BTS: scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across omics data.

ABSTRACT:

Motivation

statistics from genome-wide association studies (GWAS) are widely used in fine-mapping and colocalization analyses to identify causal variants and their enrichment in functional contexts, such as affected cell types and genomic features. With the expansion of functional genomic (FG) datasets, which now include hundreds of thousands of tracks across various cell and tissue types, it is critical to establish scalable algorithms integrating thousands of diverse FG annotations with GWAS results.

Results

We propose BTS (Bayesian Tissue Score), a novel, highly efficient algorithm uniquely designed for 1) identifying affected cell types and functional elements (context-mapping) and 2) fine-mapping potentially causal variants in a context-specific manner using large colle

SUBMITTER: Kuksa PP 

PROVIDER: S-EPMC11838512 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature