Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis
Recurrent selection is an important breeding method for population improvement and selecting elite inbreds or fixed lines from the improved germplasm. Recently, a computer simulation tool called QuMARS has been developed, which allows the simulation and optimization of various recurrent selection st...
| Autores principales: | , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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Crop Science Society of China
2020
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| Acceso en línea: | https://hdl.handle.net/10568/142418 |
| _version_ | 1855538167681122304 |
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| author | Ali, Mohsin Zhang, Luyan DeLacy, Ian Arief, Vivi Dieters, Mark Pfeiffer, Wolfgang H. Wang, Jiankang Li, Huihui |
| author_browse | Ali, Mohsin Arief, Vivi DeLacy, Ian Dieters, Mark Li, Huihui Pfeiffer, Wolfgang H. Wang, Jiankang Zhang, Luyan |
| author_facet | Ali, Mohsin Zhang, Luyan DeLacy, Ian Arief, Vivi Dieters, Mark Pfeiffer, Wolfgang H. Wang, Jiankang Li, Huihui |
| author_sort | Ali, Mohsin |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Recurrent selection is an important breeding method for population improvement and selecting elite inbreds or fixed lines from the improved germplasm. Recently, a computer simulation tool called QuMARS has been developed, which allows the simulation and optimization of various recurrent selection strategies. Our major objective in this study was to use the QuMARS tool to compare phenotypic recurrent, marker-assisted recurrent, and genomic selections (abbreviated respectively as PS, MARS and GS) for both short- and long- term breeding procedures. For MARS, two marker selection models were considered, i.e., stepwise (Rstep) and forward regressions (Forward). For GS, three prediction models were considered, i.e., genomic best linear unbiased predictors (GBLUP), ridge regression (Ridge), and regression by Moore-Penrose general inverse (InverseMP). To generate genotypes and phenotypes for a given individual during simulation, one additive and two epistasis genetic models were considered with three levels of heritability. Results demonstrated that selection responses from GBLUP-based GS and MARS (Forward) were consistently greater than those from PS under the additive model, particularly in early selection cycles. In contrast, selection response from PS was consistently superior over MARS and GS under epistatic models. For the two epistasis models, total genetic variance and the additive variance component were increased in some cases after selection. Through simulation, we concluded that GS and PS were effective recurrent selection methods for improved breeding of targeted traits controlled by additive and epistatic quantitative trait loci (QTL). QuMARS provides an opportunity for breeders to compare, optimize and integrate new technology into their conventional breeding programs. |
| format | Journal Article |
| id | CGSpace142418 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Crop Science Society of China |
| publisherStr | Crop Science Society of China |
| record_format | dspace |
| spelling | CGSpace1424182025-01-28T07:08:05Z Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis Ali, Mohsin Zhang, Luyan DeLacy, Ian Arief, Vivi Dieters, Mark Pfeiffer, Wolfgang H. Wang, Jiankang Li, Huihui models genotypes marker-assisted selection plant breeding phenotypes gene interaction Recurrent selection is an important breeding method for population improvement and selecting elite inbreds or fixed lines from the improved germplasm. Recently, a computer simulation tool called QuMARS has been developed, which allows the simulation and optimization of various recurrent selection strategies. Our major objective in this study was to use the QuMARS tool to compare phenotypic recurrent, marker-assisted recurrent, and genomic selections (abbreviated respectively as PS, MARS and GS) for both short- and long- term breeding procedures. For MARS, two marker selection models were considered, i.e., stepwise (Rstep) and forward regressions (Forward). For GS, three prediction models were considered, i.e., genomic best linear unbiased predictors (GBLUP), ridge regression (Ridge), and regression by Moore-Penrose general inverse (InverseMP). To generate genotypes and phenotypes for a given individual during simulation, one additive and two epistasis genetic models were considered with three levels of heritability. Results demonstrated that selection responses from GBLUP-based GS and MARS (Forward) were consistently greater than those from PS under the additive model, particularly in early selection cycles. In contrast, selection response from PS was consistently superior over MARS and GS under epistatic models. For the two epistasis models, total genetic variance and the additive variance component were increased in some cases after selection. Through simulation, we concluded that GS and PS were effective recurrent selection methods for improved breeding of targeted traits controlled by additive and epistatic quantitative trait loci (QTL). QuMARS provides an opportunity for breeders to compare, optimize and integrate new technology into their conventional breeding programs. 2020-06-01 2024-05-22T12:10:28Z 2024-05-22T12:10:28Z Journal Article https://hdl.handle.net/10568/142418 en Open Access Crop Science Society of China Chinese Academy of Agricultural Sciences Ali, Mohsin; Zhang, Luyan; DeLacy, Ian; Arief, Vivi; Dieters, Mark; Pfeiffer, Wolfgang H.; Wang, Jiankang; and Li, Huihui. 2020. Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis. Crop Journal 8(5): 866-877. https://doi.org/10.1016/j.cj.2020.04.002 |
| spellingShingle | models genotypes marker-assisted selection plant breeding phenotypes gene interaction Ali, Mohsin Zhang, Luyan DeLacy, Ian Arief, Vivi Dieters, Mark Pfeiffer, Wolfgang H. Wang, Jiankang Li, Huihui Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis |
| title | Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis |
| title_full | Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis |
| title_fullStr | Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis |
| title_full_unstemmed | Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis |
| title_short | Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis |
| title_sort | modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis |
| topic | models genotypes marker-assisted selection plant breeding phenotypes gene interaction |
| url | https://hdl.handle.net/10568/142418 |
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