Journal article
Data-driven consideration of genetic disorders for global genomic newborn screening programs
- Abstract:
-
Purpose
Over 30 international studies are exploring newborn sequencing (NBSeq) to expand the range of genetic disorders included in newborn screening. Substantial variability in gene selection across programs exists, highlighting the need for a systematic approach to prioritize genes.
Methods
We assembled a data set comprising 25 characteristics about each of the 4390 genes included in 27 NBSeq programs. We used regression analysis to identify several predictors of inclusion and developed a machine learning model to rank genes for public health consideration.
Results
Among 27 NBSeq programs, the number of genes analyzed ranged from 134 to 4299, with only 74 (1.7%) genes included by over 80% of programs. The most significant associations with gene inclusion across programs were presence on the US Recommended Uniform Screening Panel (inclusion increase of 74.7%, CI: 71.0%-78.4%), robust evidence on the natural history (29.5%, CI: 24.6%-34.4%), and treatment efficacy (17.0%, CI: 12.3%-21.7%) of the associated genetic disease. A boosted trees machine learning model using 13 predictors achieved high accuracy in predicting gene inclusion across programs (area under the curve = 0.915, R2 = 84%).
Conclusion
The machine learning model developed here provides a ranked list of genes that can adapt to emerging evidence and regional needs, enabling more consistent and informed gene selection in NBSeq initiatives.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
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- Publisher copy:
- 10.1016/j.gim.2025.101443
Authors
- Publisher:
- Elsevier
- Journal:
- Genetics in Medicine More from this journal
- Volume:
- 27
- Issue:
- 7
- Article number:
- 101443
- Place of publication:
- United States
- Publication date:
- 2025-05-09
- Acceptance date:
- 2025-04-11
- DOI:
- EISSN:
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1530-0366
- ISSN:
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1098-3600
- Pmid:
-
40357684
- Language:
-
English
- Keywords:
- Pubs id:
-
2128562
- Local pid:
-
pubs:2128562
- Deposit date:
-
2025-11-12
- ARK identifier:
Terms of use
- Copyright holder:
- American College of Medical Genetics and Genomics
- Copyright date:
- 2025
- Rights statement:
- © 2025 American College of Medical Genetics and Genomics. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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