Collective effects of long-range DNA methylations predict gene expressions and estimate phenotypes in cancer.

Scientific Reports
Soyeon KimDegui Zhi

Abstract

DNA methylation of various genomic regions has been found to be associated with gene expression in diverse biological contexts. However, most genome-wide studies have focused on the effect of (1) methylation in cis, not in trans and (2) a single CpG, not the collective effects of multiple CpGs, on gene expression. In this study, we developed a statistical machine learning model, geneEXPLORE (gene expression prediction by long-range epigenetics), that quantifies the collective effects of both cis- and trans- methylations on gene expression. By applying geneEXPLORE to The Cancer Genome Atlas (TCGA) breast and 10 other types of cancer data, we found that most genes are associated with methylations of as much as 10 Mb from the promoters or more, and the long-range methylation explains 50% of the variation in gene expression on average, far greater than cis-methylation. geneEXPLORE outperforms competing methods such as BioMethyl and MethylXcan. Further, the predicted gene expressions could predict clinical phenotypes such as breast tumor status and estrogen receptor status (AUC = 0.999, 0.94 respectively) as accurately as the measured gene expression levels. These results suggest that geneEXPLORE provides a means for accurate imputa...Continue Reading

References

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Datasets Mentioned

BETA
GSE39004

Methods Mentioned

BETA
RNA-seq
Hi-Seq

Software Mentioned

IlluminaHumanMethylation450kanno
R package glmnet
BioMethyl
GeneEXPLORE
R package BioMethyl
ELMER
R
MethylXcan
R package REMP
prediXcan

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