Methylomics of gene expression in human monocytes

Abstract

DNA methylation is one of several epigenetic mechanisms that contribute to the regulation of gene expression; however, the extent to which methylation of CpG dinucleotides correlates with gene expression at the genome-wide level is still largely unknown. Using purified primary monocytes from subjects in a large community-based cohort (n = 1264), we characterized methylation (>485 000 CpG sites) and mRNA expression (>48K transcripts) and carried out genome-wide association analyses of 8370 expression phenotypes. We identified 11 203 potential cis-acting CpG loci whose degree of methylation was associated with gene expression (eMS) at a false discovery rate threshold of 0.001. Most of the associations were consistent in effect size and direction of effect across sex and three ethnicities. Contrary to expectation, these eMS were not predominately enriched in promoter regions, or CpG islands, but rather in the 3' UTR, gene bodies, CpG shores or 'offshore' sites, and both positive and negative correlations between methylation and expression were observed across all locations. eMS were enriched for regions predicted to be regulatory by ENCODE (Encyclopedia of DNA Elements) data in multiple cell types, particularly enhancers. One of the strongest association signals detected (P < 2.2 × 10(-308)) was a methylation probe (cg17005068) in the promoter/enhancer region of the glutathione S-transferase theta 1 gene (GSTT1, encoding the detoxification enzyme) with GSTT1 mRNA expression. Our study provides a detailed description of the epigenetic architecture in human monocytes and its relationship to gene expression. These data may help prioritize interrogation of biologically relevant methylation loci and provide new insights into the epigenetic basis of human health and diseases.

Life course socioeconomic status and DNA methylation in genes related to stress reactivity and inflammation: The multi-ethnic study of atherosclerosis

“…However, we observed both positive and negative correlations between DNAm and gene expression in other site types, which is consistent with previous studies that have challenged the assumption that DNAm is inversely associated with gene expression. 42,52 The results of this study reflect the biological complexity of epigenetic data and underscore the need for multidisciplinary approaches to study how DNAm may contribute to the social patterning of disease. We found that childhood SES was associated with DNAm in approximately the same number of stress-and inflammationrelated genes (3 stress and 2 inflammation), whereas adult SES was primarily associated with DNAm in inflammation-related genes (5 inflammation-related genes vs. 1 stress-related gene).…”

“…Criteria for elimination included: 'detected' expression levels in <10 % of MESA samples (detection P-value cut-off D 0.01), probes that contain a SNP, probes with low variance across samples (<10 th percentile), or overlap with a non-unique region. A detailed description of the quantitation and data processing procedures used for DNAm and gene expression can be found in Liu et al 42 Chip effects were adjusted prior to analysis.…”

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

“…In addition to the genetic association analysis of DNA methylation levels (see section 3.1), the relationship between DNA methylation and Gene expression levels have been integrated at the genome-wide scale (Gibbs et al, 2010; Liu, Ding, et al, 2013). Although epigenetic markers can induce the transcriptional regulation, the epigenetic modification may not be sufficient to cause changes of gene expression levels.…”

“…The integrative approach of multi-omic data may enhance the understanding of the molecular dynamics underlying the pathophysiology of diseases, and may lead to novel strategies for early detection, prevention and treatment of human diseases. Given the increasing number of population studies collecting multi-omic data but limited overview of the methodological framework for integrative analyses (Liu, Ding, et al, 2013; Petersen et al, 2014; Shah et al, 2015), we summarize the analytical methods for high-throughput multi-omic data, and provide an updated analytical framework to incorporate genomic, epigenomic, transcriptomic, proteomic, and metabolomics data for the emerging field of multi-omic association study of human diseases. In this article, we do not cover the topic of disease classification and prediction, which does not aim to understand the biological and functional roles of omic markers (Wan & Pal, 2014).…”

Framework for the Integration of Genomics, Epigenomics and Transcriptomics in Complex Diseases

“…This strategy has been applied to HapMap cell lines [14] , whole blood from healthy human subjects [16] and human monocytes [17] . Furthermore, some studies have combined these types of data to better understand complex diseases such as breast cancer [18] or type 2 diabetes [19] .…”