Browsing by Author "Brown-Guedira, Gina L."

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    The Accuracy of Genomic Prediction between Environments and Populations for Soft Wheat Traits
    Huang, Mao; Ward, Brian P.; Griffey, Carl A.; Van Sanford, David A.; McKendry, Anne; Brown-Guedira, Gina L.; Tyagi, Priyanka; Sneller, Clay H. (2018-12)
    Genomic selection (GS) uses training population (TP) data to estimate the value of lines in a selection population. In breeding, the TP and selection population are often grown in different environments, which can cause low prediction accuracy when the correlation of genetic effects between the environments is low. Subsets of TP data may be more predictive than using all TP data. Our objectives were (i) to evaluate the effect of using subsets of TP data on GS accuracy between environments, and (ii) to assess the accuracy of models incorporating marker x environment interaction (MEI). Two wheat (Triticum aestivum L.) populations were phenotyped for 11 traits in independent environments and genotyped with single-nucleotide polymorphism markers. Within each population trait combination, environments were clustered. Data from one duster were used as the TP to predict the value of the same lines in the other cluster(s) of environments. Models were built using all TP data or subsets of markers selected for their effect and stability. The GS accuracy using all TP data was >0.25 for 9 of 11 traits. The between-environment accuracy was generally greatest using a subset of stable and significant markers; accuracy increased up to 48% relative to using all TP data. We also assessed accuracy using each population as the TP and the other as the selection population. Using subsets of TP data or the MEI models did not improve accuracy between populations. Using optimized subsets of markers within a population can improve GS accuracy by reducing noise in the prediction data set.
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    Association Analysis of Baking and Milling Quality Traits in an Elite Soft Red Winter Wheat Population
    Gaire, Rupesh; Huang, Mao; Sneller, Clay H.; Griffey, Carl A.; Brown-Guedira, Gina L.; Mohammadi, Mohsen (2019-05)
    Although grain yield is the most important trait for growers, milling and baking industries demand high-quality and scab-free grains for various end products. To accelerate breeding of wheat (Triticum aestivum L.) cultivars with high grain quality, genetic dissection of quality traits is necessary. Genome-wide association studies (GWAS) were conducted to identify genomic regions responsible for milling and baking quality traits in soft red winter wheat (SRWW). Seven quality traits were evaluated in two locations and 2 yr for 270 elite SRWW lines. These traits include flour yield, softness equivalent, flour protein, and four solvent (lactose, sodium carbonate, sucrose, and water) retention capacity measurements. In this study, 27,449 single nucleotide polymorphism (SNP) markers were developed by using both genotyping-by-sequencing and 90K SNP array technologies. A linear mixed model in GWAS, accounting for population structure and kinship, was fitted to identify 18 [-log(P) >= 4.0] genomic regions on 12 different chromosomes associated with the quality traits. Only one of these associations seems to be a previously identified quantitative trait locus, whereas others are novel associations. The most significant associations for quality traits were identified on chromosomes 1B, 2A, 2B, 4B, 5A, 7A, and 7D. Candidate gene searches, facilitated by the wheat genome assembly, led us to the identification of putative genes related to SRWW quality traits including fasciclin-like arabinogalactan. The results of this study can be used in developing high-quality SRWW varieties for the eastern region of the United States.
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    Genome-wide association studies for yield-related traits in soft red winter wheat grown in Virginia
    Ward, Brian P.; Brown-Guedira, Gina L.; Kolb, Frederic L.; Van Sanford, David A.; Tyagi, Priyanka; Sneller, Clay H.; Griffey, Carl A. (Public Library of Science, 2019-02-22)
    Grain yield is a trait of paramount importance in the breeding of all cereals. In wheat (Triticum aestivum L.), yield has steadily increased since the Green Revolution, though the current rate of increase is not forecasted to keep pace with demand due to growing world population and increasing affluence. While several genome-wide association studies (GWAS) on yield and related component traits have been performed in wheat, the previous lack of a reference genome has made comparisons between studies difficult. In this study, a GWAS for yield and yield-related traits was carried out on a population of 322 soft red winter wheat lines across a total of four rain-fed environments in the state of Virginia using single-nucleotide polymorphism (SNP) marker data generated by a genotyping-by-sequencing (GBS) protocol. Two separate mixed linear models were used to identify significant marker-trait associations (MTAs). The first was a single-locus model utilizing a leave-one-chromosome-out approach to estimating kinship. The second was a sub-setting kinship estimation multi-locus method (FarmCPU). The single-locus model identified nine significant MTAs for various yield-related traits, while the FarmCPU model identified 74 significant MTAs. The availability of the wheat reference genome allowed for the description of MTAs in terms of both genetic and physical positions, and enabled more extensive post-GWAS characterization of significant MTAs. The results indicate a number of promising candidate genes contributing to grain yield, including an ortholog of the rice aberrant panicle organization (APO1) protein and a gibberellin oxidase protein (GA2ox-A1) affecting the trait grains per square meter, an ortholog of the Arabidopsis thaliana mother of flowering time and terminal flowering 1 (MFT) gene affecting the trait seeds per square meter, and a B2 heat stress response protein affecting the trait seeds per head. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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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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    Identification of quantitative trait loci associated with nitrogen use efficiency in winter wheat
    Brasier, Kyle G.; Ward, Brian P.; Smith, Jared; Seago, John E.; Oakes, Joseph C.; Balota, Maria; Davis, Paul H.; Fountain, Myron O.; Brown-Guedira, Gina L.; Sneller, Clay H.; Thomason, Wade E.; Griffey, Carl A. (2020-02-24)
    Maintaining winter wheat (Triticum aestivum L.) productivity with more efficient nitrogen (N) management will enable growers to increase profitability and reduce the negative environmental impacts associated with nitrogen loss. Wheat breeders would therefore benefit greatly from the identification and application of genetic markers associated with nitrogen use efficiency (NUE). To investigate the genetics underlying N response, two bi-parental mapping populations were developed and grown in four site-seasons under low and high N rates. The populations were derived from a cross between previously identified high NUE parents (VA05W-151 and VA09W-52) and a shared common low NUE parent, 'Yorktown.' The Yorktown x VA05W-151 population was comprised of 136 recombinant inbred lines while the Yorktown x VA09W-52 population was comprised of 138 doubled haploids. Phenotypic data was collected on parental lines and their progeny for 11 N-related traits and genotypes were sequenced using a genotyping-by-sequencing platform to detect more than 3,100 high quality single nucleotide polymorphisms in each population. A total of 130 quantitative trait loci (QTL) were detected on 20 chromosomes, six of which were associated with NUE and N-related traits in multiple testing environments. Two of the six QTL for NUE were associated with known photoperiod (Ppd-D1 on chromosome 2D) and disease resistance (FHB-4A) genes, two were reported in previous investigations, and one QTL, QNue.151-1D, was novel. The NUE QTL on 1D, 6A, 7A, and 7D had LOD scores ranging from 2.63 to 8.33 and explained up to 18.1% of the phenotypic variation. The QTL identified in this study have potential for marker-assisted breeding for NUE traits in soft red winter wheat.
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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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    Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early-Generation Wheat Yield Trials
    Ward, Brian P.; Brown-Guedira, Gina L.; Tyagi, Priyanka; Kolb, Frederic L.; Van Sanford, David A.; Sneller, Clay H.; Griffey, Carl A. (Crop Science Society of America, 2019-02-21)
    The majority of studies evaluating genomic selection (GS) for plant breeding have used single-trait, single-site models that ignore genotype x environment interaction (GEI) effects. However, such studies do not accurately reflect the complexities of many applied breeding programs, and previous papers have found that models that incorporate GEI effects and multiple traits can increase the accuracy of genomic estimated breeding values (GEBVs). This study’s goal was to test GS methods for prediction in scenarios that simulate earlygeneration yield testing by correcting for field spatial variation, and fitting multienvironment and multitrait models on data for 14 traits of varying heritability evaluated in unbalanced designs across four environments. Corrections for spatial variation increased across-environment trait heritability by 25%, on average, but had little effect on model predictive ability. Results between all models were generally equivalent when predicting the performance of newly introduced genotypes. However, models incorporating GEI information and multiple traits increased prediction accuracy by up to 9.6% for low-heritability traits when phenotypic data were sparsely collected across environments. The results suggest that GS models using multiple traits and incorporating GEI effects may best be suited to predicting line performance in new environments when phenotypic data have already been collected across a subset of the total testing environments.
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    Registration of 'Hilliard' wheat
    Griffey, Carl A.; Malla, Subas; Brooks, Wynse S.; Seago, John E.; Christopher, Anthony; Thomason, Wade E.; Pitman, Robert M.; Markham, Robin; Vaughn, Mark E.; Dunaway, David W.; Beahm, Mary; Barrack, C. Lin; Rucker, Elizabeth; Behl, Harry D.; Hardiman, Thomas H.; Beahm, Bruce R.; Browning, Phillip; Schmale, David G. III; McMaster, Nicole J.; Custis, J. Tommy; Gulick, Steve; Ashburn, S. Bobby; Jones, Ned, Jr.; Baik, Byung-Kee; Bockelman, Harold; Marshall, David; Fountain, Myron O.; Brown-Guedira, Gina L.; Cowger, Christina; Cambron, Sue; Kolmer, James; Jin, Yue; Chen, Xianming; Garland-Campbell, Kimberly; Sparry, Ellen (2020-09)
    'Hilliard' (Reg. no. CV-1163, PI 676271), a soft red winter (SRW) wheat (Triticum aestivum L.) developed and tested as VA11W-108 by the Virginia Agricultural Experiment Station, was released in March 2015. Hilliard was derived from the cross '25R47'/'Jamestown'. Hilliard is widely adapted, from Texas to Ontario, Canada, and provides producers with a mid-season, medium height, awned, semi-dwarf (Rht2) cul tivar that has very high yield potential, good straw strength, and intermediate grain volume weight and quality. It expresses moderate to high levels of resistance to most diseases prevalent in the eastern United States and Ontario. In the 2016-2018 USDA-ARS Uniform SRW Wheat nurseries, Hilliard ranked first in grain yield in the southern nursery across all 3 yr (5,147-5,758 kg ha(-1)). In the uniform eastern nursery, it ranked first for grain yield in 2016 (6,159 kg ha(-1)) and 2017 (5,633 kg ha(-1)) and second in 2018 (5,515 kg ha(-1)). Grain volume weights of Hilliard were similar to overall trial averages in the uniform southern (73.4-75.2 kg hl(-1)) and eastern (70-75.8 kg hl(-1)) nurseries. Hilliard has soft grain texture with flour softness equivalent values varying from 58.1 to 61.7 g 100 g(-1). Straight grade flour yields on a Quadrumat Senior mill varied from 66.8 to 68.4 g kg(-1). Flour protein concentration varied from 7.0 to 9.1 g 100 g(-1) and gluten strength from 108 to 128 g 100 g(-1), as measured by lactic acid solvent retention capacity. Cookie spread diameter varied from 18.3 to 18.6 cm.
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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.