Abstract
Key message
Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction.
Abstract
The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G × E). Phenomic selection is supposed to be efficient for modelling the G × E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G × E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G × E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G × E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype–environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G × E.
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Data availability
The datasets generated during and/or analysed during the current study are not publicly available due to the breeding program privacy policy, but are available from the corresponding author on reasonable request.
Code availability
Code used to run the analysis is available from the corresponding author on request.
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Acknowledgements
The authors thank INRAE experimental units (UE PHACC Clermont-Ferrand, UE GCIE Estrées-Mons, IE UMR GQE Le Moulon, UE Domaine de la Motte Rennes), breeders from Agri-Obtentions and Florimond Desprez. The authors are grateful to Agri-Obtentions, Florimond-Desprez and the Association Nationale de la Recherche et de la Technologie (ANRT, Grant Number 2019/0060) which supported this PhD work. The authors also thank Rachel Carol (Bioscience Editing, France) for proofreading. The authors thank the editor and two anonymous reviewers for their fruitful comments.
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This work was funded by Agri-Obtentions, Florimond-Desprez, and the Association Nationale de la Recherche et de la Technologie (ANRT, Grant Number 2019/0060).
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JA, FXO, BR and EH: designed the field trials and collected the phenotypic data from Agri-Obtentions and INRAE. EGD: provided the phenotypic data and genotyping data from the Florimond Desprez company. SB: provided the genotyping data from Agri-Obtentions and INRAE and participated in discussions of this study. AC and TMH: developed the method of multivariate weighted ridge regression prediction of NIR spectra. RR: initiated the project and with JLG: supervised the study and helped improving the manuscript. PR: analysed the data and wrote the manuscript. All authors approved the final manuscript.
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Co-author Ellen Goudemand was employed by Florimond-Desprez, and co-author Jérôme Auzanneau was employed by Agri-Obtentions. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Robert, P., Goudemand, E., Auzanneau, J. et al. Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials. Theor Appl Genet 135, 3337–3356 (2022). https://doi.org/10.1007/s00122-022-04170-4
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DOI: https://doi.org/10.1007/s00122-022-04170-4