Comethyl: a network-based methylome approach to investigate the multivariate nature of health and disease

Abstract

Health outcomes are frequently shaped by difficult to dissect inter-relationships between biological, behavioral, social and environmental factors. DNA methylation patterns reflect such multivariate intersections, providing a rich source of novel biomarkers and insight into disease etiologies. Recent advances in whole-genome bisulfite sequencing enable investigation of DNA methylation over all genomic CpGs, but existing bioinformatic approaches lack accessible system-level tools. Here, we develop the R package Comethyl, for weighted gene correlation network analysis of user-defined genomic regions that generates modules of comethylated regions, which are then tested for correlations with multivariate sample traits. First, regions are defined by CpG genomic location or regulatory annotation and filtered based on CpG count, sequencing depth and variability. Next, correlation networks are used to find modules of interconnected nodes using methylation values within the selected regions. Each module containing multiple comethylated regions is reduced in complexity to a single eigennode value, which is then tested for correlations with experimental metadata. Comethyl has the ability to cover the noncoding regulatory regions of the genome with high relevance to interpretation of genome-wide association studies and integration with other types of epigenomic data. We demonstrate the utility of Comethyl on a dataset of male cord blood samples from newborns later diagnosed with autism spectrum disorder (ASD) versus typical development. Comethyl successfully identified an ASD-associated module containing regions mapped to genes enriched for brain glial functions. Comethyl is expected to be useful in uncovering the multivariate nature of health disparities for a variety of common disorders. Comethyl is available at github.com/cemordaunt/comethyl with complete documentation and example analyses.

Networks of placental DNA methylation correlate with maternal serum PCB concentrations and child neurodevelopment

“…From 199,945 total filtered regions, 928 were assigned to a module (0.46%) while the remaining regions (99.54%) were assigned to the grey module. Each of the 27 modules contained between 10 and 89 regions (Figure 4B) , which is consistent with previous Comethyl findings using cord blood samples (Mordaunt et al, 2022). However, the modules generated from placenta were unique because most modules mapped to large blocks of adjacent regions (Figure 4C) (Supplemental Table S23) .…”

“…However, the modules generated from placenta were unique because most modules mapped to large blocks of adjacent regions (Figure 4C) (Supplemental Table S23). This is in contrast to other tissues, where modules typically map to short regions across several chromosomes (Mordaunt et al, 2022). This unique finding led us to investigate where the modules mapped on the genome.…”

“…To identify patterns of DNA methylation, we used the Comethyl R package (Mordaunt et al, 2022), which performs weighted gene correlation network analysis (WGCNA) with WGBS data. This method allowed us to look at modules of co-methylated regions instead of individual CpG sites, which are more susceptible to stochastic variation.…”

mEthAE: an Explainable AutoEncoder for methylation data

“…Therefore, our current framework can not detect cross-chromosomal interactions of CpGs. While this topic is underrepresented in literature, given our current findings it would be important to test such hypothesis and train one large model for all chromosomes [48, 49].…”

The Promise of DNA Methylation in Understanding Multigenerational Factors in Autism Spectrum Disorders

“…Additionally, the difficulty of translating findings from animal studies to humans remains a challenge, particularly when examining diseases of complex etiology and clinical phenotypes, such as ASD. To help fill these critical gaps we have recently developed a systems biology approach that integrates multiple variables of exposures, genetics, demographics, and social determinants of health with regions of co-methylation ( Mordaunt et al, 2022 ).…”