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Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis.

ABSTRACT: Epigenetic mechanisms integrate genetic and environmental causes of disease, but comprehensive genome-wide analyses of epigenetic modifications have not yet demonstrated robust association with common diseases. Using Illumina HumanMethylation450 arrays on 354 anti-citrullinated protein antibody-associated rheumatoid arthritis cases and 337 controls, we identified two clusters within the major histocompatibility complex (MHC) region whose differential methylation potentially mediates genetic risk for rheumatoid arthritis. To reduce confounding factors that have hampered previous epigenome-wide studies, we corrected for cellular heterogeneity by estimating and adjusting for cell-type proportions in our blood-derived DNA samples and used mediation analysis to filter out associations likely to be a consequence of disease. Four CpGs also showed an association between genotype and variance of methylation. The associations for both clusters replicated at least one CpG (P < 0.01), with the rest showing suggestive association, in monocyte cell fractions in an independent cohort of 12 cases and 12 controls. Thus, DNA methylation is a potential mediator of genetic risk.

SUBMITTER: Liu Y

PROVIDER: S-EPMC3598632 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis.

Liu Yun Y Aryee Martin J MJ Padyukov Leonid L Fallin M Daniele MD Hesselberg Espen E Runarsson Arni A Reinius Lovisa L Acevedo Nathalie N Taub Margaret M Ronninger Marcus M Shchetynsky Klementy K Scheynius Annika A Kere Juha J Alfredsson Lars L Klareskog Lars L Ekström Tomas J TJ Feinberg Andrew P AP

Nature biotechnology 20130120 2

Epigenetic mechanisms integrate genetic and environmental causes of disease, but comprehensive genome-wide analyses of epigenetic modifications have not yet demonstrated robust association with common diseases. Using Illumina HumanMethylation450 arrays on 354 anti-citrullinated protein antibody-associated rheumatoid arthritis cases and 337 controls, we identified two clusters within the major histocompatibility complex (MHC) region whose differential methylation potentially mediates genetic risk ...[more]

PMID: 23334450

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Project description:GWAS owe their popularity to the expectation that they will make a major impact on diagnosis, prognosis and management of disease by uncovering genetics underlying clinical phenotypes. The dominant paradigm in GWAS data analysis so far consists of extensive reliance on methods that emphasize contribution of individual SNPs to statistical association with phenotypes. Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously. Recent advances in other genomics domains pinpoint multivariate causal graph-based inference as a promising principled analysis framework for high-throughput data. Designed to discover biomarkers in the local causal pathway of the phenotype, these methods lead to accurate and highly parsimonious multivariate predictive models. In this paper, we investigate the applicability of causal graph-based method TIE* to analysis of GWAS data. To test the utility of TIE*, we focus on anti-CCP positive rheumatoid arthritis (RA) GWAS datasets, where there is a general consensus in the community about the major genetic determinants of the disease.Application of TIE* to the North American Rheumatoid Arthritis Cohort (NARAC) GWAS data results in six SNPs, mostly from the MHC locus. Using these SNPs we develop two predictive models that can classify cases and disease-free controls with an accuracy of 0.81 area under the ROC curve, as verified in independent testing data from the same cohort. The predictive performance of these models generalizes reasonably well to Swedish subjects from the closely related but not identical Epidemiological Investigation of Rheumatoid Arthritis (EIRA) cohort with 0.71-0.78 area under the ROC curve. Moreover, the SNPs identified by the TIE* method render many other previously known SNP associations conditionally independent of the phenotype.Our experiments demonstrate that application of TIE* captures maximum amount of genetic information about RA in the data and recapitulates the major consensus findings about the genetic factors of this disease. In addition, TIE* yields reproducible markers and signatures of RA. This suggests that principled multivariate causal and predictive framework for GWAS analysis empowers the community with a new tool for high-quality and more efficient discovery.This article was reviewed by Prof. Anthony Almudevar, Dr. Eugene V. Koonin, and Prof. Marianthi Markatou.

Project description:BackgroundPanic disorder (PD) is considered to be a multifactorial disorder emerging from interactions among multiple genetic and environmental factors. To date, although genetic studies reported several susceptibility genes with PD, few of them were replicated and the pathogenesis of PD remains to be clarified. Epigenetics is considered to play an important role in etiology of complex traits and diseases, and DNA methylation is one of the major forms of epigenetic modifications. In this study, we performed an epigenome-wide association study of PD using DNA methylation arrays so as to investigate the possibility that different levels of DNA methylation might be associated with PD.MethodsThe DNA methylation levels of CpG sites across the genome were examined with genomic DNA samples (PD, N = 48, control, N = 48) extracted from peripheral blood. Methylation arrays were used for the analysis. β values, which represent the levels of DNA methylation, were normalized via an appropriate pipeline. Then, β values were converted to M values via the logit transformation for epigenome-wide association study. The relationship between each DNA methylation site and PD was assessed by linear regression analysis with adjustments for the effects of leukocyte subsets.ResultsForty CpG sites showed significant association with PD at 5% FDR correction, though the differences of the DNA methylation levels were relatively small. Most of the significant CpG sites (37/40 CpG sites) were located in or around CpG islands. Many of the significant CpG sites (27/40 CpG sites) were located upstream of genes, and all such CpG sites with the exception of two were hypomethylated in PD subjects. A pathway analysis on the genes annotated to the significant CpG sites identified several pathways, including "positive regulation of lymphocyte activation."ConclusionsAlthough future studies with larger number of samples are necessary to confirm the small DNA methylation abnormalities associated with PD, there is a possibility that several CpG sites might be associated, together as a group, with PD.

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Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis.

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Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis.

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