Extending the breeder’s equation to take aim at the target population of environments
A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction accuracy are viewed as pathways to improve traits in the refe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1129591/full |
| _version_ | 1797901444211277824 |
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| author | Mark Cooper Mark Cooper Owen Powell Owen Powell Carla Gho Tom Tang Carlos Messina |
| author_facet | Mark Cooper Mark Cooper Owen Powell Owen Powell Carla Gho Tom Tang Carlos Messina |
| author_sort | Mark Cooper |
| collection | DOAJ |
| description | A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction accuracy are viewed as pathways to improve traits in the reference population of genotypes and product performance in the target population of environments (TPE). To realize these breeding outcomes there must be a positive MET-TPE relationship that provides consistency between the trait variation expressed within the MET data sets that are used to train the genome-to-phenome (G2P) model for applications of genomic prediction and the realized trait and performance differences in the TPE for the genotypes that are the prediction targets. The strength of this MET-TPE relationship is usually assumed to be high, however it is rarely quantified. To date investigations of genomic prediction methods have focused on improving prediction accuracy within MET training data sets, with less attention to quantifying the structure of the TPE and the MET-TPE relationship and their potential impact on training the G2P model for applications of genomic prediction to accelerate breeding outcomes for the on-farm TPE. We extend the breeder’s equation and use an example to demonstrate the importance of the MET-TPE relationship as a key component for the design of genomic prediction methods to realize improved rates of genetic gain for the target yield, quality, stress tolerance and yield stability traits in the on-farm TPE. |
| first_indexed | 2024-04-10T09:02:09Z |
| format | Article |
| id | doaj.art-658b512b6b114aacbc1d55c08cddb52e |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-10T09:02:09Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-658b512b6b114aacbc1d55c08cddb52e2023-02-21T11:23:14ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-02-011410.3389/fpls.2023.11295911129591Extending the breeder’s equation to take aim at the target population of environmentsMark Cooper0Mark Cooper1Owen Powell2Owen Powell3Carla Gho4Tom Tang5Carlos Messina6Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, AustraliaAustralian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, AustraliaQueensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, AustraliaAustralian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, AustraliaSchool of Agriculture & Food Sciences, The University of Queensland, Brisbane, QLD, AustraliaCorteva Agriscience, Johnston, IA, United StatesHorticultural Sciences Department, University of Florida, Gainesville, FL, United StatesA major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction accuracy are viewed as pathways to improve traits in the reference population of genotypes and product performance in the target population of environments (TPE). To realize these breeding outcomes there must be a positive MET-TPE relationship that provides consistency between the trait variation expressed within the MET data sets that are used to train the genome-to-phenome (G2P) model for applications of genomic prediction and the realized trait and performance differences in the TPE for the genotypes that are the prediction targets. The strength of this MET-TPE relationship is usually assumed to be high, however it is rarely quantified. To date investigations of genomic prediction methods have focused on improving prediction accuracy within MET training data sets, with less attention to quantifying the structure of the TPE and the MET-TPE relationship and their potential impact on training the G2P model for applications of genomic prediction to accelerate breeding outcomes for the on-farm TPE. We extend the breeder’s equation and use an example to demonstrate the importance of the MET-TPE relationship as a key component for the design of genomic prediction methods to realize improved rates of genetic gain for the target yield, quality, stress tolerance and yield stability traits in the on-farm TPE.https://www.frontiersin.org/articles/10.3389/fpls.2023.1129591/fullgenotype x environment (G x E) interactionsgenotypingphenotypingenvirotypinggenomic prediction |
| spellingShingle | Mark Cooper Mark Cooper Owen Powell Owen Powell Carla Gho Tom Tang Carlos Messina Extending the breeder’s equation to take aim at the target population of environments Frontiers in Plant Science genotype x environment (G x E) interactions genotyping phenotyping envirotyping genomic prediction |
| title | Extending the breeder’s equation to take aim at the target population of environments |
| title_full | Extending the breeder’s equation to take aim at the target population of environments |
| title_fullStr | Extending the breeder’s equation to take aim at the target population of environments |
| title_full_unstemmed | Extending the breeder’s equation to take aim at the target population of environments |
| title_short | Extending the breeder’s equation to take aim at the target population of environments |
| title_sort | extending the breeder s equation to take aim at the target population of environments |
| topic | genotype x environment (G x E) interactions genotyping phenotyping envirotyping genomic prediction |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1129591/full |
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