Not just hopeful monsters : phenotypic and evolutionary properties of very large adaptive mutations
Abstract/Contents
- Abstract
- Understanding which mutations drive adaptation and mapping these mutations to their phenotypic and fitness consequences is a major goal of evolutionary biology. In particular, constructing these genotype-phenotype-fitness maps of adaptive mutations has the promise to answer numerous puz- zles in the field of evolution. One especially tricky puzzle is the existence of examples of very large adaptive mutations. Theoretical work, alongside evidence from quantitative genetics, suggests that organisms are integrated entities and mutations of very large effect should be doomed to be very strongly deleterious. However, there are numerous examples of very large adaptive mutations in nature. In this thesis, I tackle this apparent paradox by leveraging barcode lineage tracking technology to measure the fitness of very large adaptive mutations that arose in an evolution ex- periment conducted with yeast. First, I test the limits of this technology to measure fitness, finding a major source of technical variation that affects many technologies that use sequencing data as a quantitative measure. However, after accounting for this and other technical variation, we find that substantial variation persists between replicate experiments conducted on different days, which represents strong context-dependency in fitness. Second, I leverage this context dependency by conducting fitness measurement experiments in a large collection of subtly varying environments. We then use these data to gain insight into the structure of the genotype-phenotype-fitness maps for these very large adaptive mutations. We find that these mutations are able to exist because they are locally modular - affecting a small number of phenotypes that contribute to fitness in the environment they evolved in. However, these mutations are also globally pleiotropic - affecting many phenotypes that contribute to fitness in other environments. Altogether, this in-depth study of very large adaptive mutations reveals a diversity of phenotypic and evolutionary properties hidden from mere glances at a mutation's fitness, or the gene or pathway the mutation affects.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Kinsler, Grant Richard |
|---|---|
| Degree supervisor | Petrov, Dmitri Alex, 1969- |
| Thesis advisor | Petrov, Dmitri Alex, 1969- |
| Thesis advisor | Geiler Samerotte, Kerry |
| Thesis advisor | Palacios Roman, Julia Adela |
| Thesis advisor | Rosenberg, Noah |
| Thesis advisor | Sherlock, Gavin |
| Degree committee member | Geiler Samerotte, Kerry |
| Degree committee member | Palacios Roman, Julia Adela |
| Degree committee member | Rosenberg, Noah |
| Degree committee member | Sherlock, Gavin |
| Associated with | Stanford University, Department of Biology |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Grant Richard Kinsler. |
|---|---|
| Note | Submitted to the Department of Biology. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/wc650jr3033 |
Access conditions
- Copyright
- © 2022 by Grant Richard Kinsler
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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