ArticleBioinformatics (Oxford, England)2023
Genetic fine-mapping from summary data using a nonlocal prior improves the detection of multiple causal variants.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 6 citations in OpenAlex.
- Comparative fine-mapping of breast cancer susceptibility loci using summary statistics methods and multinomial regression.medRxiv : the preprint server for health sciences · 2026Article
- Integrating genetic data with biological insight: A practical guide to cis-Mendelian randomization.American journal of human genetics · 2026Review
- Towards improved fine-mapping of candidate causal variants.Nature reviews. Genetics · 2025Review
- Application of Human Genetics to Prioritize Coagulation Cascade Protein Targets for Ischemic Stroke Prevention.Stroke · 2025Article
- Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits.Cell genomics · 2025Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Finemap-MiXeR: A variational Bayesian approach for genetic finemapping.PLoS genetics · 2024Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationGenome-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.
resultsWe 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/.
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