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Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat

Author: Krause, M.
Author: Gonzalez-Perez, L.
Author: Crossa, J.
Author: Perez-Rodriguez, P.
Author: Montesinos-Lopez, O.A.
Author: Singh, R.P.
Author: Dreisigacker, S.
Author: Poland, J.A.
Author: Rutkoski, J.
Author: Sorrells, M.E.
Author: Gore, M.A.
Author: Mondal, S.
Year: 2019
ISSN: 2160-1836 (Print)
Abstract: 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: PDF
Language: English
Publisher: Genetics Society of America
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Place of Publication: Bethesda, MD (USA)
Pages: 1231-1247
Issue: 4
Volume: 9
DOI: 10.1534/g3.118.200856
Agrovoc: WHEAT
Related Datasets:
Related Datasets:
Journal: G3: Genes, Genomes, Genetics

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This item appears in the following Collection(s)

  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP
  • Wheat
    Wheat - breeding, phytopathology, physiology, quality, biotech

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