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Novel Pooling Strategies for Genetic Testing, with Application to Newborn Screening

Author

Listed:
  • Hussein El Hajj

    (Department of Management Sciences, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada)

  • Douglas R. Bish

    (Department of Information Systems, Statistics, and Management Science, University of Alabama Tuscaloosa, Alabama 35487)

  • Ebru K. Bish

    (Department of Information Systems, Statistics, and Management Science, University of Alabama Tuscaloosa, Alabama 35487)

  • Denise M. Kay

    (Wadsworth Center, New York State Department of Health Albany, New York 12208)

Abstract

Newborn screening (NBS) is a state-level initiative that detects life-threatening genetic disorders for which early treatment can substantially improve health outcomes. Cystic fibrosis (CF) is among the most prevalent disorders in NBS. CF can be caused by a large number of mutation variants to the CFTR gene. Most states use a multitest CF screening process that includes a genetic test ( DNA ). However, due to cost concerns, DNA is used only on a small subset of newborns (based on a low-cost biomarker test with low classification accuracy), and only for a small subset of CF-causing variants. To overcome the cost barriers of expanded genetic testing, we explore a novel approach, of multipanel pooled DNA testing. This approach leads not only to a novel optimization problem (variant selection for screening, variant partition into multipanels, and pool size determination for each panel), but also to novel CF NBS processes. We establish key structural properties of optimal multipanel pooled DNA designs; develop a methodology that generates a family of optimal designs at different costs; and characterize the conditions under which a 1-panel versus a multipanel design is optimal. This methodology can assist decision-makers to design a screening process, considering the cost versus accuracy trade-off. Our case study, based on published CF NBS data from the state of New York, indicates that the multipanel and pooling aspects of genetic testing work synergistically, and the proposed NBS processes have the potential to substantially improve both the efficiency and accuracy of current practices.

Suggested Citation

  • Hussein El Hajj & Douglas R. Bish & Ebru K. Bish & Denise M. Kay, 2022. "Novel Pooling Strategies for Genetic Testing, with Application to Newborn Screening," Management Science, INFORMS, vol. 68(11), pages 7994-8014, November.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:11:p:7994-8014
    DOI: 10.1287/mnsc.2021.4289
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    References listed on IDEAS

    as
    1. Hrayer Aprahamian & Ebru K. Bish & Douglas R. Bish, 2018. "Adaptive risk-based pooling in public health screening," IISE Transactions, Taylor & Francis Journals, vol. 50(9), pages 753-766, September.
    2. Hrayer Aprahamian & Douglas R. Bish & Ebru K. Bish, 2020. "Optimal Group Testing: Structural Properties and Robust Solutions, with Application to Public Health Screening," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 895-911, October.
    3. Hadi El-Amine & Ebru K. Bish & Douglas R. Bish, 2018. "Robust Postdonation Blood Screening Under Prevalence Rate Uncertainty," Operations Research, INFORMS, vol. 66(1), pages 1-17, 1-2.
    4. Saloumeh Sadeghzadeh & Ebru K. Bish & Douglas R. Bish, 2020. "Optimal data-driven policies for disease screening under noisy biomarker measurement," IISE Transactions, Taylor & Francis Journals, vol. 52(2), pages 166-180, February.
    5. Lauren N. Steimle & Brian T. Denton, 2017. "Markov Decision Processes for Screening and Treatment of Chronic Diseases," International Series in Operations Research & Management Science, in: Richard J. Boucherie & Nico M. van Dijk (ed.), Markov Decision Processes in Practice, chapter 0, pages 189-222, Springer.
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    8. Hrayer Aprahamian & Douglas R. Bish & Ebru K. Bish, 2019. "Optimal Risk-Based Group Testing," Management Science, INFORMS, vol. 65(9), pages 4365-4384, September.
    9. A. K. Chakravarty & J. B. Orlin & U. G. Rothblum, 1982. "Technical Note—A Partitioning Problem with Additive Objective with an Application to Optimal Inventory Groupings for Joint Replenishment," Operations Research, INFORMS, vol. 30(5), pages 1018-1022, October.
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