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 af dette arbejde
2 Citationer (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.

OriginalsprogEngelsk
TidsskriftClinical Genetics
Vol/bind103
Udgave nummer6
Sider (fra-til)688-692
Antal sider5
ISSN0009-9163
DOI
StatusUdgivet - jun. 2023

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