Analysis methods for diagnosing rare neurodevelopmental diseases with episignatures: a systematic review of the literature
Background: Rare diseases (RDs) and neurodevelopmental disorders (NDDs) remain under-researched due to their low prevalence, leaving significant gaps in diagnostic strategies. Beyond next-generation sequencing, epigenetic profiling and particularly episignatures have emerged as a promising complemen...
| Autores: | , , , , |
|---|---|
| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universitat Pompeu Fabra |
| Repositorio: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:dnet:rdupf_______::a957245a6ab92915398cb96e31b2cceb |
| Acesso em linha: | https://hdl.handle.net/10230/72851 http://dx.doi.org/10.3390/biomedicines13123043 |
| Access Level: | acceso abierto |
| Palavra-chave: | DMPs DMRs DNA methylation VUS Episignatures Machine learning Neurodevelopmental disorders Rare disease |
| Resumo: | Background: Rare diseases (RDs) and neurodevelopmental disorders (NDDs) remain under-researched due to their low prevalence, leaving significant gaps in diagnostic strategies. Beyond next-generation sequencing, epigenetic profiling and particularly episignatures have emerged as a promising complementary diagnostic tool and for reclassifying variants of uncertain significance (VUS). However, clinical implementation remains limited, hindered by non-standardized methodologies and restricted data sharing that impede the development of sufficiently large datasets for robust episignature development. Methods: We conducted a systematic literature review following PRISMA 2020 guidelines to identify all studies reporting episignatures published between 2014 and 2025. The review summarizes methodological approaches used for episignature detection and implementation, as well as reports of epimutations. Results: A total of 108 studies met the inclusion criteria. All but three employed Illumina methylation arrays, mostly 450 K and EPIC versions for patient sample analysis. Three main methodological phases were identified: data quality control, episignature detection, and classification model training. Despite methodological variability across these stages, most studies demonstrated high predictive capabilities, often relying on methodologies developed by a small number of leading groups. Conclusions: Epigenetic screening has significant potential to improve diagnostic yield in RDs and NDDs. Continued methodological refinement and collaborative standardization efforts will be crucial for its successful integration into clinical practice. Nevertheless, key challenges persist, including the need for secure and ethical data-sharing frameworks, external validation, and methodological standardization. |
|---|