Multi-environment genomic selection in rice elite breeding lines
Abstract Background: Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models th...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Language: | Inglés |
| Published: |
Research Square Platform LLC
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/127686 |
| _version_ | 1855534176970735616 |
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| author | Nguyen, Van Hieu Morantte, Rose Imee Zhella Lopena, Vitaliano Verdeprado, Holden Murori, Rosemary Ndayiragije, Alexis Katiyar, Sanjay Islam, Md Rafiqul Juma, Roselyne U. Galvez, Hayde Glaszmann, Jean-Christophe Cobb, Joshua N. Bartholomé, Jérôme |
| author_browse | Bartholomé, Jérôme Cobb, Joshua N. Galvez, Hayde Glaszmann, Jean-Christophe Islam, Md Rafiqul Juma, Roselyne U. Katiyar, Sanjay Lopena, Vitaliano Morantte, Rose Imee Zhella Murori, Rosemary Ndayiragije, Alexis Nguyen, Van Hieu Verdeprado, Holden |
| author_facet | Nguyen, Van Hieu Morantte, Rose Imee Zhella Lopena, Vitaliano Verdeprado, Holden Murori, Rosemary Ndayiragije, Alexis Katiyar, Sanjay Islam, Md Rafiqul Juma, Roselyne U. Galvez, Hayde Glaszmann, Jean-Christophe Cobb, Joshua N. Bartholomé, Jérôme |
| author_sort | Nguyen, Van Hieu |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Abstract
Background: Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant
genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic
prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this
context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-
environment information. We used 111 elite breeding lines representing the diversity of the International Rice Research Institute (IRRI)
breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15
environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict
untested environments using seven multi-environment models and three cross-validation scenarios.
Results: The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved
material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant
height (33% and 54% of pairwise correlation greater than 0.5 ) but low for grain yield (lower than 0.2 in most cases). Clustering analyses
based on environmental covariates separated Asia’s and Africa's environments into different clusters or subclusters. The predictive abilities
ranged from 0.06 to 0.79 for days to flowering, 0.25 to 0.88 for plant height, and -0.29 to 0.62 for grain yield. We found that models
integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects
(genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of
all available environments gave better results than a subset.
Conclusion: Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines
in targeted environments. The recommendation for the breeders is to use simple multi-environment models with all available information
for routine application in breeding programs. |
| format | Preprint |
| id | CGSpace127686 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Research Square Platform LLC |
| publisherStr | Research Square Platform LLC |
| record_format | dspace |
| spelling | CGSpace1276862025-11-12T04:58:51Z Multi-environment genomic selection in rice elite breeding lines Nguyen, Van Hieu Morantte, Rose Imee Zhella Lopena, Vitaliano Verdeprado, Holden Murori, Rosemary Ndayiragije, Alexis Katiyar, Sanjay Islam, Md Rafiqul Juma, Roselyne U. Galvez, Hayde Glaszmann, Jean-Christophe Cobb, Joshua N. Bartholomé, Jérôme rice research Abstract Background: Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi- environment information. We used 111 elite breeding lines representing the diversity of the International Rice Research Institute (IRRI) breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios. Results: The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5 ) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia’s and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25 to 0.88 for plant height, and -0.29 to 0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset. Conclusion: Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. The recommendation for the breeders is to use simple multi-environment models with all available information for routine application in breeding programs. 2022-10-10 2023-01-20T12:31:28Z 2023-01-20T12:31:28Z Preprint https://hdl.handle.net/10568/127686 en Open Access application/pdf application/pdf Research Square Platform LLC Nguyen, V. H., Morantte, R.I.Z., Lopena, V., Verdeprado, H., Murori, R., Ndayiragije, A., Katiyar, S. et al. 2022.Multi-environment genomic selection in rice elite breeding lines. (2022). |
| spellingShingle | rice research Nguyen, Van Hieu Morantte, Rose Imee Zhella Lopena, Vitaliano Verdeprado, Holden Murori, Rosemary Ndayiragije, Alexis Katiyar, Sanjay Islam, Md Rafiqul Juma, Roselyne U. Galvez, Hayde Glaszmann, Jean-Christophe Cobb, Joshua N. Bartholomé, Jérôme Multi-environment genomic selection in rice elite breeding lines |
| title | Multi-environment genomic selection in rice elite breeding lines |
| title_full | Multi-environment genomic selection in rice elite breeding lines |
| title_fullStr | Multi-environment genomic selection in rice elite breeding lines |
| title_full_unstemmed | Multi-environment genomic selection in rice elite breeding lines |
| title_short | Multi-environment genomic selection in rice elite breeding lines |
| title_sort | multi environment genomic selection in rice elite breeding lines |
| topic | rice research |
| url | https://hdl.handle.net/10568/127686 |
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