Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm
Zinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the world’s population. To study the potential of genomic selection (GS) for maize with increased Zn concentration, an association panel and two doubled haploid (DH) populations were evaluated in three environments....
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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Oxford University Press
2020
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| Acceso en línea: | https://hdl.handle.net/10568/171386 |
| _version_ | 1855519900345303040 |
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| author | Mageto, Edna K. Crossa, José Pérez-Rodríguez, Paulino Dhliwayo, Thanda Palacios-Rojas, Natalia |
| author_browse | Crossa, José Dhliwayo, Thanda Mageto, Edna K. Palacios-Rojas, Natalia Pérez-Rodríguez, Paulino |
| author_facet | Mageto, Edna K. Crossa, José Pérez-Rodríguez, Paulino Dhliwayo, Thanda Palacios-Rojas, Natalia |
| author_sort | Mageto, Edna K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Zinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the world’s population. To study the potential of genomic selection (GS) for maize with increased Zn concentration, an association panel and two doubled haploid (DH) populations were evaluated in three environments. Three genomic prediction models, M (M1: Environment + Line, M2: Environment + Line + Genomic, and M3: Environment + Line + Genomic + Genomic x Environment) incorporating main effects (lines and genomic) and the interaction between genomic and environment (G x E) were assessed to estimate the prediction ability (rMP) for each model. Two distinct cross-validation (CV) schemes simulating two genomic prediction breeding scenarios were used. CV1 predicts the performance of newly developed lines, whereas CV2 predicts the performance of lines tested in sparse multi-location trials. Predictions for Zn in CV1 ranged from -0.01 to 0.56 for DH1, 0.04 to 0.50 for DH2 and -0.001 to 0.47 for the association panel. For CV2, rMP values ranged from 0.67 to 0.71 for DH1, 0.40 to 0.56 for DH2 and 0.64 to 0.72 for the association panel. The genomic prediction model which included G x E had the highest average rMP for both CV1 (0.39 and 0.44) and CV2 (0.71 and 0.51) for the association panel and DH2 population, respectively. These results suggest that GS has potential to accelerate breeding for enhanced kernel Zn concentration by facilitating selection of superior genotypes. |
| format | Journal Article |
| id | CGSpace171386 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1713862025-01-29T12:58:06Z Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm Mageto, Edna K. Crossa, José Pérez-Rodríguez, Paulino Dhliwayo, Thanda Palacios-Rojas, Natalia genetics breeding zinc forecasting maize Zinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the world’s population. To study the potential of genomic selection (GS) for maize with increased Zn concentration, an association panel and two doubled haploid (DH) populations were evaluated in three environments. Three genomic prediction models, M (M1: Environment + Line, M2: Environment + Line + Genomic, and M3: Environment + Line + Genomic + Genomic x Environment) incorporating main effects (lines and genomic) and the interaction between genomic and environment (G x E) were assessed to estimate the prediction ability (rMP) for each model. Two distinct cross-validation (CV) schemes simulating two genomic prediction breeding scenarios were used. CV1 predicts the performance of newly developed lines, whereas CV2 predicts the performance of lines tested in sparse multi-location trials. Predictions for Zn in CV1 ranged from -0.01 to 0.56 for DH1, 0.04 to 0.50 for DH2 and -0.001 to 0.47 for the association panel. For CV2, rMP values ranged from 0.67 to 0.71 for DH1, 0.40 to 0.56 for DH2 and 0.64 to 0.72 for the association panel. The genomic prediction model which included G x E had the highest average rMP for both CV1 (0.39 and 0.44) and CV2 (0.71 and 0.51) for the association panel and DH2 population, respectively. These results suggest that GS has potential to accelerate breeding for enhanced kernel Zn concentration by facilitating selection of superior genotypes. 2020-08-01 2025-01-29T12:58:06Z 2025-01-29T12:58:06Z Journal Article https://hdl.handle.net/10568/171386 en Open Access Oxford University Press Mageto, Edna K.; Crossa, Jose; Pérez-Rodríguez, Paulino; Dhliwayo, Thanda; Palacios-Rojas, Natalia; et al. 2020. Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm. G3 Genes Genomes Genetics 10(8): 2629–2639. https://doi.org/10.1534/g3.120.401172 |
| spellingShingle | genetics breeding zinc forecasting maize Mageto, Edna K. Crossa, José Pérez-Rodríguez, Paulino Dhliwayo, Thanda Palacios-Rojas, Natalia Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm |
| title | Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm |
| title_full | Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm |
| title_fullStr | Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm |
| title_full_unstemmed | Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm |
| title_short | Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm |
| title_sort | genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm |
| topic | genetics breeding zinc forecasting maize |
| url | https://hdl.handle.net/10568/171386 |
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