Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat
Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic predict...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Oxford University Press
2016-09-01
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| Series: | G3: Genes, Genomes, Genetics |
| Subjects: | |
| Online Access: | http://g3journal.org/lookup/doi/10.1534/g3.116.032888 |
| _version_ | 1818932289567981568 |
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| author | Jessica Rutkoski Jesse Poland Suchismita Mondal Enrique Autrique Lorena González Pérez José Crossa Matthew Reynolds Ravi Singh |
| author_facet | Jessica Rutkoski Jesse Poland Suchismita Mondal Enrique Autrique Lorena González Pérez José Crossa Matthew Reynolds Ravi Singh |
| author_sort | Jessica Rutkoski |
| collection | DOAJ |
| description | Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots. |
| first_indexed | 2024-12-20T04:30:07Z |
| format | Article |
| id | doaj.art-3aabe42ccb4046a1b813dae139438a6d |
| institution | Directory Open Access Journal |
| issn | 2160-1836 |
| language | English |
| last_indexed | 2024-12-20T04:30:07Z |
| publishDate | 2016-09-01 |
| publisher | Oxford University Press |
| record_format | Article |
| series | G3: Genes, Genomes, Genetics |
| spelling | doaj.art-3aabe42ccb4046a1b813dae139438a6d2022-12-21T19:53:25ZengOxford University PressG3: Genes, Genomes, Genetics2160-18362016-09-01692799280810.1534/g3.116.03288812Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in WheatJessica RutkoskiJesse PolandSuchismita MondalEnrique AutriqueLorena González PérezJosé CrossaMatthew ReynoldsRavi SinghGenomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.http://g3journal.org/lookup/doi/10.1534/g3.116.032888Secondary traits in genomic selectionGenPredmultivariate analysisselection indexshared data resource |
| spellingShingle | Jessica Rutkoski Jesse Poland Suchismita Mondal Enrique Autrique Lorena González Pérez José Crossa Matthew Reynolds Ravi Singh Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat G3: Genes, Genomes, Genetics Secondary traits in genomic selection GenPred multivariate analysis selection index shared data resource |
| title | Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat |
| title_full | Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat |
| title_fullStr | Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat |
| title_full_unstemmed | Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat |
| title_short | Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat |
| title_sort | canopy temperature and vegetation indices from high throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat |
| topic | Secondary traits in genomic selection GenPred multivariate analysis selection index shared data resource |
| url | http://g3journal.org/lookup/doi/10.1534/g3.116.032888 |
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