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MODEL SELECTION METHODS FOR GENOME WIDE ASSOCIATION
STUDIES AND STATISTICAL ANALYSIS OF RNA SEQ DATA
by
Sudeep Srivastava
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)
August 2012
Copyright 2012 Sudeep Srivastava
Object Description
| Title | Model selection methods for genome wide association studies and statistical analysis of RNA seq data |
| Author | Srivastava, Sudeep |
| Author email | sudeepsr@usc.edu;sudeep.mentor@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computational Biology and Bioinformatics |
| School | College of Letters, Arts And Sciences |
| Date defended/completed | 2012-04-05 |
| Date submitted | 2012-06-25 |
| Date approved | 2012-06-26 |
| Restricted until | 2012-06-26 |
| Date published | 2012-06-26 |
| Advisor (committee chair) | Chen, Liang |
| Advisor (committee member) |
Nuzhdin, Sergey Sun, Fengzhu Radchenko, Peter |
| Abstract | Genome-wide association studies are important tools to reconstruct the genotype phenotype map to understand the underlying genetic architecture of complex traits. This enables us to better understand the genetic architecture of these phenotypes. With the advances in genotyping and high throughput sequencing technologies, millions of markers can be identified for individual populations in very short durations of time. Due to the multiple loci control nature of complex phenotypes, there is great interest to test markers simultaneously instead of one by one. In chapter 2, we compare three model selection methods for genome wide association studies using simulations: the Stochastic Search Variable Selection (SSVS), the Least Absolute Shrinkage and Selection Operator (LASSO) and the Elastic Net. We apply the three methods to identify genetic variants that are associated with daunorubicin-induced cytotoxicity. We also compare the LASSO and the SSVS to a dataset of two quantitative phenotypes related to Rheumatoid Arthritis. ❧ In the second part of the dissertation, a two parameter generalized Poisson(GP) model to analyze RNA Seq is proposed. Deep sequencing of RNAs (RNA-seq) has been a useful tool to characterize and quantify transcriptomes. However, there are significant challenges in the analysis of RNA-seq data, such as how to separate signals from sequencing bias and how to perform reasonable normalization. In chapter 4 ,we used the generalized Poisson model to separate out the "true" expression level from the bias. We show that the GP model fits the data much better than the traditional Poisson model. Based on the GP model, we can improve the estimates of gene or exon expression, perform a more reasonable normalization across different samples, and improve the identification of differentially expressed genes and the identification of differentially spliced exons. We also use a likelihood based approach to estimate the expression levels of transcripts using the GP model discussed in chapter 5. ❧ RNA Sequencing and genome wide associations studies have led to a rapid growth in understanding of complex genetic phenotypes and diseases. These two methods are crucial tools in the genomic age in the fields of molecular biology, genomics, population and quantitative genetics etc. Using these tools effectively, with the help of statistical and algorithmic methods, would lead to a rapid growth of knowledge in these fields and in the overall field of biology. |
| Keyword | alternative splicing; differential expression; expression; genome wide association studies; model selection; RNA seq |
| 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-m |
| Rights | Srivastava, Sudeep |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume4/etd-Srivastava-905.pdf |
Description
| Title | Page 1 |
| Full text | MODEL SELECTION METHODS FOR GENOME WIDE ASSOCIATION STUDIES AND STATISTICAL ANALYSIS OF RNA SEQ DATA by Sudeep Srivastava 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) August 2012 Copyright 2012 Sudeep Srivastava |
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