Published March 13, 2024
| Version CWAS_data_v2
Journal article
Open
Generation of annotation dataset and burden test results of Category-Wide Association Study (CWAS)
Authors/Creators
- 1. Department of Integrated Biomedical and Life Science, Korea University, Seoul, 02841, Republic of Korea
- 2. L-HOPE Program for Community-Based Total Learning Health Systems, Korea University, Seoul, 02841, Republic of Korea
- 3. School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul, 02841, Republic of Korea
- 4. Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
- 5. Department of Psychiatry, CHA Gangnam Medical Center, CHA University School of Medicine, Seoul, 06135, Republic of Korea
- 6. School of Biomedical Convergence Engineering, Pusan National University, Busan, 50612, Republic of Korea
- 7. Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- 8. Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, OX3 7TY, UK
- 9. Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94158, USA
Description
Category-Wide Association Study (CWAS) is a statistical framework that performs genome-wide assessment of noncoding associations with diseases.
The uploaded files contain scripts for generating annotation dataset and burden test results of CWAS analyses.
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.
Files
randrover/CWAS_2024_analysis-CWAS_data_v2.zip
Files
(47.3 MB)
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md5:1333c83d204b19c5d1ac84ccdfd10b0a
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Additional details
Related works
- Is supplement to
- Software: https://github.com/randrover/CWAS_2024_analysis/tree/CWAS_data_v2 (URL)