Browsing by Author "Mason, Esten, advisor"

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Open Access
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.
Open Access
Mapping Rhizoctonia root and crown rot resistance from sugar beet germplasm FC709-2 using new genomic resources
(Colorado State University. Libraries, 2024) Metz, Nicholas, author; Mason, Esten, advisor; Dorn, Kevin, committee member; Richards, Christopher, committee member; Gaines, Todd, committee member
Sugar beet (Beta vulgaris subsp. Vulgaris) provides about 35% of the refined sugar globally, and over half of the domestic production in the United States. Sugar beet are primarily grown in temperate climates from plantings in late spring and harvest in the fall. In the United States sugar beets are grown in four diverse regions: the upper Midwest (Minnesota and North Dakota), the far west (California, Idaho, Oregon, and Washington, the Great Plains (Colorado, Nebraska, Montana, and Wyoming), and the Great Lakes (Michigan). Multiple pests and pathogens continue to threaten tonnage and recoverable sugar yields. These are controlled through planting genetically resistant cultivars, agronomic cultural practices and chemical applications throughout the growing season. With a shrinking set of chemical and cultural control options to manage these production threats, the need for continued improvement upon host plant resistance is important. Decades of global breeding efforts to improved disease tolerance in sugar beet has been effective, but molecular and genomic guided breeding and disease resistance characterization in sugar beet is only now emerging. The most important root pathogen in sugar beet is Rhizoctonia Root and Crown Rot (RRCR) caused by the fungal pathogen Rhizoctonia solani. This disease is estimated to cause up to 50% localized losses, and regularly causes 57 million dollars in economic losses per year despite the use of tolerant varieties, chemical control, and cultural practices. Public sugar beet pre-breeding has developed hundreds of widely utilized lines with novel traits and combinations of traits, including for RRCR resistance. One such line, FC709-2, displayed exceptional tolerance to Rhizoctonia solani released from the United States Department of Agriculture sugar beet breeding program in Fort Collins, Colorado. This germplasm line is base for many RRCR resistant cultivars used by growers around the world. In this study, new germplasm, genetic, and genomic resources revolving around FC709-2 were developed. These resources include a new germplasm line derived from the purification of FC709-2. By using stricter selection pressure and single seed decent a more homogenous seed lot was created to be used by other breeding programs. A new reference genome created from a single highly RRCR resistant plant using the most recent sequencings and bioinformatic technologies will be used to discover genes that are responsible for a wide array of plant interactions. Last, novel QTLs associated with RRCR resistance were discovered using a bi-parental mapping population and bulk segregate analysis. Collectively, these results show that discovering novel RRCR resistance genes in a highly resistant germplasm line using a purpose-built reference genome is a streamlined and accurate method. With these new resources in place researchers around the world can use them to discover the genes responsible for RRCR resistance, create markers for more accurate selections, and follow the methods described to be implemented in other plant breeding programs.