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Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR. / Scherer, Michael; Gasparoni, Gilles; Rahmouni, Souad et al.

In: Epigenetics and Chromatin, Vol. 14, No. 1, 44, 12.2021.

Research output: Contribution to journal › Article › peer-review

Harvard

Scherer, M, Gasparoni, G, Rahmouni, S, Shashkova, T, Arnoux, M, Louis, E, Nostaeva, A, Avalos, D, Dermitzakis, ET, Aulchenko, YS, Lengauer, T, Lyons, PA, Georges, M & Walter, J 2021, 'Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR', Epigenetics and Chromatin, vol. 14, no. 1, 44. https://doi.org/10.1186/s13072-021-00415-6

APA

Scherer, M., Gasparoni, G., Rahmouni, S., Shashkova, T., Arnoux, M., Louis, E., Nostaeva, A., Avalos, D., Dermitzakis, E. T., Aulchenko, Y. S., Lengauer, T., Lyons, P. A., Georges, M., & Walter, J. (2021). Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR. Epigenetics and Chromatin, 14(1), [44]. https://doi.org/10.1186/s13072-021-00415-6

Vancouver

Scherer M, Gasparoni G, Rahmouni S, Shashkova T, Arnoux M, Louis E et al. Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR. Epigenetics and Chromatin. 2021 Dec;14(1):44. doi: 10.1186/s13072-021-00415-6

Author

Scherer, Michael ; Gasparoni, Gilles ; Rahmouni, Souad et al. / Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR. In: Epigenetics and Chromatin. 2021 ; Vol. 14, No. 1.

BibTeX

@article{f86eeba5b0b84def9e1986f32e6d0f4e,
title = "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.",
keywords = "Computational biology, DNA methylation, Quantitative trait loci, Tissue specificity",
author = "Michael Scherer and Gilles Gasparoni and Souad Rahmouni and Tatiana Shashkova and Marion Arnoux and Edouard Louis and Arina Nostaeva and Diana Avalos and Dermitzakis, {Emmanouil T.} and Aulchenko, {Yurii S.} and Thomas Lengauer and Lyons, {Paul A.} and Michel Georges and J{\"o}rn Walter",
note = "Funding Information: Open Access funding enabled and organized by Projekt DEAL. This work was supported by the EU H2020 project SYSCID (733100), an MRC Programme Grant (MR/L019027/1) to P.A.L, and by ELIXIR Luxembourg via its data hosting service. The work of A.N. was supported by the Ministry of Education and Science of the Russian Federation via the state assignment of the Novosibirsk State University (project “Graduates 2020”). Funding Information: We appreciate the help of Ivan Kuznetsov with data management and analysis and would like to thank Myriam Mni and the GIGA-Institute Genomics core facility for technical assistance. We appreciate the help from the data management team at the University of Luxembourg, especially from Wei Gu. Publisher Copyright: {\textcopyright} 2021, The Author(s).",
year = "2021",
month = dec,
doi = "10.1186/s13072-021-00415-6",
language = "English",
volume = "14",
journal = "Epigenetics and Chromatin",
issn = "1756-8935",
publisher = "BioMed Central Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

AU - Scherer, Michael

AU - Gasparoni, Gilles

AU - Rahmouni, Souad

AU - Shashkova, Tatiana

AU - Arnoux, Marion

AU - Louis, Edouard

AU - Nostaeva, Arina

AU - Avalos, Diana

AU - Dermitzakis, Emmanouil T.

AU - Aulchenko, Yurii S.

AU - Lengauer, Thomas

AU - Lyons, Paul A.

AU - Georges, Michel

AU - Walter, Jörn

N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL. This work was supported by the EU H2020 project SYSCID (733100), an MRC Programme Grant (MR/L019027/1) to P.A.L, and by ELIXIR Luxembourg via its data hosting service. The work of A.N. was supported by the Ministry of Education and Science of the Russian Federation via the state assignment of the Novosibirsk State University (project “Graduates 2020”). Funding Information: We appreciate the help of Ivan Kuznetsov with data management and analysis and would like to thank Myriam Mni and the GIGA-Institute Genomics core facility for technical assistance. We appreciate the help from the data management team at the University of Luxembourg, especially from Wei Gu. Publisher Copyright: © 2021, The Author(s).

PY - 2021/12

Y1 - 2021/12

N2 - 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.

AB - 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.

KW - Computational biology

KW - DNA methylation

KW - Quantitative trait loci

KW - Tissue specificity

UR - http://www.scopus.com/inward/record.url?scp=85115116029&partnerID=8YFLogxK

U2 - 10.1186/s13072-021-00415-6

DO - 10.1186/s13072-021-00415-6

M3 - Article

C2 - 34530905

AN - SCOPUS:85115116029

VL - 14

JO - Epigenetics and Chromatin

JF - Epigenetics and Chromatin

SN - 1756-8935

IS - 1

M1 - 44

ER -

ID: 34257738