Description:
<jats:title>Abstract</jats:title><jats:sec><jats:title>Motivation</jats:title><jats:p>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 patterns.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We present “FiniMOM” (fine-mapping using a product inverse-moment prior), a novel Bayesian fine-mapping method for summarized genetic associations. For causal effects, the method uses a nonlocal 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 linkage disequilibrium reference. The results of simulations studies aimed to mimic a typical GWAS on circulating protein levels show improved credible set coverage and power of the proposed method over current state-of-the-art fine-mapping method SuSiE, especially in the case of multiple causal variants within a locus.</jats:p></jats:sec><jats:sec><jats:title>Availability and implementation</jats:title><jats:p>https://vkarhune.github.io/finimom/.</jats:p></jats:sec>