Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat
Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are asso...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Oxford University Press
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/164705 |
| _version_ | 1855527302966804480 |
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| author | Krause, Margaret R. González-Pérez, Lorena Crossa, José Pérez-Rodríguez, Paulino Montesinos-López, Osval Singh, Ravi P. Dreisigacker, Susanne Poland, Jesse Rutkoski, Jessica Sorrells, Mark Gore, Michael A. Mondal, Suchismita |
| author_browse | Crossa, José Dreisigacker, Susanne González-Pérez, Lorena Gore, Michael A. Krause, Margaret R. Mondal, Suchismita Montesinos-López, Osval Poland, Jesse Pérez-Rodríguez, Paulino Rutkoski, Jessica Singh, Ravi P. Sorrells, Mark |
| author_facet | Krause, Margaret R. González-Pérez, Lorena Crossa, José Pérez-Rodríguez, Paulino Montesinos-López, Osval Singh, Ravi P. Dreisigacker, Susanne Poland, Jesse Rutkoski, Jessica Sorrells, Mark Gore, Michael A. Mondal, Suchismita |
| author_sort | Krause, Margaret R. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment (G × E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs. |
| format | Journal Article |
| id | CGSpace164705 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1647052025-05-24T06:51:12Z Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat Krause, Margaret R. González-Pérez, Lorena Crossa, José Pérez-Rodríguez, Paulino Montesinos-López, Osval Singh, Ravi P. Dreisigacker, Susanne Poland, Jesse Rutkoski, Jessica Sorrells, Mark Gore, Michael A. Mondal, Suchismita genetics molecular biology Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment (G × E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs. 2019-04-01 2024-12-19T12:54:13Z 2024-12-19T12:54:13Z Journal Article https://hdl.handle.net/10568/164705 en Oxford University Press Krause, Margaret R; González-Pérez, Lorena; Crossa, José; Pérez-Rodríguez, Paulino; Montesinos-López, Osval; Singh, Ravi P; Dreisigacker, Susanne; Poland, Jesse; Rutkoski, Jessica; Sorrells, Mark; Gore, Michael A and Mondal, Suchismita. 2019. Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat. G3: Genes, Genomes, Genetics, Volume 9; pages 1231-1247. |
| spellingShingle | genetics molecular biology Krause, Margaret R. González-Pérez, Lorena Crossa, José Pérez-Rodríguez, Paulino Montesinos-López, Osval Singh, Ravi P. Dreisigacker, Susanne Poland, Jesse Rutkoski, Jessica Sorrells, Mark Gore, Michael A. Mondal, Suchismita Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat |
| title | Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat |
| title_full | Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat |
| title_fullStr | Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat |
| title_full_unstemmed | Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat |
| title_short | Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat |
| title_sort | hyperspectral reflectance derived relationship matrices for genomic prediction of grain yield in wheat |
| topic | genetics molecular biology |
| url | https://hdl.handle.net/10568/164705 |
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