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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.

Original publication

DOI

10.1038/ng.3330

Type

Journal article

Journal

Nat Genet

Publication Date

07/2015

Volume

47

Pages

839 - 846

Keywords

Arthritis, Rheumatoid, Bayes Theorem, Case-Control Studies, Celiac Disease, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Models, Genetic, Multiple Sclerosis, Polymorphism, Single Nucleotide, Risk