Identifying Differentially Methylated Genomic Regions With Comethdmr**
Author(s): Gabriel J. Odom, Lissette Gomez, Tiago Chedraoui Silva, Ingrid Gonzalez, Suky Martinez, Jermaine Jones, Lily Wang
Affiliation(s): Florida International University, The University of Miami
Twitter: @RevDocGabriel
Because of the complex interdependencies inherent in epigenomics data, identifying differentially methylated regions (DMRs) remains a challenging task. However, many current methods of DMR detection focus on finding regions with a few highly significant differentially methylated CpGs, thereby failing to detect regions with small but pervasive differential methylation patterns. Additionally, these and many other methods often lose statistical power after multiple comparison corrections due to the massive volume of probes and/or subregions to test. For these reasons, we developed the coMethDMR R/Bioconductor package and DNA methylation analysis pipeline. The first step is to perform unsupervised clustering on the methylation data to detect regions of contiguous co-methylation; this clustering can also leverage additional information on CpG islands and other genomic regions. Then, the M-values in these clusters are tested against the phenotype of interest via a linear mixed model, that models both variations between CpG sites within the region and differential methylation with a continuous phenotype simultaneously. The overall slope of the cluster is tested against 0, so that statistical significance for a genomic sub-region implies that the DMR is associated with the continuous phenotype. We provide an example of this pipeline to analyze DNA methylation data for subjects addicted to heroin.
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