Person:
Poland, J.

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Poland
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Poland, J.
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0000-0002-7856-1399

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2012 - 2019162020 - 20226
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Now showing 1 - 10 of 22
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    Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data
    (John Wiley & Sons Inc., 2022) Lopez-Cruz, M.; Dreisigacker, S.; Crespo Herrera, L.A.; Bentley, A.R.; Singh, R.P.; Poland, J.; Shrestha, S.; Huerta-Espino, J.; Velu, G.; Juliana, P.; Mondal, S.; Pérez-Rodríguez, P.; Crossa, J.
    Publication
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    Bayesian multitrait kernel methods improve multienvironment genome-based prediction
    (Oxford University Press, 2022) Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Montesinos-López, A.; Ramirez-Alcaraz, J.M.; Poland, J.; Singh, R.P.; Dreisigacker, S.; Crespo Herrera, L.A.; Mondal, S.; Velu, G.; Juliana, P.; Huerta-Espino, J.; Shrestha, S.; Varshney, R.K.; Crossa, J.
    Publication
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    Response to early generation genomic selection for yield in wheat
    (Frontiers, 2022) Bonnett, D.; Yongle Li; Crossa, J.; Dreisigacker, S.; Basnet, B.R.; Pérez-Rodríguez, P.; Alvarado Beltrán, G.; Jannink, J.L.; Poland, J.; Sorrells, M.E.
    Publication
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    Aerial high‐throughput phenotyping enabling indirect selection for grain yield at the early‐generation seed‐limited stages in breeding programs
    (CSSA, 2020) Krause, M.; Mondal, S.; Crossa, J.; Singh, R.P.; Pinto Espinosa, F.; Haghighattalab, A.; Shrestha, S.; Rutkoski, J.; Gore, M.A.; Sorrells, M.E.; Poland, J.
    Publication
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    Increasing genomic-enabled prediction accuracy by modeling genotype × environment interactions in kansas wheat
    (CSSA :, 2017) Jarquin, D.; Lemes da Silva, C.; Gaynor, R.C.; Poland, J.; Fritz, A.; Howard, R.; Battenfield, S.D.; Crossa, J.
    Publication
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    Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield
    (CSSA :, 2017) Jin Sun; Rutkoski, J.; Poland, J.; Crossa, J.; Jannink, J.L.; Sorrells, M.E.
    Publication
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    Aerial high‐throughput phenotyping enables indirect selection for grain yield at the early generation, seed‐limited stages in breeding programs
    (Crop Science Society of America (CSSA), 2020) Krause, M.; Mondal, S.; Crossa, J.; Singh, R.P.; Pinto Espinosa, F.; Haghighattalab, A.; Shrestha, S.; Rutkoski, J.; Gore, M.A.; Sorrells, M.E.; Poland, J.
    Publication
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    Genome‐based prediction of multiple wheat quality traits in multiple years
    (Crop Science Society of America, 2020) Ibba, M.I.; Crossa, J.; Montesinos-Lopez, O.A.; Montesinos-López, A.; Juliana, P.; Guzman, C.; Dolorean, E.; Dreisigacker, S.; Poland, J.
    Publication
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    Joint use of genome, pedigree, and their interaction with environment for predicting the performance of wheat lines in new environments
    (Genetics Society of America, 2019) Howard, R.; Gianola, D.; Montesinos-Lopez, O.A.; Juliana, P.; Singh, R.P.; Poland, J.; Shrestha, S.; Pérez-Rodríguez, P.; Crossa, J.; Jarquin, D.
    Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance of wheat lines is highly influenced by environmental stimuli, it is important to accurately model the environment and its interaction with genetic factors in prediction models. Arguably, multi-environmental best linear unbiased prediction (BLUP) may deliver better prediction performance than single-environment genomic BLUP. We evaluated pedigree and genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information as prediction inputs in two different validation schemes. All models included main effects, but some considered interactions between the different types of pedigree and genomic covariates via Hadamard products of similarity kernels. Pedigree models always gave better prediction of new lines in observed environments than genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, genomes, and environments were included. When new lines were predicted in unobserved environments, in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design and prediction of the outcome of future breeding programs.
    Publication
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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.
    Publication