Redundancy analysis allows improved detection of methylation changes in large genomic regions

DNA methylation is an epigenetic process that regulates gene expression. Methylation can be modified by environmental exposures and changes in the methylation patterns have been associated with diseases. Methylation microarrays measure methylation levels at more than 450,000 CpGs in a single experim...

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Autores: Ruiz-Arenas, Carlos, González, Juan Ramón
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Recursos:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/69050
Acesso em linha:http://hdl.handle.net/10230/69050
http://dx.doi.org/10.1186/s12859-017-1986-0
Access Level:acceso abierto
Palavra-chave:DNA methylation
Region analysis
Microarray
Epigenomics
Gene expression
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spelling Redundancy analysis allows improved detection of methylation changes in large genomic regionsRuiz-Arenas, CarlosGonzález, Juan RamónDNA methylationRegion analysisMicroarrayEpigenomicsGene expressionDNA methylation is an epigenetic process that regulates gene expression. Methylation can be modified by environmental exposures and changes in the methylation patterns have been associated with diseases. Methylation microarrays measure methylation levels at more than 450,000 CpGs in a single experiment, and the most common analysis strategy is to perform a single probe analysis to find methylation probes associated with the outcome of interest. However, methylation changes usually occur at the regional level: for example, genomic structural variants can affect methylation patterns in regions up to several megabases in length. Existing DMR methods provide lists of Differentially Methylated Regions (DMRs) of up to only few kilobases in length, and cannot check if a target region is differentially methylated. Therefore, these methods are not suitable to evaluate methylation changes in large regions. To address these limitations, we developed a new DMR approach based on redundancy analysis (RDA) that assesses whether a target region is differentially methylated. Results. Using simulated and real datasets, we compared our approach to three common DMR detection methods (Bumphunter, blockFinder, and DMRcate). We found that Bumphunter underestimated methylation changes and blockFinder showed poor performance. DMRcate showed poor power in the simulated datasets and low specificity in the real data analysis. Our method showed very high performance in all simulation settings, even with small sample sizes and subtle methylation changes, while controlling type I error. Other advantages of our method are: 1) it estimates the degree of association between the DMR and the outcome; 2) it can analyze a targeted or region of interest; and 3) it can evaluate the simultaneous effects of different variables. The proposed methodology is implemented in MEAL, a Bioconductor package designed to facilitate the analysis of methylation data. Conclusions. We propose a multivariate approach to decipher whether an outcome of interest alters the methylation pattern of a region of interest. The method is designed to analyze large target genomic regions and outperforms the three most popular methods for detecting DMRs. Our method can evaluate factors with more than two levels or the simultaneous effect of more than one continuous variable, which is not possible with the state-of-the-art methods.BioMed Central202520252017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/69050http://dx.doi.org/10.1186/s12859-017-1986-0reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésBMC Bioinformatics. 2017 Dec;18(1):1-9This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/690502026-06-12T07:21:37Z
dc.title.none.fl_str_mv Redundancy analysis allows improved detection of methylation changes in large genomic regions
title Redundancy analysis allows improved detection of methylation changes in large genomic regions
spellingShingle Redundancy analysis allows improved detection of methylation changes in large genomic regions
Ruiz-Arenas, Carlos
DNA methylation
Region analysis
Microarray
Epigenomics
Gene expression
title_short Redundancy analysis allows improved detection of methylation changes in large genomic regions
title_full Redundancy analysis allows improved detection of methylation changes in large genomic regions
title_fullStr Redundancy analysis allows improved detection of methylation changes in large genomic regions
title_full_unstemmed Redundancy analysis allows improved detection of methylation changes in large genomic regions
title_sort Redundancy analysis allows improved detection of methylation changes in large genomic regions
dc.creator.none.fl_str_mv Ruiz-Arenas, Carlos
González, Juan Ramón
author Ruiz-Arenas, Carlos
author_facet Ruiz-Arenas, Carlos
González, Juan Ramón
author_role author
author2 González, Juan Ramón
author2_role author
dc.subject.none.fl_str_mv DNA methylation
Region analysis
Microarray
Epigenomics
Gene expression
topic DNA methylation
Region analysis
Microarray
Epigenomics
Gene expression
description DNA methylation is an epigenetic process that regulates gene expression. Methylation can be modified by environmental exposures and changes in the methylation patterns have been associated with diseases. Methylation microarrays measure methylation levels at more than 450,000 CpGs in a single experiment, and the most common analysis strategy is to perform a single probe analysis to find methylation probes associated with the outcome of interest. However, methylation changes usually occur at the regional level: for example, genomic structural variants can affect methylation patterns in regions up to several megabases in length. Existing DMR methods provide lists of Differentially Methylated Regions (DMRs) of up to only few kilobases in length, and cannot check if a target region is differentially methylated. Therefore, these methods are not suitable to evaluate methylation changes in large regions. To address these limitations, we developed a new DMR approach based on redundancy analysis (RDA) that assesses whether a target region is differentially methylated. Results. Using simulated and real datasets, we compared our approach to three common DMR detection methods (Bumphunter, blockFinder, and DMRcate). We found that Bumphunter underestimated methylation changes and blockFinder showed poor performance. DMRcate showed poor power in the simulated datasets and low specificity in the real data analysis. Our method showed very high performance in all simulation settings, even with small sample sizes and subtle methylation changes, while controlling type I error. Other advantages of our method are: 1) it estimates the degree of association between the DMR and the outcome; 2) it can analyze a targeted or region of interest; and 3) it can evaluate the simultaneous effects of different variables. The proposed methodology is implemented in MEAL, a Bioconductor package designed to facilitate the analysis of methylation data. Conclusions. We propose a multivariate approach to decipher whether an outcome of interest alters the methylation pattern of a region of interest. The method is designed to analyze large target genomic regions and outperforms the three most popular methods for detecting DMRs. Our method can evaluate factors with more than two levels or the simultaneous effect of more than one continuous variable, which is not possible with the state-of-the-art methods.
publishDate 2017
dc.date.none.fl_str_mv 2017
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/69050
http://dx.doi.org/10.1186/s12859-017-1986-0
url http://hdl.handle.net/10230/69050
http://dx.doi.org/10.1186/s12859-017-1986-0
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv BMC Bioinformatics. 2017 Dec;18(1):1-9
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv BioMed Central
publisher.none.fl_str_mv BioMed Central
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
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