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 (i) identifying affected cell types and functional elements (context-mapping) and (ii) fine-mapping potentially causal variants in a context-specific manner using large collections of cell type-specific FG annotation tracks. BTS leverages GWAS summary statistics and annotation-specific Bayesian models to analyze genome-wide annotation tracks, including enhancers, open chromatin, and histone marks. We evaluated BTS on GWAS summary statistics for immune and cardiovascular traits, such as Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Coronary Artery Disease (CAD). Our results demonstrate that BTS is over 100× more efficient in estimating functional annotation effects and context-specific variant fine-mapping compared to existing methods. Importantly, this large-scale Bayesian approach prioritizes both known and novel annotations, cell types, genomic regions, and variants and provides valuable biological insights into the functional contexts of these diseases. AVAILABILITY AND IMPLEMENTATION: 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).

At home fitness has rapidly risen recently due to the COVID-19 pandemic and stay-at-home-orders. This also produced a large set of first time users of gym equipment and structured exercise routines. Access to professional fitness trainers to assist beginners in proper exercise form has become increasingly difficult. According to the National Safety Council (NSC), approximately 468,000 injuries occurred due to exercise in 2019 before the pandemic. Without proper guidance, this statistic is bound to increase. Therefore, there is a need for systems to monitor exercise performance for both short term and long term injury prevention. We present a novel mobile app called Verum Fitness which will use the camera from a smart phone to record a user performing an exercise. Then, the app will skeletonize the user, extract angles from specific joints, and feed this data into a Fuzzy Inference System (FIS), an inherently explainable model, to classify exercise performance. With the FIS, we can provide a description of each repetition performed to determine if it could cause injury and how to improve. From our synthetically generated data, we show a training and test Accuracy of 80.42% and 71.67%, respectively, as well as high Sensitivity and Specificity for the goblet squat.