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.

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

Cappuccio, Gerarda;Brunetti-Pierri, Nicola;Merla, Giuseppe;
2022

Abstract

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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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/893580
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