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Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers

Nature Genetics volume 47, pages 381–386 (2015)Cite this article

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Abstract

Genetic studies of type 1 diabetes (T1D) have identified 50 susceptibility regions1,2, finding major pathways contributing to risk3, with some loci shared across immune disorders4,5,6. To make genetic comparisons across autoimmune disorders as informative as possible, a dense genotyping array, the Immunochip, was developed, from which we identified four new T1D-associated regions (P < 5 × 10−8). A comparative analysis with 15 immune diseases showed that T1D is more similar genetically to other autoantibody-positive diseases, significantly most similar to juvenile idiopathic arthritis and significantly least similar to ulcerative colitis, and provided support for three additional new T1D risk loci. Using a Bayesian approach, we defined credible sets for the T1D-associated SNPs. The associated SNPs localized to enhancer sequences active in thymus, T and B cells, and CD34+ stem cells. Enhancer-promoter interactions can now be analyzed in these cell types to identify which particular genes and regulatory sequences are causal.

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Figure 1: T1D Immunochip P-value enrichment analysis.
Figure 2: Heat map showing chromatin state enrichment analysis of the T1D 99% credible SNP set in Immunochip densely mapped regions versus the complement set within the Epigenomic Roadmap and ENCODE tissues.

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Acknowledgements

This research uses 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 (NIDDK), the National Institute of Allergy and Infectious Diseases (NIAID), the National Human Genome Research Institute (NHGRI), the National Institute of Child Health and Human Development (NICHD) and JDRF and supported by grant U01 DK062418 from the US National Institutes of Health. Further support was provided by grants from the NIDDK (DK046635 and DK085678) to P.C. and by a joint JDRF and Wellcome Trust grant (WT061858/09115) to the Diabetes and Inflammation Laboratory at Cambridge University, which also received support from the NIHR Cambridge Biomedical Research Centre. ImmunoBase receives support from Eli Lilly and Company. C.W. and H.G. are funded by the Wellcome Trust (089989). The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (100140).

We gratefully acknowledge the following groups and individuals who provided biological samples or data for this study. We obtained DNA samples from the British 1958 Birth Cohort collection, funded by the UK Medical Research Council and the Wellcome Trust. We acknowledge use of DNA samples from the NIHR Cambridge BioResource. We thank volunteers for their support and participation in the Cambridge BioResource and members of the Cambridge BioResource Scientific Advisory Board (SAB) and Management Committee for their support of our study. We acknowledge the NIHR Cambridge Biomedical Research Centre for funding. Access to Cambridge BioResource volunteers and to their data and samples are governed by the Cambridge BioResource SAB. Documents describing access arrangements and contact details are available at http://www.cambridgebioresource.org.uk/. We thank the Avon Longitudinal Study of Parents and Children laboratory in Bristol, UK, and the British 1958 Birth Cohort team, including S. Ring, R. Jones, M. Pembrey, W. McArdle, D. Strachan and P. Burton, for preparing and providing the control DNA samples. This study makes use of data generated by the Wellcome Trust Case Control Consortium, funded by Wellcome Trust award 076113; a full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk/.

Author information

Author notes

  1. Patrick Concannon

    Present address: Present address: University of Florida Genetics Institute and Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA.,

  2. Suna Onengut-Gumuscu, Wei-Min Chen and Oliver Burren: These authors contributed equally to this work.

  3. John A Todd, Chris Wallace, Patrick Concannon and Stephen S Rich: These authors jointly supervised this work.

Authors and Affiliations

  1. Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA

    Suna Onengut-Gumuscu, Wei-Min Chen, Aaron R Quinlan, Josyf C Mychaleckyj, Emily Farber, Jessica K Bonnie, Michal Szpak, Patrick Concannon & Stephen S Rich

  2. Department of Medicine, Division of Endocrinology, University of Virginia, Charlottesville, Virginia, USA

    Suna Onengut-Gumuscu

  3. Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, Virginia, USA

    Wei-Min Chen, Aaron R Quinlan, Josyf C Mychaleckyj & Stephen S Rich

  4. Department of Medical Genetics, Juvenile Diabetes Research Foundation (JDRF)/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, National Institute for Health Research (NIHR) Biomedical Research Centre, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK

    Oliver Burren, Nick J Cooper, Ellen Schofield, Premanand Achuthan, Hui Guo, Mary D Fortune, Helen Stevens, Neil M Walker, Jason D Cooper, John A Todd & Chris Wallace

  5. Department of Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA

    Lucas D Ward, Anshul Kundaje & Manolis Kellis

  6. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Lucas D Ward, Anshul Kundaje, Manolis Kellis & Mark J Daly

  7. Department of Genetics, Stanford University, Stanford, California, USA

    Anshul Kundaje

  8. Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA

    Anshul Kundaje & Mark J Daly

  9. Wellcome Trust Sanger Institute, Hinxton, UK

    Jeffrey C Barrett & Panos Deloukas

  10. Medical Research Council (MRC) Biostatistics Unit, Institute of Public Health, University Forvie Site, Cambridge, UK

    Chris Wallace

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  1. Suna Onengut-Gumuscu
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Consortia

Type 1 Diabetes Genetics Consortium

Contributions

The study was conceptually designed by M.J.D., J.C.B., P.D., J.A.T., C.W., P.C. and S.S.R. The study was implemented by S.O.-G., E.F., H.S., N.M.W., P.D., T1DGC, J.A.T., C.W., P.C. and S.S.R. DNA samples were managed by S.O.-G., E.F. and H.S. Genotyping and laboratory quality control were conducted by S.O.-G., E.F. and P.D. Statistical quality control methods were implemented by W.-M.C., M.S., N.J.C., H.G. and J.C.M. Statistical analyses were performed by W.-M.C., A.R.Q., J.C.M., J.D.C., O.B., J.K.B., N.J.C., M.D.F. and C.W. Chromatin state analyses were conducted by O.B., L.D.W., A.K. and M.K. ImmunoBase is maintained by O.B., E.S. and P.A. The manuscript was written by S.O.-G., W.-M.C., A.R.Q., O.B., J.A.T., C.W., P.C. and S.S.R. All authors reviewed and contributed on the final manuscript.

Corresponding author

Correspondence to Stephen S Rich.

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Competing interests

The authors declare no competing financial interests.

Additional information

A complete list of members and affiliations is provided in the Supplementary Note.

Integrated supplementary information

Supplementary Figure 1 Credible SNP set size, before and after filtering for nonsynonymous SNPs or those that overlapped predicted enhancers that had enrichment for T1D association.

Supplementary Figure 2 Relationship inference in 10,796 individuals from T1DGC affected sibling pair (ASP) and trio families.

Supplementary Figure 3 Principal-components analysis (PCA) in T1D cases and controls projected on HapMap 3 coordinates.

Supplementary Figure 4 Principal-components analysis (PCA) on T1D cases and controls projected on European HapMap 3 (CEU and TSI) data.

Supplementary Figure 5 Population structure in T1DGC affected sibling pair (ASP) families and trio families on HapMap data.

Supplementary Figure 6 Effect of shared controls on conditional posterior probability of association with disease 2 given association with disease 1.

Results with independent (red) and shared (blue) controls are shown, with the y axis showing the conditional posterior probability and the x axis showing the variable parameter and with the remaining parameters fixed at the values in Supplementary Table 3.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Table 3 and Supplementary Note. (PDF 557 kb)

Supplementary Data Set

Annotated densely mapped regions. (PDF 13992 kb)

Supplementary Table 1

List of credible SNPs and annotation for T1D. (XLS 659 kb)

Supplementary Table 2

Candidate T1D functional SNPs in credible sets. (XLS 25 kb)

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Onengut-Gumuscu, S., Chen, WM., Burren, O. 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). https://doi.org/10.1038/ng.3245

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