Person: Poland, J.
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Poland
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J.
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Poland, J.
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0000-0002-7856-1399 17 results
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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 - Incorporating genome-wide association mapping results into genomic prediction models for grain yield and yield stability in CIMMYT spring bread wheat(Frontiers, 2020) Sehgal, D.; Rosyara, U.; Mondal, S.; Singh, R.P.; Poland, J.; Dreisigacker, S.
Publication - Haplotype-based, genome-wide association study reveals stable genomic regions for grain yield in CIMMYT spring bread wheat(Frontiers, 2020) Sehgal, D.; Mondal, S.; Crespo Herrera, L.A.; Velu, G.; Juliana, P.; Huerta-Espino, J.; Shrestha, S.; Poland, J.; Singh, R.P.; Dreisigacker, S.
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 - Breeding-assisted genomics: applying meta- GWAS for milling and baking quality in CIMMYT wheat breeding program(Public Library of Science, 2018) Battenfield, S.D.; Sheridan, J.L.; Silva, L.D.C.E.; Miclaus, K.J.; Dreisigacker, S.; Wolfinger, R.D.; Peña, Roberto; Singh, R.P.; Jackson, E.W.; Fritz, A.; Guzman, C.; Poland, J.One of the biggest challenges for genetic studies on natural or unstructured populations is the unbalanced datasets where individuals are measured at different times and environments. This problem is also common in crop and animal breeding where many individuals are only evaluated for a single year and large but unbalanced datasets can be generated over multiple years. Many wheat breeding programs have focused on increasing bread wheat (Triticum aestivum L.) yield, but processing and end-use quality are critical components when considering its use in feeding the rising population of the next century. The challenges with end-use quality trait improvements are high cost and seed amounts for testing, the latter making selection in early breeding populations impossible. Here we describe a novel approach to identify marker-trait associations within a breeding program using a meta-genome wide association study (GWAS), which combines GWAS analysis from multiyear unbalanced breeding nurseries, in a manner reflecting meta-GWAS in humans. This method facilitated mapping of processing and end-use quality phenotypes from advanced breeding lines (n = 4,095) of the CIMMYT bread wheat breeding program from 2009 to 2014. Using the meta-GWAS we identified marker-trait associations, allele effects, candidate genes, and can select using markers generated in this process. Finally, the scope of this approach can be broadly applied in ‘breeding-assisted genomics’ across many crops to greatly increase our functional understanding of plant genomes.
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