State‐level newborn screening allows for early treatment of genetic disorders, which can substantially improve health outcomes for newborns. As the cost of genetic testing decreases, it is becoming an essential part of newborn screening. A genetic disorder can be caused by many mutation variants; therefore, an important decision is to determine which variants to search for (ie, the
Novel Pooling Strategies for Genetic Testing, with Application to Newborn Screening
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. This paper was accepted by Stefan Scholtes, healthcare management.
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- NSF-PAR ID:
- 10334260
- Date Published:
- Journal Name:
- Management Science
- ISSN:
- 0025-1909
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract panel design), under a testing budget. The frequency of variants that cause a disorder and the incidence of the disorder vary by racial/ethnic group. Consequently, it is important to consider equity issues in panel design, so as to reduce disparities among different groups. We study the panel design problem using cystic fibrosis (CF) as a model disorder, considering the trade‐offs between equity and accuracy, under a limited budget. Most states use a genetic test in their CF screening protocol, but panel designs vary, and, due to cost, no state's panel includes all CF‐causing variants. We develop models that design equitable genetic testing panels, and compare them with panels that maximize sensitivity in the general population. Our case study, based on realistic CF data, highlights the value of equitable panels and provides important insight for newborn screening practices. -
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Funding: This work was supported by the National Science Foundation [Grant 1761842].
Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0296 .
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1287/mnsc.2021.4289
