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

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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, Vol. 103, No. 6, 2023, p. 688-692.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Hildonen, M, Ferilli, M, Hjortshøj, TD, Dunø, M, Risom, L, Bak, M, Ek, J, Møller, RS, Ciolfi, A, Tartaglia, M & Tümer, Z 2023, 'DNA methylation signature classification of rare disorders using publicly available methylation data', Clinical Genetics, vol. 103, no. 6, pp. 688-692. https://doi.org/10.1111/cge.14304

APA

Hildonen, M., Ferilli, M., Hjortshøj, T. D., Dunø, M., Risom, L., Bak, M., Ek, J., Møller, R. S., Ciolfi, A., Tartaglia, M., & Tümer, Z. (2023). DNA methylation signature classification of rare disorders using publicly available methylation data. Clinical Genetics, 103(6), 688-692. https://doi.org/10.1111/cge.14304

Vancouver

Hildonen M, Ferilli M, Hjortshøj TD, Dunø M, Risom L, Bak M et al. DNA methylation signature classification of rare disorders using publicly available methylation data. Clinical Genetics. 2023;103(6):688-692. https://doi.org/10.1111/cge.14304

Author

Hildonen, Mathis ; Ferilli, Marco ; Hjortshøj, Tina Duelund ; Dunø, Morten ; Risom, Lotte ; Bak, Mads ; Ek, Jakob ; Møller, Rikke S. ; Ciolfi, Andrea ; Tartaglia, Marco ; Tümer, Zeynep. / DNA methylation signature classification of rare disorders using publicly available methylation data. In: Clinical Genetics. 2023 ; Vol. 103, No. 6. pp. 688-692.

Bibtex

@article{69faa52625b447fd9cdecb06d3b8db27,

title = "DNA methylation signature classification of rare disorders using publicly available methylation data",

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.",

keywords = "epigenetics, episignature, Kabuki syndrome, KMT2D, Mendelian disorders, rare disorders, VUS classification",

author = "Mathis Hildonen and Marco Ferilli and Hjortsh{\o}j, {Tina Duelund} and Morten Dun{\o} and Lotte Risom and Mads Bak and Jakob Ek and M{\o}ller, {Rikke S.} and Andrea Ciolfi and Marco Tartaglia and Zeynep T{\"u}mer",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors. Clinical Genetics published by John Wiley & Sons Ltd.",

year = "2023",

doi = "10.1111/cge.14304",

language = "English",

volume = "103",

pages = "688--692",

journal = "Clinical Genetics",

issn = "0009-9163",

publisher = "Wiley-Blackwell",

number = "6",

}

RIS

TY - JOUR

T1 - DNA methylation signature classification of rare disorders using publicly available methylation data

AU - Hildonen, Mathis

AU - Ferilli, Marco

AU - Hjortshøj, Tina Duelund

AU - Dunø, Morten

AU - Risom, Lotte

AU - Bak, Mads

AU - Ek, Jakob

AU - Møller, Rikke S.

AU - Ciolfi, Andrea

AU - Tartaglia, Marco

AU - Tümer, Zeynep

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - epigenetics

KW - episignature

KW - Kabuki syndrome

KW - KMT2D

KW - Mendelian disorders

KW - rare disorders

KW - VUS classification

U2 - 10.1111/cge.14304

DO - 10.1111/cge.14304

M3 - Journal article

C2 - 36705342

AN - SCOPUS:85147499703

VL - 103

SP - 688

EP - 692

JO - Clinical Genetics

JF - Clinical Genetics

SN - 0009-9163

IS - 6

ER -

ID: 363269700