Utility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder
Authors: Davis Christal N. , Jinwala Zeal , Hatoum Alexander S. , Toikumo Sylvanus , Agrawal Arpana , Rentsch Christopher T. , Edenberg Howard J. , Baurley James W. , Hartwell Emily E. , Crist Richard C. , Gray Joshua C. , Justice Amy C. , Gelernter Joel , Kember Rachel L. , Kranzler Henry R. ,   VA Million Veteran Program, Muralidhar Sumitra , Moser Jennifer , Deen Jennifer E. , Tsao Philip S. , Gaziano J. Michael , Hauser Elizabeth , Kilbourne Amy , Matheny Michael , Oslin Dave , Churby Lori , Whitbourne Stacey B. , Brewer Jessica V. , Shayan Shahpoor (Alex) , Selva Luis E. , Pyarajan Saiju , Cho Kelly , DuVall Scott L. , Brophy Mary T. , Stephens Brady , Connor Todd , Argyres Dean P. , Assimes Tim , Hung Adriana , Kranzler Henry , Aguayo Samuel , Ahuja Sunil , Alexander Kathrina , Androulakis Xiao M. , Balasubramanian Prakash , Ballas Zuhair , Beckham Jean , Bhushan Sujata , Boyko Edward , Cohen David , Dellitalia Louis , Faulk L. Christine , Fayad Joseph , Fujii Daryl , Gappy Saib , Gesek Frank , Greco Jennifer , Godschalk Michael , Gress Todd W. , Gupta Samir , Gutierrez Salvador , Harley John , Hamner Mark , Hurley Robin , Iruvanti Pran , Jacono Frank , Jhala Darshana , Kinlay Scott , Landry Michael , Liang Peter , Liangpunsakul Suthat , Lichy Jack , Mahan C. Scott , Marrache Ronnie , Mastorides Stephen , Mattocks Kristin , Meyer Paul , Moorman Jonathan , Morgan Timothy , Murdoch Maureen , Norton James , Okusaga Olaoluwa , Oursler Kris Ann , Poon Samuel , Rauchman Michael , Servatius Richard , Sharma Satish , Smith River , Sriram Peruvemba , Strollo Patrick , Tandon Neeraj , Villareal Gerardo , Walsh Jessica , Wells John , Whittle Jeffrey , Whooley Mary , Wilson Peter , Xu Junzhe , Yeh Shing Shing , Bast Elizabeth S. , Dryden Gerald Wayne , Hogan Daniel J. , Joshi Seema , Lo Tze Shien , Morales Providencia , Naik Eknath , Ong Michael K. , Petrakis Ismene , Rai Amneet S. , Yen Andrew W. 
Published: January 2025

ImportanceRecently, the US Food and Drug Administration gave premarketing approval to an algorithm based on its purported ability to identify individuals at genetic risk for opioid use disorder (OUD). However, the clinical utility of the candidate genetic variants included in the algorithm has not been independently demonstrated.ObjectiveTo assess the utility of 15 genetic variants from an algorithm intended to predict OUD risk.Design, Setting, and ParticipantsThis case-control study examined the association of 15 candidate genetic variants with risk of OUD using electronic health record data from December 20, 1992, to September 30, 2022. Electronic health record data, including pharmacy records, were accrued from participants in the Million Veteran Program across the US with opioid exposure (n = 452 664). Cases with OUD were identified using International Classification of Diseases, Ninth Revision, or International Classification of Diseases, Tenth Revision, diagnostic codes, and controls were individuals with no OUD diagnosis.ExposuresNumber of risk alleles present across 15 candidate genetic variants.Main Outcome and MeasuresPerformance of 15 genetic variants for identifying OUD risk assessed via logistic regression and machine learning models.ResultsA total of 452 664 individuals with opioid exposure (including 33 669 with OUD) had a mean (SD) age of 61.15 (13.37) years, and 90.46% were male; the sample was ancestrally diverse (with individuals of genetically inferred European, African, and admixed American ancestries). Using Nagelkerke R2, collectively, the 15 candidate genes accounted for 0.40% of variation in OUD risk. In comparison, age and sex alone accounted for 3.27% of the variation. The ensemble machine learning. The ensemble machine learning model using the 15 variants as predictive factors correctly classified 52.83% (95% CI, 52.07%-53.59%) of individuals in an independent testing sample.Conclusions and RelevanceResults of this study suggest that the candidate genetic variants included in the approved algorithm do not meet reasonable standards of efficacy in identifying OUD risk. Given the algorithm’s limited predictive accuracy, its use in clinical care would lead to high rates of both false-positive and false-negative findings. More clinically useful models are needed to identify individuals at risk of developing OUD.