eQTM Atlas: New Resource Maps DNA Methylation to Gene Expression Across Tissues and Diseases
Scientists have published the eQTM Atlas, a freely accessible web resource cataloguing over 11 million associations between DNA methylation sites and gene expression levels across 11 tissue types and four disease contexts. The tool addresses a longstanding gap in epigenome-wide association studies (EWAS), where disease-linked DNA methylation sites have traditionally been connected to genes only by genomic proximity rather than functional evidence. By enabling statistically grounded links between methylation and expression, the Atlas aims to accelerate the interpretation of epigenetic findings in complex diseases.
The eQTM Atlas is a newly released bioinformatics resource that aggregates more than 11 million expression quantitative trait methylation (eQTM) associations drawn from eight independent cohorts. It covers 173,886 unique CpG probes and 20,231 unique genes across 11 tissue types and four broad disease categories. The platform, built with R Shiny and hosted by the University of Pittsburgh Center for Research Computing, allows researchers to search by gene or CpG site, filter by tissue or disease type, and visualize both cis- and trans-eQTMs through genome browser and heatmap interfaces. The core motivation is to move beyond proximity-based gene annotation in EWAS — a common limitation that can misattribute functional relevance to nearby genes simply because of physical closeness on the genome. By integrating eQTM data with existing EWAS resources, users can instead identify genes whose expression levels are statistically associated with specific methylation changes. The Atlas is freely available online, with source code publicly accessible on GitHub, lowering barriers for broad adoption in the research community.
What's missing
The preprint does not detail the specific four disease contexts covered, the sample sizes within each cohort, or the statistical thresholds used to define significant eQTM associations. It also does not address potential confounding factors such as cell-type composition differences across tissue samples, which can substantially influence both DNA methylation and gene expression measurements. As a preprint, the work has not yet undergone formal peer review, and replication of the curated associations across independent datasets is not reported.
What different sources said
- bioRxivCenter
HOMED enables hierarchical and multimodal optimization of DNA methylation deconvolution across tissues
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