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High-throughput phenotyping and modeling to dissect the genetic architecture of plant plasticity and growth

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Plant development consists of a tight web of interconnected traits that are jointly affected by genetics and the environment. These traits not only are affected by internal and environmental cues but can influence and be influenced by other traits as well. In this dissertation, I describe two projects where I develop frameworks for examining potential mechanisms underlying phenotypic variation and show how perturbations of one trait may cause other traits to vary. In the first project, I use the shade avoidance response as a model system for understanding the genetic architecture of late development plasticity. I leverage multiple data sets - bolting time, rosette biomass, inflorescence growth, etc. - in conjunction with path models to better describe cascading effects of QTL to explain colocalizations of QTL for different traits. I show how combinations of direct and indirect QTL effects can lead to variation in plasticity. In the second project, I take advantage of dense time series data to develop a model for measuring relative growth rates from growth curves called SplineRGR. SplineRGR is a parsimonious and flexible framework that demonstrates how perturbations in a latent trait - relative growth rate - can result in a wide range of patterns in overall growth dynamics. Overall, this dissertation illustrates that using complex models to leverage increasingly intricate data sets can provide insight into the genetic architecture of developmental traits and a more unified view of the genotype-phenotype map.

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This item is under embargo until May 10, 2023.