BTS: scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across omics data
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Motivation
Summary statistics from genome-wide association studies (GWAS) are often used in fine-mapping or colocalization analyses to identify potentially causal variants and their enrichment in various functional contexts, such as affected cell types and genomic features. As functional genomic (FG) datasets and assay types continue to expand, it is critical to establish scalable algorithms that can integrate thousands of diverse cell type-specific FG annotations with GWAS results.
Results
We propose BTS (Bayesian Tissue Score), a novel, highly efficient algorithm for 1) identification of affected cell types and functional genomic elements (context-mapping) and 2) cell type-specific inference of potentially causal variants (context-specific variant fine-mapping) using large-scale collections of heterogenous cell type-specific FG annotation tracks. To do so, BTS uses GWAS summary statistics and estimates per-annotation Bayesian models using genome-wide annotation tracks including enhancer, open chromatin, and epigenetic histone marks from the FILER FG database. We evaluated BTS across >900 FG annotation tracks on GWAS summary statistics for immune-related and cardiovascular traits, including Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Coronary Artery Disease (CAD). Our results show that BTS scales well and is > 100x more efficient when estimating functional annotation effects and performing context-specific variant fine-mapping compared to existing methods. Importantly, the resulting large-scale Bayesian evaluation and prioritization of both known and novel annotations, cell types, genomic regions, and variants provides biological insights into the functional contexts for these diseases.
Availability and implementation
BTS R package is available from https://bitbucket.org/wanglab-upenn/BTS-R . BTS GWAS summary statistics analysis pipeline is freely available at https://bitbucket.org/wanglab-upenn/bts-pipeline . Docker image with pre-installed BTS R package and GWAS summary statistics pipeline is also available at https://hub.docker.com/r/wanglab/bts .
