Abstract
BackgroundEpigenome-wide association studies of human disease and other quantitative traits are becoming increasingly common. A series of papers reporting age-related changes in DNA methylation profiles in peripheral blood have already been published. However, blood is a heterogeneous collection of different cell types, each with a very different DNA methylation profile.ResultsUsing a statistical method that permits estimating the relative proportion of cell types from DNA methylation profiles, we examine data from five previously published studies, and find strong evidence of cell composition change across age in blood. We also demonstrate that, in these studies, cellular composition explains much of the observed variability in DNA methylation. Furthermore, we find high levels of confounding between age-related variability and cellular composition at the CpG level.ConclusionsOur findings underscore the importance of considering cell composition variability in epigenetic studies based on whole blood and other heterogeneous tissue sources. We also provide software for estimating and exploring this composition confounding for the Illumina 450k microarray.
Highlights
Epigenome-wide association studies of human disease and other quantitative traits are becoming increasingly common
DNA methylation (DNAm) profiles show large between cell type differences We downloaded Illumina HumanMethylation450 BeadChip (Illumina 450k) data from flow-sorted neutrophils, lymphocytes (CD8+ and CD4+ T cells, CD56+ natural killer cells and CD19+ B cells) and CD14+ monocytes from six adult male samples as previously described [17] and confirmed that sorted blood cell types have unique DNAm profiles (Figure S1 in Additional file 2)
Whole blood has been one of the most widely used source tissues in Epigenome-wide association studies (EWAS). In these studies, cellular composition explains much of the observed variability in DNAm
Summary
Epigenome-wide association studies of human disease and other quantitative traits are becoming increasingly common. DNA methylation (DNAm) is of particular interest because it is dynamic across the lifetime, affected by environmental insults, and previously implicated in developmental disorders and cancer [1] In these studies, DNAm levels are measured genome-wide at thousands to millions of sites in hundreds of individuals to identify loci where these levels are associated with quantitative traits or disease [1,2]. Many studies measure genome-wide DNAm in blood as obtaining diseaserelevant tissues is often invasive and/or impossible With many of these studies completed, few disease-associated loci have been reported outside of cancer [3], type 1 diabetes [4], and rheumatoid arthritis [5]. Instead a series of papers reporting age-related changes of DNAm profiles have been published [6,7,8,9,10,11,12,13,14]
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