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

Nature Biotechnology volume 31, pages 142–147 (2013)Cite this article

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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.

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Figure 1: Differential cell counts in identifying rheumatoid arthritis–associated differentially methylated positions (DMPs).
Figure 2: Identification of epigenetically mediated genetic risk factors for rheumatoid arthritis disease.
Figure 3
Figure 4: Genotype-dependent candidate DMPs that mediate genetic risk within the MHC region.
Figure 5: Replication data for ten candidate DMPs that mediate genetic risk in rheumatoid arthritis from sorted monocytes.

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Acknowledgements

We thank M. Rosenblum for helpful discussions on the application of statistical mediation methodology. We thank R.A. Irizarry for his contributions to the concepts in this work, his statistical insights on batch effects and helpful comments. We thank E.A. Houseman for codes used for estimating cell proportions. We also thank the EIRA study group18 for contributing invaluable clinical information and biological samples. This work was supported by the US National Institutes of Health Centers of Excellence in Genomic Science, 5P50HG003233 to A.P.F., and by the Swedish Research Council, the Swedish Combine project, the Swedish Strategic Foundations, the AFA Insurance and the European Research Council (ERC).

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Author notes

  1. Yun Liu, Martin J Aryee, Leonid Padyukov and M Daniele Fallin: These authors contributed equally to this work.

Authors and Affiliations

  1. Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Yun Liu, Martin J Aryee, M Daniele Fallin, Arni Runarsson, Margaret Taub & Andrew P Feinberg

  2. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Yun Liu, Arni Runarsson & Andrew P Feinberg

  3. Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Martin J Aryee

  4. Department of Medicine, Rheumatology Unit, Karolinska Institute, Stockholm, Sweden

    Leonid Padyukov, Espen Hesselberg, Marcus Ronninger, Klementy Shchetynsky & Lars Klareskog

  5. Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden

    Leonid Padyukov, Espen Hesselberg, Marcus Ronninger, Klementy Shchetynsky, Lars Klareskog & Tomas J Ekström

  6. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

    M Daniele Fallin, Margaret Taub & Andrew P Feinberg

  7. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

    M Daniele Fallin

  8. Department of Biosciences and Nutrition, Karolinska Institute, Stockholm, Sweden

    Lovisa Reinius & Juha Kere

  9. Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden

    Nathalie Acevedo & Annika Scheynius

  10. Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden

    Lars Alfredsson

  11. Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden

    Tomas J Ekström

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Contributions

Y.L. performed the experiments. Y.L. and M.J.A. analyzed data. L.P. and L.A. performed epidemiological data collection and evaluation. L.P. did sample genotyping and genotype imputation. M.D.F. performed epidemiology analysis and data interpretation, and assisted in experimental design. L.P., M.R., K.S. and E.H. prepared nucleic acids and/or cell sorting. A.R. performed the 450K arrays. M.T. assisted in statistical analysis. J.K., L.R., N.A. and A.S. provided reference normal 450K data from sorted cells for estimating cell proportions. Y.L., M.J.A., L.P., M.D.F., L.K., T.J.E. and A.P.F. conceived the experiments and wrote the paper.

Corresponding authors

Correspondence to Lars Klareskog, Tomas J Ekström or Andrew P Feinberg.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 (PDF 1658 kb)

Supplementary Table 1

Characteristics of the study population (XLSX 9 kb)

Supplementary Table 2

Differential cell type distribution from methylation signature estimation and flow cytometry (XLSX 10 kb)

Supplementary Table 3

RA-associated CpGs (DMPs) (XLSX 4047 kb)

Supplementary Table 4

Genotype-associated DMPs (XLSX 778 kb)

Supplementary Table 5

RA-associated DMPs that are controled by SNPs within the MHC region (XLSX 530 kb)

Supplementary Table 6

SNPs within the MHC region that are associated with both DMPs and RA phenotype (XLSX 348 kb)

Supplementary Table 7

Methylation-mediated genetic risk loci within the MHC region (XLSX 57 kb)

Supplementary Table 8

Genetic risk loci within the MHC region that are associated with methylation variance (XLSX 70 kb)

Supplementary Table 9

Replication on sorted cells (XLSX 61 kb)

Supplementary Table 10

Effect of batch and cell type composition adjustment on differential methylation effect sizes and p-values (XLSX 51 kb)

Supplementary Table 11

DMPs and the genes within 100kb distances (XLSX 46 kb)

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Liu, Y., Aryee, M., Padyukov, L. et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol 31, 142–147 (2013). https://doi.org/10.1038/nbt.2487

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