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Newborn Screening Quality Assurance Program for CFTR Mutation Detection and Gene Sequencing to Identify Cystic Fibrosis

Overview of attention for article published in Journal of Inborn Errors of Metabolism and Screening, October 2016
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Title
Newborn Screening Quality Assurance Program for CFTR Mutation Detection and Gene Sequencing to Identify Cystic Fibrosis
Published in
Journal of Inborn Errors of Metabolism and Screening, October 2016
DOI 10.1177/2326409816661358
Pubmed ID
Authors

Miyono M. Hendrix, Stephanie L. Foster, Suzanne K. Cordovado

Abstract

All newborn screening laboratories in the United States and many worldwide screen for cystic fibrosis. Most laboratories use a second-tier genotyping assay to identify a panel of mutations in the CF transmembrane regulator (CFTR) gene. Centers for Disease Control and Prevention's Newborn Screening Quality Assurance Program houses a dried blood spot repository of samples containing CFTR mutations to assist newborn screening laboratories and ensure high-quality mutation detection in a high-throughput environment. Recently, CFTR mutation detection has increased in complexity with expanded genotyping panels and gene sequencing. To accommodate the growing quality assurance needs, the repository samples were characterized with several multiplex genotyping methods, Sanger sequencing, and 3 next-generation sequencing assays using a high-throughput, low-concentration DNA extraction method. The samples performed well in all of the assays, providing newborn screening laboratories with a resource for complex CFTR mutation detection and next-generation sequencing as they transition to new methods.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 14%
Other 4 14%
Student > Master 3 10%
Student > Ph. D. Student 3 10%
Researcher 2 7%
Other 5 17%
Unknown 8 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 34%
Medicine and Dentistry 4 14%
Agricultural and Biological Sciences 3 10%
Computer Science 1 3%
Unspecified 1 3%
Other 2 7%
Unknown 8 28%