3.9 Article

Novel diagnostic DNA methylation episignatures expand and refine the epigenetic landscapes of Mendelian disorders

Journal

HUMAN GENETICS AND GENOMICS ADVANCES
Volume 3, Issue 1, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.xhgg.2021.100075

Keywords

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Funding

  1. London Health Sciences Molecular Diagnostics Development Fund
  2. Genome Canada Genomic Applications Partnership Program
  3. Victorian Government's Operational Infrastructure Support Program
  4. Chair in Genomic Medicine
  5. Royal Children's Hospital Foundation
  6. Italian Ministry of Health [CCR-201723669081, RCR-2020-23670068_001]
  7. Italian Ministry of Research

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Overlapping clinical phenotypes and complex genomic associations pose challenges in diagnosing and managing Mendelian disorders. This study identifies 19 new episignature disorders, in addition to 38 previously established episignatures, associated with a total of 65 genetic syndromes. The study demonstrates increasing resolution and specificity in identifying Mendelian episignatures at various molecular levels, and shows the development of accurate and disease-specific diagnostic classifiers. The findings expand the understanding of Mendelian conditions, improve clinical diagnostic capabilities, and offer potential reclassification of variants of unknown clinical significance.
Overlapping clinical phenotypes and an expanding breadth and complexity of genomic associations are a growing challenge in the diagnosis and clinical management of Mendelian disorders. The functional consequences and clinical impacts of genomic variation may involve unique, disorder-specific, genomic DNA methylation episignatures. In this study, we describe 19 novel episignature disorders and compare the findings alongside 38 previously established episignatures for a total of 57 episignatures associated with 65 genetic syndromes. We demonstrate increasing resolution and specificity ranging from protein complex, gene, sub-gene, protein domain, and even single nucleotide-level Mendelian episignatures. We show the power of multiclass modeling to develop highly accurate and disease-specific diagnostic classifiers. This study significantly expands the number and spectrum of disorders with detectable DNA methylation episignatures, improves the clinical diagnostic capabilities through the resolution of unsolved cases and the reclassification of variants of unknown clinical significance, and provides further insight into the molecular etiology of Mendelian conditions.

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