Stochastic search and joint fine-mapping increases accuracy and identifies previously unreported associations in immune-mediated diseases

Jennifer L. Asimit (), Daniel B. Rainbow, Mary D. Fortune, Nastasiya F. Grinberg, Linda S. Wicker and Chris Wallace ()
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Jennifer L. Asimit: Cambridge Biomedical Campus
Daniel B. Rainbow: University of Oxford
Mary D. Fortune: Cambridge Biomedical Campus
Nastasiya F. Grinberg: University of Cambridge
Linda S. Wicker: University of Oxford
Chris Wallace: Cambridge Biomedical Campus

Nature Communications, 2019, vol. 10, issue 1, 1-15

Abstract: Abstract Thousands of genetic variants are associated with human disease risk, but linkage disequilibrium (LD) hinders fine-mapping the causal variants. Both lack of power, and joint tagging of two or more distinct causal variants by a single non-causal SNP, lead to inaccuracies in fine-mapping, with stochastic search more robust than stepwise. We 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. MFM analysis of six immune-mediated diseases reveals 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.

Date: 2019
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DOI: 10.1038/s41467-019-11271-0

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