coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes

Abstract

Recent technology has made it possible to measure DNA methylation profiles in a cost-effective and comprehensive genome-wide manner using array-based technology for epigenome-wide association studies. However, identifying differentially methylated regions (DMRs) remains a challenging task because of the complexities in DNA methylation data. Supervised methods typically focus on the regions that contain consecutive highly significantly differentially methylated CpGs in the genome, but may lack power for detecting small but consistent changes when few CpGs pass stringent significance threshold after multiple comparison. Unsupervised methods group CpGs based on genomic annotations first and then test them against phenotype, but may lack specificity because the regional boundaries of methylation are often not well defined. We present coMethDMR, a flexible, powerful, and accurate tool for identifying DMRs. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first. Next, coMethDMR tests association between methylation levels within the sub-region and phenotype via a random coefficient mixed effects model that models both variations between CpG sites within the region and differential methylation simultaneously. coMethDMR offers well-controlled Type I error rate, improved specificity, focused testing of targeted genomic regions, and is available as an open-source R package.

Epigenome-wide meta-analysis of DNA methylation differences in prefrontal cortex implicates the immune processes in Alzheimer’s disease

“…The DMRs were identified by both coMethDMR 13 and comb-p 12 software. In the coMethDMR approach, we tested 40,010 pre-defined genomic regions to identify co-methylated and differentially methylated regions associated with Braak stage, adjusting for estimated neuron proportions, age at death, sex, and batch effects for each cohort separately.…”

“…Methods for identifying differentially methylated regions (DMRs) can be classified into supervised methods, which look for regions in the genome with consecutive small P -values, or unsupervised methods which group CpGs probes into clusters first and then test the clusters against phenotype 10 . We performed a meta-analysis of DMRs using two complementary analysis tools, a supervised method comb-p 12 and an unsupervised method coMethDMR 13 . Relevant to this meta-analysis, the coMethDMR-based meta-analysis strategy allowed us to assess between cohort heterogeneities in genomic regions.…”

Epigenetic silencing of LncRNA ANRIL enhances liver fibrosis and HSC activation through activating AMPK pathway

“…With regard to the latter, de novo DNA methylation activity catalysed by DNA methyltransferase 3A (DNMT3A) is methylated by addition of transfer methyl groups to the C‐5 position in the cytosine ring . DNA methylation can establish a docking site for transcriptional repressors to permanent gene silencing . Long non‐coding RNAs (LncRNAs) are well‐known to interact with components of the epigenetic machinery.…”

“…11,12 DNA methylation can establish a docking site for transcriptional repressors to permanent gene silencing. 13 Long non-coding RNAs (LncRNAs) are well-known to interact with components of the epigenetic machinery. LncRNAs are longer than 200 nucleotides, which were protein-non-coding genes.…”

MethylSPWNet and MethylCapsNet: Biologically Motivated Organization of DNAm Neural Network, Inspired by Capsule Networks

“…[29][30][31][32][33] More recent work has called for a greater understanding of the implications of DNAm-DNAm interactions through the incorporation of Gaussian Graphical Models, Canonical Correlation Analysis, and module discovery through weighted gene co-methylation networks. [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] There is growing support for the use of novel deep learning methods to aggregate, group, and select CpGs by their local context (e.g., genes) in an effort to connect and interpret the data with clinical outcomes. [51][52][53] Incorporation of prior biological knowledge not only improves the transparency and interpretability of the modeling approach but also reduces noise while increasing signal by meaningfully pruning redundant relationships between predictors.…”