Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino Sheep
The marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or selection (GS). If the potential of GP is to be fully utilized to optimize the effect of breeding and selection, in addition to incorporating the above fa...
| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Oxford University Press
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/114635 |
| _version_ | 1855526544919756800 |
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| author | Shaohua Zhu Tingting Guo Chao Yuan Jianbin Liu Jianye Li Mei Han Hongchang Zhao Yi Wu Weibo Sun Xijun Wang Tianxiang Wang Jigang Liu Tiambo, Christian K. Yaojing Yue Bohui Yang |
| author_browse | Bohui Yang Chao Yuan Hongchang Zhao Jianbin Liu Jianye Li Jigang Liu Mei Han Shaohua Zhu Tiambo, Christian K. Tianxiang Wang Tingting Guo Weibo Sun Xijun Wang Yaojing Yue Yi Wu |
| author_facet | Shaohua Zhu Tingting Guo Chao Yuan Jianbin Liu Jianye Li Mei Han Hongchang Zhao Yi Wu Weibo Sun Xijun Wang Tianxiang Wang Jigang Liu Tiambo, Christian K. Yaojing Yue Bohui Yang |
| author_sort | Shaohua Zhu |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or selection (GS). If the potential of GP is to be fully utilized to optimize the effect of breeding and selection, in addition to incorporating the above factors into simulated data for analysis, it is essential to incorporate these factors into real data for understanding their impact on GP accuracy, more clearly and intuitively. Herein, we studied the GP of six wool traits of sheep by two different models, including Bayesian Alphabet (BayesA, BayesB, BayesCπ, and Bayesian LASSO) and genomic best linear unbiased prediction (GBLUP). We adopted fivefold cross-validation to perform the accuracy evaluation based on the genotyping data of Alpine Merino sheep (n = 821). The main aim was to study the influence and interaction of different models and marker densities on GP accuracy. The GP accuracy of the six traits was found to be between 0.28 and 0.60, as demonstrated by the cross-validation results. We showed that the accuracy of GP could be improved by increasing the marker density, which is closely related to the model adopted and the heritability level of the trait. Moreover, based on two different marker densities, it was derived that the prediction effect of GBLUP model for traits with low heritability was better; while with the increase of heritability level, the advantage of Bayesian Alphabet would be more obvious, therefore, different models of GP are appropriate in different traits. These findings indicated the significance of applying appropriate models for GP which would assist in further exploring the optimization of GP. |
| format | Journal Article |
| id | CGSpace114635 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1146352025-01-24T14:13:04Z Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino Sheep Shaohua Zhu Tingting Guo Chao Yuan Jianbin Liu Jianye Li Mei Han Hongchang Zhao Yi Wu Weibo Sun Xijun Wang Tianxiang Wang Jigang Liu Tiambo, Christian K. Yaojing Yue Bohui Yang sheep small ruminants animal breeding genetics The marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or selection (GS). If the potential of GP is to be fully utilized to optimize the effect of breeding and selection, in addition to incorporating the above factors into simulated data for analysis, it is essential to incorporate these factors into real data for understanding their impact on GP accuracy, more clearly and intuitively. Herein, we studied the GP of six wool traits of sheep by two different models, including Bayesian Alphabet (BayesA, BayesB, BayesCπ, and Bayesian LASSO) and genomic best linear unbiased prediction (GBLUP). We adopted fivefold cross-validation to perform the accuracy evaluation based on the genotyping data of Alpine Merino sheep (n = 821). The main aim was to study the influence and interaction of different models and marker densities on GP accuracy. The GP accuracy of the six traits was found to be between 0.28 and 0.60, as demonstrated by the cross-validation results. We showed that the accuracy of GP could be improved by increasing the marker density, which is closely related to the model adopted and the heritability level of the trait. Moreover, based on two different marker densities, it was derived that the prediction effect of GBLUP model for traits with low heritability was better; while with the increase of heritability level, the advantage of Bayesian Alphabet would be more obvious, therefore, different models of GP are appropriate in different traits. These findings indicated the significance of applying appropriate models for GP which would assist in further exploring the optimization of GP. 2021-10-19 2021-08-15T07:35:02Z 2021-08-15T07:35:02Z Journal Article https://hdl.handle.net/10568/114635 en Open Access Oxford University Press Shaohua Zhu, Tingting Guo, Chao Yuan, Jianbin Liu, Jianye Li, Mei Han, Hongchang Zhao, Yi Wu, Weibo Sun, Xijun Wang, Tianxiang Wang, Jigang Liu, Tiambo, C.K., Yaojing Yue and Bohui Yang. 2021. Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino Sheep. G3: Genes, Genomes, Genetics 11(11): jkab206. |
| spellingShingle | sheep small ruminants animal breeding genetics Shaohua Zhu Tingting Guo Chao Yuan Jianbin Liu Jianye Li Mei Han Hongchang Zhao Yi Wu Weibo Sun Xijun Wang Tianxiang Wang Jigang Liu Tiambo, Christian K. Yaojing Yue Bohui Yang Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino Sheep |
| title | Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino Sheep |
| title_full | Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino Sheep |
| title_fullStr | Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino Sheep |
| title_full_unstemmed | Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino Sheep |
| title_short | Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino Sheep |
| title_sort | evaluation of bayesian alphabet and gblup based on different marker density for genomic prediction in alpine merino sheep |
| topic | sheep small ruminants animal breeding genetics |
| url | https://hdl.handle.net/10568/114635 |
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