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

Background: 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 si...

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Autores: Ruiz-Arenas, C. (Carlos)|||/items/846b8463-9aee-4a4d-9f21-50b55a99e8fa, Gonzalez, J.R. (Juan R.)|||/items/2e41788a-fef1-47d9-9d02-c70bc52be6f4
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universidad de Navarra
Repositorio:Dadun. Depósito Académico Digital de la Universidad de Navarra
Idioma:inglés
OAI Identifier:oai:dadun.unav.edu:10171/123017
Acceso en línea:https://hdl.handle.net/10171/123017
Access Level:acceso abierto
Palabra clave:DNA methylation
Epigenomics
Gene expression
Microarray
Region analysis
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spelling Redundancy analysis allows improved detection of methylation changes in large genomic regionsRuiz-Arenas, C. (Carlos)|||/items/846b8463-9aee-4a4d-9f21-50b55a99e8faGonzalez, J.R. (Juan R.)|||/items/2e41788a-fef1-47d9-9d02-c70bc52be6f4DNA methylationEpigenomicsGene expressionMicroarrayRegion analysisBackground: 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.Dadun. Depósito Académico Digital Universidad de Navarra20172017-01-0120172017-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10171/123017reponame:Dadun. Depósito Académico Digital de la Universidad de Navarrainstname:Universidad de NavarraInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dadun.unav.edu:10171/1230172026-06-21T12:47:57Z
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, C. (Carlos)|||/items/846b8463-9aee-4a4d-9f21-50b55a99e8fa
DNA methylation
Epigenomics
Gene expression
Microarray
Region analysis
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, C. (Carlos)|||/items/846b8463-9aee-4a4d-9f21-50b55a99e8fa
Gonzalez, J.R. (Juan R.)|||/items/2e41788a-fef1-47d9-9d02-c70bc52be6f4
author Ruiz-Arenas, C. (Carlos)|||/items/846b8463-9aee-4a4d-9f21-50b55a99e8fa
author_facet Ruiz-Arenas, C. (Carlos)|||/items/846b8463-9aee-4a4d-9f21-50b55a99e8fa
Gonzalez, J.R. (Juan R.)|||/items/2e41788a-fef1-47d9-9d02-c70bc52be6f4
author_role author
author2 Gonzalez, J.R. (Juan R.)|||/items/2e41788a-fef1-47d9-9d02-c70bc52be6f4
author2_role author
dc.contributor.none.fl_str_mv Dadun. Depósito Académico Digital Universidad de Navarra
dc.subject.none.fl_str_mv DNA methylation
Epigenomics
Gene expression
Microarray
Region analysis
topic DNA methylation
Epigenomics
Gene expression
Microarray
Region analysis
description Background: 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
2017-01-01
2017
2017-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10171/123017
url https://hdl.handle.net/10171/123017
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dadun. Depósito Académico Digital de la Universidad de Navarra
instname:Universidad de Navarra
instname_str Universidad de Navarra
reponame_str Dadun. Depósito Académico Digital de la Universidad de Navarra
collection Dadun. Depósito Académico Digital de la Universidad de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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