Pharmacogenomics at scale : population analysis and machine learning applications in pharmacogenomics
Abstract/Contents
- Abstract
- Pharmacogenomics promises the ability to provide personalized therapeutic guidance for patients based on their genetics. Understanding how genetic variation leads to heterogeneity in drug response could dramatically increase patient outcomes by increasing efficacy and minimizing adverse drug reactions. Pharmacogenomics research has historically suffered from small sample sizes, which limits our understanding of global allele frequencies, drug-gene associations, and the identification and functional assessment of rare variants. In recent years, biobanks containing phenotype-linked genetic data for hundreds of thousands of participants have become available, presenting an unprecedented opportunity for population-scale analysis of the effect of genetics on drug response. Simultaneously, the capabilities of deep learning algorithms have advanced significantly, enabling powerful predictions about properties of DNA sequence data. This dissertation illustrates how biobanks can be used to study pharmacogenetics and how deep neural networks can be used to predict metabolic function of haplotypes in pharmacogenes. I present results from the largest pharmacogenetic study to date, analyzing pharmacogenetic allele and phenotype frequencies and a cohort of 500,000 individuals. I demonstrate how these data can be used to discover drug-gene associations and drug-gene-response associations through genome-wide and candidate gene studies by integrating clinical records and prescription data. Finally, I present a novel deep learning approach to predicting haplotype function in an important drug-metabolizing enzyme, CYP2D6.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | McInnes, Gregory Madden |
|---|---|
| Degree supervisor | Altman, Russ |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Ashley, Euan A |
| Thesis advisor | Rivas, Manuel, 1957- |
| Thesis advisor | Zou, James |
| Degree committee member | Ashley, Euan A |
| Degree committee member | Rivas, Manuel, 1957- |
| Degree committee member | Zou, James |
| Associated with | Stanford University, Department of Biomedical Informatics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Gregory M. McInnes. |
|---|---|
| Note | Submitted to the Department of Biomedical Informatics. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/bm119xm7699 |
Access conditions
- Copyright
- © 2021 by Gregory Madden McInnes
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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