Modeling complex patterns of differential DNA methylation that associate with gene expression changes

Christopher E. Schlosberg, Nathan D. VanderKraats, John R. Edwards

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Numerous genomic studies are underway to determine which genes are abnormally regulated by DNA methylation in disease. However, we have a poor understanding of how disease-specific methylation changes affect expression. We thus developed an integrative analysis tool, Methylation-based Gene Expression Classification (ME-Class), to explain specific variation in methylation that associates with expression change. This model captures the complexity of methylation changes around a gene promoter. Using 17 whole-genome bisulfite sequencing and RNA-seq datasets from different tissues from the Roadmap Epigenomics Project, ME-Class significantly outperforms standard methods using methylation to predict differential gene expression change. To demonstrate its utility, we used ME-Class to analyze 32 datasets from different hematopoietic cell types from the Blueprint Epigenome project. Expression-associated methylation changes were predominantly found when comparing cells from distantly related lineages, implying that changes in the cell's transcriptional program precede associated methylation changes. Training ME-Class on normaltumor pairs from The Cancer Genome Atlas indicated that cancer-specific expression-associated methylation changes differ from tissue-specific changes. We further show that ME-Class can detect functionally relevant cancer-specific, expression-associated methylation changes that are reversed upon the removal of methylation. ME-Class is thus a powerful tool to identify genes that are dysregulated by DNA methylation in disease.

Original languageEnglish
Pages (from-to)5100-5111
Number of pages12
JournalNucleic acids research
Issue number9
StatePublished - May 19 2017


Dive into the research topics of 'Modeling complex patterns of differential DNA methylation that associate with gene expression changes'. Together they form a unique fingerprint.

Cite this