Multi-trait genomic prediction models enhance the predictive ability of grain trace elements in rice
Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from As...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/164057 |
| _version_ | 1855525066420256768 |
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| author | Muvunyi, Blaise Pascal Zou, Wenli Zhan, Junhui He, Sang Ye, Guoyou |
| author_browse | He, Sang Muvunyi, Blaise Pascal Ye, Guoyou Zhan, Junhui Zou, Wenli |
| author_facet | Muvunyi, Blaise Pascal Zou, Wenli Zhan, Junhui He, Sang Ye, Guoyou |
| author_sort | Muvunyi, Blaise Pascal |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (p andlt; 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly (p andlt; 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain. |
| format | Journal Article |
| id | CGSpace164057 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1640572025-12-08T10:29:22Z Multi-trait genomic prediction models enhance the predictive ability of grain trace elements in rice Muvunyi, Blaise Pascal Zou, Wenli Zhan, Junhui He, Sang Ye, Guoyou genetics medical sciences Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (p andlt; 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly (p andlt; 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain. 2022-06-22 2024-12-19T12:53:23Z 2024-12-19T12:53:23Z Journal Article https://hdl.handle.net/10568/164057 en Open Access Frontiers Media Muvunyi, Blaise Pascal; Zou, Wenli; Zhan, Junhui; He, Sang and Ye, Guoyou. 2022. Multi-trait genomic prediction models enhance the predictive ability of grain trace elements in rice. Front. Genet., Volume 13 |
| spellingShingle | genetics medical sciences Muvunyi, Blaise Pascal Zou, Wenli Zhan, Junhui He, Sang Ye, Guoyou Multi-trait genomic prediction models enhance the predictive ability of grain trace elements in rice |
| title | Multi-trait genomic prediction models enhance the predictive ability of grain trace elements in rice |
| title_full | Multi-trait genomic prediction models enhance the predictive ability of grain trace elements in rice |
| title_fullStr | Multi-trait genomic prediction models enhance the predictive ability of grain trace elements in rice |
| title_full_unstemmed | Multi-trait genomic prediction models enhance the predictive ability of grain trace elements in rice |
| title_short | Multi-trait genomic prediction models enhance the predictive ability of grain trace elements in rice |
| title_sort | multi trait genomic prediction models enhance the predictive ability of grain trace elements in rice |
| topic | genetics medical sciences |
| url | https://hdl.handle.net/10568/164057 |
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