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      Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in Rheumatoid Arthritis

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          Abstract

          Epigenetic mechanisms integrate genetic and environmental causes of disease. Comprehensive genome-wide analyses of epigenetic modifications have not demonstrated robust association with common diseases. Using Illumina HumanMethylation450 arrays on 354 ACPA positive rheumatoid arthritis (RA) cases and 337 controls, we identified two clusters within the MHC region whose differential methylation potentially mediates genetic risk for RA. To reduce confounding hampering previous epigenome-wide studies, we corrected for cellular heterogeneity by estimating and adjusting for cell-type proportions and used mediation analysis to filter out associations likely consequential to disease. Four CpGs also showed association between genotype and variance of methylation in addition to mean. The associations for both clusters replicated at least one CpG (p<0.01), with the rest showing suggestive association, in monocytes in an independent 12 cases and 12 controls. Thus, DNA methylation is a potential mediator of genetic risk.

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          The history of cancer epigenetics.

          Andrew Feinberg, Benjamin Tycko (2004)
          Article 0 comments Cited 549 times – based on 0 reviews     Review now
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            Epigenome-wide association studies for common human diseases.

            Vardhman Rakyan, Thomas A. Down, David Balding … (2011)
            Despite the success of genome-wide association studies (GWASs) in identifying loci associated with common diseases, a substantial proportion of the causality remains unexplained. Recent advances in genomic technologies have placed us in a position to initiate large-scale studies of human disease-associated epigenetic variation, specifically variation in DNA methylation. Such epigenome-wide association studies (EWASs) present novel opportunities but also create new challenges that are not encountered in GWASs. We discuss EWAS design, cohort and sample selections, statistical significance and power, confounding factors and follow-up studies. We also discuss how integration of EWASs with GWASs can help to dissect complex GWAS haplotypes for functional analysis.
            Article 0 comments Cited 466 times – based on 0 reviews     Review now
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              An integrative genomics approach to infer causal associations between gene expression and disease.

              Eric E. Schadt, John Lamb, Xia Yang … (2005)
              A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 9604648
                Journal ID (pubmed-jr-id): 20305
                Journal ID (nlm-ta): Nat Biotechnol
                Journal ID (iso-abbrev): Nat. Biotechnol.
                Title: Nature biotechnology
                ISSN (Print): 1087-0156
                ISSN (Electronic): 1546-1696
                Publication date Nihms-submitted: 5 March 2013
                Publication date (Electronic): 20 January 2013
                Publication date (Print): February 2013
                Publication date PMC-release: 01 August 2013
                Volume: 31
                Issue: 2
                Pages: 142-147
                Affiliations
                [1 ]Center for Epigenetics, Johns Hopkins University School of Medicine, 570 Rangos, 855 N. Wolfe St., Baltimore, MD 21205
                [2 ]Department of Medicine, Johns Hopkins University School of Medicine, 570 Rangos, 855 N. Wolfe St., Baltimore, MD 21205
                [3 ]Department of Oncology, Johns Hopkins University School of Medicine, 570 Rangos, 855 N. Wolfe St., Baltimore, MD 21205
                [4 ]Rheumatology Unit, Department of Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
                [5 ]Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
                [6 ]Department of Biosciences and Nutrition, Karolinska Institutet, 171 77 Stockholm, Sweden
                [7 ]Department of Medicine Solna, Karolinska Institutet, 171 77 Stockholm, Sweden
                [8 ]Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205
                [9 ]Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205
                [10 ]Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
                [11 ]Department of Clinical Neuroscience, Karolinska Institutet, 171 77 Stockholm, Sweden
                Author notes
                [† ]Address correspondence to Lars Klareskog ( lars.klareskog@ 123456ki.se ), Tomas Ekström ( tomas.ekstrom@ 123456ki.se ), and Andrew Feinberg ( afeinberg@ 123456jhu.edu )
                [*]

                Equal contribution from these authors

                Article
                Manuscript ID: NIHMS430504
                DOI: 10.1038/nbt.2487
                PMC ID: 3598632
                PubMed ID: 23334450
                SO-VID: d755386a-7d19-4d01-b2c1-026276aab001
                License:

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Funding
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: P50 HG003233 || HG
                Categories
                Subject: Article

                ScienceOpen disciplines: Biotechnology
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                ScienceOpen disciplines: Biotechnology

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