The Polygenic Score Catalog: an open database for reproducibility and systematic evaluation
Lambert, Samuel A
MacArthur, Jacqueline AL
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Lambert, S. A., Gil, L., Jupp, S., Ritchie, S., Xu, Y., Buniello, A., McMahon, A., et al. The Polygenic Score Catalog: an open database for reproducibility and systematic evaluation. Nature Genetics https://doi.org/10.17863/CAM.59754
Polygenic [risk] scores (PGS) can enhance prediction and understanding of common diseases and traits. However, the reproducibility of PGS and their subsequent applications in biological and clinical research have been hindered by several factors, including: inadequate and incomplete reporting of PGS development, heterogeneity in evaluation techniques, and inconsistent access to, and distribution of, the information necessary to calculate the scores themselves. To address this we present the PGS Catalog (www.PGSCatalog.org), an open resource for polygenic scores. The PGS Catalog currently contains 238 published PGS and data from 92 publications for 113 traits, including diabetes, cardiovascular diseases, neurological disorders, cancers, body mass index and blood lipids. Full scoring information for each PGS is available, along with consistently curated metadata required for reproducibility as well as accurate application in independent studies. Using the PGS Catalog, we demonstrate that multiple PGS can be systematically evaluated to generate comparable performance metrics. The PGS Catalog has capabilities for user deposition, expert curation and programmatic access, thus providing the community with an open platform for polygenic score dissemination, research and translation.
This work was supported by core funding from: the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194;RG/18/13/33946) and the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] [*]. This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. MI was supported by the Munz Chair of Cardiovascular Prediction and Prevention. This study was supported by the Victorian Government’s Operational Infrastructure Support (OIS) program. Research reported in this publication was supported by the National Human Genome Research Institute of the National Institutes of Health under Award Number U41HG007823. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. In addition, we acknowledge funding from the European Molecular Biology Laboratory. JD holds a British Heart Foundation Chair and is funded by the National Institute for Health Research [Senior Investigator Award] [*]. MI and SR are supported by the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust]. SL is supported by a postdoctoral fellowship award from the Canadian Institutes of Health Research.
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This record's DOI: https://doi.org/10.17863/CAM.59754
This record's URL: https://www.repository.cam.ac.uk/handle/1810/312656
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