Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids

Oliveira, I. C. M.; Bernardeli, Arthur; Guilhen, José Henrique Soler; Pastina, Maria Marta

doi:10.1007/978-1-0716-2205-6_20

Cited by 2 publications

(3 citation statements)

References 117 publications

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“…Genomic selection is proposed to boost different stages of a breeding program ( Allier et al., 2019 ; Oliveira et al., 2020 ; Powell et al., 2020 ). For hybrid crops, the prediction of a potential cross from genotyped parents represents an important tool that ensures the best candidates will reach advanced field stages ( Kadam et al., 2021 ; Oliveira et al., 2022 ). Routinely, sweet corn breeders assess hybrid performance by examining a set of target traits in several environments.…”

Section: Discussionmentioning

confidence: 99%

Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program

Peixoto,

Leach,

Jarquin

et al. 2024

Front. Plant Sci.

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Sweet corn breeding programs, like field corn, focus on the development of elite inbred lines to produce commercial hybrids. For this reason, genomic selection models can help the in silico prediction of hybrid crosses from the elite lines, which is hypothesized to improve the test cross scheme, leading to higher genetic gain in a breeding program. This study aimed to explore the potential of implementing genomic selection in a sweet corn breeding program through hybrid prediction in a within-site across-year and across-site framework. A total of 506 hybrids were evaluated in six environments (California, Florida, and Wisconsin, in the years 2020 and 2021). A total of 20 traits from three different groups were measured (plant-, ear-, and flavor-related traits) across the six environments. Eight statistical models were considered for prediction, as the combination of two genomic prediction models (GBLUP and RKHS) with two different kernels (additive and additive + dominance), and in a single- and multi-trait framework. Also, three different cross-validation schemes were tested (CV1, CV0, and CV00). The different models were then compared based on the correlation between the estimated breeding values/total genetic values and phenotypic measurements. Overall, heritabilities and correlations varied among the traits. The models implemented showed good accuracies for trait prediction. The GBLUP implementation outperformed RKHS in all cross-validation schemes and models. Models with additive plus dominance kernels presented a slight improvement over the models with only additive kernels for some of the models examined. In addition, models for within-site across-year and across-site performed better in the CV0 than the CV00 scheme, on average. Hence, GBLUP should be considered as a standard model for sweet corn hybrid prediction. In addition, we found that the implementation of genomic prediction in a sweet corn breeding program presented reliable results, which can improve the testcross stage by identifying the top candidates that will reach advanced field-testing stages.

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Section: Discussionmentioning

confidence: 99%

Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program

Peixoto,

Leach,

Jarquin

et al. 2024

Front. Plant Sci.

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“…As we know, genomic selection has been proposed for boosting different stages of a breeding program . For hybrid crops, the prediction of the potential crosses (hybrids) from genotyped parents represents an important tool that can ensure good candidates reach advanced field stages (Kadam et al, 2021;Oliveira et al, 2022). Routinely, sweet corn breeders assessed hybrids performance in several environments, and they need to keep in the consideration a set of target traits for crop production.…”

Section: Discussionmentioning

confidence: 99%

“…Hybrids represented a final and/or commercial product for multiple crops and, consequently, several breeding programs. They can be generated through a simple cross between individuals that may or may not belong to different genetic groups, such as heterotic groups (Oliveira et al, 2022). Hybrid crop breeding generally advances though an evaluation process where finish lines from a given program are crossed to testers.…”

Section: Introductionmentioning

confidence: 99%

Application of quantitative genetics tools to breeding program optimization

Peixoto

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Overall, the application of quantitative genetics theory has greatly influenced plant breeding programs over the last few decades. The aim of this study was to use and develop quantitative genetics tools to improve breeding programs. In the first chapter we simulated a hybrid crop breeding program and compared breeding pipelines with two strategies for parental updates, and we compare the gain and costs to implements genomic selection and high-throughput phenotyping into the pipeline. Our results suggest that early parental selection performs better and that high-throughput phenotyping together with genomic selection delivers the highest hybrid gain in the long-term. In the second chapter we evaluated the potential of implementing genomic selection in a sweet corn breeding program through hybrid prediction. We evaluated 506 hybrids in six environments and measured 21 traits. We considered eight statistical models for prediction and three cross-validation schemes CV1, CV0, and CV00. The results indicated RKHS model outperforming GBLUP models, and models with additive plus dominance kernels presented slight improvement for some traits. Therefore, we recommend using the RKHS model as a standard model for sweet corn hybrid prediction, and to implement genomic selection in sweet corn breeding programs to optimize the testcross stage and the candidates that reach the field stage. In the third chapter we describe SMate, a flexible R package for cross prediction and optimization, which represents a tool for breeding programs to balance genetic gains and inbreeding rate levels. The package builds a valid mate plan based on two core aspects: (i) prediction of usefulness for potential cross, and (ii) optimization of the set of crosses. In conclusion, SMate package enables to optimize cross selection in breeding programs targeting long term genetic gains while balancing genetic diversity and inbreeding rate levels. In summary, quantitative genetics tools have been largely applied in breeding programs and has evolved with it. Our study demonstrated potential to contribute to the quantitative genetics field and direct impact in breeding programs. Keywords: Mate allocation. Inbreeding. Genomic hybrid prediction. Stochastic simulation.

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Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids

Cited by 2 publications

References 117 publications

Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program

Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program

Application of quantitative genetics tools to breeding program optimization

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