Abstract
BackgroundUnderstanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL), but also for discriminating general from cell type-specific effects.ResultsHere, we present a two-step computational framework MAGAR (https://bioconductor.org/packages/MAGAR), which fully supports the identification of methQTLs from matched genotyping and DNA methylation data, and additionally allows for illuminating cell type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T cells, B cells) from healthy individuals and demonstrate the discrimination of common from cell type-specific methQTLs. We experimentally validate both types of methQTLs in an independent data set comprising additional cell types and tissues. Finally, we validate selected methQTLs located in the PON1, ZNF155, and NRG2 genes by ultra-deep local sequencing. In line with previous reports, we find cell type-specific methQTLs to be preferentially located in enhancer elements.ConclusionsOur analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell type-specific epigenomic variation.
Highlights
Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease
Strong cell type‐specific DNA methylation signals identified in bowel biopsies and purified blood cell types The data set that we used for the discovery of methylation quantitative trait loci (methQTL) comprised 409 samples from ileum (IL, n = 98) and rectum (RE, n = 95) tissue biopsies and the two FACSsorted blood cell types CD4-positive T cells (n = 119) and CD19-positive B cells (n = 97)
To alleviate the methQTL identification process, we developed the new R-based framework Methylation-Aware Genotype Association in R (MAGAR) that provides a comprehensive suite of tools enabling methQTL analysis leveraging the correlation of DNA methylation states of neighboring CpGs (Fig. 2A)
Summary
Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. Epigenetic mechanisms, including histone modifications, small RNAs, and DNA methylation, regulate gene expression in a tissue- and cell-type-specific manner [1]. Additional genetic effects that are not located in the CpG site but in genetic variants distant to the analyzed CpG can influence its DNA methylation state. Such variants influencing DNA methylation states are referred to as methylation quantitative trait loci (methQTL). These associations can range from distances of a few bases to several megabases resulting in longrange interactions [12, 13]. Combining methQTLs with expression QTLs (eQTLs) enables the investigation of associations between DNA methylation and gene expression changes [20,21,22]
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