Howard DM, Davis KAS, Deary IJ, Hotopf M, et al. Factors associated with sharing email information and mental health survey participation in two large population cohorts bioRxiv 2018 1;471433.

  63. Gaziano JM, Concato J, Brophy M, Fiore L, Pyarajan S, Breeling J, et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J Clin Epidemiol. 2016;70:214–23.

  64. Agrawal A, Freedman ND, Cheng Y-C, Lin P, Shaffer JR, Sun Q, et al. Measuring alcohol consumption for genomic meta-analyses of alcohol intake: opportunities and challenges. Am J Clin Nutr. 2012;95(3):539–47.

  65. Nutt D, King LA, Saulsbury W, Blakemore C. Development of a rational scale to assess the harm of drugs of potential misuse. Lancet. 2007;369(9566):1047–53.

  66. Jentsch JD, Ashenhurst JR, Cervantes MC, Groman SM, James AS, Pennington ZT. Dissecting impulsivity and its relationships to drug addictions. Ann N Y Acad Sci. 2014/03/21. 2014;1327:1–26.

    PubMed  PubMed Central  Google Scholar 

  67. Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, et al. The genotype-tissue expression (GTEx) project. Nat Genet. 2013;45:580–5.

    CAS  Google Scholar 

  68. Huckins LM, Dobbyn A, Ruderfer DM, Hoffman G, Wang W, Pardiñas AF, et al. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nat Genet. 2019;51(4):659–74.

  69. Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci. 2016;19:1442–53.

  70. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101(1):5–22.

  71. Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. 2015;47(9):1091–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C, et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science (80- ). 2018.

  73. • Kapoor M, Wang J-C, Farris SP, Liu Y, McClintick J, Gupta I, et al. Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism. Transl Psychiatry. 2019;9(1):89. Paper that used transcriptomic data from post-mortem brain tissue of alcoholics and controls and a variety of methods to identify genes most likely to be causal.

    PubMed  PubMed Central  Google Scholar 

  74. Berkel TDM, Pandey SC. Emerging role of epigenetic mechanisms in alcohol addiction. Alcohol Clin Exp Res. 2017;41(4):666–80.

    PubMed  PubMed Central  Google Scholar 

  75. Zhang H, Herman AI, Kranzler HR, Anton RF, Zhao H, Zheng W, et al. Array-based profiling of DNA methylation changes associated with alcohol dependence. Alcohol Clin Exp Res. 2013;37:E108–15.

    CAS  PubMed  Google Scholar 

  76. Philibert R, Plume JM, Gibbons FX, Brody GH, Beach S. The impact of recent alcohol use on genome wide DNA methylation signatures. Front Genet. 2012;3:54.

    PubMed  PubMed Central  Google Scholar 

  77. Weng JT-Y, Wu LS-H, Lee C-S, Hsu PW-C, Cheng ATA. Integrative epigenetic profiling analysis identifies DNA methylation changes associated with chronic alcohol consumption. Comput Biol Med. 2015;64:299–306.

    CAS  PubMed  Google Scholar 

  78. Lohoff FW, Sorcher JL, Rosen AD, Mauro KL, Fanelli RR, Momenan R, et al. Methylomic profiling and replication implicates deregulation of PCSK9 in alcohol use disorder. Mol Psychiatry. 2018;23(9):1900–10.

    CAS  PubMed  Google Scholar 

  79. Jung Y, Hsieh LS, Lee AM, Zhou Z, Coman D, Heath CJ, et al. An epigenetic mechanism mediates developmental nicotine effects on neuronal structure and behavior. Nat Neurosci. 2016;19(7):905–14.

  80. Tsai P-C, Glastonbury CA, Eliot MN, Bollepalli S, Yet I, Castillo-Fernandez JE, et al. Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health. Clin Epigenetics. 2018;10(1):126.

  81. Szutorisz H, Hurd YL. Epigenetic effects of cannabis exposure. Biol Psychiatry. 2016;79(7):586–94.

    CAS  PubMed  Google Scholar 

  82. • Peterson RE, Kuchenbaecker K, Walters RK, Chen C-Y, Popejoy AB, Periyasamy S, et al. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell. 2019; Important paper calling attention to best practices for analyzing non-European ancestry samples.

  83. • Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51(4):584. Paper that demonstrated how clinical application of polygenic scores in the near future could potentially contribute to inequitable benefits, with little utility for historically disadvantaged populations.

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM, Gravel S, et al. Human demographic history impacts genetic risk prediction across diverse populations. Am J Hum Genet. 2017;100(4):635–49.

  85. Martin AR, Daly MJ, Robinson EB, Hyman SE, Neale BM. Predicting polygenic risk of psychiatric disorders. Biol Psychiatry. 2018.

  86. Price AL, Patterson N, Yu F, Cox DR, Waliszewska A, McDonald GJ, et al. A genomewide admixture map for Latino populations. Am J Hum Genet. 2007/04/13. 2007;80(6):1024–36.

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Millwood IY, Walters RG, Mei XW, Guo Y, Yang L, Bian Z, et al. Conventional and genetic evidence on alcohol and vascular disease aetiology: a prospective study of 500 000 men and women in China. Lancet. 2019;393(10183):1831–42.

  88. • Lee PH, Anttila V, Won H, Feng Y-CA, Rosenthal J, Zhu Z, et al. Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. bioRxiv. 2019 528117. Largest cross-disorder GWAS of psychiatric disorders to date, although did not include substance use disorders.

  89. Leeman RF, Heilig M, Cunningham CL, Stephens DN, Duka T, O’Malley SS. REVIEW: ethanol consumption: how should we measure it? Achieving consilience between human and animal phenotypes. Addict Biol. 2010;15(2):109–24.

    PubMed  PubMed Central  Google Scholar 

  90. Baker EJ, Jay JJ, Bubier JA, Langston MA, Chesler EJ. GeneWeaver: a web-based system for integrative functional genomics. Nucleic Acids Res. 2011;40(D1):D1067–76.

    PubMed  PubMed Central  Google Scholar 

  91. Hernandez Cordero AI, Gonzales NM, Parker CC, Sokolof G, Vandenbergh DJ, Cheng R, et al. Genome-wide associations reveal human-mouse genetic convergence and modifiers of myogenesis, CPNE1 and STC2. Am J Hum Genet. 2019;105(6):1222–36.

  92. Gaspar HA, Hübel C, Breen G. Drug Targetor: a web interface to investigate the human druggome for over 500 phenotypes. Bioinformatics. 2018;35(14):2515–7.

    PubMed Central  Google Scholar 

  93. Gallagher MD, Chen-Plotkin AS. The post-GWAS era: from association to function. Am J Hum Genet. 2018;102(5):717–30.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

ECJ was funded by NIAAA (F32AA027435), and AA was funded by NIMH (MH109532) and NIDA (K02DA32573). ECJ is also supported by grant YIG-0-064-18 from the American Foundation for Suicide Prevention. Dr. Chang reports a grant stipend from Washington University in St. Louis (Division of Biology and Biological Sciences) during the conduct of the study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Emma C. Johnson.

Ethics declarations

Conflict of Interest

The authors report no conflicts of interest.

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the American Foundation for Suicide Prevention.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Neurogenetics and Psychiatric Genetics

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Johnson, E.C., Chang, Y. & Agrawal, A. An Update on the Role of Common Genetic Variation Underlying Substance Use Disorders. Curr Genet Med Rep 8, 35–46 (2020). https://doi.org/10.1007/s40142-020-00184-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40142-020-00184-w

Keywords

Navigation