Multivariate Bayesian variable selection for multi-trait genetic fine mapping.

ABSTRACT: Genome-wide association studies (GWAS) have identified thousands of single-nucleotide polymorphisms (SNPs) associated with complex traits, but determining the underlying causal variants remains challenging. Fine mapping aims to pinpoint the potentially causal variants from a large number of correlated SNPs possibly with group structure in GWAS-enriched genomic regions using variable selection approaches. In multi-trait fine mapping, we are interested in identifying the causal variants for multiple related traits. Existing multivariate variable selection methods for fine mapping select variables for all responses without considering the possible heterogeneity across different responses. Here, we develop a novel multivariate Bayesian variable selection method for multi-trait fine mapping to

SUBMITTER: Canida T 

PROVIDER: S-EPMC11905884 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature