Utility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder

Results 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.

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Publication Details

Journal
JAMA Network Open
Published
2025-01-09
DOI
https://doi.org/10.1001/jamanetworkopen.2024.53913
Citations
5
Primary Topic
Opioid Use Disorder Treatment
Type
article
Field-Weighted Citation Impact
9.39

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Utility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder

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JAMA Network Open
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article

Utility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder

Alexander S. Hatoum, Lori Churby, Luis E. Selva, Michael E. Matheny, F. A. Gesek, Elizabeth Bast, Jessica V. Brewer, Emily E. Hartwell, Arpana Agrawal, Maureen Murdoch, Kristin Mattocks, Samuel M. Aguayo, Peter S. Liang, Amy C. Justice, Robin A. Hurley, Zuhair K. Ballas, Richard C. Crist, James W. Baurley, X. Michelle Androulakis, Michael Ong, Pran Iruvanti, Paul N. Meyer, Sujata Bhushan, Christopher T. Rentsch, Gerald W. Dryden, Scott L. DuVall, Jennifer Greco, Christal N. Davis, Scott Kinlay, Samuel Poon, Joel Gelernter, Howard J. Edenberg, Jack Lichy, Olaoluwa O. Okusaga, Kelly Cho, Jonathan P. Moorman, Jennifer E. Deen, Joshua C. Gray, Louis J. Dell’Italia, Rachel L. Kember, Jeff Whittle, Daniel J. Hogan, Salvador Gutierrez, Mark B. Hamner, Richard J. Servatius, Shahpoor Alex Shayan, Saiju Pyarajan, Adriana M. Hung, Philip S. Tsao, Brady M. Stephens, Ismene Petrakis, Elizabeth R. Hauser, Andrew W. Yen, Suthat Liangpunsakul, Todd Gress, Edward J. Boyko, Amy M. Kilbourne, Providencia Morales, Patrick J. Strollo, Tze Shien Lo, Todd Connor, Michael Rauchman, Gerardo Villareal, L. Christine Faulk, Jean C. Beckham, Sylvanus Toikumo, Darshana Jhala, Henry R. Kranzler, Timothy R. Morgan, Stacey B. Whitbourne, Mary T. Brophy, Samir Gupta, Daryl Fujii, Mary A. Whooley, Eknath Naik, Dean P. Argyres, Joseph Fayad, Saib Gappy, Peruvemba Sriram, Kathrina Alexander, Frank J. Jacono, Zeal Jinwala, David Cohen, Neeraj Tandon, Seema Joshi, Sunil Ahuja, Satish Sharma, Tim Assimes, Peter Wilson, Prakash Balasubramanian, Jennifer Moser, J. Michael Gaziano, Michael Godschalk, River Smith, Michael Landry, Stephen Mastorides, Kris Ann Oursler, Shing Shing Yeh, Henry R. Kranzler, Junzhe Xu, John B. Harley, John M. Wells, Dave Oslin, Ronnie Marrache, Sumitra Muralidhar, Jessica Walsh, Charles S. Mahan, James Norton, S. Amneet
article en
5 citations

Abstract

Results 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.

JAMA Network OpenVol. 8(1)
Uniformed Services University of the Health Sciences (US), Washington University in St. Louis (US), Yale University (US), VA Connecticut Healthcare System (US), London School of Hygiene & Tropical Medicine (GB), BioRealm (United States) (US), Indiana University School of Medicine, Indiana University – Purdue University Indianapolis (US), University of Pennsylvania (US)
Office of Research and Development, VISN 4 Mental Illness Research, Education, and Clinical Center, NIH Clinical Center
Good health and well-being
Openalex Percentile: Top 2%
Opioid Use Disorder Treatment
9.39
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