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Browsing by Author "Rhodes, Davina, committee member"

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    Analysis of wheat spike characteristics using image analysis, machine learning, and genomics
    (Colorado State University. Libraries, 2022) Hammers, Mikayla, author; Mason, Esten, advisor; Ben-Hur, Asa, committee member; Mueller, Nathan, committee member; Rhodes, Davina, committee member
    Understanding genetics regulating yield component and spike traits can contribute to the development of new wheat cultivars. The flowering pathway in wheat is not entirely known, but spike architecture and its relationship with yield component traits could provide valuable information for crop improvement. Spikelets spike-1 (SPS) has previously been positively associated with kernel number spike (KNS) and negatively correlated with thousand kernel weight, meaning a further understanding of SPS could help unlock full yield potential. While genomics research has improved efficiency over time with the development of techniques such as genotyping by sequencing (GBS), phenotyping remains a labor and time intensive process, limiting the amount of phenomic data available for research. Recently, there has been more interest in generating high-throughput methods for collecting and analyzing phenotypic data. Imaging is a cheap and easily reproducible way to collect data at a specific maturity point or over time, and is a promising candidate for implementing deep learning algorithms to extract traits of interest. For this study, a population of 594 soft red winter wheat (SRWW) inbred lines were evaluated for wheat spike characteristics over two years. Images of wheat spikes were taken in a controlled environment and used to train deep learning algorithms to count SPS. A total of 12,717 images were prepared for analysis and used to train, test, and validate a basic classification and regression convolutional neural network (CNN), as well as a VGG16 and VGG19 regression model. Classification had a low accuracy and did not allow for an assessment of error margins. Regression models were more accurate. Of the regression models, VGG16 had the lowest mean absolute error (MAE) (MAE = 1.09) and mean squared error (MSE) (MSE = 2.08), and the highest coefficient of determination (R2) (R2 = 0.53) meaning it had the best fit of all models. The basic CNN was the next well fit model (MAE = 1.27, MSE = 2.61, r = 0.48) followed by the VGG19 (MAE = 1.32, MSE = 2.98, r = 0.45). With an average error of just above one spikelet, it is possible that counting methods could provide enough data with an accuracy high enough for use in statistical analyses such as genome wide association studies (GWAS), or genomic selection (GS). A GWAS was used to identify markers associated with SPS and yield component traits, while demonstrating the use of genomic selection (GS) for prediction and screening of individuals across multiple breeding programs. The GWAS results indicated similar markers and genotypic regions underpinning both KNS and SPS on chromosome 6A and spike length and SPS on chromosome 7A. It was observed that favorable alleles at each locus were associated with higher KNS and SPS on chromosome 6A and longer wheat spikes with higher SPS on chromosome 7A. Significant markers on 7A were observed in the region near WAPO1, the causal gene for SPS on the long arm of chromosome 7A, indicating they could be associated with that gene. GS results showed promise for whole genome selection, with the lowest prediction accuracy observed for heading date (rgs = 0.30) and the highest for spike area (rgs = 0.62). SPS showed prediction accuracies ranging from 0.33 to 0.42, high enough to aid in the selection process. These results indicate that knowledge of the flowering pathway and wheat spike architecture and how it relates to yield components could be beneficial for making selections and increasing grain yield.
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    Characterizing smoke taint in hops (Humulus lupulus) and investigating the impact of defoliation stress on phytocannabinoid content in industrial hemp (Cannabis sativa)
    (Colorado State University. Libraries, 2024) Sandoval, Brandon, author; Prenni, Jessica, advisor; Rhodes, Davina, committee member; Broeckling, Corey, committee member
    The family Cannabaceae contains at least 10 genera, with Cannabis (hemp) and Humulus (hop) being two of the most economically important. Both genera have long been valued by humans for their chemical constituents and are used today for both medicinal and recreational purposes. However, adverse environmental factors may impact the chemical profile of these important crops, leading them away from a true-to-type quality. This thesis will explore the effects of an abiotic stress on the chemical profile of each crop: smoke-taint in hops and defoliative hail damage of hemp. The Pacific Northwest contains 97.5% of U.S. commercial hop acreage and has also seen an increase in the number and severity of wildfire events in recent years. While there is extensive research from the wine industry on the impact of smoke taint in grapes, our knowledge of smoke taint in hops is limited. Here, we aimed to characterize smoke taint in hops using laboratory simulated wildfires with distinct fuel types and non-targeted gas chromatography-mass spectrometry. Our results reveal an overall variation in the chemical profiles between smoked and control hops and across fuel types and the detection of known and novel smoke taint markers including guaiacol, 4-methylguaiacol, and xylopyranose. This research provides evidence to support the use of established smoke taint markers for hop analysis and lays the groundwork for future studies to investigate various fuel types and their impact on hop quality. The United States has seen an abrupt increase in commercial industrial hemp production since the Agricultural Improvement Act of 2018. However, the historical prohibition of this crop has resulted in a lack of basic physiological research to guide management practices. For example, abiotic stress can stimulate plants to increase production of secondary metabolites such as phytocannabinoids and this is of high importance to farmers as they as they must balance optimization of CBD yield (crop value) with regulatory requirements (THC < 0.3% by mass) that could lead to crop loss (mandated destruction). In this study we evaluated the impact of defoliation stress (to simulate hail damage) at three different growth stages. Our results indicate that defoliation stress during late flowering yielded no significant change in phytocannabinoid production. However, defoliation stress during vegetative and early flowering yielded a significant increase in phytocannabinoids, including total CBD and THC, at harvest.
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    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 member
    Wheat (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.
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    Metabolite fingerprinting of hops (Humulus lupulus) to track chemical variations
    (Colorado State University. Libraries, 2022) Nasiatka, Katie, author; Prenni, Jessica, advisor; Rhodes, Davina, committee member; Van Buiten, Charlene, committee member
    In the brewing industry, identification of quality crops that provide unique organoleptic properties to beer flavor (aroma, taste) are of critical importance. Hops represent a key ingredient in beer and are utilized to impart specific flavors. India Pale Ales (IPAs) are a popular style of "hoppy beers" in the U.S. and customer expectations for consistency, quality, and unique organoleptic properties of hops are growing. While the contribution of chemical compounds in hops (Humulus lupulus) such as alpha-acids (e.g. humulone) is well-understood, the influence of the hop metabolome (e.g. composition of hop chemical compounds) is still in the early stages of discovery. There is a gap in the knowledge regarding our understanding of chemistry variations in hops among cultivars and growing locations that impact the sensory quality. Traditional sensory evaluation relies on the ability to organize a group of unbiased and trained panelists, who are also subject to sensory fatigue, which can add to the challenge of this method. An alternative approach, ambient mass spectrometry (AMS) is an objective, intuitive, analytical tool capable of rapid chemical fingerprinting. The overall goal of this research is to develop a robust, high-throughput assay using AMS technology to evaluate hop quality that is reflective of both cultivar and environmental variations impacting sensory. To address this goal, twelve hop samples were sourced from three different suppliers across four different farms located in Washington and Oregon over two growing seasons. The samples included three commercial cultivars, Cascade, Centennial, and Strata. The hop samples were extracted using an 80% ethanol solution and fingerprints were acquired by Direct Analysis in Real Time Mass Spectrometry (DART-MS). The resulting data were used to train predictive models and validation was performed to evaluate classification accuracy. Additionally, authentic standards of important hop compounds (hop alpha-acids, terpenes) were used to putatively annotate DART-MS signals reflective of sensory attributes. This study demonstrates the potential of this approach for rapid evaluation of hops quality and lays groundwork for further method optimization. Ultimately, implementation of this tool could have applications for quality assurance programs and for phenotyping of hops for producers and craft brewers.