Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-07

AUTHORS

Mary D Fortune, Hui Guo, Oliver Burren, Ellen Schofield, Neil M Walker, Maria Ban, Stephen J Sawcer, John Bowes, Jane Worthington, Anne Barton, Steve Eyre, John A Todd, Chris Wallace

ABSTRACT

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. More... »

PAGES

839-846

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ng.3330

    DOI

    http://dx.doi.org/10.1038/ng.3330

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1050898748

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26053495


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