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

By Suna Onengut-Gumuscu, Wei-Min Chen, O. Burren, Nicholas J. Cooper, Aaron R. Quinlan, J. Mychaleckyj, Emily Farber, J. Bonnie, M. Szpak, E. Schofield and 16 more. Nature Genetics, 2015

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Genetic studies of type 1 diabetes (T1D) have identified 50 susceptibility regions, finding major pathways contributing to risk, with some loci shared across immune disorders. 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.

Subfields: Genetics, Immunology

View Paper: https://www.semanticscholar.org/paper/1ca26f917163d897beeef4b329d46b9b63311ad2

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Citations in the corpus

Fine-mapping inflammatory bowel disease loci to single variant resolution (2017, background): “Truly high-resolution mapping data, when combined with increasingly sophisticated and comprehensive public databases annotating the putative regulatory function of DNA variants, are likely to reveal novel insights into disease pathogenesis7–9 and the mechanisms of disease-associated variants.”
Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells (2016, background): “…et al., 2015], including Crohn’s disease [CD] and ulcerative colitis [UC], multiple sclerosis [MS] [Beecham et al., 2013], type 1 diabetes [T1D] [Onengut-Gumuscu et al., 2015], and rheumatoid arthritis [RA] [Okada et al., 2014]), for which we retrieved publicly available genome-wide summary…”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, background): “However, since the SNP with the highest association signal is not necessarily the causal variant of that locus (Onengut-Gumuscu et al., 2015), geneticists often link an association signal to the locus they belong to rather than the SNP itself (Brzyski et al.”
The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog) (2016, methodology): “…of such work include the analysis of Catalog data for identification of other traits associated with type 1 diabetes loci by Onengut-Gumuscu et al. (8); for evidence of pleiotropy by Sivakumaran et al. (9); for loci associated with seven common diseases to identify possible causal missense…”
Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis (2019, background): “Improved capture of non-HLA genetic risk We then identified 32 non-HLA loci (summarized in (28)) which again each associated independently with T1D”