Abstract
Key message
Calibrating a genomic selection model on a sparse factorial design rather than on tester designs is advantageous for some traits, and equivalent for others.
Abstract
In maize breeding, the selection of the candidate inbred lines is based on topcross evaluations using a limited number of testers. Then, a subset of single-crosses between these selected lines is evaluated to identify the best hybrid combinations. Genomic selection enables the prediction of all possible single-crosses between candidate lines but raises the question of defining the best training set design. Previous simulation results have shown the potential of using a sparse factorial design instead of tester designs as the training set. To validate this result, a 363 hybrid factorial design was obtained by crossing 90 dent and flint inbred lines from six segregating families. Two tester designs were also obtained by crossing the same inbred lines to two testers of the opposite group. These designs were evaluated for silage in eight environments and used to predict independent performances of a 951 hybrid factorial design. At a same number of hybrids and lines, the factorial design was as efficient as the tester designs, and, for some traits, outperformed them. All available designs were used as both training and validation set to evaluate their efficiency. When the objective was to predict single-crosses between untested lines, we showed an advantage of increasing the number of lines involved in the training set, by (1) allocating each of them to a different tester for the tester design, or (2) reducing the number of hybrids per line for the factorial design. Our results confirm the potential of sparse factorial designs for genomic hybrid breeding.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
We thank Lidea, Limagrain Europe, Maïsadour Semences, Corteva, RAGT 2n, and Syngenta Seeds grouped in the frame of the ProMais SAM-MCR program for the funding, inbred lines development, hybrid production, and phenotyping. We are also grateful to scientists from these companies and to scientists of the INRAE “R2D2” network for helpful discussions on the results. A.L. phD contract was funded by RAGT 2n and ANRT contract n° 2020/0032 and receive support from the "Investissement d’Avenir" project "Amaizing" (Amaizing, ANR-10-BTBR-0001). GQE-Le Moulon benefits from the support of Saclay Plant Sciences-SPS (ANR-17-EUR-0007).
Funding
Lidea, Limagrain Europe, Maïsadour Semences, Corteva, RAGT 2n, and Syngenta Seeds grouped in the frame of the ProMais funded the SAM-MCR project. A.L. phD contract was funded by RAGT 2n and ANRT contract n° 2020/0032 and receive support from the "Investissement d’Avenir" project "Amaizing" (Amaizing, ANR-10-BTBR-0001). GQE-Le Moulon benefits from the support of Saclay Plant Sciences-SPS (ANR-17-EUR-0007).
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CB, AC and LM initiated this project. LM and CB coordinated it with the help of CL and CG. AC, LM and CL supervised this work. SP and CP contributed to the development of the genetic material. AL analyzed the results and prepared the manuscript. All authors discussed the results and contributed to the final manuscript. All authors revised and approved the manuscript.
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Lorenzi, A., Bauland, C., Mary-Huard, T. et al. Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage. Theor Appl Genet 135, 3143–3160 (2022). https://doi.org/10.1007/s00122-022-04176-y
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DOI: https://doi.org/10.1007/s00122-022-04176-y