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Peixoto MA, Leach KA, Jarquin D, Flannery P, Zystro J, Tracy WF, Bhering L, Resende MFR. Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program. FRONTIERS IN PLANT SCIENCE 2024; 15:1293307. [PMID: 38726298 PMCID: PMC11080654 DOI: 10.3389/fpls.2024.1293307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/26/2024] [Indexed: 05/12/2024]
Abstract
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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Affiliation(s)
- Marco Antônio Peixoto
- Laboratório de Biometria, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States
| | - Kristen A. Leach
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States
| | - Diego Jarquin
- Department of Agronomy, University of Florida, Gainesville, FL, United States
| | - Patrick Flannery
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - Jared Zystro
- Organic Seed Alliance, Port Townsend, WA, United States
| | - William F. Tracy
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - Leonardo Bhering
- Laboratório de Biometria, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | - Márcio F. R. Resende
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States
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Zhang Z, Wang L. A simulation framework for reciprocal recurrent selection-based hybrid breeding under transparent and opaque simulators. FRONTIERS IN PLANT SCIENCE 2023; 14:1174168. [PMID: 37441181 PMCID: PMC10333587 DOI: 10.3389/fpls.2023.1174168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 06/02/2023] [Indexed: 07/15/2023]
Abstract
Hybrid breeding is an established and effective process to improve offspring performance, while it is resource-intensive and time-consuming for the recurrent process in reality. To enable breeders and researchers to evaluate the effectiveness of competing decision-making strategies, we present a modular simulation framework for reciprocal recurrent selection-based hybrid breeding. Consisting of multiple modules such as heterotic separation, genomic prediction, and genomic selection, this simulation framework allows breeders to efficiently simulate the hybrid breeding process with multiple options of simulators and decision-making strategies. We also integrate the recently proposed concepts of transparent and opaque simulators into the framework in order to reflect the breeding process more realistically. Simulation results show the performance comparison among different breeding strategies under the two simulators.
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Affiliation(s)
- Zerui Zhang
- Program of Bioinformatics and Computational Biology, Iowa State University, Ames, IA, United States
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, United States
- Department of Statistics, Iowa State University, Ames, IA, United States
| | - Lizhi Wang
- Program of Bioinformatics and Computational Biology, Iowa State University, Ames, IA, United States
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, United States
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Machado IP, DoVale JC, Sabadin F, Fritsche-Neto R. On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops. FRONTIERS IN PLANT SCIENCE 2023; 14:1164555. [PMID: 37332727 PMCID: PMC10272588 DOI: 10.3389/fpls.2023.1164555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/10/2023] [Indexed: 06/20/2023]
Abstract
The advances in genomics in recent years have increased the accuracy and efficiency of breeding programs for many crops. Nevertheless, the adoption of genomic enhancement for several other crops essential in developing countries is still limited, especially for those that do not have a reference genome. These crops are more often called orphans. This is the first report to show how the results provided by different platforms, including the use of a simulated genome, called the mock genome, can generate in population structure and genetic diversity studies, especially when the intention is to use this information to support the formation of heterotic groups, choice of testers, and genomic prediction of single crosses. For that, we used a method to assemble a reference genome to perform the single-nucleotide polymorphism (SNP) calling without needing an external genome. Thus, we compared the analysis results using the mock genome with the standard approaches (array and genotyping-by-sequencing (GBS)). The results showed that the GBS-Mock presented similar results to the standard methods of genetic diversity studies, division of heterotic groups, the definition of testers, and genomic prediction. These results showed that a mock genome constructed from the population's intrinsic polymorphisms to perform the SNP calling is an effective alternative for conducting genomic studies of this nature in orphan crops, especially those that do not have a reference genome.
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Affiliation(s)
| | - Júlio César DoVale
- Department of Crop Science, Federal University of Ceará, Fortaleza, Brazil
| | - Felipe Sabadin
- School of Plant and Environmental Sciences, Virginia Tech: Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Roberto Fritsche-Neto
- LSU AgCenter, Louisiana State University Agricultural Center, Baton Rouge, LA, United States
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Terraillon J, Roeber FK, Flachenecker C, Frisch M. Training set designs for prediction of yield and moisture of maize test cross hybrids with unreplicated trials. FRONTIERS IN PLANT SCIENCE 2023; 14:1080087. [PMID: 36950349 PMCID: PMC10025381 DOI: 10.3389/fpls.2023.1080087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
Unreplicated field trials and genomic prediction are both used to enhance the efficiency in early selection stages of a hybrid maize breeding program. No results are available on the optimal experimental design when combining both approaches. Our objectives were to investigate the effect of the training set design on the accuracy of genomic prediction in unreplicated maize test crosses. We carried out a cross validation study on basis of an experimental data set consisting of 1436 hybrids evaluated for yield and moisture for which genotyping information of 461 SNP markers were available. Training set designs of different size, implementing within environment prediction, within year prediction, across year prediction, and combinations of data sources across years and environments were compared with respect to their prediction accuracy. Across year prediction did not reach prediction accuracies that are useful for genomic selection. Within year prediction across environments provided useful correlations between observed and predicted breeding values. The prediction accuracies did not improve when adding to the training set data from previous years. We conclude that using all data available from unreplicated tests of the current breeding cycle provides a good accuracy of predicting test crosses, whereas adding data from previous breeding cycles, in which the genotypes are less related to the tested material, has only limited value for increasing the prediction accuracy.
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Affiliation(s)
- Jérôme Terraillon
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Giessen, Germany
| | | | | | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Giessen, Germany
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Martins Oliveira IC, Bernardeli A, Soler Guilhen JH, Pastina MM. Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids. Methods Mol Biol 2022; 2467:543-567. [PMID: 35451790 DOI: 10.1007/978-1-0716-2205-6_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
For many plant and animal species, commercial products are hybrids between individuals from different genetic groups. For allogamous plant species such as maize, the breeding objective is to produce single-cross hybrid varieties from two inbred lines each selected in complementary groups. Efficient hybrid breeding requires methods that (1) quickly generate homozygous and homogeneous parental lines with high combining abilities, (2) efficiently choose among the large number of available parental lines the most promising ones, and (3) predict the performances of sets of non-phenotyped single-cross hybrids, or hybrids phenotyped in a limited number of environments, based on their relationship with another set of hybrids with known performances. The maize breeding community has been developing model-based prediction of hybrid performances well before the genomic era. This chapter (1) provides a reminder of the maize breeding scheme before the genomic era; (2) describes how genomic data were incorporated in the prediction models involved in different steps of genomic-based single-cross maize hybrid breeding; and (3) reviews factors affecting the accuracy of genomic prediction, approaches for optimizing GP-based single-cross maize hybrid breeding schemes, and ensuring the long-term sustainability of genomic selection.
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Affiliation(s)
| | - Arthur Bernardeli
- Department of Agronomy, Universidade Federal de Viçosa, Viçosa-MG, Brazil
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Cazenave X, Petit B, Lateur M, Nybom H, Sedlak J, Tartarini S, Laurens F, Durel CE, Muranty H. Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple. G3 (BETHESDA, MD.) 2021; 12:6459174. [PMID: 34893831 PMCID: PMC9210277 DOI: 10.1093/g3journal/jkab420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/29/2021] [Indexed: 11/12/2022]
Abstract
Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e., genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and small increases in predictive ability could be obtained for some traits when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.
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Affiliation(s)
- Xabi Cazenave
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Bernard Petit
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Marc Lateur
- Plant Breeding and Biodiversity, Centre Wallon de Recherches Agronomiques, Gembloux, Belgium
| | - Hilde Nybom
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Kristianstad, Sweden
| | - Jiri Sedlak
- Výzkumný a Šlechtitelský ústav Ovocnářský Holovousy s.r.o, Holovousy, Czech Republic
| | - Stefano Tartarini
- Department of Agricultural Sciences, University of Bologna, Bologna, Italy
| | - François Laurens
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Charles-Eric Durel
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Hélène Muranty
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France,Corresponding author:
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