Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize
Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must b...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2021.717552/full |
| _version_ | 1819135396909416448 |
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| author | Germano Costa-Neto Germano Costa-Neto Jose Crossa Jose Crossa Roberto Fritsche-Neto Roberto Fritsche-Neto |
| author_facet | Germano Costa-Neto Germano Costa-Neto Jose Crossa Jose Crossa Roberto Fritsche-Neto Roberto Fritsche-Neto |
| author_sort | Germano Costa-Neto |
| collection | DOAJ |
| description | Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an “enviromic assembly approach,” which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios. |
| first_indexed | 2024-12-22T10:18:26Z |
| format | Article |
| id | doaj.art-9cfd9645fb5941c086b9e8fc4955394a |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-22T10:18:26Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Plant Science |
| spelling | doaj.art-9cfd9645fb5941c086b9e8fc4955394a2022-12-21T18:29:41ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2021-10-011210.3389/fpls.2021.717552717552Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in MaizeGermano Costa-Neto0Germano Costa-Neto1Jose Crossa2Jose Crossa3Roberto Fritsche-Neto4Roberto Fritsche-Neto5Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, BrazilInstitute for Genomic Diversity, Cornell University, Ithaca, NY, United StatesBiometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico City, MexicoColegio de Posgraduado, Mexico City, MexicoDepartment of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, BrazilBreeding Analytics and Data Management Unit, International Rice Research Institute (IRRI), Los Baños, PhilippinesQuantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an “enviromic assembly approach,” which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.https://www.frontiersin.org/articles/10.3389/fpls.2021.717552/fullgenomic selectionadaptabilitygenotype × environmentclimate-smartselective phenotyping |
| spellingShingle | Germano Costa-Neto Germano Costa-Neto Jose Crossa Jose Crossa Roberto Fritsche-Neto Roberto Fritsche-Neto Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize Frontiers in Plant Science genomic selection adaptability genotype × environment climate-smart selective phenotyping |
| title | Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize |
| title_full | Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize |
| title_fullStr | Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize |
| title_full_unstemmed | Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize |
| title_short | Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize |
| title_sort | enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity in maize |
| topic | genomic selection adaptability genotype × environment climate-smart selective phenotyping |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2021.717552/full |
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