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.
I: Clinical Genetics, Bind 103, Nr. 6, 2023, s. 688-692.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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
N1 - Publisher Copyright: © 2023 The Authors. Clinical Genetics published by John Wiley & Sons Ltd.
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