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

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Statistical Genetics

Background:

• Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with diseases.
• Integrating GWAS with functional genomic (FG) data helps pinpoint causal variants and their cellular contexts.
• Existing methods struggle to scale with the growing volume of FG datasets for comprehensive analysis.

Purpose of the Study:

• To develop a scalable and efficient algorithm, Bayesian Tissue Score (BTS), for integrating large-scale FG annotations with GWAS summary statistics.
• To enable context-specific fine-mapping of causal variants by identifying relevant cell types and genomic features.
• To provide biological insights into the functional underpinnings of complex diseases.

Main Methods:

• Developed BTS, a novel Bayesian algorithm leveraging GWAS summary statistics and cell type-specific FG annotation tracks.
• Applied BTS to analyze diverse FG annotations, including enhancers, open chromatin, and histone marks.
• Evaluated BTS performance on GWAS data for immune (IBD, RA, SLE) and cardiovascular (CAD) diseases.

Main Results:

• BTS demonstrates over 100x greater efficiency in estimating functional annotation effects and variant fine-mapping compared to existing methods.
• The algorithm successfully identifies affected cell types and functional elements relevant to disease.
• BTS prioritizes known and novel functional annotations, cell types, genomic regions, and variants, offering significant biological insights.

Conclusions:

• BTS provides a highly efficient and scalable solution for integrating large-scale functional genomics data with GWAS.
• The algorithm facilitates context-specific variant fine-mapping and identification of disease-relevant cell types and genomic features.
• BTS offers valuable biological insights into the functional architecture of complex human diseases.