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


Type
Article
Change log
Authors
Xu, Yu 
Vuckovic, Dragana 
Ritchie, Scott C 
Akbari, Parsa 
Jiang, Tao 
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.

Description
Keywords
Blood cell trait, Disease assocations, Machine learning, Method, Polygenic score, Population stratification
Journal Title
Cell Genomics
Conference Name
Journal ISSN
2666-979X
2666-979X
Volume Title
2
Publisher
Elsevier BV
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 (IS-BRC-1215-20014)