DNA methylation signature classification of rare disorders using publicly available methylation data.
KMT2D
Kabuki syndrome
Mendelian disorders
VUS classification
epigenetics
episignature
rare disorders
Journal
Clinical genetics
ISSN: 1399-0004
Titre abrégé: Clin Genet
Pays: Denmark
ID NLM: 0253664
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
revised:
18
01
2023
received:
07
12
2022
accepted:
19
01
2023
medline:
3
5
2023
pubmed:
28
1
2023
entrez:
27
1
2023
Statut:
ppublish
Résumé
Disease-specific DNA methylation patterns (DNAm signatures) have been established for an increasing number of genetic disorders and represent a valuable tool for classification of genetic variants of uncertain significance (VUS). Sample size and batch effects are critical issues for establishing DNAm signatures, but their impact on the sensitivity and specificity of an already established DNAm signature has not previously been tested. Here, we assessed whether publicly available DNAm data can be employed to generate a binary machine learning classifier for VUS classification, and used variants in KMT2D, the gene associated with Kabuki syndrome, together with an existing DNAm signature as proof-of-concept. Using publicly available methylation data for training, a classifier for KMT2D variants was generated, and individuals with molecularly confirmed Kabuki syndrome and unaffected individuals could be correctly classified. The present study documents the clinical utility of a robust DNAm signature even for few affected individuals, and most importantly, underlines the importance of data sharing for improved diagnosis of rare genetic disorders.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
688-692Informations de copyright
© 2023 The Authors. Clinical Genetics published by John Wiley & Sons Ltd.
Références
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