BTS: a scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across >1,000s of omics datasets
Docker image is available at https://hub.docker.com/r/wanglab/bts with preinstalled BTS R package (https://bitbucket.org/wanglab-upenn/BTS-R) and BTS GWAS summary statistics analysis pipeline (https://bitbucket.org/wanglab-upenn/bts-pipeline).
Authors
- Kaylyn Clark (ORCID: https://orcid.org/0000-0002-7207-6608)
- Otto Valladares (ORCID: https://orcid.org/0000-0001-8055-2187)
- Pavel P. Kuksa (ORCID: https://orcid.org/0000-0003-2248-6403)
- Matei Ionita (ORCID: https://orcid.org/0000-0002-0514-3364)
- Yuk Yee Leung (ORCID: https://orcid.org/0000-0002-3047-5440)
- Prabhakaran Gangadharan
- Luke Carter
- Jeffrey Cifello (ORCID: https://orcid.org/0009-0000-6147-2026)
- Li-San Wang
Institutions
- Penn Center for AIDS Research (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Bioinformatics
- Published
- 2025-09-26
- DOI
- https://doi.org/10.1093/bioinformatics/btaf509
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 0.00