Genomic structural equation modelling provides a whole-system approach for the future crop breeding.
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. Breed...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Online Access: | http://hdl.handle.net/20.500.11937/83925 |
| _version_ | 1848764611850928128 |
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| author | 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 |
| author_facet | 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 |
| author_sort | He, Tianhua |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:22:07Z |
| format | Journal Article |
| id | curtin-20.500.11937-83925 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | eng |
| last_indexed | 2025-11-14T11:22:07Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-839252021-08-03T07:22:46Z Genomic structural equation modelling provides a whole-system approach for the future crop breeding. 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 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. 2021 Journal Article http://hdl.handle.net/20.500.11937/83925 10.1007/s00122-021-03865-4 eng restricted |
| spellingShingle | 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 Genomic structural equation modelling provides a whole-system approach for the future crop breeding. |
| title | Genomic structural equation modelling provides a whole-system approach for the future crop breeding. |
| title_full | Genomic structural equation modelling provides a whole-system approach for the future crop breeding. |
| title_fullStr | Genomic structural equation modelling provides a whole-system approach for the future crop breeding. |
| title_full_unstemmed | Genomic structural equation modelling provides a whole-system approach for the future crop breeding. |
| title_short | Genomic structural equation modelling provides a whole-system approach for the future crop breeding. |
| title_sort | genomic structural equation modelling provides a whole-system approach for the future crop breeding. |
| url | http://hdl.handle.net/20.500.11937/83925 |