Analysis of overlapping genetic association in type 1 and type 2 diabetes
Tóm tắt
Given the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal co-localises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently.
Using publicly available type 2 diabetes summary statistics from a genome-wide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate <0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined co-localisation of each type 1 diabetes signal with each type 2 diabetes signal using
Of the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals co-localised between both diseases (posterior probability ≥0.9): (1) chromosome 16q23.1, near
Four of five association signals that co-localise between type 1 diabetes and type 2 diabetes are in opposite directions, suggesting a complex genetic relationship between the two diseases.
Từ khóa
Tài liệu tham khảo
Onengut-Gumuscu S, Chen W-M, Burren O et al (2015) Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat Genet 47:381–386
Mahajan A, Taliun D, Thurner M et al (2018) Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet 50:1505–1513
Aylward A, Chiou J, Okino M-L, Kadakia N, Gaulton KJ (2018) Shared genetic risk contributes to type 1 and type 2 diabetes etiology. Hum Mol Genet https://doi.org/10.1093/hmg/ddy314
McCarthy S, Das S, Kretzschmar W et al (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 48:1279–1283
Guo H, Fortune MD, Burren OS, Schofield E, Todd JA, Wallace C (2015) Integration of disease association and eQTL data using a Bayesian colocalisation approach highlights six candidate causal genes in immune-mediated diseases. Hum Mol Genet 24:3305–3313
Wallace C (2020) Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLoS Genet 16:e1008720. https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008720
Hormozdiari F, van de Bunt M, Segre AV et al (2016) Colocalization of GWAS and eQTL Signals Detects Target Genes. Am J Hum Genet 99:1245–1260
Vujkovic M, Keaton JM, Lynch JA et al (2019) Discovery of 318 novel loci for type-2 diabetes and related micro- and macrovascular outcomes among 1.4 million participants in a multi-ethnic meta-analysis. MedRxiv. https://doi.org/10.1101/19012690
Spracklen CN, Horikoshi M, Kim YJ et al (2020) Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature 582:240–245. https://doi.org/10.1038/s41586-020-2263-3
Dooley J, Tian L, Schonefeldt S et al (2016) Genetic predisposition for beta cell fragility underlies type 1 and type 2 diabetes. Nat Genet 48:519–527