Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields
We explored the use of the eco-physiological crop model GECROS to identify markers for improved rice yield under well-watered (control) and water-deficit conditions. Eight model parameters were measured from the control in one season for 267 indica genotypes. The model accounted for 58% of yield var...
| Autores principales: | , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Oxford University Press
2019
|
| Acceso en línea: | https://hdl.handle.net/10568/164710 |
| _version_ | 1855515554205401088 |
|---|---|
| author | Kadam, Niteen N. Jagadish, Krishna S.V. Struik, Paul C. van der Linden, C Gerard Yin, Xinyou |
| author_browse | Jagadish, Krishna S.V. Kadam, Niteen N. Struik, Paul C. Yin, Xinyou van der Linden, C Gerard |
| author_facet | Kadam, Niteen N. Jagadish, Krishna S.V. Struik, Paul C. van der Linden, C Gerard Yin, Xinyou |
| author_sort | Kadam, Niteen N. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | We explored the use of the eco-physiological crop model GECROS to identify markers for improved rice yield under well-watered (control) and water-deficit conditions. Eight model parameters were measured from the control in one season for 267 indica genotypes. The model accounted for 58% of yield variation among genotypes under control and 40% under water-deficit conditions. Using 213 randomly selected genotypes as training set, 90 SNP loci were identified using GWAS, explaining 42-77% of crop-model parameter variation. SNPs-based parameter values estimated from the additive loci effects were fed into the model. For the training set, the SNPs-based model accounted for 37% (control) and 29% (water-deficit) of yield variation, less than the 78% explained by a statistical genomic prediction (GP) model for the control treatment. Both models failed in predicting yields of the 54 testing genotypes. However, compared with the GP model, the SNPs-based crop model was advantageous when simulating yields under either control or water-stress conditions in an independent season. Crop-model sensitivity analysis ranked the SNP loci for their relative importance in accounting for yield variation, and the rank differed greatly between control and water-deficit environments. Crop models have a potential to use single-environment information for predicting phenotypes under different environments. |
| format | Journal Article |
| id | CGSpace164710 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1647102025-05-14T10:23:52Z Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields Kadam, Niteen N. Jagadish, Krishna S.V. Struik, Paul C. van der Linden, C Gerard Yin, Xinyou We explored the use of the eco-physiological crop model GECROS to identify markers for improved rice yield under well-watered (control) and water-deficit conditions. Eight model parameters were measured from the control in one season for 267 indica genotypes. The model accounted for 58% of yield variation among genotypes under control and 40% under water-deficit conditions. Using 213 randomly selected genotypes as training set, 90 SNP loci were identified using GWAS, explaining 42-77% of crop-model parameter variation. SNPs-based parameter values estimated from the additive loci effects were fed into the model. For the training set, the SNPs-based model accounted for 37% (control) and 29% (water-deficit) of yield variation, less than the 78% explained by a statistical genomic prediction (GP) model for the control treatment. Both models failed in predicting yields of the 54 testing genotypes. However, compared with the GP model, the SNPs-based crop model was advantageous when simulating yields under either control or water-stress conditions in an independent season. Crop-model sensitivity analysis ranked the SNP loci for their relative importance in accounting for yield variation, and the rank differed greatly between control and water-deficit environments. Crop models have a potential to use single-environment information for predicting phenotypes under different environments. 2019-04-29 2024-12-19T12:54:13Z 2024-12-19T12:54:13Z Journal Article https://hdl.handle.net/10568/164710 en Open Access Oxford University Press Kadam, Niteen N; Jagadish, S V Krishna; Struik, Paul C; van der Linden, C Gerard and Yin, Xinyou. 2019. Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields. Journal of Experimental Botany, volume 70, no. 9; pages 2575-2586 |
| spellingShingle | Kadam, Niteen N. Jagadish, Krishna S.V. Struik, Paul C. van der Linden, C Gerard Yin, Xinyou Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields |
| title | Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields |
| title_full | Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields |
| title_fullStr | Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields |
| title_full_unstemmed | Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields |
| title_short | Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields |
| title_sort | incorporating genome wide association into eco physiological simulation to identify markers for improving rice yields |
| url | https://hdl.handle.net/10568/164710 |
| work_keys_str_mv | AT kadamniteenn incorporatinggenomewideassociationintoecophysiologicalsimulationtoidentifymarkersforimprovingriceyields AT jagadishkrishnasv incorporatinggenomewideassociationintoecophysiologicalsimulationtoidentifymarkersforimprovingriceyields AT struikpaulc incorporatinggenomewideassociationintoecophysiologicalsimulationtoidentifymarkersforimprovingriceyields AT vanderlindencgerard incorporatinggenomewideassociationintoecophysiologicalsimulationtoidentifymarkersforimprovingriceyields AT yinxinyou incorporatinggenomewideassociationintoecophysiologicalsimulationtoidentifymarkersforimprovingriceyields |