Through genome wide association of nonvolatile metabolites and leaf ecophysiological traits, historic breeding practices were found to have led to germplasm divergence within the cultivated sunflower Helianthus annuus. In genome-wide analyses of single nucleotide polymorphisms (SNPs) in relation to flower petal carotenoid content across the cultivated H. annuus germplasm, alternative methods of analysis proposed differing genetic architectures, which suggests that these methods can be used as complementary approach in prioritizing SNPs for function analysis. Leaf hyperspectral reflectance was leveraged in a machine learning framework to predict herbivore- and volatile induction across the genus with 95% accuracy, while characterizing changes in volatile metabolites. The body of work in this dissertation represents the first characterization of the standing genetic variation for nonvolatile specialized metabolite diversity in cultivated sunflower in the context of modern breeding practices, and the first assessment of hyperspectral reflectance and volatile metabolite diversity across the genus Helianthus.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2132 |
| Date | 01 January 2021 |
| Creators | Dowell, Jordan |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
Page generated in 0.0014 seconds