Sharing information between related diseases using Bayesian joint fine mapping increases accuracy and identifies novel associations in six immune mediated diseases

Abstract

AbstractThousands of genetic variants have been associated with human disease risk, but linkage disequilibrium (LD) hinders fine-mapping the causal variants. We show that stepwise regression, and, to a lesser extent, stochastic search fine mapping can mis-identify as causal, SNPs which jointly tag distinct causal variants. Frequent sharing of causal variants between immune-mediated diseases (IMD) motivated us to develop a computationally efficient multinomial fine-mapping (MFM) approach that borrows information between diseases in a Bayesian framework. We show that MFM has greater accuracy than single disease analysis when shared causal variants exist, and negligible loss of precision otherwise. Applying MFM to data from six IMD revealed causal variants undetected in individual disease analysis, including in IL2RA where we confirm functional effects of multiple causal variants using allele-specific expression in sorted CD4+ T cells from genotype-selected individuals. MFM has the potential to increase fine-mapping resolution in related diseases enabling the identification of associated cellular and molecular phenotypes.