DNA methylation signature classification of rare disorders using publicly available methylation data

Mathis Hildonen, Marco Ferilli, Tina Duelund Hjortshøj, Morten Dunø, Lotte Risom, Mads Bak, Jakob Ek, Rikke S Møller, Andrea Ciolfi, Marco Tartaglia, Zeynep Tümer*

*Corresponding author for this work
2 Citations (Scopus)

Abstract

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.

Original languageEnglish
JournalClinical Genetics
Volume103
Issue number6
Pages (from-to)688-692
Number of pages5
ISSN0009-9163
DOIs
Publication statusPublished - Jun 2023

Keywords

  • Abnormalities, Multiple/genetics
  • DNA Methylation
  • Hematologic Diseases/genetics
  • Humans
  • Vestibular Diseases/genetics
  • KMT2D
  • rare disorders
  • Kabuki syndrome
  • VUS classification
  • episignature
  • Mendelian disorders
  • epigenetics

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