Genetic Fine-mapping from Summary Data Using a Nonlocal Prior Improves the Detection of Multiple Causal Variants

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2023 Jun 22

PMID 37348543

Authors

Ville Karhunen

Ilkka Launonen

Marjo-Riitta Jarvelin

Sylvain Sebert

Mikko J Sillanpaa

Affiliations

Soon will be listed here.

Abstract

Motivation: 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.

Results: 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.

Availability And Implementation: https://vkarhune.github.io/finimom/.

Citing Articles

The goldmine of GWAS summary statistics: a systematic review of methods and tools.

Kontou P, Bagos P BioData Min. 2024; 17(1):31.

PMID: 39238044 PMC: 11375927. DOI: 10.1186/s13040-024-00385-x.

Finemap-MiXeR: A variational Bayesian approach for genetic finemapping.

Akdeniz B, Frei O, Shadrin A, Vetrov D, Kropotov D, Hovig E PLoS Genet. 2024; 20(8):e1011372.

PMID: 39146375 PMC: 11349196. DOI: 10.1371/journal.pgen.1011372.

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