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Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

Abstract

We report the largest and most diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 78 genome-wide-significant (P < 5 × 10−8) regions, including 36 that are new. We define credible sets of T1D-associated variants and show that they are enriched in immune-cell accessible chromatin, particularly CD4+ effector T cells. Using chromatin-accessibility profiling of CD4+ T cells from 115 individuals, we map chromatin-accessibility quantitative trait loci and identify five regions where T1D risk variants co-localize with chromatin-accessibility quantitative trait loci. We highlight rs72928038 in BACH2 as a candidate causal T1D variant leading to decreased enhancer accessibility and BACH2 expression in T cells. Finally, we prioritize potential drug targets by integrating genetic evidence, functional genomic maps and immune protein–protein interactions, identifying 12 genes implicated in T1D that have been targeted in clinical trials for autoimmune diseases. These findings provide an expanded genomic landscape for T1D.

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Fig. 1: Fine mapping of T1D regions using a Bayesian stochastic search algorithm.
Fig. 2: Fine mapping of the chromosome 4p15.2 region.
Fig. 3: Functional annotation of T1D-associated variants in the BACH2 region.

Data availability

All univariable summary statistics for genotype association with T1D (including imputed variants) are available through the NHGRI-EBI GWAS catalog (GCST90013445 and GCST90013446). The caQTL summary statistics are available through the Type 1 Diabetes Knowledge Portal (https://t1d.hugeamp.org).

Publicly available ATAC-seq: Raw FASTQ files were obtained from Gene Expression Omnibus (GEO) accession number GSE118189. These data included four individuals and 25 immune-cell types under resting conditions as well as after stimulation with anti-human CD3/CD28 Dynabeads and human IL-2 (for 24 h; T lymphocytes), F(ab)′2 anti-human IgG/IgM38 and human IL-4 (for 24 h; B lymphocytes), human IL-2 (for 48 h; natural killer cells) or lipopolysaccharide (for 6 h; monocytes)37.

The ATAC-seq data from the pancreatic islets of five donors without glucose intolerance and five EndoCβH1 cell line replicates, under resting conditions and after stimulation with IFN-γ and IL-1β for 48 h, were downloaded from the GEO (accession number GSE123404)35.

The ATAC-seq data from cardiac fibroblasts (two fetal and three adult) were downloaded from the European Nucleotide Archive (https://www.ebi.ac.uk/ena/data/view/SRX2843570 and https://www.ebi.ac.uk/ena/data/view/SRX2843571) as a control cell type that we did not expect to be involved in the etiology of T1D40.

Epigenome annotation tracks: We obtained chromHMM100 tracks from diverse primary human cells from the NIH Epigenome Roadmap, http://dcc.blueprint-epigenome.eu/#/md/secondary_analysis/Segmentation_of_ChIP-Seq_data_20140811, and additional immune-specific human primary and cell lines from the Blueprint consortium, https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/coreMarks/jointModel/final.

Whole-blood eQTL summary statistics: Summary statistics from whole-blood cis-eQTL analysis from 31,683 individuals42 were downloaded from https://eqtlgen.org.

Additional databases used in the priority-index drug target prioritization analysis were obtained through the relational database provided in the R package Pi (http://pi.well.ox.ac.uk:3010/download).

Code availability

Code used to generate the results presented in this paper is available at https://github.com/ccrobertson/t1d-immunochip-2020. The pipelines for processing ATAC-seq data are available at https://github.com/dfloresDIL/MEGA and http://pepatac.databio.org.

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Acknowledgements

We thank the investigators and their studies for contributing samples and/or data to the current work, and the participants in those studies who made this research possible. These studies include the T1DGC, British 1958 Birth Cohort, Genetic Resource Investigating Diabetes (GRID), Consortium for the Longitudinal Evaluation of African-Americans with Early Rheumatoid Arthritis (CLEAR), Epidemiology of Diabetes Interventions and Complications (EDIC), Genetics of Kidneys and Diabetes Study (GoKinD), New York Cancer Project (NYCP), SEARCH for Diabetes in Youth study (SEARCH), Type 1 Diabetes TrialNet study (TrialNet), Tyypin 1 Diabetekseen Sairastuneita Perheenjäsenineen (IDDMGEN), Tyypin 1 Diabeteksen Genetiikka (T1DGEN), Northern Ireland GRID Collection, Northern Ireland Young Hearts Project, Hvidoere Study Group on Childhood Diabetes (HSG) and International HapMap Project. Additional institutions contributing samples are: British Diabetes Association (BDA), NIHR Cambridge BioResource, UK Blood Service (UKBS), Benaroya Research Institute (BRI), National Institute of Mental Health (NIMH), University of Alabama at Birmingham (UAB), University of Colorado, University of California San Francisco (UCSF), Medical College of Wisconsin (MCW) and Steno Diabetes Center. Samples and data from the T1DGC, EDIC and GoKinD can be obtained from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository. This research utilizes resources provided by the T1DGC, a collaborative clinical study sponsored by the NIDDK, National Institute of Allergy and Infectious Diseases (NIAID), National Human Genome Research Institute (NHGRI), National Institute of Child Health and Human Development (NICHD) and Juvenile Diabetes Research Foundation (JDRF) and is supported by grant no. U01 DK062418 to S.S.R. The generation of chromatin-accessibility data on T1DGC samples was supported by grants from the NIDDK (grant nos DP3 DK111906 to S.S.R. and R01 DK115694 to P.C.). Further support was provided by the NIAID (grant no. P01 AI042288 to M.A.A.). The JDRF/Wellcome Diabetes and Inflammation Laboratory was supported by grants from the JDRF (grant no. 4-SRA-2017-473-A-A) and the Wellcome Trust (grant no. 107212/A/15/Z). Computation used the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and the NIHR Oxford Biomedical Research Centre. Financial support was provided by a Wellcome Core Award (grant no. 203141/Z/16/Z). The views expressed are those of the author(s) and not necessarily those of the NHS, NIHR or Department of Health. While working on this project, C.C.R. was supported by a training grant from the US National Library of Medicine (grant no. T32 LM012416) and the Wagner Fellowship from the UVA. This work made use of data and samples generated by the 1958 Birth Cohort (NCDS), which is managed by the Centre for Longitudinal Studies at the UCL Institute of Education, funded by the Economic and Social Research Council (grant no. ES/M001660/1). Access to these resources was enabled via the MRC and Wellcome: 58FORWARDS grant no. 108439/Z/15/Z (The 1958 Birth Cohort: Fostering new Opportunities for Research via Wider Access to Reliable Data and Samples). Before 2015, biomedical resources were maintained under the Wellcome and Medical Research Council 58READIE Project (grant nos WT095219MA and G1001799). 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 and Management Committee for their support of our study. We thank the NIHR Cambridge Biomedical Research Centre for funding. Access to Cambridge BioResource volunteers and their data and samples are governed by the Cambridge BioResource Scientific Advisory Board. Documents describing access arrangements and contact details are available at http://www.cambridgebioresource.org.uk/. The ethics for GRID were processed by the NRES Committee East of England Cambridge South MREC 00/5/44. We thank the following CLEAR investigators who performed recruiting: D. Conn (Grady Hospital and Emory University), B. Jonas and L. Callahan (University of North Carolina at Chapel Hill), E. Smith (Medical University of South Carolina), R. Brasington (Washington University) and L. W. Moreland (University of Pittsburgh). The CLEAR Registry and Repository was funded by the NIH Office of the Director (grant nos N01-AR-0-2247 (30 September 2000–29 September 2006) and N01-AR-6-2278 (30 September 2006–31 March 2012); S.L.B. Jr, principal investigator). Bio-samples and/or data for this publication were obtained from NIMH Repository and Genomics Resource, a centralized national biorepository for genetic studies of psychiatric disorders. The SEARCH for Diabetes in Youth Study (www.searchfordiabetes.org) is indebted to the many youth and their families, as well as their healthcare providers, whose participation made this study possible. SEARCH for Diabetes in Youth is funded by the Centers for Disease Control and Prevention (PA numbers 00097, DP-05-069 and DP-10-001) and supported by the NIDDK. The SEARCH site contract numbers are: Kaiser Permanente Southern California, U48/CCU919219, U01 DP000246 and U18DP002714; University of Colorado Denver, U48/CCU819241-3, U01 DP000247 and U18DP000247-06A1; Children’s Hospital Medical Center (Cincinnati), U48/CCU519239, U01 DP000248 and 1U18DP002709; University of North Carolina at Chapel Hill, U48/CCU419249, U01 DP000254 and U18DP002708; University of Washington School of Medicine, U58/CCU019235-4, U01 DP000244 and U18DP002710-01; and Wake Forest University School of Medicine, U48/CCU919219, U01 DP000250 and 200-2010-35171. We acknowledge the support of the TrialNet group (https://www.trialnet.org), which identified study participants and provided samples and follow-up data for this study. The TrialNet group is a clinical trials network funded by the NIH through the NIDDK, NIAID and The Eunice Kennedy Shriver National Institute of Child Health and Human Development—through the cooperative agreements U01 DK061010, U01 DK061016, U01 DK061034, U01 DK061036, U01 DK061040, U01 DK061041, U01 DK061042, U01 DK061055, U01 DK061058, U01 DK084565, U01 DK085453, U01 DK085461, U01 DK085463, U01 DK085466, U01 DK085499, U01 DK085505 and U01 DK085509—and the JDRF. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the NIH or JDRF. Further support was provided by grants from the NIDDK (grant nos U01 DK103282 and U01 DK127404 to C.J.G.). DNA samples from the UAB were recruited, in part, with the support of grant nos P01-AR49084, UL1-TR001417 and UL1-TR003096 (to R.P.K.). We acknowledge the involvement of the Barbara Davis Center for Diabetes at the University of Colorado, supported by the following grants from the NIH NIDDK to M.J.R.: DRC P30 DK116073 and R01 DK032493. The collection of DNA samples at UCSF was supported by grant funding from the National Multiple Sclerosis Society (grant no. SI-2001-35701 to J.R.O.). Whole-genome-sequencing data production and variant calling was funded by an NHGRI Center for Common Disease Genomics award to Washington University in St. Louis (grant no. UM1 HG008853). This study used the TOPMed program imputation panel (version TOPMed-r2) supported by the NHLBI (www.nhlbiwgs.org). The TOPMed study investigators contributed data to the reference panel, which can be accessed through the Michigan Imputation Server (https://imputationserver.sph.umich.edu). The panel was constructed and implemented by the TOPMed Informatics Research Center at the University of Michigan (3R01HL-117626-02S1; contract HHSN268201800002I). The TOPMed Data Coordinating Center (3R01HL-120393-02S1; contract HHSN268201800001I) provided additional data management, sample identity checks and overall program coordination and support. We thank the studies and participants who provided biological samples and data for TOPMed. The individual members of the T1DGC and the SEARCH for Diabetes in Youth Study are listed in the Supplementary Note.

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This study was conceptually designed by P.C., J.A.T. and S.S.R. The study was implemented by S.O.-G., P.C., J.A.T. and S.S.R. DNA samples for genotyping were managed by S.O.-G and E.F. L.S.W. contributed to data interpretation. D.J.M.C. provided statistical advice. Frozen T1DGC peripheral blood mononuclear cell samples for chromatin-accessibility profiling (ATAC-seq) were managed by P.C. and S.O.-G. ATAC-seq data generation at the UVA was led by S.O.-G. The generation of ATAC-seq data at the University of Oxford was led by A.J.C. Genotype data processing, quality control, imputation and statistical analyses were performed by W.-M.C., S.O.-G., J.R.J.I. and C.C.R. The chromatin-accessibility data processing and analysis was performed by A.J.C., D.F.S.C., J.R.J.I. and C.C.R. The EMSAs were performed by H.Y., with supervision from S.O.-G. D.B.D. provided samples for genotyping through the GRID. P.D. provided ImmunoChip genotyping data through the UKBS. J.H.B. provided samples for genotyping and data from the BRI. S.L.B. Jr provided samples for genotyping through the CLEAR consortium. P.K.G. provided samples for genotyping through the NYCP project. J.D., D.D., J.M.L., S.M. and A.S.S. provided samples for genotyping and data through the SEARCH for Diabetes in Youth study (SEARCH). C.J.G. and M.A.A. provided samples for genotyping through TrialNet. R.P.K., J.C.E., M.J.R., A.K.S., J.R.O. and F.P. provided samples for genotyping through their affiliated institutions and research programs. The manuscript was written by J.R.J.I. (under supervision by J.A.T.) and C.C.R. (under supervision by S.S.R.). All authors reviewed and approved the manuscript.

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Correspondence to John A. Todd.

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Extended data

Extended Data Fig. 1 Fine mapping of the chromosome 6q22.32 region.

European (EUR, top panel) and African (AFR, middle panel) ancestry group association z-score statistics and posterior probabilities (bottom panel) from multi-ethnic fine mapping of EUR and AFR using PAINTOR. z-scores are colored by linkage disequilibrium (LD) to the lead PAINTOR-prioritized variant.

Extended Data Fig. 2 Fine mapping of the chromosome 18q22.2 region.

European (EUR, top panel) and African (AFR, middle panel) ancestry group association z-score statistics and posterior probabilities (bottom panel) from multi-ethnic fine mapping of EUR and AFR using PAINTOR. z-scores are colored by linkage disequilibrium (LD) to the lead PAINTOR-prioritized variant.

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Supplementary Figs. 1–12 and Note

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Robertson, C.C., Inshaw, J.R.J., Onengut-Gumuscu, S. et al. Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes. Nat Genet 53, 962–971 (2021). https://doi.org/10.1038/s41588-021-00880-5

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