Published January 19, 2023
| Version v1
Dataset
Open
Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations
Creators
- 1. Program in Bioinformatics, Loyola University Chicago, Chicago, IL, 60660, USA
- 2. Department of Biology, Loyola University Chicago, Chicago, IL, 60660, USA
- 3. Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA
- 4. Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
- 5. Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO, 80045, USA
- 6. Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- 7. The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
- 8. Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT, 05446, USA
- 9. Department of Medicine, Duke University School of Medicine, Durham, NC, 27710, USA
- 10. Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD, 20892, USA
- 11. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- 12. Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA
- 13. Program in Bioinformatics, Loyola University Chicago, Chicago, IL, 60660, USA; Department of Biology, Loyola University Chicago, Chicago, IL, 60660, USA
Description
This Zenodo file collection contains transcriptome prediction models built for PrediXcan, as well as concatenated raw results from SPrediXcan. Files belong to the scientific research paper: "Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations". Please refer to the README file for a more detailed description.
Files
README.txt
Files
(1.5 GB)
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