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Clinical, environmental, and genetic risk factors for substance use disorders: characterizing combined effects across multiple cohorts


Substance use disorders (SUDs) incur serious social and personal costs. The risk for SUDs is complex, with risk factors ranging from social conditions to individual genetic variation. We examined whether models that include a clinical/environmental risk index (CERI) and polygenic scores (PGS) are able to identify individuals at increased risk of SUD in young adulthood across four longitudinal cohorts for a combined sample of N = 15,134. Our analyses included participants of European (NEUR = 12,659) and African (NAFR = 2475) ancestries. SUD outcomes included: (1) alcohol dependence, (2) nicotine dependence; (3) drug dependence, and (4) any substance dependence. In the models containing the PGS and CERI, the CERI was associated with all three outcomes (ORs = 01.37–1.67). PGS for problematic alcohol use, externalizing, and smoking quantity were associated with alcohol dependence, drug dependence, and nicotine dependence, respectively (OR = 1.11–1.33). PGS for problematic alcohol use and externalizing were also associated with any substance dependence (ORs = 1.09–1.18). The full model explained 6–13% of the variance in SUDs. Those in the top 10% of CERI and PGS had relative risk ratios of 3.86–8.04 for each SUD relative to the bottom 90%. Overall, the combined measures of clinical, environmental, and genetic risk demonstrated modest ability to distinguish between affected and unaffected individuals in young adulthood. PGS were significant but added little in addition to the clinical/environmental risk index. Results from our analysis demonstrate there is still considerable work to be done before tools such as these are ready for clinical applications.

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Fig. 1: SUD prevalence across genetic and environmental risk factors.
Fig. 2: ROC Curves for combined and baseline models.

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Data availability

All data sources are described in the manuscript and supplemental information. No new data were collected. Only data from existing studies or study cohorts were analyzed, some of which have restricted access to protect the privacy of the study participants. Add Health genetic data obtained through dbGaP (Study Accession: phs001367.v1.p1). Instructions on gaining access to Add Health restricted use data can be found at: COGA genetic data available through dbGaP (Study Accession: phs000763.v1.p1). Instructions for access to ALSPAC data available at: The process for obtaining the GWAS summary statistics used in these analyses are described in the corresponding original GWAS publications.

Code availability

No custom algorithms or software was developed in this study. All code is available by request from the corresponding author. Polygenic scores generated using PRS-CSx ( All primary analyses completed in R 4.1.0 using the data.table(1.14.0), pROC (1.18.0), lme4 (1.1–27.1), DescTools (0.99.45), sandwich (3.0–2), and base packages.


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Research reported in this publication was supported by the National Institute on Alcohol Abuse and Alcoholism and the National Institute of Drug Abuse of the National Institutes of Health under award numbers R01AA015416, R01DA050721, R01DA042090, and K02AA018755; the Academy of Finland (grants 100499, 205585, 118555, 141054, 265240, 308248, 308698 and 312073); and the Scientific and Technological Research Council of Turkey (TÜBİTAK) under award number 114C117 (FA); and the Sigrid Juselius Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding bodies. This research also used summary data from the Psychiatric Genomics Consortium (PGC), the Million Veterans Program (MVP), the GWAS and Sequencing Consortium for Alcohol and Nicotine (GSCAN), UK Biobank, the Genomic Psychiatry Cohort (GPC) and 23andMe, Inc. We would like to thank the many studies that made these consortia possible, the researchers involved, and the participants in those studies, without whom this effort would not be possible. We would also like to thank the research participants and employees of 23andMe. The Externalizing Consortium: Principal Investigators: Danielle M. Dick, Philipp Koellinger, K. Paige Harden, Abraham A. Palmer. Lead Analysts: Richard Karlsson Linnér, Travis T. Mallard, Peter B. Barr, Sandra Sanchez-Roige. Significant Contributors: Irwin D. Waldman. The Externalizing Consortium has been supported by the National Institute on Alcohol Abuse and Alcoholism (R01AA015416 - administrative supplement), and the National Institute on Drug Abuse (R01DA050721). Additional funding for investigator effort has been provided by K02AA018755, U10AA008401, P50AA022537, as well as a European Research Council Consolidator Grant (647648 EdGe to Koellinger). The content is solely the responsibility of the authors and does not necessarily represent the official views of the above funding bodies. Add Health: Add Health is directed by Robert A. Hummer and funded by the National Institute on Aging cooperative agreements U01 AG071448 (Hummer) and U01AG071450 (Aiello and Hummer) at the University of North Carolina at Chapel Hill. Waves I–V data are from the Add Health Program Project, grant P01 HD31921 (Harris) from Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), with cooperative funding from 23 other federal agencies and foundations. Add Health was designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill. ALSPAC: We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors, and Peter Barr and Danielle Dick will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website (; This research was specifically funded by the Medical Research Council (MRC) under grants MR/L022206/1, MR/M006727/1, and G0800612/86812; the Wellcome Trust under grant 086684; and the National Institute on Alcohol Abuse and Alcoholism under 5R01AA018333–05. GWAS data was generated by Sample Logistics and Genotyping Facilities at Wellcome Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe. COGA: We thank The Collaborative Study on the Genetics of Alcoholism (COGA), Principal Investigators B. Porjesz, V. Hesselbrock, T. Foroud; Scientific Director, A. Agrawal; Translational Director, D. Dick, includes eleven different centers: University of Connecticut (V. Hesselbrock); Indiana University (H.J. Edenberg, T. Foroud, Y. Liu, M.H. Plawecki); University of Iowa Carver College of Medicine (S. Kuperman, J. Kramer); SUNY Downstate Health Sciences University (B. Porjesz, J. Meyers, C. Kamarajan, A. Pandey); Washington University in St. Louis (L. Bierut, J. Rice, K. Bucholz, A. Agrawal); University of California at San Diego (M. Schuckit); Rutgers University (J. Tischfield, R. Hart, J. Salvatore); The Children’s Hospital of Philadelphia, University of Pennsylvania (L. Almasy); Virginia Commonwealth University (D. Dick); Icahn School of Medicine at Mount Sinai (A. Goate, P. Slesinger); and Howard University (D. Scott). Other COGA collaborators include: L. Bauer (University of Connecticut); J. Nurnberger Jr., L. Wetherill, X., Xuei, D. Lai, S. O’Connor, (Indiana University); G. Chan (University of Iowa; University of Connecticut); D.B. Chorlian, J. Zhang, P. Barr, S. Kinreich, G. Pandey (SUNY Downstate); N. Mullins (Icahn School of Medicine at Mount Sinai); A. Anokhin, S. Hartz, E. Johnson, V. McCutcheon, S. Saccone (Washington University); J. Moore, Z. Pang, S. Kuo (Rutgers University); A. Merikangas (The Children’s Hospital of Philadelphia and University of Pennsylvania); F. Aliev (Virginia Commonwealth University); H. Chin and A. Parsian are the NIAAA Staff Collaborators. We continue to be inspired by our memories of Henri Begleiter and Theodore Reich, founding PI and Co-PI of COGA, and also owe a debt of gratitude to other past organizers of COGA, including Ting- Kai Li, P. Michael Conneally, Raymond Crowe, and Wendy Reich, for their critical contributions. This national collaborative study is supported by NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). All code necessary to replicate this study is available upon request. We are grateful Drs. Amy Taylor and Joëlle A. Pasman for providing GWAS summary statistics with ALSPAC samples removed.

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PBB, SIK, MND, and DMD conceived the study. DMD oversaw the study. PBB led the writing of the manuscript, with substantive contributions to the writing from DMD, SIK, and MND. PBB was the lead analyst and prepared data in Add Health and FinnTwin12. SIK prepared data in COGA. MND prepared data in ALSPAC. RKL, FA, and JM provided GWAS summary statistics. MS, KPH, BP, KB, JK, AL, HJE, MHP, and AAP provided helpful advice and feedback on various aspects of the study design. All authors contributed to and critically reviewed the manuscript.

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Correspondence to Peter B. Barr.

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Barr, P.B., Driver, M.N., Kuo, S.IC. et al. Clinical, environmental, and genetic risk factors for substance use disorders: characterizing combined effects across multiple cohorts. Mol Psychiatry 27, 4633–4641 (2022).

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