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MULTIVARIATE METHODS FOR EXTRACTING GENETIC
ASSOCIATIONS FROM CORRELATED DATA
by
Mary Helen Black
__________________________________________________________
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(STATISTICAL GENETICS AND GENETIC EPIDEMIOLOGY)
August 2009
Copyright 2009 Mary Helen Black
Object Description
| Title | Multivariate methods for extracting genetic associations from correlated data |
| Author | Black, Mary Helen |
| Author email | mhbridge@usc.edu; mary_helenb@hotmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Statistical Genetics & Genetic Epidemiology |
| School | Keck School of Medicine |
| Date defended/completed | 2009-06-30 |
| Date submitted | 2009 |
| Restricted until | Restricted until 07 Aug. 2010. |
| Date published | 2010-08-07 |
| Advisor (committee chair) | Watanabe, Richard |
| Advisor (committee member) |
Gauderman, Jim Conti, David Allayee, Hooman Buchanan, Tom |
| Abstract | Multivariate methods ranging from traditional joint SNP methods to principal components analysis (PCA) have been developed for testing multiple markers in a region for association with disease and disease- related traits. However, these methods suffer from low power or the inability to identify the subset of markers contributing to the evidence for association under various scenarios. In this paper, a new approach is introduced, based on PCA with an orthoblique rotation (OPCC), in order to identify specific subsets of markers genotyped in a candidate region showing associating with an outcome of interest. When compared to traditional methods, the OPCC approach has similar or improved power, but also identifies the unique subset of markers contributing to the evidence for association. The utility of OPCC is demonstrated with an example from type 2 diabetes literature.; In this paper, the OPCC methodology is also extended to investigation of epistasis, or gene-gene interactions, which is believed to underlie most complex diseases. OPCC is directly compared to multiple linear regression and PC-modeling of multiplicative interactions, under various scenarios. In most cases, the power of OPCC to detect interactions in the presence of epistasis is greater than that of pairwise SNP or PC approaches. Power is further enhanced when analyzed in an interaction testing framework (ITF). OPCC for detecting gene-gene interactions is applied to cohort data, for two candidate genes known to have a role in cardiovascular outcomes. Overall, OPCC proves to be a powerful and efficient method to determine whether multiple variants and their interactions are associated with complex disease traits. |
| Keyword | genetic epidemiology; principal components; cluster analysis; association studies; candidate gene |
| 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-m2530 |
| Rights | Black, Mary Helen |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Black-2763 |
| Archival file | uscthesesreloadpub_Volume44/etd-Black-2763.pdf |
Description
| Title | Page 1 |
| Full text | MULTIVARIATE METHODS FOR EXTRACTING GENETIC ASSOCIATIONS FROM CORRELATED DATA by Mary Helen Black __________________________________________________________ A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (STATISTICAL GENETICS AND GENETIC EPIDEMIOLOGY) August 2009 Copyright 2009 Mary Helen Black |
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