Published August 6, 2023
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Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology
Authors/Creators
- 1. 1. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA 2. Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- 2. 1. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA 2. Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 3. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA 4. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- 3. 5. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- 4. 6. Society of Fellows, Harvard University, Cambridge, MA, 02138 USA
- 5. 4. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan; 7. Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan 8. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC); Center for Infectious Disease Education and Research (CiDER); and Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan 9. Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan.
- 6. 10. Department of Economics, and Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
Description
These are the main codes used for the paper:
Wang et al. Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology, bioRxiv
doi: https://doi.org/10.1101/2022.12.29.522270
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References
- Wang et al. (2022), Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology, bioRxiv