Abstract

DNA methylation is an important type of epigenetic modification involved in gene regulation. Although strong DNA methylation at promoters is widely recognized to be associated with transcriptional repression, many aspects of DNA methylation remain not fully understood, including the quantitative relationships between DNA methylation and expression levels, and the individual roles of promoter and gene body methylation. Here we present an integrated analysis of whole-genome bisulfite sequencing and RNA sequencing data from human samples and cell lines. We find that while promoter methylation inversely correlates with gene expression as generally observed, the repressive effect is clear only on genes with a very high DNA methylation level. By means of statistical modeling, we find that DNA methylation is indicative of the expression class of a gene in general, but gene body methylation is a better indicator than promoter methylation. These findings are general in that a model constructed from a sample or cell line could accurately fit the unseen data from another. We further find that promoter and gene body methylation have minimal redundancy, and either one is sufficient to signify low expression. Finally, we obtain increased modeling power by integrating histone modification data with the DNA methylation data, showing that neither type of information fully subsumes the other. Our results suggest that DNA methylation outside promoters also plays critical roles in gene regulation. Future studies on gene regulatory mechanisms and disease-associated differential methylation should pay more attention to DNA methylation at gene bodies and other non-promoter regions.

Highlights

  • DNA methylation is an important type of epigenetic modification involved in gene regulation

  • When the window size was increased, the number of poorly correlated regions decreased for both methylation measures, but the decrease was more rapid for methylated cytosines within CpG dinucleotides (mCG), indicating that mCG/CG is more sensitive to small fluctuations, in particular in windows that contain a small number of CpG dinucleotides

  • As two of the three samples in our study were obtained from individuals with T2D (Lee HM et al, Discovery of type 2 diabetes genes using a multiomic analysis in a family trio, submitted), our results indicated that our data were able to capture relevant information related to the physiological status of the samples

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Summary

Introduction

DNA methylation is an important type of epigenetic modification involved in gene regulation. Various high-throughput methods have been invented for large-scale detection of methylation events [8,12,13,14] These methods differ in the way genomic regions enriched for methylated or unmethylated DNA are identified, and how genomic locations of these regions or their sequences are determined. The former includes the use of methylation-sensitive restriction enzyme digestion [15,16], immunoprecipitation [17,18,19], affinity capture [20,21], and bisulfite conversion of unmethylated cytosines to uracils [2,3,4,22]. These methods have been extensively compared in terms of their genomic coverage, resolution, cost, consistency and context-specific bias [23,24]

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