A0292
Title: Novel fine-mapping method using a continuous global-local shrinkage prior
Authors: Yan Zhang - The University of Hong Kong (Hong Kong) [presenting]
Abstract: Fine mapping plays a crucial role in identifying genetic variants underlying complex traits and diseases. Traditional methods face limitations with discrete priors and fixed assumptions on causal variants. A novel Bayesian fine-mapping approach is introduced, h2-D2, utilizing a continuous global-local shrinkage prior to enhance precision. The method also addresses the challenge of defining credible sets of causal variants with continuous priors. Additionally, the benefits of leveraging diverse ancestry data are explored to improve fine-mapping accuracy by extending the h2-D2 method to a multi-ancestry setting. Real-world examples are discussed, illustrating the discovery of previously unknown causal variants and elucidating genetic architectures across diverse populations.
