Improving selection efficiency of crop breeding with genomic prediction aided sparse phenotyping
Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, bu...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/164184 |
| _version_ | 1855539126202269696 |
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| author | He, Sang Jiang, Yong Thistlethwaite, Rebecca Hayden, Matthew J. Trethowan, Richard Daetwyler, Hans D. |
| author_browse | Daetwyler, Hans D. Hayden, Matthew J. He, Sang Jiang, Yong Thistlethwaite, Rebecca Trethowan, Richard |
| author_facet | He, Sang Jiang, Yong Thistlethwaite, Rebecca Hayden, Matthew J. Trethowan, Richard Daetwyler, Hans D. |
| author_sort | He, Sang |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders’ main interest – response to selection – was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs. |
| format | Journal Article |
| id | CGSpace164184 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1641842024-12-19T14:13:33Z Improving selection efficiency of crop breeding with genomic prediction aided sparse phenotyping He, Sang Jiang, Yong Thistlethwaite, Rebecca Hayden, Matthew J. Trethowan, Richard Daetwyler, Hans D. plant science Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders’ main interest – response to selection – was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs. 2021-10-06 2024-12-19T12:53:35Z 2024-12-19T12:53:35Z Journal Article https://hdl.handle.net/10568/164184 en Open Access Frontiers Media He, Sang; Jiang, Yong; Thistlethwaite, Rebecca; Hayden, Matthew J.; Trethowan, Richard and Daetwyler, Hans D. 2021. Improving selection efficiency of crop breeding with genomic prediction aided sparse phenotyping. Front. Plant Sci., Volume 12 |
| spellingShingle | plant science He, Sang Jiang, Yong Thistlethwaite, Rebecca Hayden, Matthew J. Trethowan, Richard Daetwyler, Hans D. Improving selection efficiency of crop breeding with genomic prediction aided sparse phenotyping |
| title | Improving selection efficiency of crop breeding with genomic prediction aided sparse phenotyping |
| title_full | Improving selection efficiency of crop breeding with genomic prediction aided sparse phenotyping |
| title_fullStr | Improving selection efficiency of crop breeding with genomic prediction aided sparse phenotyping |
| title_full_unstemmed | Improving selection efficiency of crop breeding with genomic prediction aided sparse phenotyping |
| title_short | Improving selection efficiency of crop breeding with genomic prediction aided sparse phenotyping |
| title_sort | improving selection efficiency of crop breeding with genomic prediction aided sparse phenotyping |
| topic | plant science |
| url | https://hdl.handle.net/10568/164184 |
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