Methods and applications for position-specific evolutionary features in clinical genomics
Abstract/Contents
- Abstract
- One of the grand challenges in genomic medicine is to translate fundamental scientific discoveries regarding the structure, variation, and function of the genomes of individuals and populations towards improved health outcomes. The main hypothesis of this thesis is that all forms of human genetic variation contributing to the etiology and pathophysiology of modern human diseases have distinct and quantifiable evolutionary histories, which can be computed for every position in the human genome independent of human population characteristics, and used as informative quantitative priors in the discovery and assessment of variants of clinical importance in modern human populations. To enable robust evaluation of the specific questions posed by this thesis, I first explore the necessary properties and theoretical basis for a null evolutionary hypothesis for Evolutionary Genomic Medicine, and conclude that the well-established Neutral Theory of Molecular Evolution provides a sound theoretical and methodological basis for evaluating alternative hypothesis in Evolutionary Genomic Medicine. Due to advances in multiplex genotyping technologies, genome-wide associations studies (GWAS), have emerged as the premier modality for discovery and assessment clinical genomic variation. Although these efforts have been successful in revealing thousands variants robustly associated with a broad spectrum of clinical phenotypes, the variants established by the GWAS approach have so far failed to explain large proportions of the known genetic variance associated with important clinical traits such as Type 2 Diabetes and Hypertension. Because disease-associated variation is linked with genomic loci of functional importance which have undergone evolutionary selection, and even the proxy loci (e.g. tagging SNPs) used to probe for disease associated loci themselves have quantifiable evolutionary histories, I evaluate a compendium of disease-associated variants to evaluate the effect of long-term evolutionary histories on the discovery of disease-associated variants. Through this work I demonstrate that disease-associated variants have distinct evolutionary properties, and that evolutionary features of positions can be incorporated as priors to improve discovery of disease-associated variants. A similar approach is applied to evaluate pharmacogenomics variants associated with warfarin, demonstrating that evolutionary features of genomic positions improve clinical assessment of pharmacogenomics variation. Through the findings and insights gained from efforts in pursuit of my thesis which are reported here, my collaborators and I clearly demonstrate that quantitative evolutionary features can be estimated for each position in the human genome across species, and then applied to modern human population data to improve discovery and assessment of genomic variation associated with clinical phenotypes.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2011 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Dudley, Joel |
|---|---|
| Associated with | Stanford University, Department of Biomedical Informatics. |
| Primary advisor | Butte, Atul J |
| Thesis advisor | Butte, Atul J |
| Thesis advisor | Kumar, S. (Sudhir), 1958- |
| Thesis advisor | Petrov, Dmitri Alex, 1969- |
| Advisor | Kumar, S. (Sudhir), 1958- |
| Advisor | Petrov, Dmitri Alex, 1969- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Joel Thomas Dudley. |
|---|---|
| Note | Submitted to the Department of Biomedical Informatics. |
| Thesis | Ph.D. Stanford University 2011 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2011 by Joel Dudley
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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