Response to early generation genomic selection for yield in wheat
We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying...
| Autores principales: | , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2022
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| Acceso en línea: | https://hdl.handle.net/10568/126319 |
| _version_ | 1855540468932149248 |
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| author | Bonnett, David G. Yongle Li Crossa, José Dreisigacker, Susanne Basnet, Bhoja Raj Pérez Rodriguez, Paulino Alvarado Beltrán, Gregorio Jannink, Jean-Luc Poland, Jesse A. Sorrells, Mark Earl |
| author_browse | Alvarado Beltrán, Gregorio Basnet, Bhoja Raj Bonnett, David G. Crossa, José Dreisigacker, Susanne Jannink, Jean-Luc Poland, Jesse A. Pérez Rodriguez, Paulino Sorrells, Mark Earl Yongle Li |
| author_facet | Bonnett, David G. Yongle Li Crossa, José Dreisigacker, Susanne Basnet, Bhoja Raj Pérez Rodriguez, Paulino Alvarado Beltrán, Gregorio Jannink, Jean-Luc Poland, Jesse A. Sorrells, Mark Earl |
| author_sort | Bonnett, David G. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248, P < 0.001) and GBLUP (0.195, P < 0.01) but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds. |
| format | Journal Article |
| id | CGSpace126319 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1263192025-12-08T10:29:22Z Response to early generation genomic selection for yield in wheat Bonnett, David G. Yongle Li Crossa, José Dreisigacker, Susanne Basnet, Bhoja Raj Pérez Rodriguez, Paulino Alvarado Beltrán, Gregorio Jannink, Jean-Luc Poland, Jesse A. Sorrells, Mark Earl marker-assisted selection genomics wheat plant breeding breeding methods We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248, P < 0.001) and GBLUP (0.195, P < 0.01) but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds. 2022-01-11 2022-12-27T08:51:32Z 2022-12-27T08:51:32Z Journal Article https://hdl.handle.net/10568/126319 en Open Access application/pdf Frontiers Media Bonnett, D., Li, Y., Crossa, J., Dreisigacker, S., Basnet, B., Pérez-Rodríguez, P., Alvarado, G., Jannink, J. L., Poland, J., & Sorrells, M. (2022). Response to Early Generation Genomic Selection for Yield in Wheat. Frontiers in Plant Science, 12. https://doi.org/10.3389/fpls.2021.718611 |
| spellingShingle | marker-assisted selection genomics wheat plant breeding breeding methods Bonnett, David G. Yongle Li Crossa, José Dreisigacker, Susanne Basnet, Bhoja Raj Pérez Rodriguez, Paulino Alvarado Beltrán, Gregorio Jannink, Jean-Luc Poland, Jesse A. Sorrells, Mark Earl Response to early generation genomic selection for yield in wheat |
| title | Response to early generation genomic selection for yield in wheat |
| title_full | Response to early generation genomic selection for yield in wheat |
| title_fullStr | Response to early generation genomic selection for yield in wheat |
| title_full_unstemmed | Response to early generation genomic selection for yield in wheat |
| title_short | Response to early generation genomic selection for yield in wheat |
| title_sort | response to early generation genomic selection for yield in wheat |
| topic | marker-assisted selection genomics wheat plant breeding breeding methods |
| url | https://hdl.handle.net/10568/126319 |
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