Evidence map›Paper›PMID 34530905›Full record

ArticleEpigenetics & chromatin2021

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 and 4 more

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Article in Epigenetics & chromatin, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Michael SchererDepartment of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany.
Gilles GasparoniDepartment of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany.
Souad RahmouniUnit of Animal Genomics, GIGA-Institute & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium.
Tatiana ShashkovaKurchatov Genomics Center of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.
Marion ArnouxDepartment of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany.
Edouard LouisDepartment of Gastroenterology, Liège University Hospital, CHU Liège, Liège, Belgium.
Arina NostaevaNovosibirsk State University, Novosibirsk, Russia.
Diana AvalosDepartment of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland.
Emmanouil T DermitzakisDepartment of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland.
Yurii S AulchenkoKurchatov Genomics Center of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.
Thomas LengauerComputational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.
Paul A LyonsDepartment of Medicine, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Michel GeorgesUnit of Animal Genomics, GIGA-Institute & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium.
Jörn WalterDepartment of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany. j.walter@mx.uni-saarland.de.ORCID 0000-0003-0563-7417

Funding

Medical Research Council MR/L019027/1
6 · The paper itself

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.

Indexed as

DNA MethylationQuantitative Trait LociAryldialkylphosphataseEpigenomicsHumansNerve Growth FactorsAryldialkylphosphataseNerve Growth FactorsNRG2 protein, humanPON1 protein, humanComputational biologyDNA methylationQuantitative trait lociTissue specificity

Identifiers

PMID34530905
PMCPMC8444396

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.