Browsing by Author "Haley, Scott D., advisor"
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Item Open Access Characterization and molecular mapping of stripe rust resistance in a Denali/Hatcher winter wheat doubled haploid population(Colorado State University. Libraries, 2019) Afshar, Zaki, author; Haley, Scott D., advisor; Byrne, Patrick F., committee member; Broders, Kirk D., committee memberThe majority of global wheat (Triticum aestivum L.) production is subject to infection by the stripe rust pathogen (Puccinia striiformis Westend. f. sp. tritici Erikss.). The evolution of new stripe rust races appears to be occurring more rapidly than in the past, causing significant economic loss through yield reduction and increased use of fungicides. A combination of all-stage resistance and high-temperature adult plant (HTAP) resistance in new cultivars may provide complete resistance or serve to reduce disease incidence, thus providing a greater overall level of protection. In addition, knowledge of the form of resistance present in a particular cultivar may help to minimize fungicide use with cultivars that show early-season infections prior to initiation of HTAP resistance. A doubled haploid population (n=210) developed from a cross between winter wheat cultivars 'Hatcher' (PI 638512) and 'Denali' (PI 664256) was developed and characterized for response to stripe rust during 2018 and 2019 at Fort Collins, CO and Rossville, KS. A high density genetic linkage map consisting of 4,441 single nucleotide polymorphism markers derived via genotyping by sequencing was used to identify markers for stripe rust resistance in this population. Four quantitative trait loci (QTL) for infection type (IT) and disease severity (DS) (QYr.csu-1B, QYr.csu-3A, QYr.csu-3B, and QYr.csu-7B) were found to contribute to stripe rust resistance. Among the resistance QTL, QYr.csu-1B and QYr.csu-3A iii were the most consistent for single environments and combined across environments and accounted for 9.6-16.3% and 10.1-14.4% of phenotypic variation, respectively. QYr.csu-3B showed a stronger effect than QYr.csu-7B and was detected in more than one environment. Flanking markers for all the identified QTL, especially for QYr.csu-1B and QYr.csu-3A, will be useful to develop wheat cultivars with more effective and durable resistance to stripe rust.Item Open Access Characterization and quantitative trait loci (QTL) analysis for wheat stem sawfly (Hymenoptera: Cephidae) resistance in winter wheat(Colorado State University. Libraries, 2020) Pakish, Bradley, author; Haley, Scott D., advisor; Muñoz-Amatriaín, Maria, committee member; Peairs, Frank B., committee memberThe wheat stem sawfly (Cephus cinctus) (WSS) has quickly become a major pest of Colorado wheat production over the past ten years. Prior resistant cultivars have relied on the expression of a solid-stemmed trait (Qss.msub-3BL) to decrease damage from sawfly infestations, however environmental factors (sun, rain, etc.) may result in inconsistent pith expression. The limitations of solid-stemmed varieties have aided in the recent identification of novel quantitative trait loci (QTL) for reducing WSS infestation and stem cutting by host-plant preference. In this light, crosses between 'Denali'/'Hatcher' and 'Avery'/'CO11D1397' were completed in the greenhouse during Fall 2014 and Spring 2015 to create two doubled haploid (DH) populations for discovery of QTL associated with non solid-stemmed resistance. Each population was grown under naturally occurring sawfly pressure at two different northeastern Colorado locations during the 2018-19 field season, however only the Avery/CO11D1397 population was selected for planting in the 2019-20 field season due to resource limitations. Entries were evaluated for plant height, heading date, physiological maturity, cutting score, and kernel weight. Next generation sequencing data were generated through genotyping-by-sequencing and resulted in 776 single-nucleotide polymorphisms (SNP) markers in the final genetic map for Avery/CO11D1397. Quantitative trait loci analysis identified a total of 11 QTL, seven major-effect and four minor-effect, in the Avery/CO11D1397 DH population for reduced WSS cutting in multiple environments. Two QTL were associated on the same chromosomal arms as photoperiod genes Ppd-D1 (Qwss.csu-2DS) and Ppd-B1 (Qwss.csu-2BS). The Qwss.csu-1BL was also associated on the long arm of chromosome 1B with the earliness per se gene Eps-B1. Qwss.csu-7DS and Qwss.csu-5BS were the only two major-effect QTL identified that were not associated with major developmental genes, and thus could be associated with antixenosis. Results from this study suggest that a relationship between lower cutting score and a later flowering date exists for genotypes within the Avery/CO11D1397 DH population. Introgression of Qwss.csu-7DS and Qwss.csu-5BS into cultivars with stem-solidness may help in developing new wheat varieties with durable WSS resistance.Item Open Access Genome-wide association study and genomic prediction for end-use qualities in hard winter wheat(Colorado State University. Libraries, 2024) Wondifraw, Meseret A., author; Mason, R. Esten, advisor; Haley, Scott D., advisor; Rhodes, Davina, committee member; Dorn, Kevin, committee memberWheat (Triticum aestivum L.) is a widely cultivated crop used primarily for human food, animal feed, and industrial products. Numerous wheat-based products have unique nutritional and functional requirements. In the global market, wheat quality is one of the determining factors of wheat's price and baked product characteristics. Thus, after grain yield, improving these qualities is one of the major breeding objectives in wheat. Chapter One: This chapter outlines wheat's origins and global production. It explores major quality traits like water absorption and dough rheological properties, plus their measurement methods. Factors impacting wheat quality and pertinent genes are discussed. Finally, key challenges and opportunities around breeding for improved wheat quality are addressed. Chapter Two: This chapter presents a genome-wide association study of water absorption capacity in hard winter wheat. Lines were phenotyped using the solvent retention capacity test and genotyped via genotyping-by-sequencing. Forty-three marker-trait associations were identified across 17 chromosomes, especially on chromosome 1B, indicating polygenic influence. Co-localization between identified marker-trait associations and the genes that have effects on water absorption was done, and some quantitative trait nucleotides (QTNs) were located near gluten glutenin, gliadin, and glycosyltransferase genes, confirming water absorption is a complex trait affected by different flour components. Chapter Three: This chapter presents genome-wide prediction models to predict water absorption capacity using a training population of 497 hard winter wheat genotypes. Univariate models were compared to multivariate genomic prediction models using two validation approaches - cross-validation with 100 permutations and a 20-80 split and forward validation utilizing three years of data (2019-2021) from the CSU ELITE Trial. Multivariate genomic prediction models integrating highly correlated traits like break flour yield or all traits as covariates showed improved accuracy compared to univariate models in both validation approaches, demonstrating that incorporating related phenotypic traits as covariates in multivariate models can substantially improve the accuracy of predicting water absorption capacity. Chapter Four: This chapter evaluates genomic prediction models for bread-baking quality traits in 790 wheat genotypes over the 2014-2022 growing seasons. Marker-trait associations identified via genome-wide association study (GWAS) were incorporated as fixed effects. Three models were compared using cross-validation and forward validation: a model without fixed effect, with Glu-B1al allele (Bx7OE + 8 subunit) kompetitive allele-specific PCR (KASP) marker data as a fixed effect, and with GWAS-identified markers as fixed effects. Overall, the model with GWAS-identified markers as fixed effects showed the highest prediction accuracy. However, prediction accuracy decreased for bake loaf volume prediction specifically, suggesting that trait-specific tuning is needed to optimize genomic prediction models for different baking quality traits. These chapters reinforce the genetic complexity of water absorption capacity and baking quality traits in wheat. Polygenic inheritance was revealed for water absorption capacity. Genomic prediction that incorporates phenotypic covariates and GWAS-derived markers is the best approach to selecting water absorption and baking traits.Item Open Access The utility of near-infrared reflectance spectroscopy for wheat quality assessment(Colorado State University. Libraries, 2010) Butler, Joshua Donald, author; Haley, Scott D., advisor; Brick, Mark A., committee member; Chapman, Phillip L., committee member; Seabourn, Bradford W., committee memberEnd-use quality improvement is an important objective in most wheat (Triticum aestivum L.) breeding programs. Limited sample size, destructive parameter testing, and the short duration between harvest and planting of winter wheat are challenges for testing early-generation breeding material for end-use quality parameters. Near-infrared reflectance (NIR) spectroscopy is a rapid and non-destructive technique that could facilitate early-generation selection for end-use quality. The precision and accuracy of an NIR equation for prediction purposes is dependent on the construction of a reliable calibration. The objectives of this study were to: 1) develop and validate NIR calibration models for grain volume weight, kernel characteristics, and Farinograph parameters, and 2) evaluate the performance of NIR calibration models in a breeding context for grain volume weight and single kernel characteristics. Calibration models for prediction of grain volume weight and single kernel characteristics were developed using NIR spectra and laboratory reference values from up to 10,000 samples collected from breeding nurseries under multiple environments over four crop years. Models encompassing all years of data revealed R2 (validation) of 0.73 for kernel diameter, 0.74 for kernel weight, 0.70 for kernel hardness, and 0.81 for grain volume weight. Of the Farinograph parameters, only absorption was effectively predicted using NIR calibration models for whole grain and flour with R2≥0.70. Realized heritability was estimated as a response to selection using NIR predicted values and laboratory reference values and was generally larger when using the reference values when compared to predicted values (0.17-0.77 vs. 0.05-0.77), but suggested that genetic gain was possible when using NIR models for selection. Classification errors when using the NIR models were highest in the mid-range reference values (56-66%), but could allow for divergent selection of high and low reference values. The results suggest that NIR models suitable for screening grain volume weight, SKCS kernel characteristics, and Farinograph absorption could be utilized in a breeding program and could aid in the elimination of early-generation samples with unacceptable values.Item Open Access Virus-induced gene silencing and molecular marker mapping for Russian wheat aphid resistance in wheat(Colorado State University. Libraries, 2010) Valdez, Victoria A., author; Haley, Scott D., advisor; Lapitan, Nora Lyssa V., advisor; Leach, Jan E., committee member; Chisholm, Stephen Thomas, 1972-, committee memberTo view the abstract, please see the full text of the document.