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Title: | The Effects of Epistasis and Pleiotropy on the Repeatability of Evolution |
Authors: | Taverner, Andrew Michael |
Advisors: | Andolfatto, Peter |
Contributors: | Quantitative Computational Biology Department |
Keywords: | Epistasis Evolution Pleiotropy |
Subjects: | Evolution & development |
Issue Date: | 2020 |
Publisher: | Princeton, NJ : Princeton University |
Abstract: | The extent to which evolution is predictable and repeatable are open questions in evolutionary biology. Though the question about the repeatability of evolution was posed at least 30 years ago, limited headway has been made – it is an inherently difficult question to answer, as it is generally not possible to observe evolution more than once. My thesis revolves around this question regarding the repeatability of evolution. In my first chapter, I examine the spatial distribution of non-synonymous substitutions between species and conclude that substitutions alter local evolutionary rate through epistasis. This genome-wide epistasis indicates it is unlikely for evolution to be repeatable. In my second chapter, we study a key adaptation to toxin insensitivity in insect herbivores with an overwhelmingly convergent genetic architecture. We show how epistasis and pleiotropy limit which mutations are possible, resulting in repeated evolution at the molecular level. Lastly, in my third chapter, we map the genetic architecture of pigmentation in Bombyx and find that the genes underlying this trait are largely distinct from the typical genes implicated in pigmentation in Lepidoptera and Drosophila. Ultimately, we conclude that the genetic architecture of pigmentation in Bombyx is largely novel and do not involve the classical pigmentation genes such as yellow, ebony, and tan. |
URI: | http://arks.princeton.edu/ark:/88435/dsp019p290d29t |
Alternate format: | The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: catalog.princeton.edu |
Type of Material: | Academic dissertations (Ph.D.) |
Language: | en |
Appears in Collections: | Quantitative Computational Biology |
Files in This Item:
File | Description | Size | Format | |
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Taverner_princeton_0181D_13261.pdf | 3.82 MB | Adobe PDF | View/Download |
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