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: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/72851 |
| Acceso en línea: | https://hdl.handle.net/10230/72851 http://dx.doi.org/10.3390/biomedicines13123043 |
| Access Level: | acceso abierto |
| Palabra clave: | DMPs DMRs DNA methylation VUS Episignatures Machine learning Neurodevelopmental disorders Rare disease |
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Analysis methods for diagnosing rare neurodevelopmental diseases with episignatures: a systematic review of the literatureAlegret-García, AlbertCáceres, AlejandroSevilla-Porras, MartaPérez Jurado, Luis AlbertoGonzález, Juan RamónDMPsDMRsDNA methylationVUSEpisignaturesMachine learningNeurodevelopmental disordersRare diseaseBackground: 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.MDPI2026202620252026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/10230/72851http://dx.doi.org/10.3390/biomedicines13123043https://hdl.handle.net/10230/72851reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésBiomedicines. 2025;13(12):3043© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/728512026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
Analysis methods for diagnosing rare neurodevelopmental diseases with episignatures: a systematic review of the literature |
| title |
Analysis methods for diagnosing rare neurodevelopmental diseases with episignatures: a systematic review of the literature |
| spellingShingle |
Analysis methods for diagnosing rare neurodevelopmental diseases with episignatures: a systematic review of the literature Alegret-García, Albert DMPs DMRs DNA methylation VUS Episignatures Machine learning Neurodevelopmental disorders Rare disease |
| title_short |
Analysis methods for diagnosing rare neurodevelopmental diseases with episignatures: a systematic review of the literature |
| title_full |
Analysis methods for diagnosing rare neurodevelopmental diseases with episignatures: a systematic review of the literature |
| title_fullStr |
Analysis methods for diagnosing rare neurodevelopmental diseases with episignatures: a systematic review of the literature |
| title_full_unstemmed |
Analysis methods for diagnosing rare neurodevelopmental diseases with episignatures: a systematic review of the literature |
| title_sort |
Analysis methods for diagnosing rare neurodevelopmental diseases with episignatures: a systematic review of the literature |
| dc.creator.none.fl_str_mv |
Alegret-García, Albert Cáceres, Alejandro Sevilla-Porras, Marta Pérez Jurado, Luis Alberto González, Juan Ramón |
| author |
Alegret-García, Albert |
| author_facet |
Alegret-García, Albert Cáceres, Alejandro Sevilla-Porras, Marta Pérez Jurado, Luis Alberto González, Juan Ramón |
| author_role |
author |
| author2 |
Cáceres, Alejandro Sevilla-Porras, Marta Pérez Jurado, Luis Alberto González, Juan Ramón |
| author2_role |
author author author author |
| dc.subject.none.fl_str_mv |
DMPs DMRs DNA methylation VUS Episignatures Machine learning Neurodevelopmental disorders Rare disease |
| topic |
DMPs DMRs DNA methylation VUS Episignatures Machine learning Neurodevelopmental disorders Rare disease |
| description |
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. |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025 2026 2026 2026 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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https://hdl.handle.net/10230/72851 http://dx.doi.org/10.3390/biomedicines13123043 https://hdl.handle.net/10230/72851 |
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https://hdl.handle.net/10230/72851 http://dx.doi.org/10.3390/biomedicines13123043 |
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Inglés |
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Inglés |
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Biomedicines. 2025;13(12):3043 |
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http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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application/pdf application/pdf |
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MDPI |
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MDPI |
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