Browsing by Author "Runcie, Daniel E"

A general goal of biology is to understand how two or more sets of traits in an organism are related - for example, disease state and genetics, physiology and behavior, or phenotypic variation and gene function. Many of the early advancements in statistical analysis dealt with relating measured traits when one could be represented as a single number. However, many traits are inherently multi-dimensional, and technologies are advancing for rapidly measuring many types of such highly complex traits. Making efficient use of these new, larger datasets requires new statistical models for to biological inference. In this thesis, I develop a method for relating two very different types of traits in sea urchin larvae: morphological shape, and developmental gene expression. In particular, I develop an approach for regression modeling using shape as a response variable. I use this method to address the question of whether variation in the expression of regulatory genes during development predicts later morphological variation in the larvae. I propose a hierarchical random effects factor regression model with shape as a response variable for relating morphology and gene expression when the individuals in each dataset are related, but not identical. I fit an approximation to the general model by breaking it into three discrete steps. I find that gene expression can explain ~25% of mean symmetric form variation among cultures of related larvae, and identify several groups of related genes that are correlated with aspects of morphological variation.

Many factors, including gene networks, developmental processes, and the environment mediate the link between the activity of genes and complex phenotypes in higher organisms. While genetic variants are the raw material for evolution, these other factors are critical for determining which variants are actually exposed to natural selection. In this dissertation, I describe three projects in which I investigate how developmental mechanisms and the environment interact to shape phenotypic variation. In each project, I use gene expression as a window into the activity of genes, and as a tool to measure variation in and among developmental mechanisms. Two projects are experimental, focusing on early development in sea urchins, and how environmental stress caused by climate change impacts the expression of genetic variation in phenotypic traits. In these projects, I explicitly incorporate information about the biochemical functions of genes and how they interact in development, and test how such mechanisms shape the impact of genetic and environmental perturbations to development. The third project is methodological, in which I propose a unified statistical framework for inferring previously unknown developmental constraints that may underlie gene expression phenotypes. Together, these projects demonstrate that an understanding of developmental mechanisms can enhance our understanding of the processes that shape variation in populations, and can help predict the biological effects of climate change.