Newborn Screening by Sequence and the Road Ahead
Author(s) -
Neal Sondheimer
Publication year - 2013
Publication title -
clinical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.705
H-Index - 218
eISSN - 1530-8561
pISSN - 0009-9147
DOI - 10.1373/clinchem.2013.205864
Subject(s) - sequence (biology) , computational biology , computer science , biology , genetics
Newborn screening (NBS)3 programs are an anomaly in medicine—a universally applied, state-mandated evaluation of healthy individuals. The program had its origin in the presymptomatic diagnosis of phenylketonuria (1), but as technologies for the use of dried blood spots (DBS) have improved, there has been a rapid expansion of the number of disorders evaluated, as well as an increase in the governmental mandates for testing (2).DNA-based diagnosis is already a feature of NBS. The first integrated use of genetic testing was for the confirmation of hemoglobinopathies (3). Genetic testing is now used as a primary or confirmatory technique in nearly every NBS system in the US. In addition to the analysis of sequence, DNA-based testing can also be used to detect the normal maturation of cell-mediated immunity through the detection of T-cell receptor excision circles (TRECs) (4). Clearly, additional tests will be added over time. The expanded use of DNA-based testing in NBS requires a reliable extraction method that delivers genomic DNA of sufficient quality and quantity for downstream applications.In this issue of Clinical Chemistry , Saavedra-Matiz and colleagues report details of the DNA-extraction protocol used in the New York State Newborn Screening Program (5). The source of the DNA sample is DBS on 903 filter papers, which are commonly used in NBS. Therefore, changes in the collection technique (i.e., phlebotomy), the volume of blood collected, or the type of card used for collection would not be required for most screening programs. The key advantages of this protocol are its extremely low cost and simplified use of automation. The minimization of hands-on work reduces labor costs and the chances for the introduction of error. The authors' system is easily scalable to the number of samples that might be reasonably anticipated in a state …
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