Person:
Poland, J.

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Poland
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Poland, J.

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Now showing 1 - 10 of 13
  • 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
  • Genomic selection for wheat blast in a diversity panel, breeding panel and full-sibs panel
    (Frontiers, 2022) Juliana, P.; Xinyao He; Marza, F.; Islam, R.; Anwar, M.B.; Poland, J.; Shrestha, S.; Singh, G.P.; Chawade, A.; Joshi, A.K.; Singh, R.P.; Singh, P.K.
    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
  • Increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (Triticum aestivum L.)
    (Frontiers, 2021) Tomar, V.; Singh, D.; Dhillon, G.S.; Yong Suk Chung; Poland, J.; Singh, R.P.; Joshi, A.K.; Gautam, Y.; Tiwari, B.S.; Kumar, U.
    Publication
  • Evaluations of genomic prediction and identification of new loci for resistance to stripe rust disease in wheat (Triticum aestivum L.)
    (Frontiers, 2021) Tomar, V.; Dhillon, G.S.; Singh, D.; Singh, R.P.; Poland, J.; Chaudhary, A.A.; Bhati, P.; Joshi, A.K.; Kumar, U.
    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
  • Genomic selection for grain yield in the CIMMYT Wheat Breeding Program—status and perspectives
    (Frontiers, 2020) Juliana, P.; Singh, R.P.; Braun, H.J.; Huerta-Espino, J.; Crespo Herrera, L.A.; Velu, G.; Mondal, S.; Poland, J.; Shrestha, S.
    Publication
  • High-throughput phenotyping enabled genetic dissection of crop lodging in wheat
    (Frontiers, 2019) Singh, D.; Xu Wang; Kumar, U.; Liangliang Gao; Muhammad Noor; Imtiaz, M.; Singh, R.P.; Poland, J.
    Novel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits from images and sensors but is limited in advancing our understanding of crop development and architecture. Lodging is a complex trait that significantly impacts yield and quality in many crops including wheat. Conventional visual assessment methods for lodging are time-consuming, relatively low-throughput, and subjective, limiting phenotyping accuracy and population sizes in breeding and genetics studies. Here, we demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging, which significantly impacts yield and quality in many crops including wheat. We tested and validated quantitative assessment of lodging on 2,640 wheat breeding plots over the course of 2 years using differential digital elevation models from UAS. High correlations of digital measures of lodging to visual estimates and equivalent broadsense heritability demonstrate this approach is amenable for reproducible assessment of lodging in large breeding nurseries. Using these high-throughput measures to assess the underlying genetic architecture of lodging in wheat, we applied genome-wide association analysis and identified a key genomic region on chromosome 2A, consistent across digital and visual scores of lodging. However, these associations accounted for a very minor portion of the total phenotypic variance. We therefore investigated whole genome prediction models and found high prediction accuracies across populations and environments. This adequately accounted for the highly polygenic genetic architecture of numerous small effect loci, consistent with the previously described complex genetic architecture of lodging in wheat. Our study provides a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to uncover the genetic factors and increase the rate of gain for complex traits in crop breeding.
    Publication
  • Genomic selection for quantitative adult plant stem rust resistance in wheat
    (Crop Science Society of America, 2014) Rutkoski, J.; Poland, J.; Singh, R.P.; Huerta-Espino, J.; Bhavani, S.; Barbier, H.; Rouse, M.N.; Jannink, J.L.; Sorrells, M.E.
    Quantitative adult plant resistance (APR) to stem rust (Puccinia graminis f. sp. tritici) is an important breeding target in wheat (Triticum aestivum L.) and a potential target for genomic selection (GS). To evaluate the relative importance of known APR loci in applying GS, we characterized a set of CIMMYT germplasm at important APR loci and on a genome-wide profile using genotyping-by-sequencing (GBS). Using this germplasm, we describe the genetic architecture and evaluate prediction models for APR using data from the international Ug99 stem rust screening nurseries. Prediction models incorporating markers linked to important APR loci and seedling phenotype scores as fixed effects were evaluated along with the classic prediction models: Multiple linear regression (MLR), Genomic best linear unbiased prediction (G-BLUP), Bayesian Lasso (BL), and Bayes Cπ (BCπ). We found the Sr2 region to play an important role in APR in this germplasm. A model using Sr2 linked markers as fixed effects in G-BLUP was more accurate than MLR with Sr2 linked markers (p-value = 0.12), and ordinary G-BLUP (p-value = 0.15). Incorporating seedling phenotype information as fixed effects in G-BLUP did not consistently increase accuracy. Overall, levels of prediction accuracy found in this study indicate that GS can be effectively applied to improve stem rust APR in this germplasm, and if genotypes at Sr2 linked markers are available, modeling these genotypes as fixed effects could lead to better predictions.
    Publication
  • Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding
    (Crop Science Society of America, 2018) Crain, J.; Mondal, S.; Rutkoski, J.; Singh, R.P.; Poland, J.
    Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called ‘Phenocart’ was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.
    Publication