Page 1 |
Save page Remove page | Previous | 1 of 116 | Next |
|
small (250x250 max)
medium (500x500 max)
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
Subset |
TWO-STAGE GENOTYPING DESIGN AND POPULATION STRATIFICATION IN CASE-CONTROL ASSOCIATION STUDIES
by
Hansong Wang
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)
May 2007
Copyright 2007 Hansong Wang
Object Description
| Title | Two-stage genotyping design and population stratification in case-control association studies |
| Author | Wang, Hansong |
| Author email | hansongw@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Biostatistics |
| School | Keck School of Medicine |
| Date defended/completed | 2007-03-14 |
| Date submitted | 2007 |
| Restricted until | Unrestricted |
| Date published | 2007-06-08 |
| Advisor (committee chair) | Stram, Daniel O. |
| Advisor (committee member) |
Gauderman, James Nordborg, Magnus |
| Abstract | The dissertation focuses on two problems identified from the ongoing Multiethnic Cohort (MEC) study, i.e. two-stage genotyping design and population stratification. Three chapters follow a general introduction to relevant concepts in genetic epidemiological studies. Chapter one and two contain two published papers on optimal two-stage genotyping design, using Bonferroni correction and false positive rate (FDR) correction, respectively, for the overall type I error rate. The significance of this part is that a procedure was designed to search for the most cost-effective two-stage studies based on the current availability of high-throughput and very high-throughput genotyping platforms (fixed SNP array) and without considering any constraints on total sample size or available resources. The results are to provide guidance for two-stage designs under a wide range of assumptions on the per-genotype cost ratio and total number of markers. Chapter three describes the performance of the generalized linear mixed model (GLMM) in controlling for population stratification with simulated structured case-control data. Compared to Genomic Control (GC) and the relatively new EigenSTRAT methods, GLMM does not offer much advantage. To assess the likely significance of population stratification in the planned or future MEC association scans, Chapter 3 also examines evidence of population stratification within four ethnic groups (African Americans, Japanese, Latinos and Whites) represented in the newly available MEC breast cancer data consisting of 1400 SNPs. The results indicate moderate structure exists in African Americans and Latinos and the GC approach produced acceptable empirical type I error in most cases. |
| Keyword | two stage study; population stratification; association design |
| 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 |
| Type | texts |
| Legacy record ID | usctheses-m523 |
| Rights | Wang, Hansong |
| 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-Wang-20070608 |
| Archival file | uscthesesreloadpub_Volume23/etd-Wang-20070608.pdf |
Description
| Title | Page 1 |
| Full text | TWO-STAGE GENOTYPING DESIGN AND POPULATION STRATIFICATION IN CASE-CONTROL ASSOCIATION STUDIES by Hansong Wang 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) May 2007 Copyright 2007 Hansong Wang |
Comments
Post a Comment for Page 1
