Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

Abstract

Background Understanding 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. Results Here, 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. Conclusions Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell type-specific epigenomic variation.

Novel DNA methylation changes in mouse lungs associated with heavy smoking

“…The association between smoking and changes in DNAm has been well documented, especially in human blood [6][7][8][9] . However, while there may be shared DNAm patterns between tissues 14,18 , there is evidence that DNAm can be highly variable across tissues [15][16][17] , emphasizing the need to study smoke-induced DNAm alterations in more directly affected tissues such as the lungs. The major aim of this study therefore, was to identify DNAm changes in mouse lungs arising due to smoking, and determine how these patterns would change after a period of smoking cessation.…”

“…However, most of these past studies associating smoking with DNAm alterations were done in accessible tissues, and while they are important in understanding how smoking affects the body as a whole, evidence from more proximal tissues such as lungs is severely lacking 14 . Even though some DNAm patterns overlap between tissues, it is important to measure DNAm in the specific tissue of interest, as DNAm is cell type and tissue-specific [15][16][17] . When directly comparing tissues from the same patients, many CpGs are differentially methylated between lung tissue and blood 18 .…”

Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci

“…In the past few years, several computational tools have been developed for mQTL detection (Ongen et al, 2016;Scherer et al, 2021;Shabalin, 2012). These existing methods conduct single-locus, single-trait analysis, and are commonly used for detecting common variants (e.g.…”

“…In Table 3, we provide an empirical guideline to choose among methods based on their strengths and limitations. We think other existing tools, such as fastQTL and MAGAR (Ongen et al, 2016;Scherer et al, 2021), share many strengths with Matrix-eQTL.…”