Authors

Jun Liu, Erasmus University Medical Center, The Netherlands
Elena Carnero-Montoro, Erasmus University Medical Center, The Netherlands
Jenny van Dongen, Vrije Universiteit Amsterdam, The Netherlands
Samantha Lent, Boston University
Ivana Nedeljkovic, Erasmus University Medical Center, The Netherlands
Symen Ligthart, Erasmus University Medical Center, The Netherlands
Pei-Chien Tsai, King’s College London, UK
Tiphaine C. Martin, King’s College London, UK
Pooja R. Mandaviya, Erasmus University Medical Center, The Netherlands
Rick Jansen, Vrije Universiteit Amsterdam, The Netherlands
Marjolein J. Peters, Erasmus University Medical Center, The Netherlands
Liesbeth Duijts, Erasmus University Medical Center, The Netherlands
Vincent W. V. Jaddoe, Erasmus University Medical Center, The Netherlands
Henning Tiemeier, Erasmus University Medical Center, The Netherlands
Janine F. Felix, Erasmus University Medical Center, The Netherlands
Gonneke Willemsen, Vrije Universiteit Amsterdam, The Netherlands
Eco J. C. de Geus, Vrije Universiteit Amsterdam, The Netherlands
Audrey Y. Chu, National Heart, Lung and Blood Institute
Daniel Levy, National Heart, Lung and Blood Institute
Shih-Jen Hwang, National Heart, Lung and Blood Institute
Jan Bressler, University of Texas Health Science Center at Houston
Rahul Gondalia, The University of North Carolina at Chapel Hill
Elias L. Salfati, Stanford University
Christian Herder, German Center for Diabetes Research, Germany
Bertha A. Hidalgo, The University of Alabama at Birmingham
Toshiko Tanaka, National Institute on Aging
Ann Zenobia Moore, National Institute on Aging
Rozenn N. Lemaitre, University of Washington
Min A. Jhun, University of Michigan - Ann Arbor
Jennifer A. Smith, University of Michigan - Ann Arbor
Donna K. Arnett, University of KentuckyFollow

Abstract

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.

Document Type

Article

Publication Date

6-13-2019

Notes/Citation Information

Published in Nature Communications, v. 10, article no. 2581, p. 1-11.

© Crown 2019

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Due to the large number of authors, only the first 30 and the authors affiliated with the University of Kentucky are listed in the author section above. For the complete list of authors, please download this article or visit: https://doi.org/10.1038/s41467-019-10487-4

Digital Object Identifier (DOI)

https://doi.org/10.1038/s41467-019-10487-4

Funding Information

We gratefully acknowledge the BIOS consortium (https://www.bbmri.nl/?p=259) of Biobanking and BioMolecular resources Research Infrastructure of the Netherlands (BBMRI-NL) and Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. This work is part of the CardioVasculair Onderzoek Nederland (CVON 2012-03), the Common mechanisms and pathways in Stroke and Alzheimer's disease (CoSTREAM) project (https://www.costream.eu, grant agreement No 667375), Memorabel program (project number 733050814), Netherlands X-omics Research Infrastructure and U01-AG061359 NIA. The full list of funding information of each cohort is found in Supplementary Note 2. J.L., C.M.v.D. and A.Demirkan have used exchange grants from the Personalized pREvention of Chronic DIseases consortium (PRECeDI) (H2020-MSCA-RISE-2014). A.Demirkan is supported by a Veni grant (2015) from ZonMw (VENI 91616165). C.M.v.D. received funding of CardioVasculair Onderzoek Nederland (CVON2012-03) of the Netherlands Heart Foundation. B.A.H. was supported by NHLBI K01 award (K01 HL130609-02). V.W.V.J. received a grant from the Netherlands Organization for Health Research and Development (VIDI 016.136.361) and a Consolidator Grant from the European Research Council (ERC-2014-CoG-648916). J.F.F. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 633595 (DynaHEALTH). J.B.M. is supported by K24 DK080140. J.T.B. received funding support from the JPI ERA-HDHL DIMENSION project (BBSRC BB/S020845/1) and from the ESRC (ES/N000404/1).

Related Content

All relevant data supporting the key findings of this study are available within the article and its Supplementary Information files; the cohort data sets generated and analyzed during the current study are available from the authors from each cohort upon reasonable request. The summary statistics of each cohort and meta-analysis in the discovery phase and the source data underlying Supplementary Figure 2 are provided as a Data file [https://figshare.com/s/1a1e8ac0fd9a49e2be30]. The web links for the publicly available data sets used in the paper are listed in URLs. In detail, for the BIOS data, the cis-meQTL look-up files were mainly from “Full list of primary cis-meQTLs” and the results in “Cis-meQTLs independent top effects” were also checked. The trans-meQTL look-up file was from “Trans-meQTLs top effects”. The “eQTM” look-up file was from “Cis-eQTMs independent top effects”. The eQTL look-up file was from “Cis-eQTLs Gene-level all primary effects”. Fasting glucose GWAS was from both ftp://ftp.sanger.ac.uk/pub/magic/MAGIC_Metabochip_Public_data_release_25Jan.zip [ftp://ftp.sanger.ac.uk/pub/magic/MAGIC_Metabochip_Public_data_release_25Jan.zip] and ftp://ftp.sanger.ac.uk/pub/magic/MAGIC_Manning_et_al_FastingGlucose_MainEffect.txt.gz [ftp://ftp.sanger.ac.uk/pub/magic/MAGIC_Manning_et_al_FastingGlucose_MainEffect.txt.gz]; fasting insulin GWAS was from ftp://ftp.sanger.ac.uk/pub/magic/MAGIC_Manning_et_al_lnFastingInsulin_MainEffect.txt.gz [ftp://ftp.sanger.ac.uk/pub/magic/MAGIC_Manning_et_al_lnFastingInsulin_MainEffect.txt.gz]; HbA1c was from ftp://ftp.sanger.ac.uk/pub/magic/HbA1c_METAL_European.txt.gz [ftp://ftp.sanger.ac.uk/pub/magic/HbA1c_METAL_European.txt.gz]. The type 2 diabetes GWAS was downloaded from http://diagram-consortium.org/ [http://diagram-consortium.org/] “T2D GWAS meta-analysis - Trans-Ethnic Summary Statistics Published in in Mahajan et al. (2018)”. The file “T2D_TranEthnic.BMIunadjusted.txt” was used. A reporting summary for this article is available as a supplementary information file.

Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467- 019-10487-4.

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Supplementary Information

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Peer Review File

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Description of Additional Supplementary Files

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Supplementary Data 1

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Supplementary Data 2

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Supplementary Data 3

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Supplementary Data 4

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Reporting Summary

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