Abstract
The prediction accuracy of multi-environment prediction models can be affected by the complexity of the genotype by environment interaction (G×E). Moreover, depending on the trait genetic architecture, accounting for non-additive effects, such as dominance effects, may increase the prediction accuracy of genomic models. Hence, we aimed to verify empirically: (i) the impact of the genotype by environment complexity on the prediction accuracy of grain yield in maize hybrids; (ii) the advantage of dominance effects modeling for the prediction of maize hybrids in multi-environment trials; (iii) how parent information impacts on the prediction accuracy of hybrids in multi-environment genomic models. We used a dataset comprising 614 maize hybrids evaluated during two growing seasons, under two nitrogens regimes at two locations in Brazil. The prediction accuracies were obtained using four different validation systems (hybrids and half-sib families based sampling). Our results suggest that sampling entire half-sib families or individual hybrids can achieve similar accuracy estimates in multi-environment prediction models. Moreover, modeling dominance deviations in a multi-environment prediction model can significantly increase the prediction accuracy, mainly under high G×E complexity. Also, we found a linear relationship between prediction accuracy and G×E complexity. Furthermore, we observed significant increases in prediction accuracy of lowly correlated environments when information of a linking trial/environment was included in the prediction model.
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Data archiving
The phenotypic and genotypic data for the maize hybrids included in this study can be found at Mendeley (https://data.mendeley.com/datasets/fvwnvrbx2y/1). We provided the adjusted phenotypes of 614 hybrids by evaluated scenarios, and the additive and dominance kinship matrices.
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Acknowledgements
This project was supported by FAPESP (Process: 2013/24135-2), Coordination for the Improvement of Higher Level Personnel (CAPES), and the National Council for Scientific and Technological Development (CNPq).
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FCA elaborated the hypothesis, conducted the analyses, interpreted the results, and wrote the manuscript. FCA, GG, FIM, MSV, and JSM collected the phenotypic data. GG, FIM, MSV, and JSM contributed to the writing, mainly discussion. RFN jointly elaborated the hypothesis with FCA, contributing to ideas, graphs, and analyses. All authors read and approved the final manuscript.
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Alves, F.C., Galli, G., Matias, F.I. et al. Impact of the complexity of genotype by environment and dominance modeling on the predictive accuracy of maize hybrids in multi-environment prediction models. Euphytica 217, 37 (2021). https://doi.org/10.1007/s10681-021-02779-y
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DOI: https://doi.org/10.1007/s10681-021-02779-y