Epimutation detection in the clinical context : guidelines and a use case from a new Bioconductor package
Ruiz-Arenas, Carlos (Universitat Pompeu Fabra)
Abarrategui, Leire (Newcastle University)
Hernandez-Ferrer, Carles (Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública)
Escribà-Montagut, Xavier (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Pelegrí-Sisó, Dolors (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Ryser-Welch, Patricia (Newcastle University)
Vrijheid, Martine (Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública)
Bustamante, Mariona (Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública)
Grazuleviciene, Regina (Vytautas Magnus University)
Lepeule, Johanna (University Grenoble Alpes)
Mathai, Mathew (Bradford Teaching Hospitals NHS Foundation Trust)
Vafeiadi, Marina (University of Crete)
Beltran i Agulló, Sergi (Barcelona Institute of Science and Technology (BIST))
Pérez-Jurado, Luis Alberto (Institut Hospital del Mar d'Investigacions Mèdiques)
González, Juan Ramón (Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública)

Fecha:	2023
Resumen:	Epimutations are rare alterations of the normal DNA methylation pattern at specific loci, which can lead to rare diseases. Methylation microarrays enable genome-wide epimutation detection, but technical limitations prevent their use in clinical settings: methods applied to rare diseases' data cannot be easily incorporated to standard analyses pipelines, while epimutation methods implemented in R packages (ramr) have not been validated for rare diseases. We have developed epimutacions, a Bioconductor package (). epimutacions implements two previously reported methods and four new statistical approaches to detect epimutations, along with functions to annotate and visualize epimutations. Additionally, we have developed an user-friendly Shiny app to facilitate epimutations detection () to non-bioinformatician users. We first compared the performance of epimutacions and ramr packages using three public datasets with experimentally validated epimutations. Methods in epimutacions had a high performance at low sample sizes and outperformed methods in ramr. Second, we used two general population children cohorts (INMA and HELIX) to determine the technical and biological factors that affect epimutations detection, providing guidelines on how designing the experiments or preprocessing the data. In these cohorts, most epimutations did not correlate with detectable regional gene expression changes. Finally, we exemplified how epimutacions can be used in a clinical context. We run epimutacions in a cohort of children with autism disorder and identified novel recurrent epimutations in candidate genes for autism. Overall, we present epimutacions a new Bioconductor package for incorporating epimutations detection to rare disease diagnosis and provide guidelines for the design and data analyses.
Ayudas:	Instituto de Salud Carlos III FIS-PI19/00166 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-01974 Ministerio de Ciencia e Innovación CEX2018-000806-S European Commission 308333 Agencia Estatal de Investigación CEX2018-000792-MDM Fundació la Marató de TV3 504/C/2020
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Epigenetics ; Rare disease ; Bioinformatics ; Epidemiology
Publicado en:	Epigenetics, Vol. 18, Issue 1 (July 2023) , art. 2230670, ISSN 1559-2308

DOI: 10.1080/15592294.2023.2230670
PMID: 37409354

24 p, 1.4 MB

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