Genetic analysis of Charcot-Marie-Tooth disease in Denmark and the implementation of a next generation sequencing platform

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

  • Signe Vaeth
  • Rikke Christensen
  • Morten Dunø
  • Dorte Launholt Lildballe
  • Kasper Thorsen
  • Vissing, John
  • Kirsten Svenstrup
  • Jens Michael Hertz
  • Henning Andersen
  • Uffe Birk Jensen

Charcot-Marie-Tooth disease (CMT) is a heterogeneous group of hereditary polyneuropathies. Variants in more than 80 different genes have been associated with the disorder. In recent years, the introduction of next generation sequencing (NGS) techniques have completely changed the genetic diagnostic approach from the analysis of a handful of genes to the analysis of all genes associated with CMT in a single run. In this study we describe the CMT diagnostics in Denmark in 1992-2012, prior to the implementation of NGS, by combining laboratory- and national registry data. We investigate the effect of implementing a targeted NGS approach of 63 genes associated with CMT in the diagnostic laboratory setting. This was performed by analyzing a cohort of 195 samples from patients previously analyzed by Sanger sequencing and quantitative analysis for the common causes of CMT without reaching a molecular diagnosis. A total of 1442 CMT analyses were performed in Denmark in the period 1992-2012; a disease-causing variant was detected in 21.6% of the cases. Interestingly, the diagnosis was genetically confirmed in significantly more women than men; 25.9% compared to18.5%. In our study cohort, we found a 5.6% increase in the diagnostic yield with the introduction of a targeted NGS approach.

OriginalsprogEngelsk
TidsskriftEuropean Journal of Medical Genetics
Vol/bind62
Udgave nummer1
Sider (fra-til)1-8
Antal sider8
ISSN1769-7212
DOI
StatusUdgivet - jan. 2019

Bibliografisk note

Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Antal downloads er baseret på statistik fra Google Scholar og www.ku.dk


Ingen data tilgængelig

ID: 234274097