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Revisiting hybrid breeding designs using genomic predictions: simulations highlight the superiority of incomplete factorials between segregating families over topcross designs

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Simulations showed that hybrid performances issued from an incomplete factorial between segregating families of two heterotic groups enable to calibrate genomic predictions of hybrid value more efficiently than tester-based designs.

Abstract

Genomic selection offers new opportunities to revisit hybrid breeding by replacing extensive phenotyping of hybrid combinations by genomic predictions. A key question remains to identify the best design to calibrate genomic prediction models. We proposed to use single-cross hybrids issued from an incomplete factorial design between segregating populations and compared this strategy with a conventional approach based on topcross evaluation. Two multiparental segregating populations of lines, each specific of one heterotic group, were simulated. Hybrids considered as training sets were generated using either (1) a parental line from the opposite group as tester or (2) following an incomplete factorial design. Different specific combining ability (SCA) proportions were simulated by considering different levels of group divergence and dominance effects for the simulated QTL. For the incomplete factorial design, for a same number of hybrids, we considered different numbers of parental lines and different contributions of lines (one to four) to calibration hybrids. We evaluated for different training set sizes prediction accuracies of new hybrids and genetic gains along three generations. At a given training set size, factorial design was as efficient (considering accuracy) as tester design in additive scenarios, but significantly outperformed tester design when SCA was present. The contribution number of each parental line to the incomplete factorial design had a small impact on accuracies. Our simulations confirmed experimental results and showed that calibrating models on hybrids between two multiparental populations is a cost-efficient way to perform genomic predictions in both groups, opening prospects for revisiting reciprocal recurrent selection schemes.

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Acknowledgements

A. I. Seye’s PhD was funded by the Senegalese Institute of Agricultural Research (ISRA) through a Scholarship from the West Africa Agricultural Productivity Program (WAAPP) given by the National Institute of Higher Education in Agricultural Sciences—Montpellier SupAgro and the “Amaizing” project (ANR-10-BTBR-0001). We are grateful to Caussade Semences, Corteva Agriscience, Euralis Semences, KWS, Limagrain Europe, Mas Seeds, R2n and Syngenta Seeds grouped in the frame of the ProMais “SAM-MCR” project for the funding. We also thank scientists from these companies for helpful discussions on the results. We thank D. Madur for sharing the CornFed genotyping data.

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  1. Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, 91190, Gif-sur-Yvette, France

    A. I. Seye, C. Bauland, A. Charcosset & L. Moreau

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  1. A. I. Seye
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  2. C. Bauland
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  3. A. Charcosset
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  4. L. Moreau
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Contributions

CB, AC and LM initiated this project. LM and CB coordinated it. AC and LM supervised this work. AIS wrote the simulation programs, analysed 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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Correspondence to L. Moreau.

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Communicated by Albrecht E. Melchinger.

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Seye, A.I., Bauland, C., Charcosset, A. et al. Revisiting hybrid breeding designs using genomic predictions: simulations highlight the superiority of incomplete factorials between segregating families over topcross designs. Theor Appl Genet 133, 1995–2010 (2020). https://doi.org/10.1007/s00122-020-03573-5

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