An integrated -omics analysis of the epigenetic landscape of gene expression in human blood cells

作者:Kennedy Elizabeth M*; Goehring George N; Nichols Michael H; Robins Chloe; Mehta Divya; Klengel Torsten; Eskin Eleazar; Smith Alicia K; Conneely Karen N
来源:BMC Genomics, 2018, 19(1): 476.
DOI:10.1186/s12864-018-4842-3

摘要

Background: Gene expression can be influenced by DNA methylation 1) distally, at regulatory elements such as enhancers, as well as 2) proximally, at promoters. Our current understanding of the influence of distal DNA methylation changes on gene expression patterns is incomplete. Here, we characterize genome-wide methylation and expression patterns for similar to 13 k genes to explore how DNA methylation interacts with gene expression, throughout the genome.
Results: We used a linear mixed model framework to assess the correlation of DNA methylation at similar to 400 k CpGs with gene expression changes at similar to 13 k transcripts in two independent datasets from human blood cells. Among CpGs at which methylation significantly associates with transcription (eCpGs), > 50% are distal (> 50 kb) or trans (different chromosome) to the correlated gene. Many eCpG-transcript pairs are consistent between studies and similar to 90% of neighboring eCpGs associate with the same gene, within studies. We find that enhancers (P < 5e-18)and microRNA genes (P = 9e-3)are overrepresented among trans eCpGs, and insulators and long intergenic non-coding RNAs are enriched among cis and distal eCpGs. Intragenic-eCpG-transcript correlations are negative in 60-70% of occurrences and are enriched for annotated gene promoters and enhancers (P < 0.002), highlighting the importance of intragenic regulation. Gene Ontology analysis indicates that trans eCpGs are enriched for transcription factor genes and chromatin modifiers, suggesting that some trans eCpGs represent the influence of gene networks and higherorder transcriptional control.
Conclusions: This work sheds new light on the interplay between epigenetic changes and gene expression, and provides useful data for mining biologically-relevant results from epigenome-wide association studies.

  • 出版日期2018-6-19