Abstract
Genome-wide association studies (GWAS) have uncovered pervasive genetic overlap between common clinically related immune-mediated diseases (IMD). To distinguish axes of IMD risk, and extend genetic knowledge of rare IMDs and subtypes, we developed a Bayesian shrinkage approach to perform a disease-focused decomposition of IMD GWAS summary statistics. We derive 13 components which summarise the multidimensional patterns of IMD genetic risk including those related to raised eosinophil count and serum IP-10. Projection of UK Biobank data demonstrated the IMD-specificity and accuracy of our reduced dimension basis in independent datasets. By projecting 22 rare IMD or IMD subtypes onto the basis we were able to identify disease-discriminating components and suggest novel associations. Requiring only summary level data, our approach allows the genetic architectures across any range of clinically-related traits to be characterised in fewer dimensions, facilitating the analysis of studies with modest sample size, where classical GWAS approaches are challenging.
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