Genetic fine-mapping from summary data using a non-local prior improves detection of multiple causal variants

Abstract Genome-wide association studies (GWAS) have been successful in identifying genomic loci associated with complex traits. Genetic fine-mapping aims to detect independent causal variants from the GWAS-identified loci, adjusting for linkage disequilibrium (LD) patterns. The use of GWAS summary statistics and an adequate LD reference enable large sample sizes for fine-mapping, without direct access to individual-level data. We present FiniMOM (fine-mapping using a product inverse-moment priors), a novel Bayesian fine-mapping method for summarized genetic associations. For causal effects, the method uses a non-local inverse-moment prior, which is a natural prior distribution to model non-null effects in finite samples. A beta-binomial prior is set for the number of causal variants, with a parameterization that can be used to control for potential misspecifications in the LD reference. We test the performance of our method against a current state-of-the-art fine-mapping method SuSiE (sum-of-single-effects) across a range of simulated scenarios aimed to mimic a typical GWAS on circulating protein levels, and an applied example. The results show improved credible set coverage and power of the proposed method, especially in the case of multiple causal variants within a locus. The superior performance and the flexible parameterization to control for misspecified LD reference make FiniMOM a competitive alternative to other fine-mapping methods for summarized genetic data.