Browsing by Author "Russell Wolfinger, Committee Member"

Now showing 1 - 3 of 3

Accounting for Within- and Between-Locus Dependencies in Marker Association Tests
(2003-06-26) Czika, Wendy Ann; Dennis Boos, Committee Member; David Dickey, Committee Member; Dahlia Nielsen, Committee Member; Bruce S. Weir, Committee Chair; Russell Wolfinger, Committee Member
The importance of marker association tests has recently been established for locating disease susceptibility genes in the human genome, attaining finer-scaled maps than the linkage variety of tests through the detection of linkage disequilibrium (LD). Many of these association tests were originally defined for biallelic markers under ideal assumptions, with multiallelic extensions often complicated by the covariance among genotype or allele proportions. The well-established allele and genotype case-control tests based on Pearson chi-square test statistics are exceptions since they adapt easily to multiallelic versions, however each of these has its shortcomings. We demonstrate that the multiallelic trend test is an attractive alternative that lacks these limitations. A formula for marker genotype frequencies that incorporates the coefficients quantifying various disequilibria is presented, accommodating any type of disease model. This enables the simulation of samples for estimating the significance level and calculating sample sizes necessary for achieving a certain level of power. There is a similar complexity in extending the family-based tests of association to markers with more than two alleles. Fortunately, the nonparametric sibling disequilibrium test (SDT) statistic has a natural extension to a quadratic form for multiallelic markers. In the original presentation of the statistic however, information from one of the marker alleles is needlessly discarded. This is necessary for the parametric form of the statistic due to a linear dependency among the statistics for the alleles, but the nonparametric representation eliminates this dependency. We show how a statistic making use of all the allelic information can be formed. Obstacles also arise when multiple loci affect disease susceptibility. In the presence of gene-gene interaction, single-marker tests may be unable to detect an association between individual markers and disease status. We implement and evaluate tree-based methods for the mapping of multiple susceptibility genes. Adjustments to correlated p-values from markers in LD with each other are also examined. This study of epistatic gene models reveals the importance of three-locus disequilibria of which we discuss various statistical tests.
A New Method for Genetic Network Reconstruction in Expression QTL Data Sets
(2009-11-16) Duarte, Christine Woods; Zhao-Bang Zeng, Committee Chair; Russell Wolfinger, Committee Member; Jung-Ying Tzeng, Committee Member; Ronald Sederoff, Committee Member
Expression QTL (or eQTL) studies involve the collection of microarray gene expression data and genetic marker data from segregating individuals in a population in order to search for genetic determinants of differential gene expression. Previous studies have found large numbers of trans-regulated genes that link to a single locus or eQTL ``hotspot". It would be of great interest to discover the mechanism of co-regulation for these groups of genes. However, many difficulties exist with current network reconstruction algorithms such as low power and high compuatational cost. A common observation for biological networks is that they have a scale-free or power-law architecture. In such an architecture, there exist highly influential nodes that have many connections to other nodes, but most nodes in the network have very few connections. If we assume that this type of architecture applies to genetic networks, then we can simplify the problem of genetic network reconstruction by focusing on discovery of the key regulatory genes at the top of the network. We introduce the concept of ``shielding" in which a gene is conditionally independent of the QTL given the shielder gene, and we iteratively build networks from the QTL down using tests of conditional independence. We evaluate the confidence level of shielders using a two-part strategy of requiring a threshold number of genes to be shielded and requiring a high level of bootstrap support for shielders. We have performed a set of simulations to test the sensitivity and specificity of our method as a function of method parameters. We have found that our method has good performance using a significance level of 0.05 for testing the hypothesis that a gene is a shielder, with little gained by decreasing $alpha$ further. The shielder boostrap confidence level depends on the desired balance between false positives and false negatives, but our recommendation is to use 80\% bootstrap support for high confidence of discovered network features. With a small sample size (100) and a large number of network genes (as many as 600), our algorithm succeeds in finding a high percentage of the key network regulators (47\% on average) with high confidence (95\% specificity on average). We have applied our network reconstruction algorithm to a yeast expression QTL data set in which microarray and marker data were collected from the progeny of a backcross of two species of extit{Saccharomyces cerevisiae} cite{Brem2002}. Networks have been reconstructed for 11 of the largest eQTL hotspots in this data set. The regulation of shielder gene expression has been found to be primarily in trans, although about 10\% of shielder genes are found to be regulated in cis. Bioinformatic analysis of three networks generated different hypotheses for mechanisms of regulation of the shielded genes by the primary shielders. One common theme was that the shielders modulated the effect of transcription factors of which they were themselves targets. Overall our method has created a large list of potentially important regulatory genes in various yeast biological processes, and further bioinformatic analysis or laboratory experiments could lead to the generation and testing of many important hypthotheses.
Statistical Studies of Genomics Data
(2004-12-28) Feng, Sheng; Zhao-Bang Zeng, Committee Chair; Bruce Weir, Committee Co-Chair; Leonard Stefanski, Committee Member; Hao Helen Zhang, Committee Member; Russell Wolfinger, Committee Member
In recent years, studies on Genetics and Genomics have become one of the most active fields in science. The Genetic and Genomics data have several significant and unique characteristics that bring great challenges for data analysis. Three statistical studies have been presented in this dissertation. In chapter 1, an empirical Bayesian approach has been developed in a linear mixed model for Microarray data analysis. In chapter 2, a multiple order Markov chain model is applied to summarize the local correlation patterns among multiple genetic markers in linkage disequilibrium mapping. In chapter 3, a shrinkage method is being developed to integrate Biological prior knowledge presented in moment statistics. This new method may be useful in some genetic network studies.