GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection
Genomic selection (GS) has emerged as an effective technology to accelerate crop hybrid breeding by enabling early selection prior to phenotype collection. Genomic best linear unbiased prediction (GBLUP) is a robust method that has been routinely used in GS breeding programs. However, GBLUP assumes...
| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Oxford University Press
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/162555 |
| _version_ | 1855530649003229184 |
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| author | Yang Xu Yuxiang Zhang Yanru Cui Kai Zhou Guangning Yu Wenyan Yang Xin Wang Furong Li Xiusheng Guan Xuecai Zhang Zefeng Yang Shizhong Xu Chenwu Xu |
| author_browse | Chenwu Xu Furong Li Guangning Yu Kai Zhou Shizhong Xu Wenyan Yang Xin Wang Xiusheng Guan Xuecai Zhang Yang Xu Yanru Cui Yuxiang Zhang Zefeng Yang |
| author_facet | Yang Xu Yuxiang Zhang Yanru Cui Kai Zhou Guangning Yu Wenyan Yang Xin Wang Furong Li Xiusheng Guan Xuecai Zhang Zefeng Yang Shizhong Xu Chenwu Xu |
| author_sort | Yang Xu |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Genomic selection (GS) has emerged as an effective technology to accelerate crop hybrid breeding by enabling early selection prior to phenotype collection. Genomic best linear unbiased prediction (GBLUP) is a robust method that has been routinely used in GS breeding programs. However, GBLUP assumes that markers contribute equally to the total genetic variance, which may not be the case. In this study, we developed a novel GS method called GA-GBLUP that leverages the genetic algorithm (GA) to select markers related to the target trait. We defined four fitness functions for optimization, including AIC, BIC, R2, and HAT, to improve the predictability and bin adjacent markers based on the principle of linkage disequilibrium to reduce model dimension. The results demonstrate that the GA-GBLUP model, equipped with R2 and HAT fitness function, produces much higher predictability than GBLUP for most traits in rice and maize datasets, particularly for traits with low heritability. Moreover, we have developed a user-friendly R package, GAGBLUP, for GS, and the package is freely available on CRAN (https://CRAN.R-project.org/package=GAGBLUP). |
| format | Journal Article |
| id | CGSpace162555 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1625552025-12-08T10:06:44Z GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection Yang Xu Yuxiang Zhang Yanru Cui Kai Zhou Guangning Yu Wenyan Yang Xin Wang Furong Li Xiusheng Guan Xuecai Zhang Zefeng Yang Shizhong Xu Chenwu Xu genetics algorithms marker-assisted selection Genomic selection (GS) has emerged as an effective technology to accelerate crop hybrid breeding by enabling early selection prior to phenotype collection. Genomic best linear unbiased prediction (GBLUP) is a robust method that has been routinely used in GS breeding programs. However, GBLUP assumes that markers contribute equally to the total genetic variance, which may not be the case. In this study, we developed a novel GS method called GA-GBLUP that leverages the genetic algorithm (GA) to select markers related to the target trait. We defined four fitness functions for optimization, including AIC, BIC, R2, and HAT, to improve the predictability and bin adjacent markers based on the principle of linkage disequilibrium to reduce model dimension. The results demonstrate that the GA-GBLUP model, equipped with R2 and HAT fitness function, produces much higher predictability than GBLUP for most traits in rice and maize datasets, particularly for traits with low heritability. Moreover, we have developed a user-friendly R package, GAGBLUP, for GS, and the package is freely available on CRAN (https://CRAN.R-project.org/package=GAGBLUP). 2024-07-25 2024-11-21T17:21:30Z 2024-11-21T17:21:30Z Journal Article https://hdl.handle.net/10568/162555 en Open Access application/pdf Oxford University Press Xu, Y., Zhang, Y., Cui, Y., Zhou, K., Yu, G., Yang, W., Wang, X., Li, F., Guan, X., Zhang, X., Yang, Z., Xu, S., & Xu, C. (2024). GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection. Briefings in Bioinformatics, 25(5), bbae385. https://doi.org/10.1093/bib/bbae385 |
| spellingShingle | genetics algorithms marker-assisted selection Yang Xu Yuxiang Zhang Yanru Cui Kai Zhou Guangning Yu Wenyan Yang Xin Wang Furong Li Xiusheng Guan Xuecai Zhang Zefeng Yang Shizhong Xu Chenwu Xu GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection |
| title | GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection |
| title_full | GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection |
| title_fullStr | GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection |
| title_full_unstemmed | GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection |
| title_short | GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection |
| title_sort | ga gblup leveraging the genetic algorithm to improve the predictability of genomic selection |
| topic | genetics algorithms marker-assisted selection |
| url | https://hdl.handle.net/10568/162555 |
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