Browsing by Author "Dahlia M. Nielsen, Committee Co-Chair"

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    Statistical methods for the analysis of genetics marker and microarray data
    (2004-05-18) Yu, Xiang; Bruce S. Weir, Committee Chair; Dahlia M. Nielsen, Committee Co-Chair; Greg Gibson, Committee Member; Russell D. Wolfinger, Committee Member
    With the advent of high-throughput technologies in genomics study, a large volume of data has been accumulated, leaving the challenge for bioinformaticists on how to manage, analyze, and interpret the data. Analysis of genetic marker and microarray data are two important aspects in current bioinformatics studies. In this dissertation work, we tend to explore some statistical issues for such problems. We discuss two extensions of the EM algorithm to infer haplotypes from genotype data, each for a particular sampling scenario. The first one applies to a random sample of both diploid and haploid individuals from the population, in which the haplotype information from the haploid individuals is incorporated into the estimation process. The second one applies to a sample of parent-offspring trios, in which the dependencies between the parental and the offspring genotypes are correctly handled in the analysis. We show that these two modified EM algorithms perform better than the usual one when applied to their corresponding specific samples, respectively. We study the experimental designs in two-color microarray experiments and resolve some of the outstanding issues that are controversial on the use of different experiment designs. We show that the loop and balanced block designs analyzed in a mixed model are more efficient that the reference designs from a statistical point of view. We also provide general guidelines on how to optimize experimental resources to get maximal efficiency using these designs. We present an application of the mixed model to identify transcription factor-gene interactions and to infer transcriptional regulatory structures in Sacchromyces cerevisiae using microarray experiments. We demonstrate the mixed model that pools the observations across all experiments to be a powerful approach.
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    Statistical Methods in Genetic Association Studies
    (2007-08-01) Gao, Xiaoyi; Dahlia M. Nielsen, Committee Co-Chair; Bruce S. Weir, Committee Chair; Philip Awadalla, Committee Member; Jason A. Osborne, Committee Member
    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.