Browsing by Author "Dahlia M. Nielsen, Committee Member"

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Development of Linkage and Association Methods to Map Disease Genes
(2002-10-28) Liu, Wenlei; Gregory C. Gibson, Committee Member; Bruce S. Weir, Committee Chair; Zhao-Bang Zeng, Committee Member; Dahlia M. Nielsen, Committee Member
Identification of disease susceptibility genes is one of the primary aims of contemporary genetic research. With the recent development in molecular biology techniques, large-scale gene mapping with a dense genome-spanning set of markers becomes a reality. The availability of markers throughout the genome has made linkage and association studies more feasible. In the first chapter, we review many linkage and association methods and point out the potential problems with current linkage and association analysis. In the second chapter, we modify two identity-by-state (IBS) test statistics of Lange (Lange K. 1986a, A test statistic for the affected-sib-set method. Annals of Human Genetics 50, 283--290; Lange K. 1986b, The affected sib-pair method using identity by descent relations. American Journal of Human Genetics 39, 148--150.) to allow for inbreeding in the population. We evaluate the power and false positive rates of the modified tests under three disease models using simulated data. When the population inbreeding coefficient is large, both the false positive rates and power are reduced when the modified test statistics were applied, although power remained high under a recessive disease model. Allowing for inbreeding is therefore appropriate at least for diseases known to be recessive. In the third chapter, we compute the proportions of affected sib pairs sharing 0, 1 and 2 marker alleles identity-by-decent (IBD) in an inbred population and express them in terms of higher order decent measures. We perform two consistency checks on the identity state probabilities and the two consistency checks verify our calculations. We did the same thing for affected sib pairs from first cousin marriage in an inbred population. In the fourth chapter, we study linkage and linkage disequilibrium (LD) simultaneously for single QTL using family data in an attempt to increase mapping resolution and reduce false positive rates. We estimate QTL allele frequencies, LD and recombination factions between the marker loci and the QTL locus and the QTL model parameters using an EM algorithm. After performing single analysis, we extend our model to study two marker loci simultaneously so that we can increase the accuracy of the estimations. Our simulation results show that our EM algorithm can give consistent estimates of all the parameters considered.
Functional and comparative genomics of Aspergillus flavus to characterize secondary metabolism
(2010-07-07) Georgianna, David Ryan; Dahlia M. Nielsen, Committee Member; James A. Alspaugh, Committee Member; David C. Muddiman, Committee Member; Gary A. Payne, Committee Chair
Recently available genome sequences for Aspergillus flavus and A. oryzae were used to gain insight into the biosynthesis of secondary metabolites and to identify species-specific characters for these fungi. Transcriptome analyses, comparative genome hybridizations, and bottom-up proteomics were used to study two interrelated aspects of the ecology of A. flavus: 1) Regulation of secondary metabolism, with emphasis on the carcinogenic mycotoxin, aflatoxin, and 2) Discovery of key differences between A. flavus and the closely related domesticated species A. oryzae. Filamentous fungi such as A. flavus produce an abundance of diverse secondary metabolites, the most well-studied being aflatoxin. A defining feature of secondary metabolites is their production by clusters of genes. In this thesis I provide a comprehensive review of the current status of aflatoxin biosynthesis and regulation, including emerging genomic studies. I was the first to employ SILAC (stable isotope labeling by amino acids in cell culture), a technique to enable relative protein quantification by mass spectrometry, in a multicellular free-living prototroph. This technique allowed me to quantify 381 proteins during growth of A. flavus under conditions conducive (28Â°C) and non-conducive (37Â°C) for aflatoxin biosynthesis. From these studies I showed that enzymes needed for aflatoxin biosynthesis were lacking at 37Â°C. Additionally, I observed that protein concentration and transcript accumulation did not correlate well, with transcripts and proteins from genes within the aflatoxin cluster being a notable exception. I also showed through use of reporter constructs that the aflatoxin pathway specific transcription factor AflR is localized to the nucleus and active at 37Â°C even though aflatoxin is not produced and most genes in the pathway are not expressed. I have also studied the regulation of all predicted secondary metabolite gene clusters for A. flavus to better characterize their expression under environmental conditions. Of the predicted 55 secondary metabolite gene clusters in A. flavus, only three metabolites have been associated with a respective cluster. These are aflatoxin, cyclopiazonic acid, and aflatrem. Aside from aflatoxin, little knowledge is available about the regulation of other secondary metabolites in A. flavus. Transcriptional analysis of these secondary metabolite gene clusters over 28 experimental conditions showed the clusters to group into classes with similar profiles of expression. To further explore the correlations found by gene expression analysis, aflatoxin and CPA production were quantified under six cell culture environments known to be conducive or non-conducive for aflatoxin biosynthesis and in infected maize seeds. We found that CPA was not as tightly regulated as aflatoxin in response to cell culture environment however CPA and aflatoxin both accumulated similarly in developing maize seeds. I compared the genome of A. flavus with A. oryzae, a non-aflatoxigenic and domesticated species related to A. flavus. I hypothesized that insights gained from knowledge of the differences between these species would reveal new information on regulation of secondary metabolism, and possibly pathogenicity. I used comparative genome hybridization to characterize genomic content among three strains of each species. These results revealed that A. flavus and A. oryzae are strikingly similar with regard to DNA sequence. In addition to defining core sequence variations between these species I investigated the gene expression differences on substrates unique to each speciesâ€™ ecological niche. Aspergillus oryzae has been used in fermentations through cultivation for thousand of years on wheat bran. Aspergillus flavus is an opportunistic pathogen of maize causing loss of crops through contamination with aflatoxin. From the expression analyses it was clear that despite similar genomes, A. flavus and A. oryzae use their genomes in vastly different ways. Among the most interesting of these differences was that A. flavus appears to be a much more capable producer of secondary metabolites.
Functional Genomic Analysis and Protein Expression in Lactobacillus
(2008-12-12) Duong, Tri; Robert M. Kelly, Committee Member; Dahlia M. Nielsen, Committee Member; Amy M. Grunden, Committee Member; Todd R. Klaenhammer, Committee Chair
The lactic acid bacteria (LAB) are important in the production of food, industrial chemicals, and bulk ingredients. LAB, particularly probiotic lactobacilli, occupy important niches in the gastrointestinal tracts of humans and animals and are increasingly recognized as modulators of human and animal health. Major advances have been made in the genomic characterization of LAB facilitating their expanded use in bioprocessing and health. Trehalose is a cryoprotectant used to protect starter cultures from damage caused by freezing and lyophilization. Characterization of the tre locus of Lactobacillus acidophilus NCFM identified a trehalose PTS transporter, trehalose-6-phosphate hydrolase and a transcriptional regulator. Knockout mutants were used to determine that uptake and hydrolysis of trehalose is required for cryoprotection in L. acidophilus. Analysis of the FOS, lac and tre operons and pgm gene of L. acidophilus identified a number of putative promoter and repressor elements which were used to construct a series of expression vectors for use in lactobacilli. -glucuronidase reporter assays showed FOS, lac, and tre based vectors to be highly inducible by their specific carbohydrate and repressed by glucose. A construct based on the phosphoglycerate mutase (pgm) promoter was constitutively highly expressed. The development of these expression vectors is intended to support several novel applications including the delivery of vaccines and biotherapeutics by intestinal lactobacilli. The oxalate-degrading capabilities of lactobacilli have been studied with much interest in their potential use in a probiotic strategy for the management of hyperoxaluria and urinary stone disease. We describe the construction of a plasmid for the overexpression of the L. acidophilus NCFM oxalate degradation proteins, Frc and Oxc, and characterize its effect on oxalate degradation activity of L. acidophilus and Lactobacillus gasseri. This construct was able improve oxalate degradation by L. gasseri ATCC 33323 and complement an L. acidophilus Frc knockout mutant. Dendritic cells (DC) are antigen presenting cells found at mucosal surfaces that are important in directing acquired immunity. In this study, we construct an expression vector for recombinant DC-targeted Bacillus anthracis protective antigen for oral delivery by Lactobacillus acidophilus as a potential vaccine strategy against anthrax and evaluate its protective capability using a mouse model.
Likelihood ratio tests for association with multiple disease susceptibility alleles, genotyping errors, or missing parental data
(2003-06-27) Morris, Richard Wayne; Norman L. Kaplan, Committee Co-Chair; Jeffery L. Thorne, Committee Co-Chair; Bruce S. Weir, Committee Member; Dahlia M. Nielsen, Committee Member
Multiple disease susceptibility alleles, genotype errors, or missing genotype data can create problems when testing for association between alleles or genotypes at a genetic marker and a dichotomous phenotype. I used likelihood methods to study the impact of each of these factors on detecting association. In the presence of multiple disease susceptibility alleles, I found that power of the likelihood ratio test (LRT) declines less when based on haplotypes made up of tightly linked single nucleotide polymorphisms (SNPs) than when based on individual SNPs. The result suggests that statistical methods based on haplotypes may be useful to identify and locate complex disease genes. Genotype errors can lead to excess type I error in nuclear family (case-parents) studies when errors resulting in Mendelian inconsistent families are corrected but other errors remain in the data. I developed a LRT for single SNPs or haplotypes that incorporates nuisance parameters for genotype errors and showed that type I error rate can be controlled at little cost to power. For nuclear family data in which missing parents and additional siblings create a diversity of family structures, I developed a unified approach to computing LRT power for a test of association. Comparison of LRT power with power of a family-based association test showed that LRT has greater power.
Statistical Methods for Family-Based Association Studies for Complex Human Diseases: Single-Locus and Haplotype Methods
(2006-12-15) Chung, Ren-Hua; Bruce S. Weir, Committee Co-Chair; Eden R. Martin, Committee Co-Chair; Trudy F.C. Mackay, Committee Member; Dahlia M. Nielsen, Committee Member; Jung-Ying Tzeng, Committee Member
Disease-gene fine-mapping is an important task in human genetics. Linkage and association analyses are the two main approaches for exploring disease susceptibility genes. In Chapter 1, we introduce the development of methods for disease-gene mapping in the past decades and present the rationale behind our new method development. Family-based association analyses have provided powerful tools for disease-gene mapping. The Association in the Presence of Linkage test (APL), a family-based association method, can use nuclear families with multiple affected siblings and infer missing parental genotypes properly in the linkage region. In Chapter 2, we generalized and extended APL so that it can be applied to general nuclear family structures using a bootstrap variance estimator. Unlike the original APL that can handle at most two affected siblings, the new APL can handle up to three affected siblings. We also extended APL from a single-marker test to a multiple-marker haplotype analysis. According to our simulations, the new APL has a correct type I error rate and more power than other family-based association methods such as PDT, FBAT⁄HBAT, and PDTPHASE in nuclear families with missing parents. The robustness of APL when there are rare alleles or haplotypes and when there is population substructure such that the allele frequencies in the population deviated from the Hardy-Weinberg Equilibrium (HWE) assumption was also examined in Chapter 2. Genes on the X chromosome play a role in many common diseases. Linkage analyses have identified regions on the X chromosome with high linkage peaks for several diseases. Currently there are few family-based association methods available for X-chromosome markers. In order to fill in this gap, we proposed a novel family-based association method, X-APL, in Chapter 3. X-APL is a modification of APL and shares some important properties with APL. X-APL can also perform haplotype analyses, which is the only family-based test of association we are aware of for testing haplotypes for the X-chromosome markers. Our simulation results showed that X-APL has a correct type I error rate and has more power than other family-based association methods for X chromosome such as XS-TDT, XPDT and XMCPDT for single-marker analysis in nuclear families. The robustness of X-APL when there are deviations of genotype frequencies from HWE was also examined in Chapter 3. Linkage and family-based association analyses are often applied simultaneously in the same data in order to maximize use of family data sets. However, it is not intuitively clear under what conditions association and linkage tests performed in the same data set may be correlated. In Chapter 4, we used computer simulations and theoretical statements to estimate the correlation between linkage statistics (affected sib pair maximum LOD scores) and family-based association statistics (PDT and APL) under various hypotheses. Different types of pedigrees were studied: nuclear families with affected sib pairs, extended pedigrees and incomplete pedigrees. Both simulation and theoretical results showed that when there is either no linkage or no association, the linkage and association statistics are not correlated. When there is linkage and association in the data, the two tests have a positive correlation.