bioRxivGeneticsPreprint
Genetic Basis, Quantitative Nature, and Functional Relevance of Evolutionarily Conserved DNA Methylation
Zheng DongiD, Samantha L. SchaffneriD, Maggie Fu, Joanne WhiteheadiD, Julia L. MacIsaaciD, David H. RehkopfiD, William Thomas BoyceiD, Luis Rosero‐BixbyiD, Lluís Quintana‐MurciiD, Étienne PatiniD, Gregory E. MilleriD, Keegan D. KorthaueriD, Michael Steffen KoboriD
Abstract
DNA methylation (DNAm) is a key epigenetic mark that modulates regulatory elements and gene expression, playing a crucial role in mammalian development and physiological function. Despite extensive characterization of DNAm profiles across species, little is known about its evolutionary conservation. Here, we conducted a comparative epigenome-wide analysis of great apes to identify and characterize sequence- and methylation-conserved CpGs (MCCs). Using 202 DNAm arrays, alongside 6 matched genotype and 13 matched transcriptomic datasets, we identified 11,500 MCCs for which methylation was evolutionarily related to sequences of CpGs and methylation quantitative trait loci. MCCs were the most stable across healthy human tissues and exhibited weaker genetic associations than other CpGs. Moreover, MCCs showed minimal associations with demographic, environmental factors, and noncancer diseases, yet demonstrated stronger associations with certain cancers than other CpGs, particularly gastrointestinal cancers. Functional enrichment analysis revealed that genes associated with MCC methylation in cancer were enriched for cancer driver genes and canonical cancer pathways, highlighting a significant regulatory role for MCCs in tumorigenesis. Collectively, our findings reveal the extent of DNAm conservation in great ape evolution, its association with genetic conservation, and its relevance to human diseases. These integrative analyses offer evolutionary insights into epigenetic variation and its functional implications in human populations.
- organism: human
- assay: DNA methylation
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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