To define potentially causal variants for autoimmune disease, we fine-mapped1,2 76 rheumatoid arthritis (11,475 cases, 15,870 controls)3 and type 1 diabetes loci (9,334 cases, 11,111 controls)4. After sequencing 799 1-kilobase regulatory (H3K4me3) regions within these loci in 568 individuals, we observed accurate imputation for 89% of common variants. We defined credible sets of ≤5 causal variants at 5 rheumatoid arthritis and 10 type 1 diabetes loci. We identified potentially causal missense variants at DNASE1L3, PTPN22, SH2B3, and TYK2, and noncoding variants at MEG3, CD28–CTLA4, and IL2RA. We also identified potential candidate causal variants at SIRPG and TNFAIP3. Using functional assays, we confirmed allele-specific protein binding and differential enhancer activity for three variants: the CD28–CTLA4 rs117701653 SNP, MEG3 rs34552516 indel, and TNFAIP3 rs35926684 indel.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Data availability

Summary statistics for all variants are available through the following GitHub repository: https://github.com/immunogenomics/harmjan/tree/master/RA-T1D-Finemap-SummaryStats. Genotype data have been previously published3,4 and are available from Rheumatoid Arthritis Consortium International and the Type 1 Diabetes Genetics Consortium upon request. The ATAC-Seq data discussed in this publication have been deposited in the Gene Expression Omnibus under accession number GSE116497.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Maller, J. B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).

  2. 2.

    Wakefield, J. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 81, 208–227 (2007).

  3. 3.

    Eyre, S. et al. High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat. Genet. 44, 1336–1340 (2012).

  4. 4.

    Onengut-Gumuscu, S. et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381–386 (2015).

  5. 5.

    Klareskog, L., Catrina, A. I. & Paget, S. Rheumatoid arthritis. Lancet 373, 659–672 (2009).

  6. 6.

    Palmer, J. P. et al. Insulin antibodies in insulin-dependent diabetics before insulin treatment. Science 222, 1337–1339 (1983).

  7. 7.

    Baekkeskov, S. et al. Identification of the 64K autoantigen in insulin-dependent diabetes as the GABA-synthesizing enzyme glutamic acid decarboxylase. Nature 347, 151–156 (1990).

  8. 8.

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

  9. 9.

    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).

  10. 10.

    Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

  11. 11.

    Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).

  12. 12.

    Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173–178 (2017).

  13. 13.

    Gaulton, K. J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 1415–1425 (2015).

  14. 14.

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

  15. 15.

    Jostins, L. & McVean, G. Trinculo: Bayesian and frequentist multinomial logistic regression for genome-wide association studies of multi-category phenotypes. Bioinformatics 32, 1898–1900 (2016).

  16. 16.

    Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).

  17. 17.

    Begovich, A. B. et al. A missense single-nucleotide polymorphism in a gene encoding a protein tyrosine phosphatase (PTPN22) is associated with rheumatoid arthritis. Am. J. Hum. Genet. 75, 330–337 (2004).

  18. 18.

    Bottini, N. et al. A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes. Nat. Genet. 36, 337–338 (2004).

  19. 19.

    Diogo, D. et al. TYK2 protein-coding variants protect against rheumatoid arthritis and autoimmunity, with no evidence of major pleiotropic effects on non-autoimmune complex traits. PLoS One 10, e0122271 (2015).

  20. 20.

    Sisirak, V. et al. Digestion of chromatin in apoptotic cell microparticles prevents autoimmunity. Cell 166, 88–101 (2016).

  21. 21.

    Zochling, J. et al. An ImmunoChip-based interrogation of scleroderma susceptibility variants identifies a novel association at DNASE1L3. Arthritis Res. Ther. 16, 438 (2014).

  22. 22.

    Al-Mayouf, S. M. et al. Loss-of-function variant in DNASE1L3 causes a familial form of systemic lupus erythematosus. Nat. Genet. 43, 1186–1188 (2011).

  23. 23.

    Ueki, M. et al. Caucasian-specific allele in non-synonymous single nucleotide polymorphisms of the gene encoding deoxyribonuclease I-like 3, potentially relevant to autoimmunity, produces an inactive enzyme. Clin. Chim. Acta 407, 20–24 (2009).

  24. 24.

    Nettleship, J. E. et al. Crystal structure of signal regulatory protein gamma (SIRPγ) in complex with an antibody Fab fragment. BMC Struct. Biol. 13, 13 (2013).

  25. 25.

    Brooke, G., Holbrook, J. D., Brown, M. H. & Barclay, A. N. Human lymphocytes interact directly with CD47 through a novel member of the signal regulatory protein (SIRP) family. J. Immunol. 173, 2562–2570 (2004).

  26. 26.

    Piccio, L. et al. Adhesion of human T cells to antigen-presenting cells through SIRPbeta2–CD47 interaction costimulates T-cell proliferation. Blood 105, 2421–2427 (2005).

  27. 27.

    Liu, Y. et al. Functional elements on SIRPalpha IgV domain mediate cell surface binding to CD47. J. Mol. Biol. 365, 680–693 (2007).

  28. 28.

    Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

  29. 29.

    Sveinbjornsson, G. et al. Weighting sequence variants based on their annotation increases power of whole-genome association studies. Nat. Genet. 48, 314–317 (2016).

  30. 30.

    Simeonov, D. R. et al. Discovery of stimulation-responsive immune enhancers with CRISPR activation. Nature 549, 111–115 (2017).

  31. 31.

    Fortune, M. D. et al. Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls. Nat. Genet. 47, 839–846 (2015).

  32. 32.

    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).

  33. 33.

    Zhou, Y. et al. Activation of p53 by MEG3 non-coding RNA. J. Biol. Chem. 282, 24731–24742 (2007).

  34. 34.

    Wallace, C. et al. The imprinted DLK1–MEG3 gene region on chromosome 14q32.2 alters susceptibility to type 1 diabetes. Nat. Genet. 42, 68–71 (2010).

  35. 35.

    Tsoi, L. C. et al. Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity. Nat. Genet. 44, 1341–1348 (2012).

  36. 36.

    Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

  37. 37.

    Beecham, A. H. et al. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat. Genet. 45, 1353–1360 (2013).

  38. 38.

    Lessard, C. J. et al. Variants at multiple loci implicated in both innate and adaptive immune responses are associated with Sjögren’s syndrome. Nat. Genet. 45, 1284–1292 (2013).

  39. 39.

    Cordell, H. J. et al. International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways. Nat. Commun. 6, 8019 (2015).

  40. 40.

    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).

  41. 41.

    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).

  42. 42.

    McGovern, A. et al. Capture Hi-C identifies a novel causal gene, IL20RA, in the pan-autoimmune genetic susceptibility region 6q23. Genome. Biol. 17, 212 (2016).

  43. 43.

    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).

  44. 44.

    Hinrichs, A. S. et al. The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 34, D590–D598 (2006).

  45. 45.

    Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

  46. 46.

    Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).

  47. 47.

    Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).

  48. 48.

    Delaneau, O., Marchini, J. & Zagury, J.-F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2011).

  49. 49.

    Durbin, R. Efficient haplotype matching and storage using the positional Burrows–Wheeler transform (PBWT). Bioinformatics 30, 1266–1272 (2014).

  50. 50.

    Fuchsberger, C., Abecasis, G. R. & Hinds, D. A. minimac2: faster genotype imputation. Bioinformatics 31, 782–784 (2015).

  51. 51.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  52. 52.

    Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics. 43, 11.10.1–11.10.33 (2013).

  53. 53.

    Powell, J. E., Visscher, P. M. & Goddard, M. E. Reconciling the analysis of IBD and IBS in complex trait studies. Nat. Rev. Genet. 11, 800–805 (2010).

  54. 54.

    Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2016).

  55. 55.

    Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).

  56. 56.

    Yang, J., Fritsche, L. G., Zhou, X. & Abecasis, G. A scalable Bayesian method for integrating functional information in genome-wide association studies. Am. J. Hum. Genet. 101, 404–416 (2017).

  57. 57.

    Lonsdale, J. et al. The Genotype–Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  58. 58.

    Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

  59. 59.

    Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

  60. 60.

    Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  61. 61.

    Ward, L. D. & Kellis, M. HaploRegv4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 44, D877–D881 (2016).

  62. 62.

    Bernstein, B. E. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  63. 63.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

  64. 64.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

  65. 65.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

Download references


This work is supported in part by funding from the National Institutes of Health (U01GM092691, UH2AR067677, 1U01HG009088, and 1R01AR063759 to S.R.), and Doris Duke Charitable Foundation Grant number 2013097. This work is part of the research program Rubicon ALW with project number 825.14.019 (H.-J.W.), which is partly financed by the Netherlands Organization for Scientific Research. Further support was provided by Wellcome (107212/Z/15/Z) and the Juvenile Diabetes Research Foundation (5-SRA-2015-130-A-N) to the Diabetes and Inflammation Laboratory, and by Wellcome (203141/Z/16/Z) to the Wellcome Centre for Human Genetics (J.A.T.). P.K.G. was supported in part by the Feinstein Institute and a generous gift from E. L. Greenland. P.A.N. is supported by a Rheumatology Research Foundation Disease Targeted Research Grant, NIH P30 AR070253 and R01 AR065538, and the Fundación Bechara. S.S.R., W.-M.C. and S.O. were supported in part by funding from the National Institutes of Health (R01DK096926). This research makes use of resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development and Juvenile Diabetes Research Foundation International, and is supported by grant U01DK062418 (S.S.R.).

Author information

Author notes

  1. These authors contributed equally: Harm-Jan Westra, Marta Martínez-Bonet.


  1. Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    • Harm-Jan Westra
    • , Yang Luo
    • , Nikola Teslovich
    •  & Soumya Raychaudhuri
  2. Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    • Harm-Jan Westra
    • , Yang Luo
    • , Nikola Teslovich
    •  & Soumya Raychaudhuri
  3. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    • Harm-Jan Westra
    • , Yang Luo
    • , Nikola Teslovich
    •  & Soumya Raychaudhuri
  4. Division of Rheumatology, Immunology, and Allergy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    • Harm-Jan Westra
    • , Marta Martínez-Bonet
    • , Yang Luo
    • , Nikola Teslovich
    • , Peter A. Nigrovic
    •  & Soumya Raychaudhuri
  5. Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    • Harm-Jan Westra
  6. Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA

    • Suna Onengut-Gumuscu
    • , Wei-Min Chen
    • , Aaron Quinlan
    •  & Stephen S. Rich
  7. Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA

    • Suna Onengut-Gumuscu
    • , Wei-Min Chen
    •  & Stephen S. Rich
  8. The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, USA

    • Annette Lee
    •  & Peter K. Gregersen
  9. Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK

    • Jane Worthington
    • , Steve Eyre
    •  & Soumya Raychaudhuri
  10. NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, UK

    • Jane Worthington
    •  & Steve Eyre
  11. Instituto de Parasitología y Biomedicina López-Neyra, Consejo Superior de Investigaciones Científicas, Granada, Spain

    • Javier Martin
  12. Department of Rheumatology, Leiden University Medical Centre, Leiden, The Netherlands

    • Tom Huizinga
  13. Rheumatology Unit, Department of Medicine, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

    • Lars Klareskog
  14. Department of Public Health and Clinical Medicine, Division of Rheumatology, Umeå University, Umeå, Sweden

    • Solbritt Rantapaa-Dahlqvist
  15. Department of Human Genetics, University of Utah, Salt Lake City, UT, USA

    • Aaron Quinlan
  16. Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA

    • Aaron Quinlan
  17. JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK

    • John A. Todd
  18. Division of Immunology, Boston Children’s Hospital, Boston, MA, USA

    • Peter A. Nigrovic
  19. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    • Soumya Raychaudhuri


  1. Search for Harm-Jan Westra in:

  2. Search for Marta Martínez-Bonet in:

  3. Search for Suna Onengut-Gumuscu in:

  4. Search for Annette Lee in:

  5. Search for Yang Luo in:

  6. Search for Nikola Teslovich in:

  7. Search for Jane Worthington in:

  8. Search for Javier Martin in:

  9. Search for Tom Huizinga in:

  10. Search for Lars Klareskog in:

  11. Search for Solbritt Rantapaa-Dahlqvist in:

  12. Search for Wei-Min Chen in:

  13. Search for Aaron Quinlan in:

  14. Search for John A. Todd in:

  15. Search for Steve Eyre in:

  16. Search for Peter A. Nigrovic in:

  17. Search for Peter K. Gregersen in:

  18. Search for Stephen S. Rich in:

  19. Search for Soumya Raychaudhuri in:


H.-J.W., Y.L. and S.R. performed the analyses. M.M.-B. and P.A.N. performed the functional assays. H.-J.W., M.M.-B., P.A.N. and S.R. designed the study. S.O., A.L., N.T., J.W., J.M., T.H., L.K., S.R.-D., W.-M.C., A.Q., J.A.T., S.E., P.K.G., S.S.R. and S.R. acquired the data. H.-J.W., M.M.-B., Y.L., J.A.T., P.A.N., P.K.G., S.S.R. and S.R. wrote and edited the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Soumya Raychaudhuri.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–29 and Supplementary Note

  2. Reporting Summary

  3. Supplementary Tables

    Supplementary Tables 1–29

About this article

Publication history






Further reading