Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data
Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Associations (EPA) aimed a...
| Autores principales: | , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Oxford University Press
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/126482 |
| _version_ | 1855534983422148608 |
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| author | Costa Neto, Germano Crespo-Herrera, Leonardo A. Fradgley, Nick Gardner, Keith A. Bentley, Alison R. Dreisigacker, Susanne Fritsche-Neto, Roberto Montesinos López, Osval A. Crossa, José |
| author_browse | Bentley, Alison R. Costa Neto, Germano Crespo-Herrera, Leonardo A. Crossa, José Dreisigacker, Susanne Fradgley, Nick Fritsche-Neto, Roberto Gardner, Keith A. Montesinos López, Osval A. |
| author_facet | Costa Neto, Germano Crespo-Herrera, Leonardo A. Fradgley, Nick Gardner, Keith A. Bentley, Alison R. Dreisigacker, Susanne Fritsche-Neto, Roberto Montesinos López, Osval A. Crossa, José |
| author_sort | Costa Neto, Germano |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Associations (EPA) aimed at recycling important G × E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes; (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to development of four new G × E kernels considering genomics, enviromics and EPA outcomes. The wheat trial data used included 36 locations, eight years and three target populations of environments (TPE) in India. Four prediction scenarios and six kernel-models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G × E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G × E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as “covariable selection” unfeasible because G × E is year-germplasm specific. We propose that the EPA effectively serves as a “reinforcement learner” algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level. |
| format | Journal Article |
| id | CGSpace126482 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1264822025-11-06T13:08:10Z Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data Costa Neto, Germano Crespo-Herrera, Leonardo A. Fradgley, Nick Gardner, Keith A. Bentley, Alison R. Dreisigacker, Susanne Fritsche-Neto, Roberto Montesinos López, Osval A. Crossa, José marker-assisted selection climate change wheat breeding adaptability environment Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Associations (EPA) aimed at recycling important G × E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes; (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to development of four new G × E kernels considering genomics, enviromics and EPA outcomes. The wheat trial data used included 36 locations, eight years and three target populations of environments (TPE) in India. Four prediction scenarios and six kernel-models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G × E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G × E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as “covariable selection” unfeasible because G × E is year-germplasm specific. We propose that the EPA effectively serves as a “reinforcement learner” algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level. 2023-02-09 2023-01-03T12:05:51Z 2023-01-03T12:05:51Z Journal Article https://hdl.handle.net/10568/126482 en Open Access application/pdf Oxford University Press Costa-Neto, G., Crespo-Herrera, L., Fradgley, N., Gardner, K., Bentley, A. R., Dreisigacker, S., Fritsche-Neto, R., Montesinos-López, O. A., & Crossa, J. (2022). Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data. G3, 13(2). https://doi.org/10.1093/g3journal/jkac313 |
| spellingShingle | marker-assisted selection climate change wheat breeding adaptability environment Costa Neto, Germano Crespo-Herrera, Leonardo A. Fradgley, Nick Gardner, Keith A. Bentley, Alison R. Dreisigacker, Susanne Fritsche-Neto, Roberto Montesinos López, Osval A. Crossa, José Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data |
| title | Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data |
| title_full | Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data |
| title_fullStr | Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data |
| title_full_unstemmed | Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data |
| title_short | Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data |
| title_sort | envirome wide associations enhance multi year genome based prediction of historical wheat breeding data |
| topic | marker-assisted selection climate change wheat breeding adaptability environment |
| url | https://hdl.handle.net/10568/126482 |
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