Optimizing Control Definitions in Opioid Use Disorder Genetic Research Using Electronic Health Records

doi:10.1111/adb.70094

Optimizing Control Definitions in Opioid Use Disorder Genetic Research Using Electronic Health Records

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Abstract

Amidst the opioid crisis, understanding the genetic basis of opioid use disorder (OUD) is crucial for identifying biological mechanisms and intervention points. However, genome-wide association studies (GWASs) have been hampered by inadequate sample sizes and often the use of control populations not assessed for prior opioid exposure. Because opioid exposure is a prerequisite for the development of OUD, consideration of exposure history in controls is important. Electronic health record data (EHR) paired with genomic information allow a broader sampling of patients with OUD and exposed controls. We leveraged data across two healthcare systems to evaluate the impact of using controls not screened for opioid exposure (‘generic’) versus minimally opioid-exposed control (‘exposed’). First, at the phenotypic level, we conducted phenome-wide association studies (PheWAS) to compare the medical comorbidity profiles of OUD cases when using generic versus exposed controls. While PheWAS results for OUD-related comorbidities were more pronounced when using the generic group, 83% of the disease associations were overlapping and of similar effect sizes. Second, at the genetic level, we conducted GWAS (cases vs. generic; cases vs. exposed) and assessed differences in genetic correlations and degrees of phenotypic misclassification. Genetic results were concordant across control groups based on heritability (generic: 0.16 ± 0.07 vs. 0.10 ± 0.07), associations with the coding OPRM1 variant rs1799971 (p_generic = 8.83E-03 vs. p_exposed = 1.83E-02) and genetic correlations with prior OUD GWAS (r_g-generic = 0.83 ± 0.26 vs. r_g-exposed = 0.78 ± 0.27). Although GWASs were limited by sample size (N_generic = 6269, N_exposed = 6365), compared to an independent OUD GWAS (N = 425 944), the dilution value for the two GWAS was not different from 1, suggesting no major impact of phenotypic misclassification. This study represents the first effort to enhance OUD genetic research through optimization of control definitions using EHR data. Generic controls ascertained within the US health systems, where exposure to prescription opioids is high, offer a practical alternative for genetic studies of OUD.

Original language	English
Article number	e70094
Number of pages	10
Journal	Addiction Biology
Volume	31
Issue number	1
DOIs	https://doi.org/10.1111/adb.70094
State	Published - Jan 2026

Bibliographical note

Publisher Copyright:
© 2026, John Wiley and Sons Inc. All rights reserved.

Funding

L.K.D., V.T., and S.S.R. are funded through the National Institute on Drug Abuse (NIDA DA054071). S.S.R. is also funded through the National Institute on Drug Abuse (NIDA) DP1DA054394 and 1R01DA061977-01. The project described was supported by the National Center for Research Resources, grant UL1 RR024975-01, and is now at the National Center for Advancing Translational Sciences, grant 2 UL1 TR000445-06. The project was also supported by the National Institute of Mental Health (NIMH R01MH137220). J.W.S. was supported in part by 1R01MH118233. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The dataset(s) used for the analyses described were obtained from Vanderbilt University Medical Center's BioVU, which is supported by numerous sources: institutional funding, private agencies and federal grants. These include the NIH funded Shared Instrumentation grant S10RR025141 and CTSA grants UL1TR002243, UL1TR000445 and UL1RR024975. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711 and additional funding sources listed at https://victr.vumc.org/biovu-funding/. All other PsycheMERGE Substance Use Disorders Workgroup members: Alexander S Hatoum, Natasia Courchesne-Krak, Kyra Feuer, Melissa N Poulsen, Laura Vilar-Ribo. The authors thank the participants and staff of the Vanderbilt University Medical Center BioVU and the Mass General Brigham (MGB) Biobank for their invaluable contributions to this research. This work would not have been possible without the efforts of the clinicians, researchers, and support teams involved in data collection, management, and analysis at both institutions. We are deeply grateful to all individuals whose participation and collaboration made this study possible. L.K.D., V.T., and S.S.R. are funded through the National Institute on Drug Abuse (NIDA DA054071). S.S.R. is also funded through the National Institute on Drug Abuse (NIDA) DP1DA054394 and 1R01DA061977‐01. The project described was supported by the National Center for Research Resources, grant UL1 RR024975‐01, and is now at the National Center for Advancing Translational Sciences, grant 2 UL1 TR000445‐06. The project was also supported by the National Institute of Mental Health (NIMH R01MH137220). J.W.S. was supported in part by 1R01MH118233. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The dataset(s) used for the analyses described were obtained from Vanderbilt University Medical Center's BioVU, which is supported by numerous sources: institutional funding, private agencies and federal grants. These include the NIH funded Shared Instrumentation grant S10RR025141 and CTSA grants UL1TR002243, UL1TR000445 and UL1RR024975. Genomic data are also supported by investigator‐led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711 and additional funding sources listed at https://victr.vumc.org/biovu‐funding/ .

Funders	Funder number
Massachusetts General Hospital
National Institutes of Health (NIH)	S10RR025141, UL1TR002243
Vanderbilt Digestive Diseases Research Center, Vanderbilt University Medical Center	U01HG006378, R01NS032830, U19HL065962, RC2GM092618, U01HG004798, UL1TR000445, S10RR025141, UL1RR024975, P50GM115305, UL1TR002243, R01HD074711
National Center for Advancing Translational Sciences (NCATS)	2 UL1 TR000445‐06
National Institute on Drug Abuse	1R01DA061977‐01, DA054071, DP1DA054394
National Institute of Mental Health	1R01MH118233, R01MH137220
National Center for Research Resources	UL1RR024975

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Medicine (miscellaneous)
Pharmacology
Psychiatry and Mental health

Access to Document

10.1111/adb.70094

Cite this

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title = "Optimizing Control Definitions in Opioid Use Disorder Genetic Research Using Electronic Health Records",

abstract = "Amidst the opioid crisis, understanding the genetic basis of opioid use disorder (OUD) is crucial for identifying biological mechanisms and intervention points. However, genome-wide association studies (GWASs) have been hampered by inadequate sample sizes and often the use of control populations not assessed for prior opioid exposure. Because opioid exposure is a prerequisite for the development of OUD, consideration of exposure history in controls is important. Electronic health record data (EHR) paired with genomic information allow a broader sampling of patients with OUD and exposed controls. We leveraged data across two healthcare systems to evaluate the impact of using controls not screened for opioid exposure ({\textquoteleft}generic{\textquoteright}) versus minimally opioid-exposed control ({\textquoteleft}exposed{\textquoteright}). First, at the phenotypic level, we conducted phenome-wide association studies (PheWAS) to compare the medical comorbidity profiles of OUD cases when using generic versus exposed controls. While PheWAS results for OUD-related comorbidities were more pronounced when using the generic group, 83\% of the disease associations were overlapping and of similar effect sizes. Second, at the genetic level, we conducted GWAS (cases vs. generic; cases vs. exposed) and assessed differences in genetic correlations and degrees of phenotypic misclassification. Genetic results were concordant across control groups based on heritability (generic: 0.16 ± 0.07 vs. 0.10 ± 0.07), associations with the coding OPRM1 variant rs1799971 (pgeneric = 8.83E-03 vs. pexposed = 1.83E-02) and genetic correlations with prior OUD GWAS (rg-generic = 0.83 ± 0.26 vs. rg-exposed = 0.78 ± 0.27). Although GWASs were limited by sample size (Ngeneric = 6269, Nexposed = 6365), compared to an independent OUD GWAS (N = 425 944), the dilution value for the two GWAS was not different from 1, suggesting no major impact of phenotypic misclassification. This study represents the first effort to enhance OUD genetic research through optimization of control definitions using EHR data. Generic controls ascertained within the US health systems, where exposure to prescription opioids is high, offer a practical alternative for genetic studies of OUD.",

author = "Maria Niarchou and Ellen L Tsai and Mariela V Jennings and Annika Faucon and Hyunjoon Lee and Kritika Singh and Justin D Tubbs and Panos Roussos and Tian Ge and Richard C Crist and Rachel Vickers-Smith and Howard Edenberg and Amy Moore and Bradley T Webb and Eric O Johnson and Rachel L Kember and Jordan W Smoller and Lea K Davis and Brandon J Coombes and Georgios Voloudakis and David Burstein and Travis T Mallard and Vanessa Troiani and Sandra Sanchez-Roige",

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doi = "10.1111/adb.70094",

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AU - Lee, Hyunjoon

AU - Singh, Kritika

AU - Tubbs, Justin D

AU - Roussos, Panos

AU - Ge, Tian

AU - Crist, Richard C

AU - Vickers-Smith, Rachel

AU - Edenberg, Howard

AU - Moore, Amy

AU - Webb, Bradley T

AU - Johnson, Eric O

AU - Kember, Rachel L

AU - Smoller, Jordan W

AU - Davis, Lea K

AU - Coombes, Brandon J

AU - Voloudakis, Georgios

AU - Burstein, David

AU - Mallard, Travis T

AU - Troiani, Vanessa

AU - Sanchez-Roige, Sandra

PY - 2026/1

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Optimizing Control Definitions in Opioid Use Disorder Genetic Research Using Electronic Health Records

Abstract

Bibliographical note

Funding

UN SDGs

ASJC Scopus subject areas

Access to Document

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