Fortune Mary D, Guo Hui, Burren Oliver, Schofield Ellen, Walker Neil M, Ban Maria, Sawcer Stephen J, Bowes John, Worthington Jane, Barton Anne, Eyre Steve, Todd John A, Wallace Chris
JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.
1] JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. [2] Centre for Biostatistics, Institute of Population Health, University of Manchester, Manchester, UK.
Nat Genet. 2015 Jul;47(7):839-46. doi: 10.1038/ng.3330. Epub 2015 Jun 8.
Determining whether potential causal variants for related diseases are shared can identify overlapping etiologies of multifactorial disorders. Colocalization methods disentangle shared and distinct causal variants. However, existing approaches require independent data sets. Here we extend two colocalization methods to allow for the shared-control design commonly used in comparison of genome-wide association study results across diseases. Our analysis of four autoimmune diseases--type 1 diabetes (T1D), rheumatoid arthritis, celiac disease and multiple sclerosis--identified 90 regions that were associated with at least one disease, 33 (37%) of which were associated with 2 or more disorders. Nevertheless, for 14 of these 33 shared regions, there was evidence that the causal variants differed. We identified new disease associations in 11 regions previously associated with one or more of the other 3 disorders. Four of eight T1D-specific regions contained known type 2 diabetes (T2D) candidate genes (COBL, GLIS3, RNLS and BCAR1), suggesting a shared cellular etiology.
确定相关疾病的潜在因果变异是否共享，可以识别多因素疾病的重叠病因。共定位方法能够区分共享和不同的因果变异。然而，现有方法需要独立的数据集。在此，我们扩展了两种共定位方法，以允许在跨疾病的全基因组关联研究结果比较中常用的共享对照设计。我们对四种自身免疫性疾病——1型糖尿病（T1D）、类风湿性关节炎、乳糜泻和多发性硬化症——的分析确定了90个与至少一种疾病相关的区域，其中33个（37%）与两种或更多疾病相关。然而，在这33个共享区域中的14个区域，有证据表明因果变异不同。我们在之前与其他3种疾病中的一种或多种相关的11个区域中发现了新的疾病关联。8个T1D特异性区域中的4个包含已知的2型糖尿病（T2D）候选基因（COBL、GLIS3、RNLS和BCAR1），这表明存在共享的细胞病因。
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