ArticleAmerican journal of human genetics2024
A Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis.
Article in American journal of human genetics, 2024. 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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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- Genome-wide fine-mapping improves identification of causal variants.Nature genetics · 2026Article
- Towards improved fine-mapping of candidate causal variants.Nature reviews. Genetics · 2025Review
- Genome-wide fine-mapping improves identification of causal variants.medRxiv : the preprint server for health sciences · 2025Article
- MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies.Genome biology · 2025Article
- Fine-Mapping the Results From Genome-Wide Association Studies of Primary Biliary Cholangitis Using SuSiE and h2-D2.Genetic epidemiology · 2025Article
- TWAS-GKF: a novel method for causal gene identification in transcriptome-wide association studies with knockoff inference.Bioinformatics (Oxford, England) · 2024Article
- XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias.Nature communications · 2023Article
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
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Abstract
The aim of fine mapping is to identify genetic variants causally contributing to complex traits or diseases. Existing fine-mapping methods employ Bayesian discrete mixture priors and depend on a pre-specified maximum number of causal variants, which may lead to sub-optimal solutions. In this work, we propose a Bayesian fine-mapping method called h2-D2, utilizing a continuous global-local shrinkage prior. We also present an approach to define credible sets of causal variants in continuous prior settings. Simulation studies demonstrate that h2-D2 outperforms current state-of-the-art fine-mapping methods such as SuSiE and FINEMAP in accurately identifying causal variants and estimating their effect sizes. We further applied h2-D2 to prostate cancer analysis and discovered some previously unknown causal variants. In addition, we inferred 369 target genes associated with the detected causal variants and several pathways that were significantly over-represented by these genes, shedding light on their potential roles in prostate cancer development and progression.
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