Identifying 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 datasets. 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, revealed 90 regions that were associated with at least one disease, 33 (37%) of which with two or more disorders. Nevertheless, for 14 of these 33 shared regions there was evidence that causal variants differed. We identified novel disease associations in 11 regions previously associated with one or more of the other three disorders. Four of eight T1D-specific regions contained known type 2 diabetes candidate genes: COBL, GLIS3, RNLS and BCAR1, suggesting a shared cellular etiology.
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