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      An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation

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          Abstract

          Background

          Schizophrenia is a highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated.

          Results

          We performed a multi-stage epigenome-wide association study, quantifying genome-wide patterns of DNA methylation in a total of 1714 individuals from three independent sample cohorts. We have identified multiple differentially methylated positions and regions consistently associated with schizophrenia across the three cohorts; these effects are independent of important confounders such as smoking. We also show that epigenetic variation at multiple loci across the genome contributes to the polygenic nature of schizophrenia. Finally, we show how DNA methylation quantitative trait loci in combination with Bayesian co-localization analyses can be used to annotate extended genomic regions nominated by studies of schizophrenia, and to identify potential regulatory variation causally involved in disease.

          Conclusions

          This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological approach that can be used to inform epigenome-wide association study analyses of other complex traits and diseases. We demonstrate the utility of using a polygenic risk score to identify molecular variation associated with etiological variation, and of using DNA methylation quantitative trait loci to refine the functional and regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-016-1041-x) contains supplementary material, which is available to authorized users.

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          Most cited references59

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            An Integrated Encyclopedia of DNA Elements in the Human Genome

            Summary The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure, and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall the project provides new insights into the organization and regulation of our genes and genome, and an expansive resource of functional annotations for biomedical research.
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              DNA methylation age of human tissues and cell types

              Background It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure. Results I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance. Conclusions I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
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                Author and article information

                Contributors
                E.J.Hannon@exeter.ac.uk
                E.L.Dempster@exeter.ac.uk
                J.Viana@exeter.ac.uk
                J.Burrage@exeter.ac.uk
                as541@exeter.ac.uk
                ruby.macdonald@keble.ox.ac.uk
                d.stclair@abdn.ac.uk
                10006535@uhi.ac.uk
                gerome.breen@kcl.ac.uk
                Sebastian.therman@thl.fi
                jaakko.kaprio@helsinki.fi
                timothea@hku.hk
                H.E.Hulshoff@umcutrecht.nl
                m.bohlken@umcutrecht.nl
                R.kahn@umcutrecht.nl
                igor.nenadic@uni-jena.de
                Christina.Hultman@ki.se
                robin.murray@kcl.ac.uk
                collier_david_andrew@lilly.com
                n.bass@ucl.ac.uk
                a.mcquillin@ucl.ac.uk
                lschal@essex.ac.uk
                J.Mill@exeter.ac.uk
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                30 August 2016
                30 August 2016
                2016
                : 17
                : 1
                : 176
                Affiliations
                [1 ]University of Exeter Medical School, University of Exeter, Exeter, UK
                [2 ]The Institute of Medical Sciences, Aberdeen University, Aberdeen, UK
                [3 ]University of the Highlands and Islands, Inverness, UK
                [4 ]Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King’s College London, London, UK
                [5 ]National Institute for Health and Welfare, Helsinki, Finland
                [6 ]Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
                [7 ]Department of Public Health, University of Helsinki, Helsinki, Finland
                [8 ]Department of Psychology, The University of Hong Kong, Pokfulam, Hong Kong
                [9 ]Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
                [10 ]Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
                [11 ]Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
                [12 ]Eli Lilly and Company Ltd, Windlesham, UK
                [13 ]Division of Psychiatry, University College London, London, UK
                [14 ]School of Biological Sciences, University of Essex, Colchester, UK
                [15 ]Royal Devon & Exeter Hospital, RILD Building, Level 4, Barrack Rd, Exeter, EX2 5DW UK
                Article
                1041
                10.1186/s13059-016-1041-x
                5004279
                27572077
                384532e0-84f2-4c37-98be-bd8dfd887088
                © The Author(s). 2016

                Open AccessThis 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.

                History
                : 13 April 2016
                : 9 August 2016
                Funding
                Funded by: Medical Research Council (GB)
                Award ID: MR/K013807/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 AG036039
                Award Recipient :
                Funded by: Suomen Akatemia (FI)
                Award ID: 213506
                Award ID: 129680
                Award ID: 265240
                Funded by: FundRef http://dx.doi.org/10.13039/501100002341, Suomen Akatemia;
                Award ID: 263278
                Funded by: FundRef http://dx.doi.org/10.13039/501100004047, Karolinska Institutet;
                Award ID: ALF 20090183
                Award ID: ALF 20100305
                Award Recipient :
                Funded by: National Institutes of Health (US)
                Award ID: R01 MH52857
                Categories
                Research
                Custom metadata
                © The Author(s) 2016

                Genetics
                schizophrenia,dna methylation,epigenetics,genetics,polygenic risk score (prs),genome-wide association study (gwas),epigenome-wide association study (ewas)

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