Using Long-Term Follow-Up Data to Classify Genetic Variants in Newborn Screened Conditions.

Autor:	Wilhelm K; Newborn Screening Translational Research Network, American College of Medical Genetics and Genomics, Bethesda, MD, United States.; Graduate Program in Genetics and Genomics, Graduate School of Biological Sciences, Baylor College of Medicine, Houston, TX, United States., Edick MJ; Michigan Public Health Institute, Okemos, MI, United States., Berry SA; Department of Pediatrics, Division of Genetics and Metabolism, University of Minnesota, Minneapolis, MN, United States., Hartnett M; Newborn Screening Translational Research Network, American College of Medical Genetics and Genomics, Bethesda, MD, United States., Brower A; Newborn Screening Translational Research Network, American College of Medical Genetics and Genomics, Bethesda, MD, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in genetics [Front Genet] 2022 May 26; Vol. 13, pp. 859837. Date of Electronic Publication: 2022 May 26 (Print Publication: 2022).
DOI:	10.3389/fgene.2022.859837
Abstrakt:	With the rapid increase in publicly available sequencing data, healthcare professionals are tasked with understanding how genetic variation informs diagnosis and affects patient health outcomes. Understanding the impact of a genetic variant in disease could be used to predict susceptibility/protection and to help build a personalized medicine profile. In the United States, over 3.8 million newborns are screened for several rare genetic diseases each year, and the follow-up testing of screen-positive newborns often involves sequencing and the identification of variants. This presents the opportunity to use longitudinal health information from these newborns to inform the impact of variants identified in the course of diagnosis. To test this, we performed secondary analysis of a 10-year natural history study of individuals diagnosed with metabolic disorders included in newborn screening (NBS). We found 564 genetic variants with accompanying phenotypic data and identified that 161 of the 564 variants (29%) were not included in ClinVar. We were able to classify 139 of the 161 variants (86%) as pathogenic or likely pathogenic. This work demonstrates that secondary analysis of longitudinal data collected as part of NBS finds unreported genetic variants and the accompanying clinical information can inform the relationship between genotype and phenotype. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Wilhelm, Edick, Berry, Hartnett and Brower.)
Databáze:	MEDLINE
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