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An epigenome-wide association study of total serum immunoglobulin E concentration

Nature volume 520, pages 670–674 (2015)Cite this article

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Abstract

Immunoglobulin E (IgE) is a central mediator of allergic (atopic) inflammation. Therapies directed against IgE can alleviate hay fever1 and allergic asthma1,2. Genetic association studies have not yet identified novel therapeutic targets or pathways underlying IgE regulation3,4,5,6. We therefore surveyed epigenetic associations between serum IgE concentrations and methylation at loci concentrated in CpG islands genome wide in 95 nuclear pedigrees, using DNA from peripheral blood leukocytes. We validated positive results in additional families and in subjects from the general population. Here we show replicated associations—with a meta-analysis false discovery rate less than 10−4—between IgE and low methylation at 36 loci. Genes annotated to these loci encode known eosinophil products, and also implicate phospholipid inflammatory mediators, specific transcription factors and mitochondrial proteins. We confirmed that methylation at these loci differed significantly in isolated eosinophils from subjects with and without asthma and high IgE levels. The top three loci accounted for 13% of IgE variation in the primary subject panel, explaining the tenfold higher variance found compared with that derived from large single-nucleotide polymorphism genome-wide association studies3,4. This study identifies novel therapeutic targets and biomarkers for patient stratification for allergic diseases.

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Figure 1: Manhattan plot of the results of the genome-wide methylation association study.
Figure 2: Boxplots of methylation at selected CpG loci in isolated eosinophils from subjects with and without asthma and high total serum IgE concentrations (>110 IU l−1).
Figure 3: Association of selected CpG loci to total serum IgE concentrations in the MRCA panel, partitioned by eosinophil counts.

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Acknowledgements

This work was supported by the Freemasons’ Grand Charity. The study was also funded by the Wellcome Trust under grants WT 077959 and WT096964, the UK Medical Research Council, a grant to G.M.L. from Génome Québec, le Ministère de l’Enseignement supérieur, de la Recherche, de la Science et de la Technologie (MESRST), Québec and McGill University, and the National Institutes of Health R01s HL101251-01 and P01-ES18181. M.F.M. and W.O.C.M.C. are Joint Wellcome Trust Senior Investigators, W.O.C.M.C. is a National Institute for Health Research Senior Investigator and C.L. is the Chairholder of the Canada Research Chair on Genetic Determinants in Asthma. We thank A.-M. Madore and V. T. Vaillancourt for the eosinophil isolation and M. Laviolette and N. Flamand for their advice on this technique.

Author information

Author notes

  1. Saffron A. G. Willis-Owen and Catherine Laprise: These authors contributed equally to this work.

  2. G. Mark Lathrop, Miriam F. Moffatt and William O. C. M. Cookson: These authors jointly supervised this work.

Authors and Affiliations

  1. Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, 02115, Massachusetts, USA

    Liming Liang

  2. National Heart and Lung Institute, Imperial College, London SW3 6LY, UK,

    Saffron A. G. Willis-Owen, Kenny C. C. Wong, Aristea Binia, Miriam F. Moffatt & William O. C. M. Cookson

  3. Université du Québec à Chicoutimi, Saguenay, Québec G7H 2B1, Canada,

    Catherine Laprise

  4. Institute of Life Science, College of Medicine, Swansea University, Swansea SA2 8PP, UK,

    Gwyneth A. Davies & Julian M. Hopkin

  5. Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada,

    Thomas J. Hudson

  6. Departments of Medical Biophysics and Molecular Genetics, University of Toronto, Ontario M5S 1A1, Canada,

    Thomas J. Hudson

  7. University of Colorado School of Medicine and National Jewish Health, Denver, 80206, Colorado, USA

    Ivana V. Yang & David A. Schwartz

  8. Department of Human Genetics, McGill University and Génome Québec Innovation Centre, Montréal H3A 1B1, Canada,

    Elin Grundberg, Stephan Busche, Tomi M. Pastinen & G. Mark Lathrop

  9. Jewish General Hospital and Lady Davis Research Institute, Montréal H3T 1E2, Canada,

    Marie Hudson

  10. Department of Medical Sciences, SciLifeLab, Uppsala University, Uppsala SE-751 44, Sweden,

    Lars Rönnblom

  11. Department of Medical Genetics, McGill University Health Centre, Montréal H3H 1P3, Canada,

    Tomi M. Pastinen

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  1. Liming Liang
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Contributions

S.A.G.W.-O., W.O.C.M.C., G.M.L. and M.F.M. planned the initial study. S.A.G.W.-O., A.B. and K.C.C.W. performed measurements of methylation status. L.L. and W.O.C.M.C. led statistical analyses of the data with S.A.G.W.-O. and G.M.L.: most analyses were carried out by L.L. G.M.L. and T.M.P. led discussions on replication strategy, methylation assays and cell-specific methylation, with contributions from M.F.M., D.A.S. and I.V.Y. E.G. validated Ilumina probes with bisulphite sequencing. C.L. led studies of SLSJ families with T.J.H., and G.A.D. and J.M.H. led studies of the PAPA subjects. C.L. led studies of isolated eosinophils. M.H., L.R. and S.B. recruited subjects and studied lymphocyte subsets. W.O.C.M.C. wrote the first draft of the paper. All authors contributed to the interpretation of the results and the writing of the paper.

Corresponding author

Correspondence to William O. C. M. Cookson.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Concordance in methylation status at IgE-associated loci when comparing whole-genome bisulphite sequencing with the Illumina platform.

These results were produced by us (E.G. and T.M.P.) at the Genome Québec Innovation Centre. The figures show a comparison between IgE-associated CpG probes using Illumina 450K (x-axis) and whole-genome bisulphite sequencing (WGBS) (y-axis) platforms for two samples (left and right panels) with 20-fold sequence coverage. The results show a high R2 between platforms (0.76 and 0.73). The median of the correlation coefficients for our IgE-associated loci across 30 different samples (using WGBS at various depths) was R2 = 0.76. This can be compared with the global assessment of all overlapping 450K sites, which is R2 = 0.81.

Extended Data Figure 2 Distribution of methylation status at IgE-associated loci in isolated leukocyte subsets.

a–l, The figure shows the distribution of methylation in PBL subsets at the most strongly IgE-associated loci. CpG methylation was measured by the Illumina Infinium 450K platform. Boxplots show means and interquartile ranges. a, c, e, g, i, k, Results from publically available data derived from six healthy controls21. Lower levels of methylation with wider variation are observed in eosinophils when compared to whole blood (WB) and subsets comprising CD14+ monocytes (CD14+M); CD19+ B cells (CD19+B); CD4+ T cells (CD4+T); CD56+ natural killer cells (CD56+NK); CD8+ T cells (CD8+T); granulocytes (Gran); neutrophils (Neu) and peripheral blood mononuclear cells (PBMCs). b, d, f, h, j, l, Results from cells isolated and analysed by us at the McGill University Genome Québec Innovation Centre (MUGQIC). Eosinophils (Eos) (from 24 subjects in the SLSJ panel) also show lower levels of methylation with wider variation compared to whole blood (22 SLSJ subjects), and to subsets including B cells (BC; 9 control subjects), monocytes (Mono; 76 control subjects) and T cells (TC; 74 control subjects). KW Test, Kruskal–Wallis one-way analysis of variance.

Extended Data Figure 3 Power estimations to detect eosinophil-specific effects in DNA from PBL.

The figure shows that our original MRCA data set (green line) and our combined data set (blue line) are well powered to detect signals of the magnitude observed in our three groups of subjects. The red line shows the power of a sample size of six, as described previously21, to detect differences in CpG methylation in unfractionated PBLs. The mean variance (as standard deviation (s.d.)) for the IgE-associated loci was 0.036 in PBLs from our primary MRCA panel and 0.023 in the whole blood normal samples from ref. 21, demonstrating that our results were consistent with the previous experiment.

Extended Data Table 1 Comparison of surrogate variable analyses with direct white cell counts in association models
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Liang, L., Willis-Owen, S., Laprise, C. et al. An epigenome-wide association study of total serum immunoglobulin E concentration. Nature 520, 670–674 (2015). https://doi.org/10.1038/nature14125

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