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A Multi-Omic Approach to Evaluating Soybean Iron Deficiency Chlorosis in the Field
Iron deficiency chlorosis (IDC) is an abiotic stress and yield limiting factor of soybean [Glycine max (L.) Merrill] grown on calcareous, high pH soils throughout the North Central United States. Breeders typically rank soybean varieties for IDC tolerance using subjective visual scores based on degrees of yellowing, stunting, and necrosis. The application of multiple “-omics” tools may advance IDC evaluation techniques and lead to a better understanding of the molecular mechanisms underlying IDC tolerance through improved characterization of the soil and gene expression changes in response to stress in the field environment. To explore the value of these tools, a biparental recombinant inbred line (RIL) population diverse in IDC expression was grown in replicated, short row evaluation plots and imaged using a ground cart and unmanned aerial vehicle (UAV) equipped with RGB cameras. Image properties along with apparent soil electrical conductivity and elevation data were used as predictors to train and evaluate machine learning algorithms. Both imaging methods and their corresponding models had significant relationships with IDC visual scores in year one (rCART=0.96; rUAV=0.89), year two (rCART=0.95), and over years (rCART=0.95), achieving a root mean squared error of less than 0.5 in all instances. The precise phenotypic data enabled the detection of major quantitative trait loci (QTL) on chromosomes 5 and 13 which explained a total of 26% of the IDC score variance over two environments. Additionally, 5,467 genes with significant expression differences between the RIL parents under IDC field conditions were identified via next generation transcriptomic sequencing which assisted in candidate gene and gene ontology term identification. This dissertation explored a set of complementary phenomic, enviromic, genomic, and transcriptomic approaches which may offer more precise, objective, and high-throughput alternatives for IDC evaluation. Assessment of gene expression differences between the susceptible and tolerant IDC parents of the RIL population in a field environment helped identify potential candidate genes within the IDC QTL regions.
Oswald, Cody Joe, "A Multi-Omic Approach to Evaluating Soybean Iron Deficiency Chlorosis in the Field" (2020). ETD collection for University of Nebraska-Lincoln. AAI28086503.