Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize
Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by speci...
| Autores principales: | , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/162513 |
| _version_ | 1855535483676786688 |
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| author | Loladze, Alexander Rodrigues, Francelino A. Petroli, Cesar D. Muñoz-Zavala, Carlos Naranjo, Sergio San Vicente García, Felix M. Gerard, Bruno G. Montesinos-Lopez, Osval A. Crossa, Jose Martini, Johannes W.R. |
| author_browse | Crossa, Jose Gerard, Bruno G. Loladze, Alexander Martini, Johannes W.R. Montesinos-Lopez, Osval A. Muñoz-Zavala, Carlos Naranjo, Sergio Petroli, Cesar D. Rodrigues, Francelino A. San Vicente García, Felix M. |
| author_facet | Loladze, Alexander Rodrigues, Francelino A. Petroli, Cesar D. Muñoz-Zavala, Carlos Naranjo, Sergio San Vicente García, Felix M. Gerard, Bruno G. Montesinos-Lopez, Osval A. Crossa, Jose Martini, Johannes W.R. |
| author_sort | Loladze, Alexander |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher −logp values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1. Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities. |
| format | Journal Article |
| id | CGSpace162513 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1625132025-10-26T12:56:24Z Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize Loladze, Alexander Rodrigues, Francelino A. Petroli, Cesar D. Muñoz-Zavala, Carlos Naranjo, Sergio San Vicente García, Felix M. Gerard, Bruno G. Montesinos-Lopez, Osval A. Crossa, Jose Martini, Johannes W.R. rusts remote sensing vegetation index maize chromosome mapping Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher −logp values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1. Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities. 2024-03 2024-11-21T14:14:42Z 2024-11-21T14:14:42Z Journal Article https://hdl.handle.net/10568/162513 en Open Access application/pdf Elsevier Loladze, A., Rodrigues, F., Petroli, C. D., Muñoz-Zavala, C., Naranjo, S., San Vicente, F. M., Gérard, B., Montesinos-López, O. A., Crossa, J., & Martini, J. W. R. (2024). Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize. Field Crops Research, 308, 109281. https://doi.org/10.1016/j.fcr.2024.109281 |
| spellingShingle | rusts remote sensing vegetation index maize chromosome mapping Loladze, Alexander Rodrigues, Francelino A. Petroli, Cesar D. Muñoz-Zavala, Carlos Naranjo, Sergio San Vicente García, Felix M. Gerard, Bruno G. Montesinos-Lopez, Osval A. Crossa, Jose Martini, Johannes W.R. Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize |
| title | Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize |
| title_full | Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize |
| title_fullStr | Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize |
| title_full_unstemmed | Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize |
| title_short | Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize |
| title_sort | use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize |
| topic | rusts remote sensing vegetation index maize chromosome mapping |
| url | https://hdl.handle.net/10568/162513 |
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