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González Pérez, L.

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González Pérez
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L.
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González Pérez, L.
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0000-0002-5840-0803

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2017 - 20192
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Author: Mondal, S. × AGROVOC: GENOMICS ×

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Now showing 1 - 2 of 2
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    Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat
    (Genetics Society of America, 2019) Krause, M.; González Pérez, L.; Crossa, J.; Pérez-Rodríguez, P.; Montesinos-Lopez, O.A.; Singh, R.P.; Dreisigacker, S.; Poland, J.; Rutkoski, J.; Sorrells, M.E.; Gore, M.A.; Mondal, S.
    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.
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    Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper‑spectral image data
    (BioMed Central, 2017) Montesinos-López, A.; Montesinos-Lopez, O.A.; Cuevas, J.; Mata Lopez, W.A.; Burgueño, J.; Mondal, S.; Huerta-Espino, J.; Singh, R.P.; Autrique, E.; González Pérez, L.; Crossa, J.
    Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1–8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1–23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information.
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