Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Strong and weak cross-inheritance of substance use disorders in a nationally representative sample

Molecular Psychiatry volume 27, pages 1742–1753 (2022)Cite this article

Subjects

Abstract

Substance use disorders (SUDs) are moderately to highly heritable and are in part cross-transmitted genetically, as observed in twin and family studies. We performed exome-focused genotyping to examine the cross-transmission of four SUDs: alcohol use disorder (AUD, n = 4487); nicotine use disorder (NUD, n = 4394); cannabis use disorder (CUD, n = 954); and nonmedical prescription opioid use disorder (NMPOUD, n = 346) within a large nationally representative sample (n = 36,309), the National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III). All diagnoses were based on in-person structured psychiatric interview (AUDADIS-5). SUD cases were compared alone and together to 3959 “super controls” who had neither a SUD nor a psychiatric disorder using an exome-focused array assaying 363,496 SNPs, yielding a representative view of within-disorder and cross-disorder genetic influences on SUDs. The 29 top susceptibility genes for one or more SUDs overlapped highly with genes previously implicated by GWAS of SUD. Polygenic scores (PGS) were computed within the European ancestry (EA) component of the sample (n = 12,505) using summary statistics from each of four clinically distinct SUDs compared to the 3959 “super controls” but then used for two distinctly different purposes: to predict SUD severity (mild, moderate, or severe) and to predict each of the other 3 SUDs. Our findings based on PGS highlight shared and unshared genetic contributions to the pathogenesis of SUDs, confirming the strong cross-inheritance of AUD and NUD as well as the distinctiveness of inheritance of opioid use disorder.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Multidimensional scaling (MDS) of ancestry using the top two MDS scores for 22,848 NESARC-III samples from a nationally representative survey: NESARC-III.
Fig. 2: Clinical overlap (comorbidity) of four substance use disorders (SUDs) in EA in NESARC-III, a nationally representative, psychiatrically interviewed sample: AUD (n = 4487), NUD (n = 4394), CUD (n = 954), and NMPOUD (n = 346).
Fig. 3: Relationship of AUD polygenic score to severity of four SUDs.
Fig. 4: Relationship of NUD polygenic score to severity of four SUDs.
Fig. 5: Relationship of CUD polygenic score to severity of four SUDs.
Fig. 6: Relationship of NMPOUD polygenic score to severity of four SUDs.

Similar content being viewed by others

Data availability

The individual-level genetic data (22,848 samples) with phenotypic variables (n = 4,320) for NESARC-III (family history, adverse childhood experiences, substance use, mood, anxiety, personality and posttraumatic stress disorders) are available in dbGaP (accession: phs001590.v2.p1). [https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001590.v2.p1].

References

  1. 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;382:1575–86.

    Article  PubMed  Google Scholar 

  2. 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;60:929–37.

    Article  PubMed  Google Scholar 

  3. Ducci F, Goldman D. The genetic basis of addictive disorders. Psychiatr Clin North Am. 2012;35:495–519.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Goldman D, Oroszi G, Ducci F. The genetics of addictions: uncovering the genes. Nat Rev Genet. 2005;6:521–32.

    Article  CAS  PubMed  Google Scholar 

  5. Goldman D, Bergen A. General and specific inheritance of substance abuse and alcoholism. Arch Gen Psychiatry. 1998;55:964–5.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Li D, Zhao H, Kranzler HR, Li MD, Jensen KP, Zayats T, et al. Genome-wide association study of copy number variations (CNVs) with opioid dependence. Neuropsychopharmacology. 2015;40:1016–26.

    Article  CAS  PubMed  Google Scholar 

  8. 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;51:237–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sanchez-Roige S, Palmer AA, Fontanillas P, Elson SL, Adams MJ, Howard DM, et al. Genome-wide association study meta-analysis of the alcohol use disorders identification test (AUDIT) in two population-based cohorts. Am J Psychiatry. 2019;176:107–18.

    Article  PubMed  Google Scholar 

  10. 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;73:472–80.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Stringer S, Minică CC, Verweij KJ, Mbarek H, Bernard M, Derringer J, et al. Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32,330 subjects from the International Cannabis Consortium. Transl Psychiatry. 2016;6:e769.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Tobacco, Genetics C. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet. 2010;42:441–7.

    Article  CAS  Google Scholar 

  13. 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;21:1656–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Goldman D. Polygenic risk scores in psychiatry. Biol Psychiatry. 2017;82:698–9.

    Article  PubMed  Google Scholar 

  15. 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;101:5–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Grant BF, Goldstein RB, Smith SM, Jung J, Zhang H, Chou SP, et al. The Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5): reliability of substance use and psychiatric disorder modules in a general population sample. Drug Alcohol Depend. 2015;148:27–33.

    Article  PubMed  Google Scholar 

  17. Grant BF, Chu, A, Sigman, R, Amsbary, M, Kali, J, Sugawara Y, et al. Source and Accuracy Statement: National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III) [https://www.niaaa.nih.gov/sites/default/files/NESARC_Final_Report_FINAL_1_8_15.pdf]. National Institute on Alcohol Abuse and Alcoholism. 2014.

  18. Jorgenson E, Thai KK, Hoffmann TJ, Sakoda LC, Kvale MN, Banda Y, et al. Genetic contributors to variation in alcohol consumption vary by race/ethnicity in a large multi-ethnic genome-wide association study. Mol Psychiatry. 2017;22:1359–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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;10:1499.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Clarke TK, 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). Mol Psychiatry. 2017;22:1376–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Thorgeirsson TE, Geller F, Sulem P, Rafnar T, Wiste A, Magnusson KP, et al. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature. 2008;452:638–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chou SP, Goldstein RB, Smith SM, Huang B, Ruan WJ, Zhang H, et al. The epidemiology of DSM-5 nicotine use disorder: results from the national epidemiologic survey on alcohol and related conditions-III. J Clin Psychiatry. 2016;77:1404–12.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Grant BF, Chou SP, Saha TD, Pickering RP, Kerridge BT, Ruan WJ, et al. Prevalence of 12-month alcohol use, high-risk drinking, and DSM-IV alcohol use disorder in the United States, 2001-2 to 2012-3: Results From the National Epidemiologic Survey on Alcohol and Related Conditions. JAMA Psychiatry. 2017;74:911–23.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Grant BF, Saha TD, Ruan WJ, Goldstein RB, Chou SP, Jung J, et al. Epidemiology of DSM-5 drug use disorder: results from the national epidemiologic survey on alcohol and related conditions-III. JAMA Psychiatry. 2016;73:39–47.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhan X, Hu Y, Li B, Abecasis GR, Liu DJ. RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data. Bioinformatics. 2016;32:1423–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Feng S, Liu D, Zhan X, Wing MK, Abecasis GR. RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics. 2014;30:2828–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481–7.

    Article  CAS  PubMed  Google Scholar 

  31. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. van der Sluis S, Posthuma D, Dolan CV. TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLoS Genet. 2013;9:e1003235.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Euesden J, Lewis CM, O’Reilly PF. PRSice: polygenic risk score software. Bioinformatics 2015;31:1466–8.

    Article  CAS  PubMed  Google Scholar 

  34. Song W, Kossowsky J, Torous J, Chen CY, Huang H, Mukamal KJ, et al. Genome-wide association analysis of opioid use disorder: A novel approach using clinical data. Drug Alcohol Depend. 2020;217:108276.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Watanabe K, Stringer S, Frei O, Umicevic Mirkov M, de Leeuw C, Polderman TJC, et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. 2019;51:1339–48.

    Article  CAS  PubMed  Google Scholar 

  36. Yu C, McClellan J. Genetics of substance use disorders. Child Adolesc Psychiatr Clin N. Am. 2016;25:377–85.

    Article  PubMed  Google Scholar 

  37. Hodgkinson CA, Yuan Q, Xu K, Shen PH, Heinz E, Lobos EA, et al. Addictions biology: haplotype-based analysis for 130 candidate genes on a single array. Alcohol Alcohol. 2008;43:505–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zhou H, Rentsch CT, Cheng Z, Kember RL, Nunez YZ, Sherva RM, et al. Association of OPRM1 functional coding variant with opioid use disorder: a genome-wide association study. JAMA Psychiatry. 2020;77:1072–80.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Demontis D, Rajagopal VM, Thorgeirsson TE, Als TD, Grove J, Leppala K, et al. Genome-wide association study implicates CHRNA2 in cannabis use disorder. Nat Neurosci. 2019;22:1066–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Panjwani N, Wang F, Mastromatteo S, Bao A, Wang C, He G, et al. LocusFocus: Web-based colocalization for the annotation and functional follow-up of GWAS. PLoS Comput Biol. 2020;16:e1008336.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47:1236–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Zhou H, Sealock JM, Sanchez-Roige S, Clarke TK, Levey DF, Cheng Z, et al. Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nat Neurosci. 2020;23:809–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Duncan L, Shen H, Gelaye B, Meijsen J, Ressler K, Feldman M, et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat Commun. 2019;10:3328.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Heath AC, Martin NG. Genetic models for the natural history of smoking: evidence for a genetic influence on smoking persistence. Addict Behav. 1993;18:19–34.

    Article  CAS  PubMed  Google Scholar 

  45. Han C, McGue MK, Iacono WG. Lifetime tobacco, alcohol and other substance use in adolescent Minnesota twins: univariate and multivariate behavioral genetic analyses. Addiction. 1999;94:981–93.

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  47. Kandel DB, Kandel ER. A molecular basis for nicotine as a gateway drug. N Engl J Med. 2014;371:2038–9.

    Article  PubMed  CAS  Google Scholar 

  48. Kwako LE, Momenan R, Litten RZ, Koob GF, Goldman D. Addictions neuroclinical assessment: a neuroscience-based framework for addictive disorders. Biol Psychiatry. 2016;80:179–89.

    Article  PubMed  Google Scholar 

  49. Kwako LE, Schwandt ML, Ramchandani VA, Diazgranados N, Koob GF, Volkow ND, et al. Neurofunctional domains derived from deep behavioral phenotyping in alcohol use disorder. Am J Psychiatry. 2019;176:744–53.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the study participants and their families. The National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III) is funded by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) with supplemental support from the National Institute on Drug Abuse. This work utilized the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov).

Author information

Author notes

  1. These authors contributed equally: David Goldman, S. Patricia Chou.

Authors and Affiliations

  1. Epidemiology and Biometry Branch, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, 20817, USA

    Haitao Zhang, Bridget F. Grant, W. June Ruan, Bradley T. Kerridge, Boji Huang, Tulshi D. Saha, Amy Z. Fan, Veronica Wilson & S. Patricia Chou

  2. Laboratory of Neurogenetics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, 20817, USA

    Colin A. Hodgkinson & David Goldman

  3. Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, 20817, USA

    Jeesun Jung

  4. Division of Neuroscience and Behavior, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, 20817, USA

    Abbas Parsian

  5. Office of the Clinical Director, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, 20817, USA

    David Goldman

Authors
  1. Haitao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bridget F. Grant
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Colin A. Hodgkinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. W. June Ruan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bradley T. Kerridge
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Boji Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tulshi D. Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Amy Z. Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Veronica Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jeesun Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Abbas Parsian
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. David Goldman
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. S. Patricia Chou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Study conception and design: HZ, DG, SPC; Acquisition of data: BFG, HZ, SPC, WJR, BTK, BH, TDS, AZF, VW, JJ; Analysis and interpretation of data: HZ, DG, SPC, CAH; Drafting manuscript: HZ, DG, SPC; Administrative & technical functions: BFG, HZ, SPC; Clerical & material support: VW; Comments & discussion: AP, CAH.

Corresponding author

Correspondence to Haitao Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Grant, B.F., Hodgkinson, C.A. et al. Strong and weak cross-inheritance of substance use disorders in a nationally representative sample. Mol Psychiatry 27, 1742–1753 (2022). https://doi.org/10.1038/s41380-021-01370-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-021-01370-0

Search

Advanced search

Quick links