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Cystic fibrosis (CF) is a life-threatening genetic disorder. Early treatment of CF-positive newborns can extend life span, improve quality of life, and reduce healthcare expenditures. As a result, newborns are screened for CF throughout the United States. Genetic testing is costly; therefore, CF screening processes start with a relatively inexpensive but not highly accurate biomarker test. Newborns with elevated biomarker levels are further screened via genetic testing for a panel of variants (types of mutations), selected from among hundreds of CF-causing variants, and newborns with mutations detected are referred for diagnostic testing, which corrects any false-positive screening results. Conversely, a false negative represents a missed CF diagnosis and delayed treatment. Therefore, an important decision is which CF-causing variants to include in the genetic testing panel so as to reduce the probability of a false negative under a testing budget that limits the number of variants in the panel. We develop novel deterministic and robust optimization models and identify key structural properties of optimal genetic testing panels. These properties lead to efficient, exact algorithms and key insights. Our case study underscores the value of our optimization-based approaches for CF newborn screening compared with current practices. Our findings have important implications for public policy.
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Equity in genetic newborn screening
Abstract 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, thepaneldesign), 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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- Award ID(s):
- 2052575
- PAR ID:
- 10453283
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Naval Research Logistics (NRL)
- Volume:
- 68
- Issue:
- 1
- ISSN:
- 0894-069X
- Format(s):
- Medium: X Size: p. 44-64
- Size(s):
- p. 44-64
- Sponsoring Org:
- National Science Foundation
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