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

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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References

  1. Fujinami, R. S., von Herrath, M. G., Christen, U. & Whitton, J. L. Molecular mimicry, bystander activation, or viral persistence: infections and autoimmune disease. Clin. Microbiol. Rev. 19, 80–94 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ercolini, A. M. & Miller, S. D. The role of infections in autoimmune disease. Clin. Exp. Immunol. 155, 1–15 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sener, A. G. & Afsar, I. Infection and autoimmune disease. Rheumatol. Int. 32, 3331–3338 (2012).

    Article  CAS  PubMed  Google Scholar 

  4. James, J. A. et al. An increased prevalence of Epstein-Barr virus infection in young patients suggests a possible etiology for systemic lupus erythematosus. J. Clin. Invest. 100, 3019–3026 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hanlon, P., Avenell, A., Aucott, L. & Vickers, M. A. Systematic review and meta-analysis of the sero-epidemiological association between Epstein-Barr virus and systemic lupus erythematosus. Arthritis Res. Ther. 16, R3 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  6. McClain, M. T. et al. Early events in lupus humoral autoimmunity suggest initiation through molecular mimicry. Nat. Med. 11, 85–89 (2005).

    Article  CAS  PubMed  Google Scholar 

  7. Harley, J. B. & James, J. A. Epstein-Barr virus infection induces lupus autoimmunity. Bull. NYU Hosp. Jt. Dis. 64, 45–50 (2006).

    PubMed  Google Scholar 

  8. Ascherio, A. & Munger, K. L. EBV and autoimmunity. Curr. Top. Microbiol. Immunol. 390, 365–385 (2015).

    CAS  PubMed  Google Scholar 

  9. Draborg, A. H., Duus, K. & Houen, G. Epstein-Barr virus in systemic autoimmune diseases. Clin. Dev. Immunol. 2013, 535738 (2013).

  10. Vaughn, S. E., Kottyan, L. C., Munroe, M. E. & Harley, J. B. Genetic susceptibility to lupus: the biological basis of genetic risk found in B cell signaling pathways. J. Leukoc. Biol. 92, 577–591 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Alarcón-Riquelme, M. E. et al. Genome-wide association study in an Amerindian ancestry population reveals novel systemic lupus erythematosus risk loci and the role of European admixture. Arthritis Rheumatol. 68, 932–943 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Bentham, J. et al. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat. Genet. 47, 1457–1464 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sun, C. et al. High-density genotyping of immune-related loci identifies new SLE risk variants in individuals with Asian ancestry. Nat. Genet. 48, 323–330 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 9362–9367 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Fang, H., Knezevic, B., Burnham, K. L. & Knight, J. C. XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits. Genome Med. 8, 129 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Schweizer, M. T. & Yu, E. Y. Persistent androgen receptor addiction in castration-resistant prostate cancer. J. Hematol. Oncol. 8, 128 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Asch-Kendrick, R. & Cimino-Mathews, A. The role of GATA3 in breast carcinomas: a review. Hum. Pathol. 48, 37–47 (2016).

    Article  CAS  PubMed  Google Scholar 

  19. Almohmeed, Y. H., Avenell, A., Aucott, L. & Vickers, M. A. Systematic review and meta-analysis of the sero-epidemiological association between Epstein Barr virus and multiple sclerosis. PLoS One 8, e61110 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Pender, M. P. & Burrows, S. R. Epstein-Barr virus and multiple sclerosis: potential opportunities for immunotherapy. Clin. Transl. Immunology 3, e27 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Márquez, A. C. & Horwitz, M. S. The role of latently infected B cells in CNS autoimmunity. Front. Immunol. 6, 544 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ricigliano, V. A. et al. EBNA2 binds to genomic intervals associated with multiple sclerosis and overlaps with vitamin D receptor occupancy. PLoS One 10, e0119605 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hu, X. et al. Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets. Am. J. Hum. Genet. 89, 496–506 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Trynka, G. et al. Disentangling the effects of colocalizing genomic annotations to functionally prioritize non-coding variants within complex-trait loci. Am. J. Hum. Genet. 97, 139–152 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhou, H. et al. Epstein-Barr virus oncoprotein super-enhancers control B cell growth. Cell Host Microbe 17, 205–216 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gewurz, B. E. et al. Canonical NF-κB activation is essential for Epstein-Barr virus latent membrane protein 1 TES2/CTAR2 gene regulation. J. Virol. 85, 6764–6773 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ersing, I., Bernhardt, K. & Gewurz, B. E. NF-κB and IRF7 pathway activation by Epstein-Barr virus Latent Membrane Protein 1. Viruses 5, 1587–1606 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Price, A. M. et al. Analysis of Epstein-Barr virus-regulated host gene expression changes through primary B-cell outgrowth reveals delayed kinetics of latent membrane protein 1-mediated NF-κB activation. J. Virol. 86, 11096–11106 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Farh, K. K. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Zimber-Strobl, U. et al. Epstein-Barr virus nuclear antigen 2 exerts its transactivating function through interaction with recombination signal binding protein RBP-Jκ, the homologue of Drosophila Suppressor of Hairless. EMBO J. 13, 4973–4982 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Grossman, S. R., Johannsen, E., Tong, X., Yalamanchili, R. & Kieff, E. The Epstein-Barr virus nuclear antigen 2 transactivator is directed to response elements by the J kappa recombination signal binding protein. Proc. Natl. Acad. Sci. USA 91, 7568–7572 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Henkel, T., Ling, P. D., Hayward, S. D. & Peterson, M. G. Mediation of Epstein-Barr virus EBNA2 transactivation by recombination signal-binding protein J kappa. Science 265, 92–95 (1994).

    Article  CAS  PubMed  Google Scholar 

  34. Scala, G. et al. Epstein-Barr virus nuclear antigen 2 transactivates the long terminal repeat of human immunodeficiency virus type 1. J. Virol. 67, 2853–2861 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Wang, J. H. et al. Aiolos regulates B cell activation and maturation to effector state. Immunity 9, 543–553 (1998).

    Article  CAS  PubMed  Google Scholar 

  36. Lu, F. et al. EBNA2 drives formation of new chromosome binding sites and target genes for B-cell master regulatory transcription factors RBP-jκ and EBF1. PLoS Pathog. 12, e1005339 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Bailey, S. D., Virtanen, C., Haibe-Kains, B. & Lupien, M. ABC: a tool to identify SNVs causing allele-specific transcription factor binding from ChIP-Seq experiments. Bioinformatics 31, 3057–3059 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Buchkovich, M. L. et al. Removing reference mapping biases using limited or no genotype data identifies allelic differences in protein binding at disease-associated loci. BMC Med. Genomics 8, 43 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kumasaka, N., Knights, A. J. & Gaffney, D. J. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat. Genet. 48, 206–213 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Shi, W., Fornes, O., Mathelier, A. & Wasserman, W. W. Evaluating the impact of single nucleotide variants on transcription factor binding. Nucleic Acids Res. 44, 10106–10116 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ma, B., Huang, J. & Liang, L. RTeQTL: real-time online engine for expression quantitative trait loci analyses. Database (Oxford) 2014, bau066 https://doi.org/10.1093/database/bau066 (2014).

  42. Kryworuckho, M., Diaz-Mitoma, F. & Kumar, A. CD44 isoforms containing exons V6 and V7 are differentially expressed on mitogenically stimulated normal and Epstein-Barr virus-transformed human B cells. Immunology 86, 41–48 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Gonnella, R. et al. PKC theta and p38 MAPK activate the EBV lytic cycle through autophagy induction. Biochim. Biophys. Acta 1853, 1586–1595 (2015).

    Article  CAS  PubMed  Google Scholar 

  44. Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Griffith, M. et al. DGIdb: mining the druggable genome. Nat. Methods 10, 1209–1210 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Harter, M. R. et al. BS69/ZMYND11 C-terminal domains bind and inhibit EBNA2. PLoS Pathog. 12, e1005414 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Li, Y. et al. A genome-wide association study in Han Chinese identifies a susceptibility locus for primary Sjögren’s syndrome at 7q11.23. Nat. Genet. 45, 1361–1365 (2013).

    Article  CAS  PubMed  Google Scholar 

  48. Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    Article  CAS  PubMed  Google Scholar 

  49. GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  PubMed Central  Google Scholar 

  50. Mifsud, B. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genet. 47, 598–606 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384.e19 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Liang, L. et al. A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines. Genome Res. 23, 716–726 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Stranger, B. E. et al. Population genomics of human gene expression.Nat. Genet. 39, 1217–1224 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Veyrieras, J. B. et al. High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet. 4, e1000214 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Pickrell, J. K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Montgomery, S. B. et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 464, 773–777 (2010).

    Article  CAS  PubMed  Google Scholar 

  57. Mangravite, L. M. et al. A statin-dependent QTL for GATM expression is associated with statin-induced myopathy. Nature 502, 377–380 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Dimas, A. S. et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 325, 1246–1250 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Gaffney, D. J. et al. Dissecting the regulatory architecture of gene expression QTLs. Genome Biol. 13, R7 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Trynka, G. et al. Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat. Genet. 43, 1193–1201 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  Google Scholar 

  63. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Liu, T. et al. Cistrome: an integrative platform for transcriptional regulation studies. Genome Biol. 12, R83 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Portales-Casamar, E. et al. The PAZAR database of gene regulatory information coupled to the ORCA toolkit for the study of regulatory sequences. Nucleic Acids Res. 37, D54–D60 (2009).

    Article  CAS  PubMed  Google Scholar 

  66. Griffon, A. et al. Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape. Nucleic Acids Res. 43, e27 (2015).

    Article  PubMed  Google Scholar 

  67. Barrett, T. et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 41, D991–D995 (2013).

    Article  CAS  PubMed  Google Scholar 

  68. Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  70. Smigielski, E. M., Sirotkin, K., Ward, M. & Sherry, S. T. dbSNP: a database of single nucleotide polymorphisms. Nucleic Acids Res. 28, 352–355 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Kottyan, L. C. et al. Genome-wide association analysis of eosinophilic esophagitis provides insight into the tissue specificity of this allergic disease. Nat. Genet. 46, 895–900 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Verma, S. S. et al. Imputation and quality control steps for combining multiple genome-wide datasets. Front. Genet. 5, 370 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Flicek, P. et al. Ensembl 2013. Nucleic Acids Res. 41, D48–D55 (2013).

    Article  CAS  PubMed  Google Scholar 

  76. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Birkenbach, M., Josefsen, K., Yalamanchili, R., Lenoir, G. & Kieff, E. Epstein-Barr virus-induced genes: first lymphocyte-specific G protein-coupled peptide receptors. J. Virol. 67, 2209–2220 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Chen, C. C. et al. NF-κB-mediated transcriptional upregulation of TNFAIP2 by the Epstein-Barr virus oncoprotein, LMP1, promotes cell motility in nasopharyngeal carcinoma. Oncogene 33, 3648–3659 (2014).

    Article  CAS  PubMed  Google Scholar 

  80. Craig, F. E. et al. Gene expression profiling of Epstein-Barr virus-positive and -negative monomorphic B-cell posttransplant lymphoproliferative disorders. Diagn. Mol. Pathol. 16, 158–168 (2007).

    Article  CAS  PubMed  Google Scholar 

  81. Smith, N. et al. Induction of interferon-stimulated genes on the IL-4 response axis by Epstein-Barr virus infected human B cells; relevance to cellular transformation. PLoS One 8, e64868 (2013). 8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Portis, T., Dyck, P. & Longnecker, R. Epstein-Barr virus (EBV) LMP2A induces alterations in gene transcription similar to those observed in Reed-Sternberg cells of Hodgkin lymphoma. Blood 102, 4166–4178 (2003).

    Article  CAS  PubMed  Google Scholar 

  83. Lee, I. S., Shin, Y. K., Chung, D. H. & Park, S. H. LMP1-induced downregulation of CD99 molecules in Hodgkin and Reed-Sternberg cells. Leuk. Lymphoma 42, 587–594 (2001).

    Article  CAS  PubMed  Google Scholar 

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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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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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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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