Ruiz-Arenas, CarlosAbarrategui, LeireHernandez-Ferrer, Carles, 1987-Pelegrí-Sisó, DolorsRyser-Welch, PatriciaVrijheid, MartineBustamante Pineda, MarionaGražulevičienė, ReginaLepeule, JohannaMathai, MathewVafeiadi, Marina, 1983-Beltran, SergiPérez Jurado, Luis AlbertoGonzález, Juan Ramón2023-10-032023-10-032023Ruiz-Arenas C, Abarrategui L, Hernandez-Ferrer C, Escribà-Montagut X, Pelegrí-Sisó D, Ryser-Welch P, Vrijheid M, Bustamante M, Grazuleviciene R, Lepeule J, Mathai M, Vafeiadi M, Beltran S, Pérez-Jurado LA, González JR. Epimutation detection in the clinical context: guidelines and a use case from a new Bioconductor package. Epigenetics. 2023 Dec;18(1):2230670. DOI: 10.1080/15592294.2023.22306701559-2294http://hdl.handle.net/10230/58019Epimutations 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 (https://bioconductor.org/packages/release/bioc/html/epimutacions.html). 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 (https://github.com/isglobal-brge/epimutacionsShiny) 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.application/pdfeng© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1080/15592294.2023.2230670EpigeneticsBioinformaticsEpidemiologyRare diseaseinfo:eu-repo/semantics/openAccess