Abstract
Identifying whether potential causal variants for related diseases are shared can increase understanding of the shared etiology between diseases. Colocalization methods are designed to disentangle shared and distinct causal variants in regions where two diseases show association, but existing methods are limited by assuming independent datasets. We extended existing methods to allow for the shared control design common in GWAS and applied them to four autoimmune diseases: type 1 diabetes (T1D); rheumatoid arthritis; celiac disease (CEL) and multiple sclerosis (MS). Ninety regions associated with at least one disease. In 22 regions (24%), we identify association to precisely one of our four diseases and can find no published association of any other disease to the same region; some of these may reflect effects mediated by the target of immune attack. Thirty-three regions (37%) were associated with two or more, but in 14 of these there was evidence that causal variants differed between diseases. By leveraging information across datasets, we identified novel disease associations to 12 regions previously associated with one or more of the other three autoimmune disorders. For instance, we link the CEL-associated FASLG region to T1D and identify a single SNP, rs78037977, as a likely causal variant. We also highlight several particularly complex association patterns, including the CD28-CTLA4-ICOS region, in which it appears that three distinct causal variants associate with three diseases in three different patterns. Our results underscore the complexity in genetic variation underlying related but distinct autoimmune diseases and help to approach its dissection.
