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)

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)

KU Leuven
Boston Children's Hospital
Massachusetts General Hospital
Harvard University
Brigham and Women’s Hospital
Stanford University
National Institutes of Health
University of Liege
Illumina, Inc.
ICoNS
Ariadne Labs
University of Ferrara
University of Freiburg
University of California at Los Angeles
University of Oxford
Nurture Genomics
FirstSteps-BNSI
Broad Institute of MIT and Harvard
Genomics England
Mass General Brigham

Research output: Contribution to journal › Article › Academic › peer-review

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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.

Original language	English
Article number	101443
Journal	Genetics in medicine
Volume	27
Issue number	7
DOIs	https://doi.org/10.1016/j.gim.2025.101443
Publication status	Published - 1 Jul 2025

Keywords

Gene selection
Gene-disorder associations
Genomic sequencing
Machine learning
Newborn screening

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10.1016/j.gim.2025.101443

Data-driven-consideration-of-genetic-disorders-for-global-genomic-newborn-screening-programs.pdfFinal published version, 2 MBLicence: Taverne

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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) (2025). Data-driven consideration of genetic disorders for global genomic newborn screening programs. Genetics in medicine, 27(7), Article 101443. https://doi.org/10.1016/j.gim.2025.101443

@article{f0919f554e2a4f96b586f14ecd1d375a,

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 = "Gene selection, Gene-disorder associations, Genomic sequencing, Machine learning, Newborn screening",

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 \{ICoNS Gene List Contributors\} and Aldona Zygmunt and Athina Ververi and Carol Siu and Emanuela Ponzi and Enrico Bertini and Huang Xinwen and Jovanka King and Karin Kassahn and Maria Koutsogianni and Valente, \{Maria Luisa\} and Pelo, \{Matthew J.\} and Mattia Gentile and Paola Orsini and Romina Ficarella and Stefaan Sansen and Xiao Rui and Zhao Zhengyan and David Bick and \{International Consortium on Newborn Sequencing (ICoNS)\} and Aaron Goldenberg and Aditi Satija and Alberte Lundquist and Alessandra Ferlini and Alexandra Wiedemann and Alice Tuff-Lacey and Aljazi Al-Maraghi and Amanda Pichini and Akil, \{Ammira Alshabeeb\} and Amy Brower and Amy Gaviglio and Amy Ponte and Andrea Oza and Andreas Posch and Andrew Barry and Andy Rohrwasser and Ankit Malhotra and Anna Lewis and Anna Wedell and Annelotte Duintjer and Annette Grueters and Antonio Novelli and Arindam Bhatatcharjee and Arwa Nusair and Asaf Bitton and Asbj{\o}rg Stray-Pedersen and Audrey Thurm and Aymeric Harmant and Becca Hernan and Beomhee Lee and Bethany Zettler and Brenna Boyd and Brian Chung and Brian Kirmse and Brooke Greenstein and Bruce Bennetts and Bryn Webb and Bugrahan Duz and Caleb Bupp and Camille Level and Candance Cameron and Carol Siu and Carolyn Philstrom and Cassie Goldman and Celine Lewis and Charlotte Hobbs and Christian Schaaf and Christopher Greene and Ciara Leckie and Cindy Wu and Clare Kingsley and Cora Vacher and Cynthia Powell and Dalia Kasperaviciute and Daniel Gottlieb and Daniel Uribe and Dau-Ming Niu and David Bick and David Eckstein and David Godler and David Ledbetter and Derek Ansel and Dhayo Khangsar and Diana Carli and Dimitrios Thanos and Dominique Pichard and Don Bailey and Donna Bernstein and \{Paul Daniel\}, \{Earnest James\} and Elizabeth Vengoechea and Elizaveta Krupoderova and Els Voorhoeve and Emanuele Agolini and Emeline Davoine and Emmanuelle Lecommandeur and Erin Drake and \{van Steijvoort\}, Eva and Cole, \{F. Sessions\} and Fiona Ulph and Fowzan Alkuraya and Fran{\c c}ois Boemer and Gandhi, \{Geethanjali Devadoss\} and Gemma Brown and George Bowley and George Sahyoun and Georgia Charalambidou and Gert Matthijs and Guanjun Jin and Gulcin Gumus and Hadley Smith and Harriet Etheredge and Heather Turner and Heidi Cope and Heiko Brennenstuhl and Holly Peay and Holly Pickering and Ines Kander and Ingrid Holm and Irakli Rtskhiladze and James Buchanan and Jamie Galarza-Cornejo and Jelili Ojodu and Jennifer Schleit and Jessica Giordano and Jessica Merritt and Jillian Chance and Joanna Ziff and Jonathan Berg and Jorune Balciuniene and \{Gonz{\'a}lez de Aledo-Castillo\}, \{Jos{\'e} Manuel\} and Joseph Dewulf and Jovanka King and Judit Garcia-Villoria and Julie Yeo and Justin O'Sullivan and Karin Kassahn and Karla Alex and Katarzyna Ellsworth and Katarzyna Mosiewicz and Kate MacDuffie and Katherine Langley and Katie GoldenGrant and Danielsen, \{Katrin Eivindardottir\} and Katrina Paleologos and Katya Broomberg and Kee Chan and Kelly Hagman and Kelsey Kalbfleisch and Ken Irvine and Kevin Hall and \{van den Bogaert\}, Kris and Kristen Sund and Laura Milko and Lauren Capacchione and Lauren Scully and Laurence Faivre and Laurent Servais and Layla Horwitz and Lene Rottensten and Leni Kauko and Liana Protopsaltis and Lidewij Henneman and Lilian Downie and Lionel Marcelis and Lisa Diller and King, \{Lisa Sniderman\} and Liz Gardner and Lou Metherell and \{St George\}, Lourdes and Lucy Kaplun and Madhuri Hegde and Maija Siitonen and Mairead Bresnahan and Malgorzata Ponikowska and Maria Iascone and Maria Martinez-Fresno and Marianna Raia and Martijn Dolle and Martin Vu and Matthew Aujla and Matthew Ellinwood and Matthew Pelo and Mattia Gentile and Mauro Longoni and Meekai To and Megan Clarke and Mei Baker and Melissa Berenger and Melissa Wasserstein and Meredith Wright and Mette Nyegaard and Michael Gelb and Michael Watson and Michela Zuccolo and Michelle Pirreca and Minna Niemela and \{van der Burg\}, Mirjam and Monica Ferrie and Munira Alshehri and Nabihah Sachedina and \{Yin-Hsiu Chien\}, Nancy and Mikkelsen, \{Nanna Balle\} and Natasha Bonhomme and Neal Sondheimer and Nicolas Encina and Nicole Kelly and Nicole Miller and Nidhi Shah and Niki Armstrong and Nina Gold and Ceballo, \{Onassis Castillo\} and Pakhi Dixit and Pamela Lee and Paola Orsini and Paris Tafas and Paul Kruszka and Paul Rollier and Petra Furu and Petros Tsipouras and Philip Twiss and Brooks, \{P. J.\} and Polakit Teekakirikul and Rachel Klein and Badji, \{Radja Messai\} and Rebecca Heiner-Fokkema and Rebecca Reimers and Rebekah Zimmerman and Richard Parad and Richard Scott and Riina Kaukonen and Robert Green and Robert Steiner and Roberts Melbardis and Robin Hayeems and Rose Heald and Ruby Liu and Sally Hartmanis and Sarah Elsea and Sarah Norris and Scott Grosse and Sebastian Lunke and Shamila Yusuff and Sian Morgan and Sikha Singh and Sophia Adelson and Stacey Seeloff and Stefan Koelker and Stephanie Coury and Stephen Kingsmore and Steven Brenner and Sucheta Bhatt and Sukhvinder Nicklen and Susan Clasper and Suzanne Drury and Tamara Dangouloff and Terri Finkel and Thomas Brewster and Thomas Minten and Thomas Westover and Tiina Urv and Tom Defay and Tomasz Zemojtel and Tony Cox and Tracey Bishop and Jensen, \{Ulrich Glumer\} and Ulrik Stoltze and Ulrike M{\"u}tze and Wendy Chung and Winnie Chen and Ya Gao and Yeyson Quevedo and Yiota Veloudi and Youssef Kousa and Yun-Ru Chen and Zhanzhi Hu and Zornitza Stark and Green, \{Robert C.\} and Gold, \{Nina B.\}",

note = "Publisher Copyright: {\textcopyright} 2025 American College of Medical Genetics and Genomics",

year = "2025",

month = jul,

day = "1",

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

language = "English",

volume = "27",

journal = "Genetics in medicine",

issn = "1098-3600",

publisher = "Lippincott Williams and Wilkins",

number = "7",

}

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) 2025, 'Data-driven consideration of genetic disorders for global genomic newborn screening programs', Genetics in medicine, vol. 27, no. 7, 101443. https://doi.org/10.1016/j.gim.2025.101443

TY - JOUR

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 - ICoNS Gene List Contributors

AU - Zygmunt, Aldona

AU - Ververi, Athina

AU - Siu, Carol

AU - Ponzi, Emanuela

AU - Bertini, Enrico

AU - Xinwen, Huang

AU - King, Jovanka

AU - Kassahn, Karin

AU - Koutsogianni, Maria

AU - Valente, Maria Luisa

AU - Pelo, Matthew J.

AU - Gentile, Mattia

AU - Orsini, Paola

AU - Ficarella, Romina

AU - Sansen, Stefaan

AU - Rui, Xiao

AU - Zhengyan, Zhao

AU - Bick, David

AU - International Consortium on Newborn Sequencing (ICoNS)

AU - Goldenberg, Aaron

AU - Satija, Aditi

AU - Lundquist, Alberte

AU - Ferlini, Alessandra

AU - Wiedemann, Alexandra

AU - Tuff-Lacey, Alice

AU - Al-Maraghi, Aljazi

AU - Pichini, Amanda

AU - Akil, Ammira Alshabeeb

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AU - Gaviglio, Amy

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AU - Posch, Andreas

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AU - Webb, Bryn

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AU - Siu, Carol

AU - Philstrom, Carolyn

AU - Goldman, Cassie

AU - Lewis, Celine

AU - Hobbs, Charlotte

AU - Schaaf, Christian

AU - Greene, Christopher

AU - Leckie, Ciara

AU - Wu, Cindy

AU - Kingsley, Clare

AU - Vacher, Cora

AU - Powell, Cynthia

AU - Kasperaviciute, Dalia

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AU - Uribe, Daniel

AU - Niu, Dau-Ming

AU - Bick, David

AU - Eckstein, David

AU - Godler, David

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AU - Ansel, Derek

AU - Khangsar, Dhayo

AU - Carli, Diana

AU - Thanos, Dimitrios

AU - Pichard, Dominique

AU - Bailey, Don

AU - Bernstein, Donna

AU - Paul Daniel, Earnest James

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AU - Krupoderova, Elizaveta

AU - Voorhoeve, Els

AU - Agolini, Emanuele

AU - Davoine, Emeline

AU - Lecommandeur, Emmanuelle

AU - Drake, Erin

AU - van Steijvoort, Eva

AU - Cole, F. Sessions

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AU - Alkuraya, Fowzan

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AU - Hegde, Madhuri

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AU - Bresnahan, Mairead

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AU - Iascone, Maria

AU - Martinez-Fresno, Maria

AU - Raia, Marianna

AU - Dolle, Martijn

AU - Vu, Martin

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AU - Ellinwood, Matthew

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AU - Gentile, Mattia

AU - Longoni, Mauro

AU - To, Meekai

AU - Clarke, Megan

AU - Baker, Mei

AU - Berenger, Melissa

AU - Wasserstein, Melissa

AU - Wright, Meredith

AU - Nyegaard, Mette

AU - Gelb, Michael

AU - Watson, Michael

AU - Zuccolo, Michela

AU - Pirreca, Michelle

AU - Niemela, Minna

AU - van der Burg, Mirjam

AU - Ferrie, Monica

AU - Alshehri, Munira

AU - Sachedina, Nabihah

AU - Yin-Hsiu Chien, Nancy

AU - Mikkelsen, Nanna Balle

AU - Bonhomme, Natasha

AU - Sondheimer, Neal

AU - Encina, Nicolas

AU - Kelly, Nicole

AU - Miller, Nicole

AU - Shah, Nidhi

AU - Armstrong, Niki

AU - Gold, Nina

AU - Ceballo, Onassis Castillo

AU - Dixit, Pakhi

AU - Lee, Pamela

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AU - Tafas, Paris

AU - Kruszka, Paul

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AU - Tsipouras, Petros

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AU - Brooks, P. J.

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AU - Klein, Rachel

AU - Badji, Radja Messai

AU - Heiner-Fokkema, Rebecca

AU - Reimers, Rebecca

AU - Zimmerman, Rebekah

AU - Parad, Richard

AU - Scott, Richard

AU - Kaukonen, Riina

AU - Green, Robert

AU - Steiner, Robert

AU - Melbardis, Roberts

AU - Hayeems, Robin

AU - Heald, Rose

AU - Liu, Ruby

AU - Hartmanis, Sally

AU - Elsea, Sarah

AU - Norris, Sarah

AU - Grosse, Scott

AU - Lunke, Sebastian

AU - Yusuff, Shamila

AU - Morgan, Sian

AU - Singh, Sikha

AU - Adelson, Sophia

AU - Seeloff, Stacey

AU - Koelker, Stefan

AU - Coury, Stephanie

AU - Kingsmore, Stephen

AU - Brenner, Steven

AU - Bhatt, Sucheta

AU - Nicklen, Sukhvinder

AU - Clasper, Susan

AU - Drury, Suzanne

AU - Dangouloff, Tamara

AU - Finkel, Terri

AU - Brewster, Thomas

AU - Minten, Thomas

AU - Westover, Thomas

AU - Urv, Tiina

AU - Defay, Tom

AU - Zemojtel, Tomasz

AU - Cox, Tony

AU - Bishop, Tracey

AU - Jensen, Ulrich Glumer

AU - Stoltze, Ulrik

AU - Mütze, Ulrike

AU - Chung, Wendy

AU - Chen, Winnie

AU - Gao, Ya

AU - Quevedo, Yeyson

AU - Veloudi, Yiota

AU - Kousa, Youssef

AU - Chen, Yun-Ru

AU - Hu, Zhanzhi

AU - Stark, Zornitza

AU - Green, Robert C.

AU - Gold, Nina B.

PY - 2025/7/1

Y1 - 2025/7/1

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 - Gene selection

KW - Gene-disorder associations

KW - Genomic sequencing

KW - Machine learning

KW - Newborn screening

UR - https://www.scopus.com/pages/publications/105006735005

U2 - 10.1016/j.gim.2025.101443

DO - 10.1016/j.gim.2025.101443

M3 - Article

C2 - 40357684

SN - 1098-3600

VL - 27

JO - Genetics in medicine

JF - Genetics in medicine

IS - 7

M1 - 101443

ER -

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

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Keywords

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