Data-driven consideration of genetic disorders for global genomic newborn screening programs

Thomas Minten; Sarah Bick; Sophia Adelson; Nils Gehlenborg; Laura M Amendola; François Boemer; Alison J Coffey; Nicolas Encina; Alessandra Ferlini; Janbernd Kirschner; Bianca E Russell; Laurent Servais; Kristen L Sund; Ryan J Taft; Petros Tsipouras; Hana Zouk; ICoNS Gene List Contributors; International Consortium on Newborn Sequencing (ICoNS); Ulrik Kristoffer  Stoltze

doi:10.1016/j.gim.2025.101443

Data-driven consideration of genetic disorders for global genomic newborn screening programs

Thomas Minten, Sarah Bick, Sophia Adelson, Nils Gehlenborg, Laura M Amendola, François Boemer, Alison J Coffey, Nicolas Encina, Alessandra Ferlini, Janbernd Kirschner, Bianca E Russell, Laurent Servais, Kristen L Sund, Ryan J Taft, Petros Tsipouras, Hana Zouk, ICoNS Gene List Contributors, International Consortium on Newborn Sequencing (ICoNS), Ulrik Kristoffer Stoltze (Medlem af forfattergruppering)

Afdeling for Genetik DIA

12 Citationer (Scopus)

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.

Originalsprog	Engelsk
Artikelnummer	101443
Tidsskrift	Genetics in medicine : official journal of the American College of Medical Genetics
Vol/bind	27
Udgave nummer	7
Sider (fra-til)	101443
ISSN	1098-3600
DOI	https://doi.org/10.1016/j.gim.2025.101443
Status	Udgivet - jul. 2025

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10.1016/j.gim.2025.101443

Citationsformater

Minten, T., Bick, S., Adelson, S., Gehlenborg, N., Amendola, L. M., Boemer, F., Coffey, A. J., Encina, N., Ferlini, A., Kirschner, J., Russell, B. E., Servais, L., Sund, K. L., Taft, R. J., Tsipouras, P., Zouk, H., ICoNS Gene List Contributors, International Consortium on Newborn Sequencing (ICoNS), & Stoltze, U. K. (2025). Data-driven consideration of genetic disorders for global genomic newborn screening programs. Genetics in medicine : official journal of the American College of Medical Genetics, 27(7), 101443. Artikel 101443. https://doi.org/10.1016/j.gim.2025.101443

Minten, T, Bick, S, Adelson, S, Gehlenborg, N, Amendola, LM, Boemer, F, Coffey, AJ, Encina, N, Ferlini, A, Kirschner, J, Russell, BE, Servais, L, Sund, KL, Taft, RJ, Tsipouras, P, Zouk, H, ICoNS Gene List Contributors, International Consortium on Newborn Sequencing (ICoNS) & Stoltze, UK 2025, 'Data-driven consideration of genetic disorders for global genomic newborn screening programs', Genetics in medicine : official journal of the American College of Medical Genetics, bind 27, nr. 7, 101443, s. 101443. https://doi.org/10.1016/j.gim.2025.101443

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title = "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.",

keywords = "Humans, Neonatal Screening/methods, Infant, Newborn, Genetic Diseases, Inborn/genetics, Genomics/methods, Genetic Testing/methods, Machine Learning",

author = "Thomas Minten and Sarah Bick and Sophia Adelson and Nils Gehlenborg and Amendola, {Laura M} and Fran{\c c}ois Boemer and Coffey, {Alison J} and Nicolas Encina and Alessandra Ferlini and Janbernd Kirschner and Russell, {Bianca E} and Laurent Servais and Sund, {Kristen L} and Taft, {Ryan J} and Petros Tsipouras and Hana Zouk and David Bick and Green, {Robert C} and Gold, {Nina B} and {ICoNS Gene List Contributors} and {International Consortium on Newborn Sequencing (ICoNS)} and Stoltze, {Ulrik Kristoffer}",

year = "2025",

month = jul,

doi = "10.1016/j.gim.2025.101443",

language = "English",

volume = "27",

pages = "101443",

journal = "Genetics in medicine : official journal of the American College of Medical Genetics",

issn = "1098-3600",

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T1 - Data-driven consideration of genetic disorders for global genomic newborn screening programs

AU - Minten, Thomas

AU - Bick, Sarah

AU - Adelson, Sophia

AU - Gehlenborg, Nils

AU - Amendola, Laura M

AU - Boemer, François

AU - Coffey, Alison J

AU - Encina, Nicolas

AU - Ferlini, Alessandra

AU - Kirschner, Janbernd

AU - Russell, Bianca E

AU - Servais, Laurent

AU - Sund, Kristen L

AU - Taft, Ryan J

AU - Tsipouras, Petros

AU - Zouk, Hana

AU - Bick, David

AU - Green, Robert C

AU - Gold, Nina B

AU - ICoNS Gene List Contributors

AU - International Consortium on Newborn Sequencing (ICoNS)

A2 - Stoltze, Ulrik Kristoffer

PY - 2025/7

Y1 - 2025/7

N2 - 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.

AB - 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.

KW - Humans

KW - Neonatal Screening/methods

KW - Infant, Newborn

KW - Genetic Diseases, Inborn/genetics

KW - Genomics/methods

KW - Genetic Testing/methods

KW - Machine Learning

U2 - 10.1016/j.gim.2025.101443

DO - 10.1016/j.gim.2025.101443

M3 - Journal article

C2 - 40357684

SN - 1098-3600

VL - 27

SP - 101443

JO - Genetics in medicine : official journal of the American College of Medical Genetics

JF - Genetics in medicine : official journal of the American College of Medical Genetics

IS - 7

M1 - 101443

ER -

Data-driven consideration of genetic disorders for global genomic newborn screening programs

Abstract

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