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Population improvement via recurrent selection drives genetic gain in upland rice breeding

Heredity volume 131, pages 201–210 (2023)Cite this article

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Abstract

One of the main challenges of breeding programs is to identify superior genotypes from a large number of candidates. By gradually increasing the frequency of favorable alleles in the breeding population, recurrent selection improves the population mean for target traits, increasing the chance to identify promising genotypes. In rice, population improvement through recurrent selection has been used very little to date, except in Latin America. At Embrapa (Brazilian Agricultural Research Corporation), the upland rice breeding program is conducted in two phases: population improvement followed by product development. In this study, the CNA6 population, evaluated over five cycles (3 to 7) of selection, including 20 field trials, was used to assess the realized genetic gain. A high rate of genetic gain was observed for grain yield, at 215 kg.ha−1 per cycle or 67.8 kg.ha−1 per year (3.08%). The CNA6 population outperformed the controls only for the last cycle, with a yield difference of 1128 kg.ha−1. An analysis of the product development pipeline, based on 29 advanced yield trials with lines derived from cycles 3 to 6, showed that lines derived from the CNA6 population had high grain yield, but did not outperform the controls. These results demonstrate that the application of recurrent selection to a breeding population with sufficient genetic variability can result in significant genetic gains for quantitative traits, such as grain yield. The integration of this strategy into a two-phase breeding program also makes it possible to increase quantitative traits while selecting for other traits of interest.

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Fig. 1: Schematic representation of the Embrapa rice breeding program, including the population improvement phase and the product development phase.
Fig. 2: Trend analysis of CNA6 families against the controls in cycles 3 to 7.
Fig. 3: Distribution of phenotypic values for advanced lines from the product development pipeline.

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Data availability

The data used in this study are available at the following address: https://doi.org/10.5061/dryad.1g1jwsv28.

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Acknowledgements

The authors acknowledge the work of all collaborators from partner institutions who contributed to the conduction of the field trials and collected the data used in our study. We also want to thank CIRAD for the historical and fruitful partnership and Embrapa for granting a scholarship to the first author. Most of the data from CNA6 population was generated under the coordination of Dr. Orlando Peixoto de Morais, a brilliant scientist and human being who dedicated his life to rice improvement, guaranteeing enormous benefits to Brazilian society.

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Authors and Affiliations

  1. Embrapa Rice and Beans, Santo Antonio de Goiás, Goiás, Brazil

    Adriano Pereira de Castro, Flávio Breseghello & Isabela Volpi Furtini

  2. Embrapa Rondonia, Vilhena, Rondônia, Brazil

    Marley Marico Utumi

  3. Embrapa Meio Norte, Teresina, Piauí, Brazil

    José Almeida Pereira

  4. AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France

    Tuong-Vi Cao & Jérôme Bartholomé

  5. CIRAD, UMR AGAP Institut, F-34398, Montpellier, France

    Tuong-Vi Cao & Jérôme Bartholomé

  6. Alliance Bioversity-CIAT, Cali, Colombia

    Jérôme Bartholomé

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  1. Adriano Pereira de Castro
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  2. Flávio Breseghello
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  5. José Almeida Pereira
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  6. Tuong-Vi Cao
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  7. Jérôme Bartholomé
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Contributions

The experiments were performed by AC, FB, IVF, MMU and JAP. TVC, AC and JB performed the analysis. AC and JB wrote the first draft of the manuscript, which was further edited by all the co-authors. AC and JB designed and coordinated the study. All the authors have read and approved the final manuscript.

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Correspondence to Adriano Pereira de Castro.

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Pereira de Castro, A., Breseghello, F., Furtini, I.V. et al. Population improvement via recurrent selection drives genetic gain in upland rice breeding. Heredity 131, 201–210 (2023). https://doi.org/10.1038/s41437-023-00636-3

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