Cited by 302Diabetes and associated disordersGenetic Associations and EpidemiologyGenomics and Chromatin Dynamics

The DOI matches its Crossref record; 109 of the 109 references it cites were found in a registry or an index.

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

Open access

Robertson, Catherine C., Inshaw, Jamie R. J., Onengut-Gumuscu, Suna, Chen, Wei-Min, Santa Cruz, David Flores, Yang, Hanzhi, Cutler, Antony J., Crouch, Daniel J. M., Farber, Emily, Bridges, S. Louis, Edberg, Jeffrey C., Kimberly, Robert P., Buckner, Jane H., Deloukas, Panos, Divers, Jasmin, Dabelea, Dana, Lawrence, Jean M., Marcovina, Santica, Shah, Amy S., Greenbaum, Carla J., Atkinson, Mark A., Gregersen, Peter K., Oksenberg, Jorge R., Pociot, Flemming, Rewers, Marian J., Steck, Andrea K., Dunger, David B., Type 1 Diabetes Genetics Consortium, Wicker, Linda S., Concannon, Patrick, Todd, John A., Rich, Stephen S.·2021·Nature Genetics

DOI 10.1038/s41588-021-00880-5PMC PMC8273124

Published version of a preprint

Open this paper in CoChat — free account Open PDF

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. A large-scale genetic analysis of type 1 diabetes identifies new susceptibility variants, highlights potential regulatory mechanisms and provides genetic support for therapeutic targets for immune intervention.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/s41588-021-00880-5

Cites

109 of 109 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
Minimap2: Pairwise alignment for nucleotide sequences
Li, H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty191 · Cited by 14,807
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,724
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Self-citation
A scaling normalization method for differential expression analysis of RNA-seq data
Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876
The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible
Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., Santos, A., Doncheva, N. T., Roth, A., Bork, P., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw937 · Cited by 6,980
METAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205
GENCODE: The reference human genome annotation for The ENCODE Project
Harrow, J., Frankish, A., Gonzalez, J. M., Tapanari, E., Diekhans, M., Kokocinski, F., Aken, B. L., Barrell, D., Zadissa, A., Searle, S., et al. (2012) · Genome Research · doi:10.1101/gr.135350.111 · Cited by 4,585
Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics
Giambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004383 · Cited by 4,261
Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM).
Spielman, R. S., McGinnis, R. E., & Ewens, W. J. (1993) · PubMed · Cited by 3,679
Robust relationship inference in genome-wide association studies
Manichaikul, A., Mychaleckyj, J. C., Rich, S. S., Daly, K., Sale, M., & Chen, W.-M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq559 · Cited by 3,541
Self-citation
ATAC-seq: a method for assaying chromatin accessibility genome-wide
Buenrostro, J. D., Wu, B., Chang, H. Y., & Greenleaf, W. J. (2015) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb2129s109 · Cited by 3,289
Annotation of functional variation in personal genomes using RegulomeDB
Boyle, A. P., Hong, E. L., Hariharan, M., Cheng, Y., Schaub, M. A., Kasowski, M., Karczewski, K. J., Park, J., Hitz, B. C., Weng, S., et al. (2012) · Genome Research · doi:10.1101/gr.137323.112 · Cited by 2,722
Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis
Võsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Kasela, S., et al. (2018) · doi:10.1101/447367 · Cited by 2,607
Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression
Võsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Yazar, S., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00913-z · Cited by 2,607
An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues
Corces, M. R., Trevino, A. E., Hamilton, E. G., Greenside, P. G., Sinnott-Armstrong, N. A., Vesuna, S., Satpathy, A. T., Rubin, A. J., Montine, K. S., Wu, B., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4396 · Cited by 2,496
Genetics of rheumatoid arthritis contributes to biology and drug discovery
the RACI consortium, Okada, Y., the GARNET consortium, Wu, D., Trynka, G., Raj, T., Terao, C., Ikari, K., Kochi, Y., Ohmura, K., et al. (2014) · Nature · doi:10.1038/nature12873 · Cited by 2,310
Self-citation
Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program
Taliun, D., Harris, D. N., Kessler, M. D., Carlson, J., Szpiech, Z. A., Torres, R., Taliun, S. A. G., Corvelo, A., Gogarten, S. M., Kang, H. M., et al. (2021) · Nature · doi:10.1038/s41586-021-03205-y · Cited by 2,213
Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population.
(1995) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a040269 · Cited by 1,950
Tofacitinib as Induction and Maintenance Therapy for Ulcerative Colitis
Sandborn, W. J., Su, C. G., Sands, B. E., D’Haens, G., Vermeire, S., Schreiber, S., Danese, S., Feagan, B. G., Reinisch, W., Niezychowski, W., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1606910 · Cited by 1,783
Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes
The Type 1 Diabetes Genetics Consortium, Barrett, J. C., Clayton, D. G., Concannon, P., Akolkar, B., Cooper, J. D., Erlich, H. A., Julier, C., Morahan, G., Nerup, J., et al. (2009) · Nature Genetics · doi:10.1038/ng.381 · Cited by 1,746
Self-citation
Matrix eQTL: ultra fast eQTL analysis via large matrix operations
Shabalin, A. A. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts163 · Cited by 1,694
Genotype imputation for genome-wide association studies
Marchini, J., & Howie, B. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2796 · Cited by 1,686
The support of human genetic evidence for approved drug indications
Nelson, M. R., Tipney, H., Painter, J. L., Shen, J., Nicoletti, P., Shen, Y., Floratos, A., Sham, P. C., Li, M. J., Wang, J., et al. (2015) · Nature Genetics · doi:10.1038/ng.3314 · Cited by 1,666
Systematic identification of trans eQTLs as putative drivers of known disease associations
Westra, H.-J., Peters, M. J., Esko, T., Yaghootkar, H., Schurmann, C., Kettunen, J., Christiansen, M. W., Fairfax, B. P., Schramm, K., Powell, J. E., et al. (2013) · Nature Genetics · doi:10.1038/ng.2756 · Cited by 1,658
Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads
Jiang, H., Lei, R., Ding, S., & Zhu, S. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-182 · Cited by 1,525
Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes
Finland, G. O. T. 1. D. I., Todd, J. A., the Wellcome Trust Case Control Consortium, Walker, N. M., Cooper, J. D., Smyth, D. J., Downes, K., Plagnol, V., Bailey, R., Nejentsev, S., et al. (2007) · Nature Genetics · doi:10.1038/ng2068 · Cited by 1,456
Self-citation
An anti-CD3 antibody, teplizumab, in relatives at risk for type 1 diabetes
Herold, K. C., Bundy, B. N., Long, S. A., Bluestone, J. A., DiMeglio, L. A., Dufort, M. J., Gitelman, S. E., Gottlieb, P. A., Krischer, J. P., Linsley, P. S., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1902226 · Cited by 1,189
Self-citation
Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters
Javierre, B. M., Burren, O. S., Wilder, S. P., Kreuzhuber, R., Hill, S. M., Sewitz, S., Cairns, J., Wingett, S. W., Várnai, C., Thiecke, M. J., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.037 · Cited by 1,046
Self-citation
Genetic analyses of diverse populations improves discovery for complex traits
Wojcik, G. L., Graff, M., Nishimura, K. K., Tao, R., Haessler, J., Gignoux, C. R., Highland, H. M., Patel, Y. M., Sorokin, E. P., Avery, C. L., et al. (2019) · Nature · doi:10.1038/s41586-019-1310-4 · Cited by 1,033
Measuring reproducibility of high-throughput experiments
Li, Q., Brown, J. B., Huang, H., & Bickel, P. J. (2011) · The Annals of Applied Statistics · doi:10.1214/11-aoas466 · Cited by 1,032
BEDOPS: high-performance genomic feature operations
Neph, S. J., Kuehn, M. S., Reynolds, A. P., Haugen, E., Thurman, R. E., Johnson, A., Rynes, E., Maurano, M. T., Vierstra, J. D., Thomas, S., et al. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts277 · Cited by 971
HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease
Ward, L. D., & Kellis, M. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1340 · Cited by 948
Measuring reproducibility of high-throughput experiments
Li, Q., Brown, J. B., Huang, H., & Bickel, P. J. (2010) · UC Berkeley · Cited by 915
Chromatin-state discovery and genome annotation with ChromHMM
Ernst, J., & Kellis, M. (2017) · Nature Protocols · doi:10.1038/nprot.2017.124 · Cited by 905
Gateways to the FANTOM5 promoter level mammalian expression atlas
Lizio, M., Harshbarger, J., Shimoji, H., Severin, J., Kasukawa, T., Sahin, S., Abugessaisa, I., Fukuda, S., Hori, F., Ishikawa-Kato, S., et al. (2015) · Genome Biology · doi:10.1186/s13059-014-0560-6 · Cited by 871
Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression
Schmiedel, B. J., Singh, D., Madrigal, A., Valdovino-Gonzalez, A. G., White, B. M., Zapardiel-Gonzalo, J., Ha, B., Altay, G., Greenbaum, J. A., McVicker, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.022 · Cited by 867
Innate Immune Activity Conditions the Effect of Regulatory Variants upon Monocyte Gene Expression
Fairfax, B. P., Humburg, P., Makino, S., Naranbhai, V., Wong, D., Lau, E., Jostins, L., Plant, K., Andrews, R., McGee, C., et al. (2014) · Science · doi:10.1126/science.1246949 · Cited by 764
Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers
Önengüt-Gümüşcü, S., Chen, W., Burren, O. S., Cooper, N. J., Quinlan, A. R., Mychaleckyj, J. C., Farber, E., Bonnie, J. K., Szpak, M., Schofield, E. C., et al. (2015) · Nature Genetics · doi:10.1038/ng.3245 · Cited by 694
Self-citation
Environmental risk factors for type 1 diabetes
Rewers, M., & Ludvigsson, J. (2016) · The Lancet · doi:10.1016/s0140-6736(16)30507-4 · Cited by 676
Self-citation
Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval
King, E. A., Davis, J. W., & Degner, J. F. (2019) · PLOS Genetics · doi:10.1371/journal.pgen.1008489 · Cited by 649
Pervasive Sharing of Genetic Effects in Autoimmune Disease
Cotsapas, C., Voight, B. F., Rossin, E., Lage, K., Neale, B. M., Wallace, C., Abecasis, G. R., Barrett, J. C., Behrens, T., Cho, J., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002254 · Cited by 606
Self-citation
Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci
Cooper, J. D., Smyth, D. J., Smiles, A. M., Plagnol, V., Walker, N. M., Allen, J. E., Downes, K., Barrett, J. C., Healy, B. C., Mychaleckyj, J. C., et al. (2008) · Nature Genetics · doi:10.1038/ng.249 · Cited by 545
Self-citation
Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles
Fairfax, B. P., Makino, S., Radhakrishnan, J., Plant, K., Leslie, S., Dilthey, A., Ellis, P., Langford, C., Vannberg, F. O., & Knight, J. C. (2012) · Nature Genetics · doi:10.1038/ng.2205 · Cited by 486
Promise and pitfalls of the Immunochip
Cortes, A., & Brown, M. A. (2010) · Arthritis Research & Therapy · doi:10.1186/ar3204 · Cited by 479
Interpreting type 1 diabetes risk with genetics and single-cell epigenomics
Chiou, J., Geusz, R. J., Okino, M.-L., Han, J. Y., Miller, M., Melton, R. L., Beebe, E., Benaglio, P., Huang, S., Korgaonkar, K., et al. (2021) · Nature · doi:10.1038/s41586-021-03552-w · Cited by 447
Long-Range LD Can Confound Genome Scans in Admixed Populations
Price, A. L., Weale, M. E., Patterson, N., Myers, S. R., Need, A. C., Shianna, K. V., Ge, D., Rotter, J. I., Torres, E., Taylor, K. D., et al. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.06.005 · Cited by 443
Trends in Incidence of Type 1 and Type 2 Diabetes Among Youths — Selected Counties and Indian Reservations, United States, 2002–2015
Divers, J., Mayer‐Davis, E. J., Lawrence, J. M., Isom, S. P., Dabelea, D., Dolan, L. M., Imperatore, G., Marcovina, S. M., Pettitt, D. J., Pihoker, C., et al. (2020) · MMWR Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6906a3 · Cited by 372
Self-citation
Development and standardization of an improved type 1 diabetes genetic risk score for use in newborn screening and incident diagnosis
Sharp, S. A., Rich, S. S., Wood, A. R., Jones, S. E., Beaumont, R. N., Harrison, J. W., Schneider, D. A., Locke, J. M., Tyrrell, J., Weedon, M. N., et al. (2019) · Diabetes Care · doi:10.2337/dc18-1785 · Cited by 366
Self-citation
Abundant associations with gene expression complicate GWAS follow-up
Liu, B., Gloudemans, M. J., Rao, A. S., Ingelsson, E., & Montgomery, S. B. (2019) · Nature Genetics · doi:10.1038/s41588-019-0404-0 · Cited by 354
A Genome-Wide Meta-Analysis of Six Type 1 Diabetes Cohorts Identifies Multiple Associated Loci
Bradfield, J. P., Qu, H., Wang, K., Zhang, H., Abou‐Sleiman, P. M., Kim, C. E., Mentch, F. D., Qiu, H., Glessner, J., Thomas, K., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002293 · Cited by 348
Safety and efficacy of tofacitinib for up to 9.5 years in the treatment of rheumatoid arthritis: final results of a global, open-label, long-term extension study
Wollenhaupt, J., Lee, E.-B., Curtis, J. R., Silverfield, J. C., Terry, K. K., Soma, K., Mojcik, C., DeMasi, R., Strengholt, S., Kwok, K. K., et al. (2019) · Arthritis Research & Therapy · doi:10.1186/s13075-019-1866-2 · Cited by 321
BACH2 regulates CD8+ T cell differentiation by controlling access of AP-1 factors to enhancers
Roychoudhuri, R., Clever, D. C., Li, P., Wakabayashi, Y., Quinn, K. M., Klebanoff, C. A., Ji, Y., Sukumar, M., Eil, R. L., Yu, Z., et al. (2016) · Nature Immunology · doi:10.1038/ni.3441 · Cited by 317
Functional IL6R 358Ala Allele Impairs Classical IL-6 Receptor Signaling and Influences Risk of Diverse Inflammatory Diseases
Ferreira, R. C., Freitag, D. F., Cutler, A. J., Howson, J. M. M., Rainbow, D. B., Smyth, D. J., Kaptoge, S., Clarke, P., Boreham, C., Coulson, R. M., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003444 · Cited by 316
Self-citation
Improved peak-calling with MACS2Preprint
Gaspar, J. M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/496521 · Cited by 305
Landscape of stimulation-responsive chromatin across diverse human immune cells
Calderon, D., Nguyen, M. L. T., Mezger, A., Kathiria, A. S., Müller, F., Nguyen, V. Q., Lescano, N., Wu, B., Trombetta, J. J., Ribado, J. V., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0505-9 · Cited by 269
Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types
Chun, S., Casparino, A., Patsopoulos, N. A., Croteau-Chonka, D. C., Raby, B. A., De Jager, P. L., Sunyaev, S. R., & Cotsapas, C. (2017) · Nature Genetics · doi:10.1038/ng.3795 · Cited by 259
Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies
Benner, C., Havulinna, A. S., Järvelin, M., Salomaa, V. V., Ripatti, S., & Pirinen, M. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.08.012 · Cited by 230
A genetics-led approach defines the drug target landscape of 30 immune-related traits
Fang, H., Wolf, H. D., Knezevic, B., Burnham, K. L., Osgood, J. A., Sanniti, A., Lara, A. L., Kasela, S., Cesco, S. D., Wegner, J. K., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0456-1 · Cited by 214
Genetic regulatory signatures underlying islet gene expression and type 2 diabetes
Varshney, A., Scott, L. J., Welch, R., Erdos, M. R., Chines, P. S., Narisu, N., Albanus, R. D., Orchard, P., Wolford, B. N., Kursawe, R., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621192114 · Cited by 198
Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies
Kichaev, G., & Paşaniuc, B. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.06.007 · Cited by 188
Whole-exome sequencing for finding de novo mutations in sporadic mental retardation
Robinson, P. (2010) · Genome Biology · doi:10.1186/gb-2010-11-12-144 · Cited by 180
Evolutionary stochastic search for Bayesian model exploration
Bottolo, L., & Richardson, S. T. (2010) · Bayesian Analysis · doi:10.1214/10-ba523 · Cited by 176
Bach2 maintains T cells in a naive state by suppressing effector memory-related genes
Tsukumo, S.-I., Unno, M., Muto, A., Takeuchi, A., Kometani, K., Kurosaki, T., Igarashi, K., & Saito, T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1306691110 · Cited by 168
Follow-Up Analysis of Genome-Wide Association Data Identifies Novel Loci for Type 1 Diabetes
Grant, S. F. A., Qu, H., Bradfield, J. P., Marchand, L., Kim, C., Glessner, J., Grabs, R., Taback, S. P., Frackelton, E. C., Eckert, A. W., et al. (2008) · Diabetes · doi:10.2337/db08-1022 · Cited by 166
Integrating Case-control and TDT Studies
Kazeem, G., & Farrall, M. (2005) · Annals of Human Genetics · doi:10.1046/j.1529-8817.2005.00156.x · Cited by 159
Risankizumab in patients with moderate to severe Crohn's disease: an open-label extension study
Feagan, B. G., Panés, J., Ferrante, M., Kaser, A., D’Haens, G., Sandborn, W. J., Louis, E. J., Neurath, M. F., Franchimont, D. P., Dewit, O., et al. (2018) · The Lancet. Gastroenterology & hepatology · doi:10.1016/s2468-1253(18)30233-4 · Cited by 158
The impact of proinflammatory cytokines on the β-cell regulatory landscape provides insights into the genetics of type 1 diabetes
Ramos-Rodríguez, M., Raurell‐Vila, H., Colli, M. L., Alvelos, M. I., Subirana-Granés, M., Juan‐Mateu, J., Norris, R., Turatsinze, J., Nakayasu, E. S., Webb‐Robertson, B., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0524-6 · Cited by 158
Self-citation
BACH2 immunodeficiency illustrates an association between super-enhancers and haploinsufficiency
Afzali, B., Grönholm, J., Vandrovcová, J., O'Brien, C., Sun, H., Vanderleyden, I., Davis, F. P., Khoder, A., Zhang, Y., Hegazy, A. N., et al. (2017) · Nature Immunology · doi:10.1038/ni.3753 · Cited by 154
A Novel Susceptibility Locus for Type 1 Diabetes on Chr12q13 Identified by a Genome-Wide Association Study
Hakonarson, H. H., Qu, H., Bradfield, J. P., Marchand, L., Kim, C., Glessner, J., Grabs, R., Casalunovo, T., Taback, S. P., Frackelton, E. C., et al. (2008) · Diabetes · doi:10.2337/db07-1305 · Cited by 153
Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes
Westra, H.-J., Martínez‐Bonet, M., Önengüt-Gümüşcü, S., Lee, A., Luo, Y., Teslovich, N. C., Worthington, J., Martin, J. J., Huizinga, T. W. J., Klareskog, L., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0216-7 · Cited by 147
Self-citation
Genetic predisposition for beta cell fragility underlies type 1 and type 2 diabetes
Dooley, J., Tian, L., Schönefeldt, S., Delghingaro‐Augusto, V., Garcia‐Perez, J. E., Pasciuto, E., Marino, D. D., Carr, E. J., Oskolkov, N., Lyssenko, V., et al. (2016) · Nature Genetics · doi:10.1038/ng.3531 · Cited by 145
1000 Genomes-based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data
Huang, J., Ellinghaus, D., Franke, A., Howie, B., & Li, Y. (2012) · European Journal of Human Genetics · doi:10.1038/ejhg.2012.3 · Cited by 134
Correlation between endothelial function and carotid atherosclerosis in rheumatoid arthritis patients with long-standing disease
González‐Juanatey, C., Llorca, J., & González-Gay, M. Á. (2011) · Arthritis Research & Therapy · doi:10.1186/ar3382 · Cited by 133
Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls
Fortune, M. D., Guo, H., Burren, O. S., Schofield, E. C., Walker, N. M., Ban, M., Sawcer, S. J., Bowes, J., Worthington, J., Barton, A. C., et al. (2015) · Nature Genetics · doi:10.1038/ng.3330 · Cited by 130
Self-citation
Type 1 Diabetes Risk in African-Ancestry Participants and Utility of an Ancestry-Specific Genetic Risk Score
Önengüt-Gümüşcü, S., Chen, W., Robertson, C. C., Bonnie, J. K., Farber, E., Zhu, Z., Oksenberg, J. R., Brant, S. R., Bridges, S. L., Edberg, J. C., et al. (2019) · Diabetes Care · doi:10.2337/dc18-1727 · Cited by 130
Self-citation
Probabilistic colocalization of genetic variants from complex and molecular traits: promise and limitations
Hukku, A., Pividori, M., Luca, F., Piqué-Regi, R., Im, H. K., & Wen, X. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.11.012 · Cited by 129
Predicting Islet Cell Autoimmunity and Type 1 Diabetes: An 8-Year TEDDY Study Progress Report
Krischer, J. P., Liu, X., Vehik, K., Akolkar, B., Hagopian, W., Rewers, M. J., She, J., Toppari, J., Ziegler, A., Lernmark, Å., et al. (2019) · Diabetes Care · doi:10.2337/dc18-2282 · Cited by 122
Self-citation
Pathogenic implications for autoimmune mechanisms derived by comparative eQTL analysis of CD4+ versus CD8+ T cells
Kasela, S., Kisand, K., Tserel, L., Kaleviste, E., Remm, A., Fischer, K., Esko, T., Westra, H.-J., Fairfax, B. P., Makino, S., et al. (2017) · PLOS Genetics · doi:10.1371/journal.pgen.1006643 · Cited by 113
Disentangling the Effects of Colocalizing Genomic Annotations to Functionally Prioritize Non-coding Variants within Complex-Trait Loci
Trynka, G., Westra, H.-J., Slowikowski, K., Hu, X., Xu, H., Stranger, B. E., Klein, R. J., Han, B., & Raychaudhuri, S. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.05.016 · Cited by 109
Identification of Novel T1D Risk Loci and Their Association With Age and Islet Function at Diagnosis in Autoantibody-Positive T1D Individuals: Based on a Two-Stage Genome-Wide Association Study
Zhu, M., Xu, K., Chen, Y., Gu, Y., Zhang, M., Luo, F., Liu, Y., Gu, W., Hu, J., Xu, H., et al. (2019) · Diabetes Care · doi:10.2337/dc18-2023 · Cited by 100
Targeting IL-23 in psoriasis: current perspectives
Fotiadou, C., Lazaridou, E., Sotiriou, E., & Ioannides, D. (2018) · Psoriasis Targets and Therapy · doi:10.2147/ptt.s98893 · Cited by 89
Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Burren, O. S., Rubio García, A., Javierre, B.-M., Rainbow, D. B., Cairns, J., Cooper, N. J., Lambourne, J. J., Schofield, E., Castro Dopico, X., Ferreira, R. C., et al. (2017) · Genome Biology · doi:10.1186/s13059-017-1285-0 · Cited by 80
Self-citation
PEPATAC: an optimized pipeline for ATAC-seq data analysis with serial alignments
Smith, J. P., Corces, M. R., Xu, J., Reuter, V. P., Chang, H. Y., & Sheffield, N. C. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab101 · Cited by 75
Dynamic evolution of regulatory element ensembles in primate CD4+ T cells
Danko, C. G., Choate, L. A., Marks, B. A., Rice, E. J., Wang, Z., Chu, T., Martins, A. L., Dukler, N., Coonrod, S. A., Wojno, E. D. T., et al. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-017-0447-5 · Cited by 75
Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for 12 immune-mediated diseases
Kundu, K., Tardáguila, M., Mann, A., Watt, S., Ponstingl, H., Vasquez, L. J., Schiller, D. V., Morrell, N. W., Stegle, O., Pastinen, T., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01025-y · Cited by 66
A Method for Gene‐Based Pathway Analysis Using Genomewide Association Study Summary Statistics Reveals Nine New Type 1 Diabetes Associations
Evangelou, M., Smyth, D. J., Fortune, M. D., Burren, O. S., Walker, N. M., Guo, H., Önengüt-Gümüşcü, S., Chen, W., CONCANNON, P. J., Rich, S. S., et al. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21853 · Cited by 63
Self-citation
MBV : a method to solve sample mislabeling and detect technical bias in large combined genotype and sequencing assay datasets
Fort, A., Panousis, N. I., Garieri, M., Antonarakis, S. E., Lappalainen, T., Dermitzakis, E. T., & Delaneau, O. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx074 · Cited by 60
Hope vs hype: where are we in type 1 diabetes?
Skyler, J. S. (2018) · Diabetologia · doi:10.1007/s00125-017-4530-x · Cited by 51
Shared genetic risk contributes to type 1 and type 2 diabetes etiology
Aylward, A., Chiou, J., Okino, M.-L., Kadakia, N., & Gaulton, K. J. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy314 · Cited by 50
Mapping genetic effects on cell type-specific chromatin accessibility and annotating complex immune trait variants using single nucleus ATAC-seq in peripheral blood
Benaglio, P., Newsome, J. M., Han, J. Y., Chiou, J., Aylward, A., Corban, S., Miller, M., Okino, M.-L., Kaur, J., Preißl, S., et al. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010759 · Cited by 50
Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping
Wallace, C., Cutler, A. J., Pontikos, N., Pękalski, M. Ł., Burren, O. S., Cooper, J. D., García, A. R., Ferreira, R. C., Guo, H., Walker, N. M., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005272 · Cited by 50
Self-citation
A Transcriptomic and Epigenomic Comparison of Fetal and Adult Human Cardiac Fibroblasts Reveals Novel Key Transcription Factors in Adult Cardiac Fibroblasts
Jonsson, M. K. B., Hartman, R. J. G., Ackers-Johnson, M. A., Tan, W. L. W., Lim, B., van Veen, T. A. B., & Foo, R. (2016) · JACC Basic to Translational Science · doi:10.1016/j.jacbts.2016.07.007 · Cited by 47
Anti-CD3 Antibody for the Prevention of Type 1 Diabetes: A Story of Perseverance
Gaglia, J. L., & Kissler, S. (2019) · Biochemistry · doi:10.1021/acs.biochem.9b00707 · Cited by 47
The chromosome 6q22.33 region is associated with age at diagnosis of type 1 diabetes and disease risk in those diagnosed under 5 years of age
Inshaw, J. R. J., Walker, N. M., Wallace, C., Bottolo, L., & Todd, J. A. (2017) · Diabetologia · doi:10.1007/s00125-017-4440-y · Cited by 41
Self-citation
The Role of ZFYVE27/Protrudin in Hereditary Spastic Paraplegia
Martignoni, M., Riano, E., & Rugarli, E. I. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.05.014 · Cited by 36
Genome-wide analysis of the genetic regulation of gene expression in human neutrophils
Andiappan, A. K., Melchiotti, R., Poh, T. Y., Nah, M., Puan, K. J., Viganò, E., Haase, D., Yusof, N., Luis, B. S., Lum, J., et al. (2015) · Nature Communications · doi:10.1038/ncomms8971 · Cited by 28
Stochastic search and joint fine-mapping increases accuracy and identifies previously unreported associations in immune-mediated diseases
Asimit, J. L., Rainbow, D. B., Fortune, M. D., Grinberg, N. F., Wicker, L. S., & Wallace, C. (2019) · Nature Communications · doi:10.1038/s41467-019-11271-0 · Cited by 25
Self-citation
Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for twelve immune-mediated diseasesPreprint
Kundu, K., Mann, A., Tardáguila, M., Watt, S. B., Ponstingl, H., Vasquez, L. J., Morrell, N. W., Stegle, O., Pastinen, T., Sawcer, S. J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.15.907436 · Cited by 17
PEPATAC: An optimized pipeline for ATAC-seq data analysis with serial alignmentsPreprint
Smith, J. P., Corces, M. R., Xu, J., Reuter, V. P., Chang, H. Y., & Sheffield, N. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.21.347054 · Cited by 16
Incorporating Genotype Uncertainties Into the Genotypic TDT for Main Effects and Gene‐Environment Interactions
Taub, M. A., Schwender, H. R., Beaty, T. H., Louis, T. A., & Ruczinski, I. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21615 · Cited by 13
Enhanced genetic analysis of type 1 diabetes by selecting variants on both effect size and significance, and by integration with autoimmune thyroid diseasePreprint
Crouch, D. J. M., Inshaw, J., Robertson, C. C., Zhang, J.-Y., Chen, W.-M., Onengut-Gumuscu, S., Cutler, A. J., Sidore, C., Cucca, F., Pociot, F., et al. (2021) · bioRxiv · doi:10.1101/2021.02.05.429962 · Cited by 13
Self-citation
Mapping genetic effects on cell type-specific chromatin accessibility and annotating complex trait variants using single nucleus ATAC-seqPreprint
Benaglio, P., Newsome, J. M., Han, J. Y., Chiou, J., Aylward, A., Corban, S., Okino, M.-L., Kaur, J., Gorkin, D. U., & Gaulton, K. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.03.387894 · Cited by 7
Large-scale genetic association and single cell accessible chromatin mapping defines cell type-specific mechanisms of type 1 diabetes risk
Chiou, J., Geusz, R. J., Okino, M.-L., et al. (2021) · bioRxiv · doi:10.1101/2021.01.13.426472 · Cited by 6
An anti-CD3 antibody, teplizumab, in relatives at risk for type 1 diabetes
Herold, K., Bundy, B. N., Long, S. A., Bluestone, J. A., DiMeglio, L. A., Dufort, M. J., Gitelman, S., Gottlieb, P. A., Jp, K., Linsley, P. S., et al. (2020) · Yearbook of pediatric endocrinology · doi:10.1530/ey.17.10.8 · Cited by 5

Sorted by citations

Pathogenesis of autoimmune disease
Pisetsky, D. S. (2023) · Nature Reviews Nephrology · doi:10.1038/s41581-023-00720-1 · Cited by 715
Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus
Sacks, D. B., Arnold, M., Bakris, G. L., Bruns, D. E., Horvath, A. R., Lernmark, Å., Metzger, B. E., Nathan, D. M., & Kirkman, M. S. (2023) · Diabetes Care · doi:10.2337/dci23-0036 · Cited by 351
Synergistic insights into human health from aptamer- and antibody-based proteomic profiling
Pietzner, M., Wheeler, E., Carrasco-Zanini, J., Kerrison, N. D., Oerton, E., Koprulu, M., Luan, J., Hingorani, A. D., Williams, S. A., Wareham, N. J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27164-0 · Cited by 234
Type 1 diabetes
Quattrin, T., Mastrandrea, L. D., & Walker, L. S. K. (2023) · The Lancet · doi:10.1016/s0140-6736(23)00223-4 · Cited by 292
The immunology of type 1 diabetes
Herold, K. C., Delong, T., Perdigoto, A. L., Biru, N., Brusko, T. M., & Walker, L. S. K. (2024) · Nature reviews. Immunology · doi:10.1038/s41577-023-00985-4 · Cited by 241

See all 302

Authors: Robertson, C. C., Inshaw, J. R. J., Onengut-Gumuscu, S., Chen, W.-M., Santa Cruz, D. F., Yang, H., Cutler, A. J., Crouch, D. J. M., Farber, E., Bridges, S. L., Edberg, J. C., Kimberly, R. P., Buckner, J. H., Deloukas, P., Divers, J., Dabelea, D., Lawrence, J. M., Marcovina, S., Shah, A. S., Greenbaum, C. J., Atkinson, M. A., Gregersen, P. K., Oksenberg, J. R., Pociot, F., Rewers, M. J., Steck, A. K., Dunger, D. B., Type 1 Diabetes Genetics Consortium, Wicker, L. S., Concannon, P., Todd, J. A., & Rich, S. S.
Affiliations: University of Virginia (US)
Centre for Human Genetics (GB)
Hospital for Special Surgery (US)
University of Alabama at Birmingham (US)
Benaroya Research Institute (US)
Queen Mary University of London (GB)
NYU Grossman Long Island School of Medicine (US)
Colorado School of Public Health (US)
Kaiser Permanente (US)
Medpace (United States) (US)
Cincinnati Children's Hospital Medical Center (US)
University of Florida (US)
Northwell Health (US)
University of California, San Francisco (US)
University of Copenhagen (DK)
University of Colorado Anschutz (US)
University of Cambridge (GB)
Corresponding author: Todd, John A.
ORCID: Robertson, Catherine C.
Inshaw, Jamie R. J.
Onengut-Gumuscu, Suna
Chen, Wei-Min
Yang, Hanzhi
Cutler, Antony J.
Crouch, Daniel J. M.
Farber, Emily
Bridges, S. Louis
Kimberly, Robert P.
Buckner, Jane H.
Deloukas, Panos
Divers, Jasmin
Dabelea, Dana
Lawrence, Jean M.
Marcovina, Santica
Shah, Amy S.
Greenbaum, Carla J.
Atkinson, Mark A.
Gregersen, Peter K.
Oksenberg, Jorge R.
Pociot, Flemming
Rewers, Marian J.
Steck, Andrea K.
Dunger, David B.
Wicker, Linda S.
Concannon, Patrick
Todd, John A.
Rich, Stephen S.
Venue: Nature Genetics
Publisher: Springer Science and Business Media LLC
Published: 2021-06-14
Volume: 53
Issue: 7
Pages: 962-971
ISSN: 1061-4036 (ISSN-L)
1546-1718
DOI: 10.1038/s41588-021-00880-5
PMC: PMC8273124
OpenAlex: W3171115266
Semantic Scholar: e41e3ba0695f1da1b7c897ccfd64b223b9963243
Cited by: 302
References: 107
Citations by year: 2026: 42 · 2025: 63 · 2024: 66 · 2023: 65 · 2022: 39 · 2021: 26
FWCI: 27.56
Type: journal article
Language: en
Open access: green
doi.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Genetics (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
Funders: Juvenile Diabetes Research Foundation (4-SRA-2017-473-A-A)
Wellcome Trust (107212/A/15/Z)
U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (5T32LM012416)
University of Virginia
U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (N01-AR-0-2247, N01 AR-6-2278)
U.S. Department of Health & Human Services | National Institutes of Health (UL1-TR001417)
U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (U48/CCU919219, U01 DP000250, 107212/A/15/Z, U01 DP000247, U18DP000247-06A1, U01 DP000246, U58/CCU019235-4, U01 DP000244, U18DP002710-01, U48/CCU519239, U01 DP000248, 1U18DP002709, U01 DK103282, U01 DK127404, R01 DK032493, P30 DK116073, DK-115694, DP3-111906, U01 DK-062418)
U.S. Department of Health & Human Services | Centers for Disease Control and Prevention (200-2010-35171)
U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (P01AI042288)
National Multiple Sclerosis Society (SI-2001-35701)
U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (UL1 TR000154)
JDRF
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords: type 1 diabetes, genetic risk variants, fine mapping, chromatin accessibility, CD4+ T cells, BACH2, enhancer accessibility, causal variants, therapeutic target, immune cells
MeSH terms: Alleles, Chromosome Mapping, Diabetes Mellitus, Type 1, Humans, Genetic Variation, CD4-Positive T-Lymphocytes, Autoimmunity, Gene Expression, Genetic Predisposition to Disease, Genomics, Protein Interaction Mapping, Drug Discovery, Molecular Targeted Therapy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks