STRING-assisted module searching with applications
Abstract/Contents
- Abstract
- Analyzing genome wide association data in the context of biological pathways increases statistical power and helps us understand how genetic variation influences phenotype. However, the utility of pathway-based analysis tools is hampered by undercuration and reliance on a distribution of signal across all of the genes in a pathway. Methods that combine genome wide association results with genetic networks to infer the key phenotype- modulating subnetworks combat these issues, but have primarily been limited to network definitions with binary (yes/no) labels for gene-gene interactions. A recent method (EW dmGWAS) incorporates a biological network with weighted edge probability by requiring a secondary phenotype-specific expression dataset. In this dissertation, I combine an algorithm for weighted-edge module searching and a probabilistic interaction network in order to develop a method, STAMS, for recovering modules of genes with strong associations to the phenotype and probable biologic coherence. My method builds on EW dmGWAS but does not require a secondary expression dataset and performs better in six test cases. I show that my algorithm improves over EW dmGWAS and standard gene-based analysis by measuring precision and recall of each method on associations found in other datasets of the same phenotype. In the Wellcome Trust rheumatoid arthritis study, STAMS- identified modules were more enriched for associations replicated in other datasets than EW dmGWAS (STAMS p-value 3.0x10-4; EW dmGWAS- p-value=0.8). I demonstrate that the area under the precision-recall curve is 5.9 times higher with STAMS than with EW dmGWAS run on the Wellcome Trust type 1 diabetes data. I applied STAMS to two real world discovery Genome Wide Association Studies (GWAS). One of the top modules found in the AGRE autism GWAS contains CTTNBP2. Rare loss-of-function mutations in CTTNBP2 have been associated with autism, but have not been reported in common-variant autism GWAS. The application of STAMS to a large Alzheimer's disease GWAS also identified a module of genes in Complex I of the mitochondrial electron transport chain, which is a new association with Alzheimer's disease. I present compelling evidence for a mechanistic role for the module.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Hillenmeyer, Sara Parker |
|---|---|
| Associated with | Stanford University, Program in Biomedical Informatics. |
| Primary advisor | Altman, Russ |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Owen, Art B |
| Thesis advisor | Sherlock, Gavin |
| Advisor | Owen, Art B |
| Advisor | Sherlock, Gavin |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Sara Parker Hillenmeyer. |
|---|---|
| Note | Submitted to the Program in Biomedical Informatics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Sara Parker Hillenmeyer
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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