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      Novel regional age-associated DNA methylation changes within human common disease-associated loci

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          Abstract

          Background

          Advancing age progressively impacts on risk and severity of chronic disease. It also modifies the epigenome, with changes in DNA methylation, due to both random drift and variation within specific functional loci.

          Results

          In a discovery set of 2238 peripheral-blood genome-wide DNA methylomes aged 19–82 years, we identify 71 age-associated differentially methylated regions within the linkage disequilibrium blocks of the single nucleotide polymorphisms from the NIH genome-wide association study catalogue. This included 52 novel regions, 29 within loci not covered by 450 k or 27 k Illumina array, and with enrichment for DNase-I Hypersensitivity sites across the full range of tissues. These age-associated differentially methylated regions also show marked enrichment for enhancers and poised promoters across multiple cell types. In a replication set of 2084 DNA methylomes, 95.7 % of the age-associated differentially methylated regions showed the same direction of ageing effect, with 80.3 % and 53.5 % replicated to p < 0.05 and p < 1.85 × 10 –8, respectively.

          Conclusion

          By analysing the functionally enriched disease and trait-associated regions of the human genome, we identify novel epigenetic ageing changes, which could be useful biomarkers or provide mechanistic insights into age-related common diseases.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-016-1051-8) contains supplementary material, which is available to authorized users.

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          Most cited references45

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          DNA methylation age of blood predicts all-cause mortality in later life

          Background DNA methylation levels change with age. Recent studies have identified biomarkers of chronological age based on DNA methylation levels. It is not yet known whether DNA methylation age captures aspects of biological age. Results Here we test whether differences between people’s chronological ages and estimated ages, DNA methylation age, predict all-cause mortality in later life. The difference between DNA methylation age and chronological age (Δage) was calculated in four longitudinal cohorts of older people. Meta-analysis of proportional hazards models from the four cohorts was used to determine the association between Δage and mortality. A 5-year higher Δage is associated with a 21% higher mortality risk, adjusting for age and sex. After further adjustments for childhood IQ, education, social class, hypertension, diabetes, cardiovascular disease, and APOE e4 status, there is a 16% increased mortality risk for those with a 5-year higher Δage. A pedigree-based heritability analysis of Δage was conducted in a separate cohort. The heritability of Δage was 0.43. Conclusions DNA methylation-derived measures of accelerated aging are heritable traits that predict mortality independently of health status, lifestyle factors, and known genetic factors. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0584-6) contains supplementary material, which is available to authorized users.
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            The UCSC Genome Browser database: update 2011

            The University of California, Santa Cruz Genome Browser (http://genome.ucsc.edu) offers online access to a database of genomic sequence and annotation data for a wide variety of organisms. The Browser also has many tools for visualizing, comparing and analyzing both publicly available and user-generated genomic data sets, aligning sequences and uploading user data. Among the features released this year are a gene search tool and annotation track drag-reorder functionality as well as support for BAM and BigWig/BigBed file formats. New display enhancements include overlay of multiple wiggle tracks through use of transparent coloring, options for displaying transformed wiggle data, a ‘mean+whiskers’ windowing function for display of wiggle data at high zoom levels, and more color schemes for microarray data. New data highlights include seven new genome assemblies, a Neandertal genome data portal, phenotype and disease association data, a human RNA editing track, and a zebrafish Conservation track. We also describe updates to existing tracks.
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              R: A Language for Data Analysis and Graphics

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                Author and article information

                Contributors
                christopher.g.bell@kcl.ac.uk , cgb@mrc.soton.ac.uk
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                23 September 2016
                23 September 2016
                2016
                : 17
                : 193
                Affiliations
                [1 ]Department of Twin Research & Genetic Epidemiology, King’s College London, London, UK
                [2 ]MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
                [3 ]Human Development and Health Academic Unit, Institute of Developmental Sciences, University of Southampton, Southampton, UK
                [4 ]Epigenomic Medicine, Biological Sciences, Faculty of Environmental and Natural Sciences, University of Southampton, Southampton, UK
                [5 ]BGI-Shenzhen, Shenzhen, 518083 China
                [6 ]Institute of Cancer Research, Sutton, UK
                Article
                1051
                10.1186/s13059-016-1051-8
                5034469
                27663977
                ff5d2bf5-41d3-4878-86cd-6ca679724b26
                © 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
                : 17 May 2016
                : 31 August 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 250157
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Categories
                Research
                Custom metadata
                © The Author(s) 2016

                Genetics
                epigenetics,dna methylation,ageing,common disease,human
                Genetics
                epigenetics, dna methylation, ageing, common disease, human

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