Login / Signup

Molecular Pathway-Based Classification of Ectodermal Dysplasias: First Five-Yearly Update.

Nicolai PeschelJohn T WrightMaranke I KosterAngus John ClarkeGianluca TadiniMary FeteSmail Hadj-RabiaVirginia P SybertJohanna NorderydSigrun Maier-WohlfartTimothy J FeteNina PagnanAtila F VisinoniHolm Schneider
Published in: Genes (2022)
To keep pace with the rapid advancements in molecular genetics and rare diseases research, we have updated the list of ectodermal dysplasias based on the latest classification approach that was adopted in 2017 by an international panel of experts. For this purpose, we searched the databases PubMed and OMIM for the term "ectodermal dysplasia", referring mainly to changes in the last 5 years. We also tried to obtain information about those diseases on which the last scientific report appeared more than 15 years ago by contacting the authors of the most recent publication. A group of experts, composed of researchers who attended the 8th International Conference on Ectodermal Dysplasias and additional members of the previous classification panel, reviewed the proposed amendments and agreed on a final table listing all 49 currently known ectodermal dysplasias for which the molecular genetic basis has been clarified, including 15 new entities. A newly reported ectodermal dysplasia, linked to the gene LRP6 , is described here in more detail. These ectodermal dysplasias, in the strict sense, should be distinguished from syndromes with features of ectodermal dysplasia that are related to genes extraneous to the currently known pathways involved in ectodermal development. The latter group consists of 34 syndromes which had been placed on the previous list of ectodermal dysplasias, but most if not all of them could actually be classified elsewhere. This update should streamline the classification of ectodermal dysplasias, provide guidance to the correct diagnosis of rare disease entities, and facilitate the identification of individuals who could benefit from novel treatment options.
Keyphrases
  • machine learning
  • deep learning
  • genome wide
  • gene expression
  • transcription factor
  • dna methylation
  • big data
  • social media