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    Genomic structural equation modelling provides a whole-system approach for the future crop breeding.

    Access Status
    Fulltext not available
    Authors
    He, Tianhua
    Angessa, Tefera Tolera
    Hill, Camilla Beate
    Zhang, Xiao-Qi
    Chen, Kefei
    Luo, Hao
    Wang, Yonggang
    Karunarathne, Sakura D
    Zhou, Gaofeng
    Tan, Cong
    Wang, Penghao
    Westcott, Sharon
    Li, Chengdao
    Date
    2021
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    He, T. and Angessa, T.T. and Hill, C.B. and Zhang, X.-Q. and Chen, K. and Luo, H. and Wang, Y. et al. 2021. Genomic structural equation modelling provides a whole-system approach for the future crop breeding. Theoretical and Applied Genetics.
    Source Title
    Theoretical and Applied Genetics
    DOI
    10.1007/s00122-021-03865-4
    ISSN
    0040-5752
    Faculty
    Faculty of Science and Engineering
    School
    School of Molecular and Life Sciences (MLS)
    URI
    http://hdl.handle.net/20.500.11937/83925
    Collection
    • Curtin Research Publications
    Abstract

    KEY MESSAGE: Using genomic structural equation modelling, this research demonstrates an efficient way to identify genetically correlating traits and provides an effective proxy for multi-trait selection to consider the joint genetic architecture of multiple interacting traits in crop breeding. Breeding crop cultivars with optimal value across multiple traits has been a challenge, as traits may negatively correlate due to pleiotropy or genetic linkage. For example, grain yield and grain protein content correlate negatively with each other in cereal crops. Future crop breeding needs to be based on practical yet accurate evaluation and effective selection of beneficial trait to retain genes with the best agronomic score for multiple traits. Here, we test the framework of whole-system-based approach using structural equation modelling (SEM) to investigate how one trait affects others to guide the optimal selection of a combination of agronomically important traits. Using ten traits and genome-wide SNP profiles from a worldwide barley panel and SEM analysis, we revealed a network of interacting traits, in which tiller number contributes positively to both grain yield and protein content; we further identified common genetic factors affecting multiple traits in the network of interaction. Our method demonstrates an efficient way to identify genetically correlating traits and underlying pleiotropic genetic factors and provides an effective proxy for multi-trait selection within a whole-system framework that considers the joint genetic architecture of multiple interacting traits in crop breeding. Our findings suggest the promise of a whole-system approach to overcome challenges such as the negative correlation of grain yield and protein content to facilitating quantitative and objective breeding decisions in future crop breeding.

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