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Type of Document Dissertation Author Gao, Xiaoyi , Author's Email Address xgao4@ncsu.edu URN etd-07032006-153645 Title Statistical Methods in Genetic Association Studies Degree PhD Graduate Program Bioinformatics Advisory Committee
Advisor Name Title Bruce S. Weir Committee Chair Dahlia M. Nielsen Committee Co-Chair Jason A. Osborne Committee Member Philip Awadalla Committee Member Keywords
- population structure
- multiple testing
- genotyping error
- single nucleotide polymorphism
Date of Defense 2006-07-06 Availability unrestricted Abstract Population structure is a serious confounding factor in genetic association studies.It may lead to false positive results or failure to detect true association. We propose a
hierarchical clustering algorithm, AW-clust, for using single nucleotide polymorphism
(SNP) genetic data to assign individuals to populations. We show that the algorithm
can assign sample individuals highly accurately to their corresponding ethic groups:
CEU, YRI, CHB+JPT in our tests using HapMap SNP data and it is also robust
to admixed populations when tested on Perlegen SNP data. Moreover, it can detect
fine-scale population structure as subtle as that between Chinese and Japanese by
using genome-wide hight diversity SNP loci. Genotyping errors exist in most genetic
data and can influence the biological conclusions of the studies. A simple method is to
conduct the Hardy-Weinberg equilibrium (HWE) test in population-based association
studies. We investigated the power issue of using the HWE test on genotyping error
detection in the presence of current genotyping technologies. Multiple testing is a
challenging issue in genetic studies using SNPs that are in linkage disequilibrium (LD)
with each other. Failure to adjust for multiple testing appropriately may produce
excess false positives or overlook true positive signals. We propose a new multiple
testing correction method, CLDMeff , for association studies using SNP markers. It
is shown to be simpler and more accurate than the recently developed methods and is
comparable to the permutation-based correction using both simulated and real data.
The efficiency and accuracy of the CLDMeff method makes it an attractive choice
for multiple testing correction when there is high intermarker LD in the SNP dataset.
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