Clinical Epigenomics in Rare Diseases: Interpreting DNA Methylation Episignatures.

Authors

Himanshu Goel, Shuxiang Goh, Luke Stuart

Published in

Molecular genetics & genomic medicine. Volume 14. Issue 10. Pages e70319.

Abstract

Epigenomic testing complements sequence-based analysis by detecting downstream changes in epigenomic state associated with genetic variation. Genome-wide DNA methylation episignatures are reproducible molecular phenotypes that can serve as biomarkers of specific Mendelian disorders, particularly those involving chromatin regulators, DNA methylation machinery, and transcriptional regulatory pathways.
We reviewed the biological basis, laboratory methodology, analytical approaches and clinical applications of DNA methylation episignature testing, with emphasis on neurodevelopmental disorders and rare diseases. We also considered current computational tools, limitations of clinical interpretation and emerging epigenomic and epitranscriptomic approaches.
DNA methylation episignature testing is now used clinically to support molecular diagnosis, assist interpretation of variants of uncertain significance and distinguish overlapping neurodevelopmental and chromatin-related disorders. Interpretation integrates methylation-array data, statistical and machine-learning classification, phenotype, genotype and assay-specific validation. Important limitations include tissue specificity, mosaicism, developmental effects, incomplete disorder coverage and dependence on reference datasets. Emerging approaches include tissue-agnostic classifiers, long-read methylation profiling, additional epigenomic signatures and multi-omic integration.
DNA methylation episignatures provide a clinically useful functional layer of evidence by detecting downstream epigenomic consequences of genomic variation. They should be interpreted as an adjunct to sequence-based diagnosis and clinical assessment rather than as a replacement for either. Continued expansion of reference datasets and integration with other functional genomic approaches should broaden their diagnostic utility.

PMID:
42827301
Bibliographic data and abstract were imported from PubMed on 03 Oct 2026.

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