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dc.contributor.authorHe, Tianhua
dc.contributor.authorAngessa, Tefera Tolera
dc.contributor.authorHill, Camilla Beate
dc.contributor.authorZhang, Xiao-Qi
dc.contributor.authorChen, Kefei
dc.contributor.authorLuo, Hao
dc.contributor.authorWang, Yonggang
dc.contributor.authorKarunarathne, Sakura D
dc.contributor.authorZhou, Gaofeng
dc.contributor.authorTan, Cong
dc.contributor.authorWang, Penghao
dc.contributor.authorWestcott, Sharon
dc.contributor.authorLi, Chengdao
dc.date.accessioned2021-06-08T02:28:14Z
dc.date.available2021-06-08T02:28:14Z
dc.date.issued2021
dc.identifier.citationHe, 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. Theor Appl Genet.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/83925
dc.identifier.doi10.1007/s00122-021-03865-4
dc.description.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.

dc.languageeng
dc.titleGenomic structural equation modelling provides a whole-system approach for the future crop breeding.
dc.typeJournal Article
dcterms.source.issn0040-5752
dcterms.source.titleTheor Appl Genet
dc.date.updated2021-06-08T02:28:14Z
curtin.departmentSchool of Molecular and Life Sciences (MLS)
curtin.accessStatusIn process
curtin.facultyFaculty of Science and Engineering
dcterms.source.eissn1432-2242


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