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

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Scherer, Michael 
Gasparoni, Gilles 
Rahmouni, Souad 
Shashkova, Tatiana 
Arnoux, Marion 


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 (, 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.


Funder: Universität des Saarlandes (1036)


Research, Quantitative trait loci, DNA methylation, Tissue specificity, Computational biology

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Epigenetics & Chromatin

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BioMed Central
Horizon 2020 Framework Programme (733100)
Medical Research Council (MR/L019027/1)
ministry of education and science of the russian federation (Graduates 2020)