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Transcription factors operate across disease loci, with EBNA2 implicated in autoimmunity

Nature Genetics volume 50, pages 699–707 (2018)Cite this article

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Abstract

Explaining the genetics of many diseases is challenging because most associations localize to incompletely characterized regulatory regions. Using new computational methods, we show that transcription factors (TFs) occupy multiple loci associated with individual complex genetic disorders. Application to 213 phenotypes and 1,544 TF binding datasets identified 2,264 relationships between hundreds of TFs and 94 phenotypes, including androgen receptor in prostate cancer and GATA3 in breast cancer. Strikingly, nearly half of systemic lupus erythematosus risk loci are occupied by the Epstein–Barr virus EBNA2 protein and many coclustering human TFs, showing gene–environment interaction. Similar EBNA2-anchored associations exist in multiple sclerosis, rheumatoid arthritis, inflammatory bowel disease, type 1 diabetes, juvenile idiopathic arthritis and celiac disease. Instances of allele-dependent DNA binding and downstream effects on gene expression at plausibly causal variants support genetic mechanisms dependent on EBNA2. Our results nominate mechanisms that operate across risk loci within disease phenotypes, suggesting new models for disease origins.

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Fig. 1: Intersection between autoimmune risk loci and TF binding interactions with the genome.
Fig. 2: Properties of EBNA2-bound autoimmune disease loci.
Fig. 3: Allele-dependent binding of EBNA2 to autoimmune-associated genetic variants.
Fig. 4: Cell types and TFs at disease-associated loci.

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Acknowledgements

We thank J. Lee, C. Schroeder, Y. Huang, X. Lu, Z. Patel, E. Zoller and The CCHMC DNA Sequencing and Genotyping Core for experimental support; C. Gunawan, K. Ernst and T. Hong for analytical support; B. Cobb for administrative support; R. Kopan, C. Karp, W. Miller, J. Whitsett, M. Fisher, A. Strauss, S. Hamlin, L. Muglia, H. Singh, J. Oksenberg, I. Chepelev, S. Waggoner, S. Thompson and H. Moncrieffe for constructive feedback and guidance; and Y. Yuan (University of Penn) and D. Thorley-Lawson (Tufts Institute) for generous donation of cell lines (Mutu and IB4, respectively). We also thank our colleagues who have made their data available to us, without which this project and its results would not have been possible. Funding sources: National Institutes of Health (NIH) R01 NS099068, NIH R21 HG008186, Lupus Research Alliance “Novel Approaches”, CCRF Endowed Scholar, CCHMC CpG Pilot study award and CCHMC Trustee Awards to M.T.W.; NIH R01 AI024717, NIH U01 HG008666, NIH U01 AI130830, NIH P30 AR070549, NIH R24 HL105333, NIH KL2 TR001426, NIH R01 AI031584, Kirkland Scholar Award and US Department of Veterans Affairs I01 BX001834 to J.B.H.; NIH R01 DK107502 to L.C.K; NIH DP2 GM119134 to A.B.

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Author notes

  1. These authors contributed equally: John B. Harley, Xiaoting Chen and Mario Pujato.

Authors and Affiliations

  1. Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    John B. Harley, Xiaoting Chen, Mario Pujato, Daniel Miller, Avery Maddox, Carmy Forney, Albert F. Magnusen, Arthur Lynch, Kenneth M. Kaufman, Leah C. Kottyan & Matthew T. Weirauch

  2. Division of Immunobiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    John B. Harley & Kenneth M. Kaufman

  3. Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    John B. Harley & Matthew T. Weirauch

  4. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    John B. Harley, Artem Barski, Nathan Salomonis, Kenneth M. Kaufman, Leah C. Kottyan & Matthew T. Weirauch

  5. US Department of Veterans Affairs Medical Center, Cincinnati, OH, USA

    John B. Harley & Kenneth M. Kaufman

  6. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Kashish Chetal, Nathan Salomonis & Matthew T. Weirauch

  7. Division of Allergy & Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Masashi Yukawa & Artem Barski

  8. Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Artem Barski

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Contributions

The manuscript was written by J.B.H. and M.T.W., with critical feedback from L.C.K., K.M.K., N.S., A.B., X.C., M.P., D.M. and C.F. M.T.W., X.C., M.P. and J.B.H. designed, interpreted and performed the main computational analyses. K.M.K., N.S., L.C.K., A.M. and K.C. designed, interpreted and performed additional computational analyses. L.C.K., J.B.H., M.T.W. and A.B. designed and interpreted laboratory experiments. D.M., C.F., A.F.M., A.L. and M.Y. performed the laboratory experiments.

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Correspondence to John B. Harley, Leah C. Kottyan or Matthew T. Weirauch.

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Competing interests

J.B.H., M.T.W. and L.C.K. have a submitted patent application relating to these findings. A.B. is a cofounder of Datirium, LLC.

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Supplementary information

Supplementary Tables and Figures

Supplementary Figures 1–11 and Supplementary Tables 1 and 2

Reporting Summary

Supplementary Dataset 1

List of all variants for each phenotype. Spreadsheet providing all genetic variants used in this study with associated information.

Supplementary Dataset 2

Sources of functional genomics datasets. Spreadsheet providing information and references for all functional genomics datasets used in this study.

Supplementary Dataset 3

Full RELI results. Spreadsheet providing all RELI results for (1) TF ChIP-seq datasets; (2) non-TF datasets (e.g., histone marks, DNase-seq); (3) Autoimmune ‘fine mapping’ variants; (4) Random ChIP-seq libraries (False Positive Rate estimation).

Supplementary Dataset 4

Locus plots of EBV+/– analysis for all seven EBNA2 disorders. Plots showing the full results of intersections for all TFs with available EBV+ and EBV– B cell ChIP-seq datasets.

Supplementary Dataset 5

Locus plots for additional phenotypes of interest. Full locus plot results for the diseases shown in Figure 1 and other phenotypes.

Supplementary Dataset 6

Full RELI results for EBNA2 cofactor analysis. Spreadsheet providing the RELI results and a summary table identifying potential EBNA2 cofactors occupying the seven EBNA2 disorder loci.

Supplementary Dataset 7

Additional information for allele-dependent EBNA2 autoimmune variants. Table providing additional information for the variants shown in Table 2.

Supplementary Dataset 8

Full MARIO allelic ChIP-seq analysis results. Spreadsheet providing information for all disease-associated genetic variants with allelic EBNA2 binding.

Supplementary Dataset 9

RNA-seq differential expression results. Spreadsheet providing the full results from the differential expression analysis between EBV+ and EBV– Ramos B cells.

Supplementary Dataset 10

Allelic RNA-seq results. Spreadsheet providing information for all genetic variants with allelic RNA-seq reads.

Supplementary Dataset 11

Full RELI cell type results broken down by data type and disease. Plots showing the significance of the intersection between the loci of each of the seven EBNA2 disorders and various markers of active regulatory regions across cell types (related to Fig. 4a,b).

Supplementary Dataset 12

Locus plots broken into EBV-infected B cell and T cell datasets for the seven EBNA2 disorders. Plots showing the presence and absence of ChIP-seq peaks in B and T cells at the loci of each of the EBNA2 disorders (related to Fig. 1)

Supplementary Dataset 13

Phenotypes examined in this study, with associated information. Spreadsheet providing information for all phenotypes examined in this study.

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Harley, J.B., Chen, X., Pujato, M. et al. Transcription factors operate across disease loci, with EBNA2 implicated in autoimmunity. Nat Genet 50, 699–707 (2018). https://doi.org/10.1038/s41588-018-0102-3

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