BTS

BTS leverages annotation-specific Bayesian models to identify affected cell types and functional elements and perform context-specific fine-mapping of GWAS variants across large collections of functional genomic annotation tracks.

Key Features:

• Context-mapping: Identifies affected cell types and functional elements using GWAS summary statistics and cell type-specific functional genomic annotation tracks.
• Context-specific fine-mapping: Prioritizes potentially causal variants in a context-specific manner by integrating thousands of diverse functional genomic annotations with GWAS results.
• Scalable Bayesian computation: Estimates functional annotation effects and performs context-specific variant fine-mapping with over 100x greater efficiency compared to existing methods.
• Genome-wide annotation track analysis: Processes annotation tracks including enhancers, open chromatin, and histone marks across hundreds of thousands of tracks from various cell and tissue types.

Scientific Applications:

• Immune disease GWAS analysis: Evaluates GWAS summary statistics for Inflammatory Bowel Disease, Rheumatoid Arthritis, and Systemic Lupus Erythematosus to identify affected cell types and functional contexts.
• Cardiovascular disease GWAS analysis: Analyzes Coronary Artery Disease GWAS summary statistics to prioritize causal variants and their enrichment in functional genomic contexts.
• Novel annotation and variant prioritization: Identifies both known and novel annotations, cell types, genomic regions, and variants to provide biological insights into disease functional contexts.

Methodology:

BTS uses annotation-specific Bayesian models with GWAS summary statistics to analyze genome-wide functional genomic annotation tracks.