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BETWEEN GENES AND PHENOTYPES: AN INTEGRATIVE
NETWORK-BASED MONTE CARLO METHOD FOR THE
PREDICTION OF HUMAN-GENE PHENOTYPE ASSOCIATIONS
by
Michael R. Mehan
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTATIONAL BIOLOGY AND BIOINFORMATICS)
December 2009
Copyright 2009 Michael R. Mehan
Object Description
| Title | Between genes and phenotypes: an integrative network-based Monte Carlo method for the prediction of human-gene phenotype associations |
| Author | Riel-Mehan, Michael |
| Author email | rielmeha@usc.edu; mmehan@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computational Biology & Bioinformatics |
| School | College of Letters, Arts and Sciences |
| Date defended/completed | 2009-08-21 |
| Date submitted | 2009 |
| Restricted until | Unrestricted |
| Date published | 2009-12-08 |
| Advisor (committee chair) | Waterman, Michael S. |
| Advisor (committee member) |
Zhou, Xianghong Jasmine Lei, Le Kempe, David |
| Abstract | Complex human diseases are often caused by multiple mutations, each of which contributes only a minor effect to the disease phenotype. To study the basis for these complex phenotypes, we developed a network-based approach to identify coexpression modules specifically activated in particular phenotypes.; We integrated these modules, protein-protein interaction data, Gene Ontology annotations, and our database of gene-phenotype associations derived from literature to predict novel human gene-phenotype associations. Our systematic predictions provide us with the opportunity to perform a global analysis of human gene pleiotropy and its underlying regulatory mechanisms.; We applied this method to 338 microarray datasets, covering 178 phenotype classes, and identified 193,145 phenotype-specific coexpression modules. We trained random forest classifiers for each phenotype and predicted a total of 6,558 gene-phenotype associations. We showed that 40.9% genes are pleiotropic, highlighting that pleiotropy is more prevalent than previously expected. We collected 77 ChIP-chip datasets studying 69 transcription factors binding over 16,000 targets under various phenotypic conditions. Utilizing this unique data source, we confirmed that dynamic transcriptional regulation is an important force driving the formation of phenotype specific gene modules.; We created a genome-wide gene to phenotype mapping that has many potential implications, including providing potential new drug targets and uncovering the basis for human disease phenotypes. Our analysis of these phenotype-specific coexpression modules reveals a high prevalence of gene pleiotropy, and suggests that phenotype-specific transcription factor binding may contribute to phenotypic diversity. All resources from our study are made freely available on our online Phenotype Prediction Database. |
| Keyword | bioinformatics; microarray; Expression; regulation; pleiotropy; phenotype prediction |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m2789 |
| Contributing entity | University of Southern California |
| Rights | Riel-Mehan, Michael |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@dots.usc.edu |
| Filename | etd-RielMehan-3312 |
| Archival file | uscthesesreloadpub_Volume23/etd-RielMehan-3312-0.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@dots.usc.edu |
| Full text | BETWEEN GENES AND PHENOTYPES: AN INTEGRATIVE NETWORK-BASED MONTE CARLO METHOD FOR THE PREDICTION OF HUMAN-GENE PHENOTYPE ASSOCIATIONS by Michael R. Mehan A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTATIONAL BIOLOGY AND BIOINFORMATICS) December 2009 Copyright 2009 Michael R. Mehan |
