Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population
Abstract Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland r...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-09-01
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| Series: | Rice |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12284-023-00661-0 |
| _version_ | 1797451463234945024 |
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| author | Hugues de Verdal Cédric Baertschi Julien Frouin Constanza Quintero Yolima Ospina Maria Fernanda Alvarez Tuong-Vi Cao Jérôme Bartholomé Cécile Grenier |
| author_facet | Hugues de Verdal Cédric Baertschi Julien Frouin Constanza Quintero Yolima Ospina Maria Fernanda Alvarez Tuong-Vi Cao Jérôme Bartholomé Cécile Grenier |
| author_sort | Hugues de Verdal |
| collection | DOAJ |
| description | Abstract Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S0:2, S0:3, and S0:4 generations. The PCT27 was then divided into two sets. The S0:2 and S0:3 progenies for PCT27A and the S0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program. |
| first_indexed | 2024-03-09T14:55:01Z |
| format | Article |
| id | doaj.art-3e1fabe84b434af99fcc5ef97626aaa6 |
| institution | Directory Open Access Journal |
| issn | 1939-8425 1939-8433 |
| language | English |
| last_indexed | 2024-03-09T14:55:01Z |
| publishDate | 2023-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Rice |
| spelling | doaj.art-3e1fabe84b434af99fcc5ef97626aaa62023-11-26T14:14:46ZengSpringerOpenRice1939-84251939-84332023-09-0116111810.1186/s12284-023-00661-0Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice PopulationHugues de Verdal0Cédric Baertschi1Julien Frouin2Constanza Quintero3Yolima Ospina4Maria Fernanda Alvarez5Tuong-Vi Cao6Jérôme Bartholomé7Cécile Grenier8CIRAD, UMR AGAP InstitutCIRAD, UMR AGAP InstitutCIRAD, UMR AGAP InstitutAlliance Bioversity-CIATAlliance Bioversity-CIATAlliance Bioversity-CIATCIRAD, UMR AGAP InstitutCIRAD, UMR AGAP InstitutCIRAD, UMR AGAP InstitutAbstract Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S0:2, S0:3, and S0:4 generations. The PCT27 was then divided into two sets. The S0:2 and S0:3 progenies for PCT27A and the S0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program.https://doi.org/10.1186/s12284-023-00661-0Training set optimizationGenomic selectionOryza sativaGenotype-by-environment interaction |
| spellingShingle | Hugues de Verdal Cédric Baertschi Julien Frouin Constanza Quintero Yolima Ospina Maria Fernanda Alvarez Tuong-Vi Cao Jérôme Bartholomé Cécile Grenier Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population Rice Training set optimization Genomic selection Oryza sativa Genotype-by-environment interaction |
| title | Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population |
| title_full | Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population |
| title_fullStr | Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population |
| title_full_unstemmed | Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population |
| title_short | Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population |
| title_sort | optimization of multi generation multi location genomic prediction models for recurrent genomic selection in an upland rice population |
| topic | Training set optimization Genomic selection Oryza sativa Genotype-by-environment interaction |
| url | https://doi.org/10.1186/s12284-023-00661-0 |
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