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Browsing by Author "Johnson, Jerry W."

Now showing 1 - 3 of 3
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    Identification of Quantitative Resistance to Puccinia striiformis and Puccina triticinia in the Soft Red Winter Wheat Cultivar 'Jamestown'
    Carpenter, Neal R.; Griffey, Carl A.; Malla, Subas; Barnett, Marla D.; Marshall, David; Fountain, Myron O.; Murphy, Joseph Paul; Milus, Eugene; Johnson, Jerry W.; Buck, James; Chao, Shiaoman; Brown-Guedira, Gina L.; Wright, Emily (2017-11)
    Disease resistance is critical in soft red winter wheat (Triticum aestivum L.) cultivars. Leaf rust caused by Puccinia triticina Eriks and stripe rust caused by Puccinia striiformis Westend. f. sp. tritici Eriks. are destructive pathogens of wheat. Phenotypic data were collected at diverse locations for resistance to leaf rust (North Carolina, Texas, and Virginia) and stripe rust (Arkansas, North Carolina, Georgia, Texas, and Virginia) in a Pioneer '25R47' /'Jamestown' (P47/JT) population composed of 186 FDX5:9 recombinant inbred lines (RILs). The P47/JT RILs were geno-typed with a public 90K iSelect single-nucleotide polymorphism array. Analysis of the P47/ JT population identified two quantitative trait loci (QTL) for leaf rust resistance on chromosome 5B and two QTL for stripe rust resistance on chromosomes 3B and 6A. These QTL were associated with both infection type and disease severity. Phenotypic variation (%) explained by the putative leaf rust resistance QTL of Jamestown on 5B was as high as 22.1%. Variation explained by the putative stripe rust resistance QTL of Jamestown on 3B and 6A was as high as 11.1 and 14.3%, respectively. Introgression and pyramiding of these QTL with other genes conferring resistance to leaf and stripe rusts via marker-assisted selection will facilitate development of soft red winter wheat cultivars having more durable resistance.
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    Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes
    Guo, Jia; Khan, Jahangir; Pradhan, Sumit; Shahi, Dipendra; Khan, Naeem; Avci, Muhsin; McBreen, Jordan; Harrison, Stephen; Brown-Guedira, Gina L.; Murphy, Joseph Paul; Johnson, Jerry W.; Mergoum, Mohamed; Mason, Richard Esten; Ibrahim, Amir M. H.; Sutton, Russell L.; Griffey, Carl A.; Babar, Md Ali (MDPI, 2020-10-28)
    The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.
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    Training population selection and use of fixed effects to optimize genomic predictions in a historical USA winter wheat panel
    Sarinelli, J. Martin; Murphy, Joseph Paul; Tyagi, Priyanka; Holland, James B.; Johnson, Jerry W.; Mergoum, Mohamed; Mason, Richard Esten; Babar, Ali; Harrison, Stephen; Sutton, Russell L.; Griffey, Carl A.; Brown-Guedira, Gina L. (2019-04)
    Key message: The optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Abstract: Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat (Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici). Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes.