Health
Machine learning models guide smarter gene selection in neonatal screening
More than a decade ago, researchers launched the BabySeq project, a pilot program to return neonatal genome sequencing results to parents and measure their impact on neonatal care. Today, more than 30 international initiatives are investigating the expansion of neonatal screening using genome sequencing (NBSEQ), but a new study by researchers at Mass General Brigham highlights substantial variation in gene selection between these programs. In a paper published in Medical Geneticsthe official journal of the American University of Medical Genetics and Genomics, they offer a data-driven approach that prioritizes genes for public health considerations.
It is important to be thoughtful about which genes and conditions are included in the genomic neonatal screening program. By leveraging machine learning, policymakers and clinicians can provide tools that can help policy makers and clinicians make more informed choices and ultimately improve the impact of genomic screening programs. ”
NINA GOLD, MD, co-author, Director of Prenatal Medical Genetics and Metabolism at Massachusetts General Hospital (MGH)
The authors present machine learning models that bring structure and consistency to gene selection in the NBSEQ program. This is the first publication from the International Consortium of Neonatal Sequences (Icon) founded in 2021 by senior author Robert C. Greene, MD.
The researchers analyzed 4,390 genes in 27 NBSEQ programs and identified key factors affecting gene inclusion. The number of genes analyzed in each program ranged from 134 to 4,299, but only genes (1.7%) consistently included in over 80% of the programs. The most powerful predictors of gene inclusion were whether the uniform screening panel recommended for the US had conditions, whether there was robust natural history data, and whether there was strong evidence of treatment. Effectiveness.
Using these insights, the team developed a machine learning model that incorporates 13 predictors, achieving high accuracy in predicting gene selection across the program. This model provides a ranked list of new evidence and genes that can adapt to local needs, allowing for more consistent and informed decision-making across NBSEQ initiatives around the world.
“This study represents an important step in harmonizing the NBSEQ program and ensuring that gene selection reflects the latest scientific evidence and public health priorities,” Greene said.
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Journal Reference:
Minten, T. , et al. (2025). Data-driven considerations for genetic disorders in the Global Genomic Neonatal Screening Program. Medical Genetics. doi.org/10.1016/j.gim.2025.101443.
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