Statistical methods for the analysis of genetics marker and microarray data

Abstract

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.