Person: Poland, J.
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Poland
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Poland, J.
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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 - 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 - 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 - 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 - 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 - Prospects and challenges of applied genomic selection-a new paradigm in breeding for grain yield in bread wheat(Crop Science Society of America, 2018) Juliana, P.; Singh, R.P.; Poland, J.; Mondal, S.; Crossa, J.; Montesinos-Lopez, O.A.; Dreisigacker, S.; Pérez-Rodríguez, P.; Huerta-Espino, J.; Crespo Herrera, L.A.; Velu, G.Genomic selection (GS) has been promising for increasing genetic gains in several species. Therefore, we evaluated the potential integration of GS for grain yield (GY) in bread wheat (Triticum aestivum L.) in CIMMYT's elite yield trial nurseries. We observed that the genomic prediction accuracies within nurseries (0.44 and 0.35) were substantially higher than across-nursery accuracies (0.15 and 0.05) for GY evaluated in the bed and flat planting systems, respectively. The accuracies from using only a subset of 251 genotyping-by-sequencing markers were comparable to the accuracies using all 2038 markers. We also used the item-based collaborative filtering approach for incorporating other related traits in predicting GY and observed that it outperformed genomic predictions across nurseries, but was less predictive when trait correlations with GY were low. Furthermore, we compared GS and phenotypic selections (PS) and observed that at a selection intensity of 0.5, GS could select a maximum of 70.9 and 61.5% of the top lines and discard 71.5 and 60.5% of the poor lines selected or discarded by PS within and across nurseries, respectively. Comparisons of GS and pedigree-based predictions revealed that the advantage of GS over the pedigree was moderate in populations without full-sibs. However, GS was less advantageous for within-family selections in elite families with few full-sibs and minimal Mendelian sampling variance. Overall, our results demonstrate the importance of applying GS for GY at the appropriate stage of the breeding cycle, and we speculate that gains can be maximized if it is implemented in early-generation within-family selections.
Publication - Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat(Springer, 2019) Juliana, P.; Montesinos-Lopez, O.A.; Crossa, J.; Mondal, S.; González Pérez, L.; Poland, J.; Huerta-Espino, J.; Crespo Herrera, L.A.; Velu, G.; Dreisigacker, S.; Shrestha, S.; Pérez-Rodríguez, P.; Pinto Espinosa, F.; Singh, R.P.Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center?s elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress?resilience within years.
Publication - Utilizing genomics and phenomics in CIMMYT wheat breeding(CIMMYT, 2016) Mondal, S.; Poland, J.; Haghighattalab, A.; Singh, D.; Rahmanm, M.; Sorrells, M.E.; Jin Sun; Singh, R.P.; Crossa, J.; Dreisigacker, S.; Kumar, U.; Imtiaz, M.; Juliana, P.
Publication - Wheat quality improvement at CIMMYT and the use of genomic selection on it(Elsevier, 2016) Guzman, C.; Peña, Roberto; Singh, R.G.; Autrique, E.; Dreisigacker, S.; Crossa, J.; Rutkoski, J.; Poland, J.; Battenfield, S.D.The International Center for Maize and Wheat Improvement (CIMMYT) leads the Global Wheat Program, whose main objective is to increase the productivity of wheat cropping systems to reduce poverty in developing countries. The priorities of the program are high grain yield, disease resistance, tolerance to abiotic stresses (drought and heat), and desirable quality. The Wheat Chemistry and Quality Laboratory has been continuously evolving to be able to analyze the largest number of samples possible, in the shortest time, at lowest cost, in order to deliver data on diverse quality traits on time to the breeders for making selections for advancement in the breeding pipeline. The participation of wheat quality analysis/selection is carried out in two stages of the breeding process: evaluation of the parental lines for new crosses and advanced lines in preliminary and elite yield trials. Thousands of lines are analyzed which requires a big investment in resources. Genomic selection has been proposed to assist in selecting for quality and other traits in breeding programs. Genomic selection can predict quantitative traits and is applicable to multiple quantitative traits in a breeding pipeline by attaining historical phenotypes and adding high-density genotypic information. Due to advances in sequencing technology, genome-wide single nucleotide polymorphism markers are available through genotyping-by-sequencing at a cost conducive to application for genomic selection. At CIMMYT, genomic selection has been applied to predict all of the processing and end-use quality traits regularly tested in the spring wheat breeding program. These traits have variable levels of prediction accuracy, however, they demonstrated that most expensive traits, dough rheology and baking final product, can be predicted with a high degree of confidence. Currently it is being explored how to combine both phenotypic and genomic selection to make more efficient the genetic improvement for quality traits at CIMMYT spring wheat breeding program.
Publication - Increased prediction accuracy in wheat breeding trials using a marker x environment interaction Genomic Selection model(Genetics Society of America, 2015) Lopez-Cruz, M.; Crossa, J.; Bonnett, D.; Dreisigacker, S.; Poland, J.; Jannink, J.L.; Singh, R.P.; Autrique, E.; De Los Campos, G.Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype · environment interaction( G·E). Several authors have proposed extensions of the single-environment GS model that accommodate G·E using either covariance functions or environmental covariates. In this study, we model G·E using a marker · environment interaction (M·E) GS model; the approach is conceptually simple and can be implemented with existing GS software.We discuss how themodel can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M·E model to analyze three CIMMYT wheat data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT’s research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems.We compared the M·E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) GS model that assumes that effects are constant across environments (i.e., ignoring G·E). The prediction accuracy of the M·E model was substantially greater of that of an across-environment analysis that ignores G·E. Depending on the prediction problem, the M·E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M·E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G·E. The data set and the scripts required to reproduce the analysis are publicly available as Supporting Information.
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