Improving newborn screening accuracy through genome sequencing, targeted metabolomics, and machine learning
Targeted metabolomics with AI/ML showed high sensitivity for identifying true positives, though its ability to reduce false positives varied by condition. Genome sequencing effectively reduced false positives but lacked sufficient sensitivity as a standalone test. The elevated false-positive rate among pathogenic variant carriers underscores the potential value of parental or prenatal carrier screening to improve NBS accuracy. Integrating genomic and metabolomic data may enhance NBS precision and enable earlier diagnosis and intervention for rare diseases.
Authors
- Hongyu Zhao (ORCID: https://orcid.org/0000-0003-3213-9923)
- Yuhan Xie (ORCID: https://orcid.org/0000-0002-1738-0885)
- Irina Tikhonova (ORCID: https://orcid.org/0000-0002-5260-0476)
- Gregory M. Enns (ORCID: https://orcid.org/0000-0001-9968-0840)
- Tina M. Cowan (ORCID: https://orcid.org/0000-0002-2839-3200)
- Curt Scharfe (ORCID: https://orcid.org/0000-0001-6322-2039)
- Gang Peng (ORCID: https://orcid.org/0000-0003-0307-9351)
Institutions
- Yale University (US)
- Indiana University School of Medicine
- Indiana University – Purdue University Indianapolis (US)
- Stanford University (US)
Publication Details
- Journal
- BMC Medical Genomics
- Published
- 2025-11-19
- DOI
- https://doi.org/10.1186/s12920-025-02261-x
- Citations
- 1
- Primary Topic
- Genomics and Rare Diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.82
Funders
- California Department of Public Health
- Yale University
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development