Stochastic search and joint fine-mapping increases accuracy and identifies previously unreported associations in immune-mediated diseases.
Published version
Peer-reviewed
Repository URI
Repository DOI
Type
Change log
Authors
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.
Description
Keywords
Journal Title
Conference Name
Journal ISSN
2041-1723
Volume Title
Publisher
Publisher DOI
Rights
Sponsorship
MRC (1185)
Medical Research Council (MR/R021368/1)
Wellcome Trust (107212/Z/15/Z)
Juvenile Diabetes Research Foundation Ltd (JDRF) (5-SRA-2015-130-A-N)
Wellcome Trust (099772/Z/12/Z)
Wellcome Trust (096388/Z/11/Z)
National Institute of Diabetes and Digestive and Kidney Diseases (U01DK062418)
Medical Research Council (MC_UU_00002/4)
Wellcome Trust (076113/C/04/Z)
CCF (None)