A Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis

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 suboptimal 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.