The epigenomic basis of common diseases

Rodger and Chatterjee Clinical Epigenetics The epigenomic basis of common diseases Euan J. Rodger 0 1 Aniruddha Chatterjee 0 1 0 Department of Pathology, Dunedin School of Medicine, University of Otago , Hanover Street, P.O. Box 56, Dunedin 9054 , New Zealand 1 Maurice Wilkins Centre for Molecular Biodiscovery , Level 2, 3A Symonds Street, Auckland , New Zealand - Introduction Epigenetic modification provides a stable mechanism by which cells with the same genotype can modulate their gene expression and exhibit different phenotypes. In the past two decades, excellent progress has been made to profile these modifications and our understanding of epigenetic marks has surpassed beyond the basic phenomenon of cellular heterogeneity. It is now established that epigenetic marks are altered in almost all common human diseases. The Epigenomics of Common Diseases meeting, 1–4 November 2016, provided an account of the progress made in this area and also indicated future areas that are yet to be addressed. Although disease focussed, several other aspects were discussed that are relevant to epigenetics as a field, including cellular heterogeneity, epigenomic association studies, emerging concepts in cancer epigenetics and new innovative techniques of broad application (such as single-cell analysis and epigenomic editing approaches). Here, we provide a brief report of some of the key ideas and themes discussed in this meeting and based on these, we speculate on future research directions. A needle in the haystack: insights from epigenome-wide association studies Recent advances in high-throughput DNA analysis now enable researchers to examine epigenetic modifications across the genome, primarily DNA methylation marks, for association with numerous disease phenotypes. As such, epigenome-wide association studies (EWASs) have been fruitful in their findings but they also harbour their own unique challenges. EWASs provide an opportunity to investigate a large number of CpGs across large numbers of patients and controls to detect aberrant methylation signals at a population level [1]. Further, EWASs are an ideal platform to tap into large international resources and compare multiple datasets with custom-generated EWAS data. Examples of well-curated epigenomic datasets include the International Human Epigenome Consortium (IHEC), the EU-funded BLUEPRINT project, and the International Cancer Genome Consortium (ICGC). Although EWASs have been in use for several years now and thousands of datasets and several analytical tools have been reported, there is still a need to understand the potential biases and the nature of factors that could influence the interpretation of results. More sophisticated tools need to be developed to account for these factors. One observation to come out of this meeting, based on the commentary of multiple speakers, was that EWASs require robust analytical tools to detect epigenetic variants of interest and to adjust for confounding factors such as genetic effects and cellular heterogeneity. Several speakers presented vignettes of many interesting EWAS findings, including Stephan Beck (University College London, UK) who shared some “new twists” in EWAS analytics and started off with a plea for authors and editors alike to ensure EWAS papers include CpG numbers. This oversight, he suggested, was akin to publishing a genome-wide association study without including rs numbers for single-nucleotide polymorphisms. Methylation of a CpG site and its relationship to the expression of a corresponding or nearby gene is very context specific. For example, the methylation of a particular gene promoter is likely to be very different to the methylation of the gene body. Methylation in either of these elements could have a different function for the specificity of the corresponding transcription [2]. Therefore, providing the methylation of a gene as a whole is almost meaningless if the context (i.e. the CpG number) © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. is not described. In their analysis of >700 haemopoietic effector cell methylomes from monozygotic twins discordant for type 1 diabetes, Beck and colleagues were only able to identify a single significant differentially methylated CpG position (DMP). Undeterred, they shifted their focus from differences in mean methylation and used a novel approach to detect differentially variable positions (DVPs). (...truncated)