Single nucleotide polymorphism (SNP) selection for genotype-phenotype association studies

With hundreds of single nucleotide polymorphisms (SNPs) in a candidate gene and millions of SNPs across the genome, selecting an informative subset of SNPs to maximize the ability to detect genotype-phenotype association is of great interest and importance. In addition, with a large number of SNPs, analytic methods are needed that allow investigators to control the false positive rate resulting from large numbers of SNP genotype-phenotype analyses. This dissertation uses simulated data to explore methods for selecting SNPs for genotype-phenotype association studies. I examined the pattern of linkage disequilibrium (LD) across a candidate gene region and used this pattern to aid in localizing a disease-influencing mutation. The results indicate that the r2 measure of linkage disequilibrium is preferred over the common D′ measure for use in genotype-phenotype association studies. Using step-wise linear regression, the best predictor of the quantitative trait was not usually the single functional mutation. Rather it was a SNP that was in high linkage disequilibrium with the functional mutation. Next, I compared three strategies for selecting SNPs for application to phenotype association studies: based on measures of linkage disequilibrium, based on a measure of haplotype diversity, and random selection. The results demonstrate that SNPs selected based on maximum haplotype diversity are more informative and yield higher power than randomly selected SNPs or SNPs selected based on low pair-wise LD. The data also indicate that for genes with small contribution to the phenotype, it is more prudent for investigators to increase their sample size than to continuously increase the number of SNPs in order to improve statistical power. When typing large numbers of SNPs, researchers are faced with the challenge of utilizing an appropriate statistical method that controls the type I error rate while maintaining adequate power. We show that an empirical genotype based multi-locus global test that uses permutation testing to investigate the null distribution of the maximum test statistic maintains a desired overall type I error rate while not overly sacrificing statistical power. The results also show that when the penetrance model is simple the multi-locus global test does as well or better than the haplotype analysis. However, for more complex models, haplotype analyses offer advantages. The results of this dissertation will be of utility to human geneticists designing large-scale multi-locus genotype-phenotype association studies. ^

Variation of genotype and prevalence of Noro virus in U.S. students in Mexico with Traveler's Diarrhea, summer of 2004 and 2005

Noro virus, a positive single stranded RNA virus has been identified as a major etiologic agent in food borne gastroenteritis and diarrheal diseases. The emergence of this organism as a major non-bacterial cause in such outbreaks is partly due to the improved diagnostic tools like Reverse Transcription Polymerase chain reaction (RTPCR) that enable its detection. Noro virus accounts for nearly 96% of non-bacterial gastroenteritis outbreaks in US (1). Travelers' Diarrhea (TD) has remained a constant public health risk in the developed nations for decades and bacteria like Entero toxigenic Escherichia coli, Entero aggregative Escherichia coli have been described as the main etiologic agents for TD (2-4). A possible viral contribution to TD has been discovered in two studies (5, 6). The current study was designed to determine the prevalence of Noro virus in a population of 107 US students with TD acquired in Mexico in 2005 and to compare the prevalence to the prevalence of Noro virus in a similar study done in 2004. This study involved the testing of clinical stool specimens from 107 subjects in 2005 for the presence of Noro virus using RTPCR. The prevalence of Noro virus in 2004 used for comparison to 2005 data was obtained from published data (5). All subjects were recruited as TD subjects in a randomized, double-blinded clinical trial comparing a standard three day dosing of Rifaximin with and without an anti motility drug Loperamide. The prevalence of Noro virus geno group I was similar in both years, but geno group II prevalence differed across the two years (p = 0.003). This study finding suggests that the prevalence of Noro virus geno groups varies with time even within a specific geographic location. This study emphasizes the need for further systematic epidemiologic studies to determine the molecular epidemiology and the prevalence patterns of different geno groups of this virus. These are essential to planning and implementation of public health measures to lessen the burden of TD due to Noro virus infection among US travelers. ^

Genotypic spectrum and genotype-phenotype correlation of trimethylaminuria

Trimethylaminuria (TMAU) or Fish odor syndrome is an autosomal recessive disease that is characterized by pungent body odor with subsequent psychosocial complications. There are limited studies of the sequence variants causing TMAU in the literature with most studies describing only one or two patients and lacking genotype-phenotype correlations. Also to date, there is no laboratory in the US or Europe that offers TMA genetic testing on a clinical basis. We have recently validated genetic testing in the University of Colorado DNA Diagnostic Laboratory. We have a database of a few dozen patients with a biochemical diagnosis of TMA at the University of Colorado at Denver Health Sciences Center (UCDHSC) which includes a few patients with the classical form of the disease. We have used the newly established clinical test in our institution to attempt to characterize the genotype (sequence variants including mutations and polymorphisms) of classical TMAU patients and to establish a genotype-phenotype (biochemical and clinical) association. The questionnaire results confirmed most of the previously reported epidemiological findings of TMAU and also indicated that TMAU patients use multiple intervention measures in attempt to control their symptoms with dietary control being most effective. Despite the complexity of intervention, most patients did not have any medical follow up and there was underutilization of specialist care. In a set of our patients, two deleterious mutations were identified in 4/12 patients including a novel T237P sequence variant, while the majority of our patients (8/12) did not reveal any mutations. Some of the latter were double heterozygous for the E158K and E308G polymorphisms which could explain a mild phenotype while others had only the E158K variant which raised the question of undetected mutations. These results indicate that further experiments are needed to further delineate the full mutational spectrum of the FMO3 gene. ^

The effect of genotype-by-environment interaction on longitudinal triglyceride profiles in the Bogalusa Heart Study

Triglyceride levels are a component of plasma lipids that are thought to be an important risk factor for coronary heart disease and are influenced by genetic and environmental factors, such as single nucleotide polymorphisms (SNPs), alcohol intake, and smoking. This study used longitudinal data from the Bogalusa Heart Study, a biracial community-based survey of cardiovascular disease risk factors. A sample of 1191 individuals, 4 to 38 years of age, was measured multiple times from 1973 to 2000. The study sample consisted of 730 white and 461 African American participants. Individual growth models were developed in order to assess gene-environment interactions affecting plasma triglycerides over time. After testing for inclusion of significant covariates and interactions, final models, each accounting for the effects of a different SNP, were assessed for fit and normality. After adjustment for all other covariates and interactions, LIPC -514C/T was found to interact with age3, age2, and age and a non-significant interaction of CETP -971G/A genotype with smoking status was found (p = 0.0812). Ever-smokers had higher triglyceride levels than never smokers, but persons heterozygous at this locus, about half of both races, had higher triglyceride levels after smoking cessation compared to current smokers. Since tobacco products increase free fatty acids circulating in the bloodstream, smoking cessation programs have the potential to ultimately reduce triglyceride levels for many persons. However, due to the effect of smoking cessation on the triglyceride levels of CETP -971G/A heterozygotes, the need for smoking prevention programs is also demonstrated. Both smoking cessation and prevention programs would have a great public health impact on minimizing triglyceride levels and ultimately reducing heart disease. ^

Functional data analysis approaches for genotype-phenotype association studies from next-generation sequencing

Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^

DETERMINING THE GENOTYPE-PHENOTYPE CONNECTION IN SYNTHETIC INDUCIBLE GENE EXPRESSION SYSTEMS

Introduction Gene expression is an important process whereby the genotype controls an individual cell’s phenotype. However, even genetically identical cells display a variety of phenotypes, which may be attributed to differences in their environment. Yet, even after controlling for these two factors, individual phenotypes still diverge due to noisy gene expression. Synthetic gene expression systems allow investigators to isolate, control, and measure the effects of noise on cell phenotypes. I used mathematical and computational methods to design, study, and predict the behavior of synthetic gene expression systems in S. cerevisiae, which were affected by noise. Methods I created probabilistic biochemical reaction models from known behaviors of the tetR and rtTA genes, gene products, and their gene architectures. I then simplified these models to account for essential behaviors of gene expression systems. Finally, I used these models to predict behaviors of modified gene expression systems, which were experimentally verified. Results Cell growth, which is often ignored when formulating chemical kinetics models, was essential for understanding gene expression behavior. Models incorporating growth effects were used to explain unexpected reductions in gene expression noise, design a set of gene expression systems with “linear” dose-responses, and quantify the speed with which cells explored their fitness landscapes due to noisy gene expression. Conclusions Models incorporating noisy gene expression and cell division were necessary to design, understand, and predict the behaviors of synthetic gene expression systems. The methods and models developed here will allow investigators to more efficiently design new gene expression systems, and infer gene expression properties of TetR based systems.