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Author Notes:

Elizabeth M. Kennedy, Email: ekennedy983@gmail.com

EMK carried out the data analysis to find and analyze eCpGs, and drafted the manuscript. GNG wrote python and SQL code that summarized the association tests.

MHN prepared and analyzed HiC data for distance decay analysis. CR made substantial editorial contributions to the manuscript.

DM and TK contributed lab work and processing to generate the GTP data analyzed here.

EE aided in the running of models using pyLMM. AKS aided in interpretation and made substantial revisions to the manuscript.

KNC conceived of the study, supervised the data analysis and interpretation, and made substantial revisions to the manuscript.

All authors participated in interpretation of the data and manuscript revision. All authors read and approved the final manuscript.

We would like to thank the study participants who made this work possible, as well as the staff of the Grady Trauma Project.

We also thank Benjamin Barwick for helpful discussions and for sharing his CpG annotation and code for the gene ontology analysis.

The authors declare that they have no competing interests.


Research Funding:

EMK received support from BWF training grant ID 1008188 and the NIH National Institute of General Medical Sciences (T32GM008490).

Most analyses were run on Emory’s high-powered computing cluster, which was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award UL1TR000454.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Biotechnology & Applied Microbiology
  • Genetics & Heredity
  • DNA methylation
  • Gene expression
  • Transcriptional regulation
  • Blood cells

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


Journal Title:

BMC Genomics


Volume 19, Number 1


, Pages 476-476

Type of Work:

Article | Final Publisher PDF


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 ~ 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 ~ 400 k CpGs with gene expression changes at ~ 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 ~ 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 higher-order 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.

Copyright information:

© The Author(s). 2018

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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