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

Michael Scherer; Gilles Gasparoni; Souad Rahmouni; Tatiana Shashkova; Marion Arnoux; Edouard Louis; Arina Nostaeva; Diana Avalos; Emmanouil T Dermitzakis; Yurii S Aulchenko; Thomas Lengauer; Paul A Lyons; Michel Georges; Jörn Walter

doi:10.1186/s13072-021-00415-6

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

Epigenetics Chromatin. 2021 Sep 16;14(1):44. doi: 10.1186/s13072-021-00415-6.

Authors

Michael Scherer^{1

2

3

4}, Gilles Gasparoni¹, Souad Rahmouni⁵, Tatiana Shashkova^{6

7}, Marion Arnoux¹, Edouard Louis⁸, Arina Nostaeva⁹, Diana Avalos^{10

11

12}, Emmanouil T Dermitzakis^{10

11

12}, Yurii S Aulchenko^{6

9

13

14}, Thomas Lengauer², Paul A Lyons^{15

16}, Michel Georges⁵, Jörn Walter¹⁷

Affiliations

¹ Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany.
² Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.
³ Graduate School of Computer Science, Saarland Informatics Campus, Saarbrücken, Germany.
⁴ Department of Bioinformatics and Genomics, Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.
⁵ Unit of Animal Genomics, GIGA-Institute & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium.
⁶ Kurchatov Genomics Center of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.
⁷ Research and Training Center on Bioinformatics, A.A. Kharkevich Institute for Information Transmission Problems, Moscow, Russia.
⁸ Department of Gastroenterology, Liège University Hospital, CHU Liège, Liège, Belgium.
⁹ Novosibirsk State University, Novosibirsk, Russia.
¹⁰ Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland.
¹¹ Swiss Institute of Bioinformatics (SIB), University of Geneva, Geneva, Switzerland.
¹² Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland.
¹³ Moscow Institute of Physics and Technology (State University), Moscow, Russia.
¹⁴ PolyKnomics BV, 's-Hertogenbosch, The Netherlands.
¹⁵ Department of Medicine, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
¹⁶ Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge, CB2 0AW, UK.
¹⁷ Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany. [email protected].

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.

Keywords: Computational biology; DNA methylation; Quantitative trait loci; Tissue specificity.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aryldialkylphosphatase
DNA Methylation*
Epigenomics
Humans
Nerve Growth Factors
Quantitative Trait Loci*

Substances

NRG2 protein, human
Nerve Growth Factors
Aryldialkylphosphatase
PON1 protein, human

Grants and funding

MR/L019027/1/MRC_/Medical Research Council/United Kingdom