Person:
Poland, J.

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Poland
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Poland, J.

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Now showing 1 - 8 of 8
  • 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
  • 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
  • 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.
    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
  • Genomic selection for processing and end-use quality traits in the CIMMYT spring bread wheat breeding program
    (Crop Science Society of America, 2016) Battenfield, S.D.; Guzman, C.; Gaynor, R.C.; Singh, R.P.; Peña, Roberto; Dreisigacker, S.; Fritz, A.; Poland, J.
    Wheat (Triticum aestivum L.) cultivars must possess suitable end-use quality for release and consumer acceptability. However, breeding for quality traits is often considered a secondary target relative to yield largely because of amount of seed needed and expense. Without testing and selection, many undesirable materials are advanced, expending additional resources. Here, we develop and validate whole-genome prediction models for end-use quality phenotypes in the CIMMYT bread wheat breeding program. Model accuracy was tested using forward prediction on breeding lines (n = 5520) tested in unbalanced yield trials from 2009 to 2015 at Ciudad Obregon, Sonora, Mexico. Quality parameters included test weight, 1000-kernel weight, hardness, grain and flour protein, flour yield, sodium dodecyl sulfate sedimentation, Mixograph and Alveograph performance, and loaf volume. In general, prediction accuracy substantially increased over time as more data was available to train the model. Reflecting practical implementation of genomic selection (GS) in the breeding program, forward prediction accuracies (r) for quality parameters were assessed in 2015 and ranged from 0.32 (grain hardness) to 0.62 (mixing time). Increased selection intensity was possible with GS since more entries can be genotyped than phenotyped and expected genetic gain was 1.4 to 2.7 times higher across all traits than phenotypic selection. Given the limitations in measuring many lines for quality, we conclude that GS is a powerful tool to facilitate early generation selection for end-use quality in wheat, leaving larger populations for selection on yield during advanced testing and leading to better gain for both quality and yield in bread wheat breeding programs.
    Publication