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Clinical utility and implementation of genomic newborn screening: findings from the SNUseq Baby Project

Journal
European journal of human genetics : EJHG (Q1)
Published
5 November 2026
Study design
Unclassified
Evidence level
Level 5, Expert Opinion (CEBM 5)
Authors
Jee Min Kim, Man Jin Kim, Seungbok Lee, Jin Sook Lee, Ju Sun Heo, Young Hwa Jung, et al.
PMID
42988931
DOI
10.1038/s41431-026-02263-y

Why clinicians should know about it

  • Picked for Genetics (clinical) (paper of the day, 9 November 2026): Prospective genomic newborn screening study evaluates clinical utility

Abstract

Genomic newborn screening (NBS) has emerged as a powerful complement to conventional screening; however, its clinical utility remains to be fully established. We conducted a prospective multicenter pilot study within the Seoul National University Hospital network from November 2025 to February 2026. A panel of 419 genes was curated by a multidisciplinary expert team based on early childhood onset, disease severity, the presence of an asymptomatic window, and the local availability of effective treatments. Genome sequencing was performed on 399 newborns, including six monozygotic twin pairs analyzed as single data points. In addition to 385 inborn neonates, 14 neonates referred because of abnormal conventional NBS results were included. As a result, the screen-positive rate was 1.3% (5/393). Two positive cases with variants in SLC25A15 and MYO15A were missed by conventional NBS, and we also identified three cases harboring pathogenic variants in arrhythmia-associated genes, a category inherently beyond the scope of conventional screening. Among 49 neonates with abnormal conventional NBS results, all were negative on genomic NBS. The median turnaround time from sample collection to final report was 26 days. Genomic NBS demonstrated favorable precision and complementary clinical utility beyond conventional screening. Regular multidisciplinary curation of target genes and long-term longitudinal follow-up are essential for successful clinical implementation.

Abstract as published, via PubMed.

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For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.