Importance: Recently, the Food and Drug Administration gave pre-marketing approval to algorithm based on its purported ability to identify genetic risk for opioid use disorder. However, the clinical utility of the candidate genes comprising the algorithm has not been independently demonstrated. Objective: To assess the utility of 15 variants in candidate genes from an algorithm intended to predict opioid use disorder risk. Design: This case-control study examined the association of 15 candidate genetic variants with risk of opioid use disorder using available electronic health record data from December 20, 1992 to September 30, 2022. Setting: Electronic health record data, including pharmacy records, from Million Veteran Program participants across the United States. Participants: Participants were opioid-exposed individuals enrolled in the Million Veteran Program (n = 452,664). Opioid use disorder cases were identified using International Classification of Disease diagnostic codes, and controls were individuals with no opioid use disorder diagnosis. Exposures: Number of risk alleles present across 15 candidate genetic variants. Main Outcome and Measures: Predictive performance of 15 genetic variants for opioid use disorder risk assessed via logistic regression and machine learning models. Results: Opioid exposed individuals (n=33,669 cases) were on average 61.15 (SD = 13.37) years old, 90.46% male, and had varied genetic similarity to global reference panels. Collectively, the 15 candidate genetic variants accounted for 0.4% of variation in opioid use disorder risk. The accuracy of the ensemble machine learning model using the 15 genes as predictors was 52.8% (95% CI = 52.1 - 53.6%) in an independent testing sample. Conclusions and Relevance: Candidate genes that comprise the approved algorithm do not meet reasonable standards of efficacy in predicting opioid use disorder risk. Given the limited predictive accuracy of the algorithm, its use in clinical care would lead to high rates of false positive and negative findings. More clinically useful models are needed to identify individuals at risk of developing opioid use disorder.

Dr. Kranzler is a member of advisory boards for Dicerna Pharmaceuticals, Sophrosyne Pharmaceuticals, Enthion Pharmaceuticals, and Clearmind Medicine; a consultant to Sobrera Pharmaceuticals; the recipient of research funding and medication supplies for an investigator-initiated study from Alkermes; and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which was supported in the last three years by Alkermes, Dicerna, Ethypharm, Lundbeck, Mitsubishi, Otsuka, and Pear Therapeutics, Drs. Kranzler and Gelernter hold U.S. patent 10,900,082 titled: "Genotype-guided dosing of opioid agonists," issued 26 January 2021.

This research is based on data from Million Veteran Program, Office of Research and Development, Veterans Health Administration and was supported by awards #I01 BX003341 and IK2 CX002336 (to EEH); the VISN 4 Mental Illness Research, Education, and Clinical Center; NIAAA grant K01 AA028292 (to RLK); and NIDA grant P30 DA046345. An MVP Core Acknowledgement list is provided in the supplemental materials. This publication does not represent the views of the Department of Veteran Affairs, the Department of Defense, Uniformed Services University, or the United States Government.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Million Veteran Program (MVP), an initiative of the U.S. Department of Veterans Affairs to discover the genetic etiology of diseases relevant to Veterans, was approved by the Central Veterans Affairs Institutional Review Board (IRB) and all site-specific IRBs. We followed all relevant ethical regulations for research with human subjects.

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Individual-level Million Veteran Program data is available to approved Veterans Affairs researchers.