Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease

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Authors
Xu, Yu
Vuckovic, Dragana
  Ritchie, Scott
Akbari, Parsa
Jiang, Tao
Grealey, Jason
  Butterworth, Adam
Publication Date
2022-01-12
Journal Title
Cell Genomics
ISSN
2666-979X
Publisher
Elsevier BV
Volume
2
Issue
1
Number
100086
Pages
100086-100086
Type
Article
This Version
VoR
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Citation
Xu, Y., Vuckovic, D., Ritchie, S., Akbari, P., Jiang, T., Grealey, J., Butterworth, A., et al. (2022). Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease. Cell Genomics, 2 (1. 100086), 100086-100086. https://doi.org/10.1016/j.xgen.2021.100086
Abstract
In this issue of Cell Genomics, Xu et al. report a comprehensive analysis of the genetics of 26 blood cell traits, leveraging data from two large biobanks to construct and make available machine-learning optimized polygenic scores (PGSs). In addition to delivering insights into the biology and clinical associations of these traits, the authors evaluate and provide recommendations on methods for PGS construction.
Sponsorship
Medical Research Council (MR/L003120/1)
British Heart Foundation (None)
British Heart Foundation (RG/18/13/33946)
ESRC (ES/T013192/1)
European Commission Horizon 2020 (H2020) Societal Challenges (101016775)
National Institute for Health Research (NIHRDH-IS-BRC-1215-20014)
Identifiers
External DOI: https://doi.org/10.1016/j.xgen.2021.100086
This record's URL: https://www.repository.cam.ac.uk/handle/1810/332819
Rights
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/
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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International