Cost-effective and robust genotyping using double-mismatch allele-specific quantitative PCR.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
15 02 2019
Historique:
received: 06 02 2017
accepted: 06 12 2018
entrez: 17 2 2019
pubmed: 17 2 2019
medline: 11 8 2020
Statut: epublish

Résumé

For a wide range of diseases, SNPs in the genome are the underlying mechanism of dysfunction. Therefore, targeted detection of these variations is of high importance for early diagnosis and (familial) screenings. While allele-specific PCR has been around for many years, its adoption for SNP genotyping or somatic mutation detection has been hampered by its low discriminating power and high costs. To tackle this, we developed a cost-effective qPCR based method, able to detect SNPs in a robust and specific manner. This study describes how to combine the basic principles of allele-specific PCR (the combination of a wild type and variant primer) with the straightforward readout of DNA-binding dye based qPCR technology. To enhance the robustness and discriminating power, an artificial mismatch in the allele-specific primer was introduced. The resulting method, called double-mismatch allele-specific qPCR (DMAS-qPCR), was successfully validated using 12 SNPs and 15 clinically relevant somatic mutations on 48 cancer cell lines. It is easy to use, does not require labeled probes and is characterized by high analytical sensitivity and specificity. DMAS-qPCR comes with a complimentary online assay design tool, available for the whole scientific community, enabling researchers to design custom assays and implement those as a diagnostic test.

Identifiants

pubmed: 30770838
doi: 10.1038/s41598-019-38581-z
pii: 10.1038/s41598-019-38581-z
pmc: PMC6377641
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2150

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Auteurs

Steve Lefever (S)

Center for Medical Genetics Ghent, Ghent University, Ghent, 9000, Belgium. Steve.Lefever@UGent.be.
Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, 9000, Belgium. Steve.Lefever@UGent.be.
Bioinformatics Institute Ghent (BIG), Ghent University, Ghent, 9000, Belgium. Steve.Lefever@UGent.be.

Ali Rihani (A)

Center for Medical Genetics Ghent, Ghent University, Ghent, 9000, Belgium.
Karolinska Institute, Stockholm, SE-171 77, Sweden.

Joni Van der Meulen (J)

Center for Medical Genetics Ghent, Ghent University, Ghent, 9000, Belgium.

Filip Pattyn (F)

Center for Medical Genetics Ghent, Ghent University, Ghent, 9000, Belgium.
Ontoforce, Ghent, 9000, Belgium.

Tom Van Maerken (T)

Center for Medical Genetics Ghent, Ghent University, Ghent, 9000, Belgium.
Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, 9000, Belgium.

Jo Van Dorpe (J)

Department of Pathology, University Hospital Ghent, Ghent, 9000, Belgium.

Jan Hellemans (J)

Biogazelle, Zwijnaarde, 9052, Belgium.

Jo Vandesompele (J)

Center for Medical Genetics Ghent, Ghent University, Ghent, 9000, Belgium.
Biogazelle, Zwijnaarde, 9052, Belgium.
Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, 9000, Belgium.
Bioinformatics Institute Ghent (BIG), Ghent University, Ghent, 9000, Belgium.

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Classifications MeSH