: 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. This article is protected by copyright. All rights reserved.

DNA methylation signature classification of rare disorders using publicly available methylation data / Hildonen, Mathis; Ferilli, Marco; Hjortshøj, Tina Duelund; Dunø, Morten; Risom, Lotte; Bak, Mads; Ek, Jakob; Møller, Rikke S; Ciolfi, Andrea; Tartaglia, Marco; Tümer, Zeynep. - In: CLINICAL GENETICS. - ISSN 0009-9163. - 103:6(2023), pp. 688-692. [10.1111/cge.14304]

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

Ferilli, Marco
Secondo
;
Ciolfi, Andrea;
2023

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. This article is protected by copyright. All rights reserved.
2023
KMT2D; Kabuki syndrome; VUS classification; epigenetics; episignature; mendelian disorders; rare disorders
01 Pubblicazione su rivista::01a Articolo in rivista
DNA methylation signature classification of rare disorders using publicly available methylation data / Hildonen, Mathis; Ferilli, Marco; Hjortshøj, Tina Duelund; Dunø, Morten; Risom, Lotte; Bak, Mads; Ek, Jakob; Møller, Rikke S; Ciolfi, Andrea; Tartaglia, Marco; Tümer, Zeynep. - In: CLINICAL GENETICS. - ISSN 0009-9163. - 103:6(2023), pp. 688-692. [10.1111/cge.14304]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1667240
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