Repository logo
 

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

Published version
Peer-reviewed

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 and Care Research (IS-BRC-1215-20014)