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      Increased DNA methylation variability in type 1 diabetes across three immune effector cell types

      research-article
      a , 1 , 2 , 3 , 4 , 5 , 5 , 5 , 1 , 5 , 5 , 6 , 7 , 8 , 9 , 8 , 9 , 8 , 9 , 8 , 9 , 8 , 9 , 10 , 8 , 9 , 11 , 12 , 11 , 12 , 11 , 12 , 11 , 12 , 11 , 12 , 11 , 12 , 11 , 12 , 13 , 13 , 13 , 13 , 1 , 14 , 15 , 14 , 15 , 14 , 15 , 8 , 9 , 10 , 16 , 11 , 12 , 8 , 16 , 17 , 18 , 19 , 19 , 20 , 21 , 22 , 23 , 7 , 7 , 7 , 6 , 5 , 1 , b , 5
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          Abstract

          The incidence of type 1 diabetes (T1D) has substantially increased over the past decade, suggesting a role for non-genetic factors such as epigenetic mechanisms in disease development. Here we present an epigenome-wide association study across 406,365 CpGs in 52 monozygotic twin pairs discordant for T1D in three immune effector cell types. We observe a substantial enrichment of differentially variable CpG positions (DVPs) in T1D twins when compared with their healthy co-twins and when compared with healthy, unrelated individuals. These T1D-associated DVPs are found to be temporally stable and enriched at gene regulatory elements. Integration with cell type-specific gene regulatory circuits highlight pathways involved in immune cell metabolism and the cell cycle, including mTOR signalling. Evidence from cord blood of newborns who progress to overt T1D suggests that the DVPs likely emerge after birth. Our findings, based on 772 methylomes, implicate epigenetic changes that could contribute to disease pathogenesis in T1D.

          Abstract

          The incidence of type 1 diabetes is increasing, potentially implicating non-genetic factors. Here the authors conduct an epigenome-wide association study in disease-discordant twins and find increased DNA methylation variability at genes associated with immune cell metabolism and the cell cycle.

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          Most cited references74

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            Fast unfolding of communities in large networks

            Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
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              The sva package for removing batch effects and other unwanted variation in high-throughput experiments.

              Heterogeneity and latent variables are now widely recognized as major sources of bias and variability in high-throughput experiments. The most well-known source of latent variation in genomic experiments are batch effects-when samples are processed on different days, in different groups or by different people. However, there are also a large number of other variables that may have a major impact on high-throughput measurements. Here we describe the sva package for identifying, estimating and removing unwanted sources of variation in high-throughput experiments. The sva package supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                29 November 2016
                2016
                : 7
                : 13555
                Affiliations
                [1 ]Medical Genomics, UCL Cancer Institute, University College London , London WC1E 6BT, UK
                [2 ]Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory , Cambridge CB1 8RN, UK
                [3 ]CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031, China
                [4 ]Statistical Cancer Genomics, UCL Cancer Institute, University College London , London WC1E 6BT, UK
                [5 ]The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London E1 2AT, UK
                [6 ]Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine , Aurora, Colorado 80045, USA
                [7 ]Department of Clinical Sciences, Lund University, Skåne University Hospital , SE-20502 Malmö, Sweden
                [8 ]Department of Haematology, University of Cambridge, Cambridge Biomedical Campus , Cambridge CB2 0PT, UK
                [9 ]National Health Service Blood and Transplant, Cambridge Biomedical Campus , Cambridge CB2 0PT, UK
                [10 ]British Heart Foundation Centre of Excellence, Cambridge Biomedical Campus , Cambridge CB2 0QQ, UK
                [11 ]Department of Human Genetics, McGill University , Montreal, Québec, Canada H3A 0G1
                [12 ]McGill University and Genome Quebec Innovation Centre , Montreal, Québec, Canada H3A 0G1
                [13 ]European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus , Hinxton, Cambridge CB10 1SD, UK
                [14 ]CNAG-CRG, Centre for Genomic Regulation, Barcelona Institute of Science and Technology (BIST) , Baldiri Reixac 4, 08028 Barcelona, Spain
                [15 ]Universitat Pompeu Fabra, Plaça de la Mercè 10 , 08002 Barcelona, Spain
                [16 ]Human Genetics, Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton , Cambridge CB10 1SA, UK
                [17 ]Department of Pediatrics, Medical University of Innsbruck , 6020 Innsbruck, Austria
                [18 ]Division of Endocrinology and Diabetes, RWTH Aachen University , 52074 Aachen, Germany
                [19 ]German Center for Diabetes Research (DZD) , 85764 Neuherberg, Germany
                [20 ]Department of General Pediatrics, Neonatology and Pediatric Cardiology, University Children's Hospital, Heinrich Heine University of Düsseldorf , 40225 Düsseldorf, Germany
                [21 ]Division of Endocrinology, Department of Internal Medicine I, Ulm University Medical Centre , 89081 Ulm, Germany
                [22 ]Lee Kong Chian School of Medicine, Nanyang Technological University , Singapore 636921, Singapore
                [23 ]Imperial College London , London SW7 2AZ, UK
                Author notes
                [*]

                These authors contributed equally to this work

                Author information
                http://orcid.org/0000-0002-8230-0116
                http://orcid.org/0000-0001-8074-6299
                http://orcid.org/0000-0002-5989-6898
                http://orcid.org/0000-0002-3897-7955
                http://orcid.org/0000-0002-9550-0897
                http://orcid.org/0000-0003-1095-3852
                http://orcid.org/0000-0001-5290-2151
                Article
                ncomms13555
                10.1038/ncomms13555
                5141286
                27898055
                347269ee-9033-489b-90cf-e2caa352cd9c
                Copyright © 2016, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 17 April 2016
                : 04 October 2016
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