2026-04-14T08:24:29Zhttps://recercat.cat/oai/requestoai:recercat.cat:2445/1190112025-12-05T01:14:49Zcom_2072_1057col_2072_478917
RECERCAT
author
Ruiz Arenas, Carlos
author
González, Juan Ramón
2018-01-12T14:22:50Z2018-01-12T14:22:50Z2017-12-142017-12-20T18:59:53Z
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.
cc by (c) Ruiz Arenas, Carlos; González, Juan R., 2017 http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess
ADN
Expressió gènica
DNA
Gene expression
Redundancy analysis allows improved detection of methylation
changes in large genomic regions
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion