Abstract

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.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu.

Graduation Date

2021

Semester

Summer

Advisor

Mason, Chase

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

Biology

Degree Program

Conservation Biology; Integrative Biology

Format

application/pdf

Identifier

CFE0009104; DP0026437

URL

https://purls.library.ucf.edu/go/DP0026437

Language

English

Release Date

February 2027

Length of Campus-only Access

5 years

Access Status

Doctoral Dissertation (Campus-only Access)

Restricted to the UCF community until February 2027; it will then be open access.

Share

COinS