Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat

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dc.contributor.author Rutkoski, J.
dc.contributor.author Poland, Jesse A.
dc.contributor.author Mondal, S.
dc.contributor.author Autrique, E.
dc.contributor.author Perez, L. G.
dc.contributor.author Crossa, J.
dc.contributor.author Reynolds, M.
dc.contributor.author Singh, R.
dc.date.accessioned 2017-11-30T21:53:40Z
dc.date.available 2017-11-30T21:53:40Z
dc.date.issued 2016-07-06
dc.identifier.uri http://hdl.handle.net/2097/38409
dc.description Citation: Rutkoski, J., . . . Singh, R. (2016). 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, 6(9), 2799-2808. https://doi.org/10.1534/g3.116.032888
dc.description.abstract 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.
dc.relation.uri https://doi.org/10.1534/g3.116.032888
dc.rights Copyright © 2016 Rutkoski et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Secondary Traits In Genomic Selection
dc.subject Genpred
dc.subject Multivariate Analysis
dc.subject Selection Index
dc.subject Shared Data Resource
dc.subject Infer Leaf-Area
dc.title Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat
dc.type Text
dc.date.published 2016
dc.citation.doi 10.1534/g3.116.032888
dc.citation.epage 2808
dc.citation.issn 2160-1836
dc.citation.issue 9
dc.citation.jtitle G3-Genes Genomes Genetics
dc.citation.spage 2799
dc.citation.volume 6
dc.citation Rutkoski, J., . . . Singh, R. (2016). 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, 6(9), 2799-2808. https://doi.org/10.1534/g3.116.032888
dc.contributor.authoreid jpoland
dc.description.version Article: Version of Record
dc.contributor.kstate Poland, Jesse A.


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Copyright © 2016 Rutkoski et al. This is an open-access article distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Except where otherwise noted, the use of this item is bound by the following: Copyright © 2016 Rutkoski et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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