Published August 24, 2023
| Version 0.1.3
Journal article
Open
Annotation datasets for Category-Wide Association Study
Creators
- 1. Department of Integrated Biomedical and Life Science, Korea University, Seoul, 02841, Republic of Korea
- 2. School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul, 02841, Republic of Korea
- 3. Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
- 4. Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- 5. Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, OX3 7TY, UK
Description
Category-Wide Association Study (CWAS) is a statistical framework that performs genome-wide assessment of noncoding associations with diseases.
The uploaded files are functional annotations curated for CWAS analysis. The annotations are cell-type-specific cis-regulatory elements sourced from Herring et al. (2022) (DOI: https://doi.org/10.1016/j.cell.2022.09.039). The elements are identified using the strategy from Herring et al. (2022).
The CWAS framework is implemented as a Python package. For the most current version of CWAS-Plus package, please see https://github.com/joonan-lab/cwas.
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