Thesis (Ph. D.)--University of Wisconsin--Madison, 1970. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

In order to understand common, complex disease it is necessary to consider not just genetic risks and environmental risks, but also the interplay between them. This thesis aims to develop methodology for the detection of gene-environment interactions specifically; both by looking at the strengths and weaknesses of traditional approaches and through the development and testing of a novel statistical method. Developments in genotyping technology enable researchers to collect large volumes of polymorphisms in human genes, yet very few statistical methods are able to handle the volume, variation and complexity of this data, especially in combination with environmental risk factors. Interactions between genes and the environment are often subject to the curse of dimensionality, with each new variable increasing the potential number of interactions exponentially, leading to low power and a high false positive rate. The Mixed Tree Method (MTM) exploits the differences between environmental and genetic variables, by selecting the most appropriate features from conventional methods (including recursive partitioning, random forests and logistic regression) and combining them with new comparison algorithms which rank the genetic variables by the likelihood that they interact with the environmental variable under study. Results show the MTM to be as effective as the most successful current method for identification of interactions, but maintaining a much lower false positive rate and computational burden. As the number of SNPs in the dataset increases, the success of MTM compared to other methods becomes greater while the comparator approaches exhibit computational problems and rapidly increasing processing times. The MTM is also applied to a colorectal cancer dataset to show its use in a practical setting. The results together suggest that MTM could be a useful strategy for identifying gene environment interactions in future studies into complex disease.

Death from infectious disease is common heritable, and in many cases a consequence of the host response, rather than direct effects of the pathogen. Since the host response in sepsis is orchestrated by the transmission of a variety of signals, both intra-cellular and inter-cellular, with which we have at least some capacity to intervene, it follows that it should be possible to prevent death through pharmaceutical modulation of inflammatory cascades. So far, it is not. The best candidate therapy for sepsis, activated protein C, failed to live up to initial promise and was ultimately withdrawn from the market in dismal failure. The premise of the work presented here is that a different approach – to develop an understanding of the host response at a genomic level – may yield more tractable insights, specifically into the problem of host susceptibility to influenza, a heritable cause of death in otherwise healthy people and a significant global threat. Since the sequencing of the human genome, it has become possible to identify genomic loci underlying host susceptibility to disease using genome-wide association studies (GWAS), best exemplified by the Wellcome Trust Case Control Consortium. This new technology creates substantial new challenges. The genetic markers associated with a phenotype are rarely causative, frequently in poorly-understood intergenic regions, and tend to have small effect sizes, such that tens or even hundreds of thousands of subjects must be recruited to have sufficient power to detect them. It is therefore not straightforward to translate these genotype-phenotype associations into useful understanding of the role of genes and gene products in disease pathogenesis. Attempts to overcome these challenges in order to discover genomic loci underlying individual susceptibility to infection form the core of this thesis. Ultimately these efforts converge with the development of a new computational method to detect phenotype-associated loci from genome-wide association studies (GWAS) using co-expression at regulatory regions of the genome.

Thesis (M. Sc.)--Carleton University, 2008. / Includes bibliographical references (p. 92-97). Also available in electronic format on the Internet.

Recent advances in genotyping technology coupled with an improved understanding of the architecture of linkage disequilibrium across the human genome have resulted in genome-wide association studies (GWAS) becoming a useful and widely applied tool for discovering common genetic variants associated with both quantitative traits and disease risk. After each GWAS was completed, it left behind a set of genotypes and phenotypes, often including anthropometric measures used as covariates. Genetic associations with anthropometric measures are not well characterized, perhaps due to lack of power to detect them in the sample sizes of individual studies. To improve power to detect variants associated with complex phenotypes such as anthropometric traits, data from multiple GWAS can be combined. This thesis describes the methods and results of several such analyses performed as part of the Genome-wide Investigation of ANThropemtric measures (GIANT) consortium, and compares various different methods that can be used to perform combined analyses of GWAS. In particular, the comparisons focus on comparing differences between meta-analysis methods, in which only summary statistics that result from within-study association testing are shared between studies, and mega-analysis methods in which individual-level genotype and phenotype data is analysed together. Finally, a brief discussion of technological means that have the potential to help overcome some of the challenges associated with performing mega-analyses is offered in order to suggest future work that could be undertaken in this area.

O presente trabalho tem os seguintes objetivos: propor uma sistemática para o estudo e a interpretação da estabilidade e adaptabilidade fenotípica, através de duas técnicas de análise multiway (PARAFAC e Tucker3); propor a construção de um gráfico, denominado de Triplot, que possibilita avaliar as relç]oesoes entre os 3 modos (genótipos, locais e anos); implementar uma rotina computacional para a análise de dados, segundo os modelos multiway; implementar uma rotina computacional para a construção do Triplot. Os dados a serem uti- lizados são relativos a experimentos com 13 genótipos de feijão que foram conduzidos em 9 ex- perimentos distintos constituídos pelos anos agrícolas de 2000/2001, 2001/2002 e 2005/2006, pelos municípios de Dourados e Aquidauana, sendo que os experimentos foram instalados na época das águas (Dourados)e também na época da seca (Dourados e Aquidauana). Cada local é constituído de município e uma época de instalação. Os resultados indicaram que o gráfico triplot e joint plot, facilitam o entendimento da interação tripla e traz ao pesquisador informações mais reais sobre a interação tripla, do que a modelagem AMMI de duas entradas; o gráfico triplot, ajuda a identificar genótipos, locais e anos estáveis, dentro de um grande grupo de genótipos, locais e anos; de uma maneira geral recomenda-se, utilizar o triplot e o joint plot juntos, para obter melhores interpretações dos resultados; dentre os genótipos estudados, o genótipo 6 é o que menos contribui para a interação e o os genótipos 12, 9 e 5 são os que mais contribuem para a interação. / The present work has the following objectives: to propose a systematics for the study and the interpretation of the phenotypic stability and adaptability, through several multiway models (PARAFAC and Tucker3); to propose a graphic, called of Triplot, that it makes possible to evaluate the relations between the 3 ways (genotypes, locations and years); to implement a computational routine for the data analysis, according multiway models; to implement a computational routine for the construction of Triplot. The used data are relative the experiments with 13 genotypes of beans that had been lead in 9 experimental distinct ones constituted by agricultural years of 2000/2001, 2001/2002 and 2005/2006, by Dourados and Aquidauana cities, where the experiments had been installed at the time of waters (Dourados) and also at the time of dries (Dourados and Aquidauana). Each location is constituted of city and time of installation. The results indicated that the graphic triplot and joint plot, facilitate the agreement of triple interaction and bring to the researcher more real information about triple interaction, of what AMMI model of two way; the graphic triplot, helps to identify stabels genotypes, locations and years, inside of a great group of genotypes, location and years; in a general recommend to use triplot and joint plot together, to get better interpretations of the results; the genotype 6 is what less contributes for the triple interaction and genotypes 12, 9 and 5 are the that more contribute for the interaction.

Thesis (M.A.)--University of North Carolina at Wilmington, 2006. / Includes bibliographical references (leaf: 24)

O presente trabalho tem os seguintes objetivos: propor uma sistemática para o estudo e a interpretação da estabilidade e adaptabilidade fenotípica, através de duas técnicas de análise multiway (PARAFAC e Tucker3); propor a construção de um gráfico, denominado de Triplot, que possibilita avaliar as relç]oesoes entre os 3 modos (genótipos, locais e anos); implementar uma rotina computacional para a análise de dados, segundo os modelos multiway; implementar uma rotina computacional para a construção do Triplot. Os dados a serem uti- lizados são relativos a experimentos com 13 genótipos de feijão que foram conduzidos em 9 ex- perimentos distintos constituídos pelos anos agrícolas de 2000/2001, 2001/2002 e 2005/2006, pelos municípios de Dourados e Aquidauana, sendo que os experimentos foram instalados na época das águas (Dourados)e também na época da seca (Dourados e Aquidauana). Cada local é constituído de município e uma época de instalação. Os resultados indicaram que o gráfico triplot e joint plot, facilitam o entendimento da interação tripla e traz ao pesquisador informações mais reais sobre a interação tripla, do que a modelagem AMMI de duas entradas; o gráfico triplot, ajuda a identificar genótipos, locais e anos estáveis, dentro de um grande grupo de genótipos, locais e anos; de uma maneira geral recomenda-se, utilizar o triplot e o joint plot juntos, para obter melhores interpretações dos resultados; dentre os genótipos estudados, o genótipo 6 é o que menos contribui para a interação e o os genótipos 12, 9 e 5 são os que mais contribuem para a interação. / The present work has the following objectives: to propose a systematics for the study and the interpretation of the phenotypic stability and adaptability, through several multiway models (PARAFAC and Tucker3); to propose a graphic, called of Triplot, that it makes possible to evaluate the relations between the 3 ways (genotypes, locations and years); to implement a computational routine for the data analysis, according multiway models; to implement a computational routine for the construction of Triplot. The used data are relative the experiments with 13 genotypes of beans that had been lead in 9 experimental distinct ones constituted by agricultural years of 2000/2001, 2001/2002 and 2005/2006, by Dourados and Aquidauana cities, where the experiments had been installed at the time of waters (Dourados) and also at the time of dries (Dourados and Aquidauana). Each location is constituted of city and time of installation. The results indicated that the graphic triplot and joint plot, facilitate the agreement of triple interaction and bring to the researcher more real information about triple interaction, of what AMMI model of two way; the graphic triplot, helps to identify stabels genotypes, locations and years, inside of a great group of genotypes, location and years; in a general recommend to use triplot and joint plot together, to get better interpretations of the results; the genotype 6 is what less contributes for the triple interaction and genotypes 12, 9 and 5 are the that more contribute for the interaction.

The Major Histocompatibility Complex (MHC) is a genomic region in chromosome 6 which has been consistently found to be associated with the risk of developing virtually all common autoimmune diseases. Although its importance in disease pathogenesis has been known for decades, efforts to disentangle the roles of the classical human leukocyte antigens (HLA) and other variants responsible for the susceptibility to disease have often met with limited success, owing to the complex structure and extreme heterogeneity of the region. In this thesis, I interrogate the MHC for association with three common autoimmune diseases, ankylosing spondylitis, psoriasis and multiple sclerosis, with the aim of confirming the previously-reported associations and of identifying novel ones. To do so, I employ a systematic, joint analysis of single nucleotide polymorphism (SNP) and HLA allele data, in a logistic regression framework, using a recently developed algorithm to predict the HLA alleles for samples where such information is unavailable. To ensure the reliability of the analysis, I apply stringent quality control procedures and integrate over the uncertainty of the HLA allele predictions. Moreover, I resolve the haplotype phase of individuals from the HapMap project to create reliable reference panels, used in both HLA prediction and in quality control procedures. By directly testing HLA subtypes for association with the disease, the power to detect such associations is increased. I present the results of the analysis on the three disease phenotypes and discuss the evidence for important novel findings amongst both SNPs and HLA alleles in two of the diseases. In the final part of this thesis, I introduce a novel, model-based approach to detect inconsistencies in the data and show how it can be used to flag problematic SNPs which conventional quality control procedures may fail to identify.

Genome wide association studies (GWAS) have revolutionized our approach to mapping genetic determinants of complex human diseases. However, even with success from recent studies, we have typically been able to explain only a fraction of the trait heritability. GWAS are typically analysed by testing for the marginal effects of single variants. Consequently, it has been suggested that gene-gene interactions might contribute to the missing heritability of complex diseases. GWAS incorporating interaction effects have not been routinely applied because of statistical and computational challenges relating to the number of tests performed, genome-wide. To overcome this issue, I have developed novel methodology to allow rapid testing of pairwise interactions in GWAS of complex traits, implemented in the IntRapid software. Simulations demonstrated that the power of this approach was equivalent to computationally demanding exhaustive searches of the genome, but required only a fraction of the computing time. Application of IntRapid to GWAS of a range of complex human traits undertaken by the Wellcome Trust Case Control Consortium (WTCCC) identified several interaction effects at nominal significance, which warrant further investigation in independent studies. In an attempt to fine-map the identified interacting loci, I undertook imputation of the WTCCC genotype data up to the 1000 Genomes Project reference panel (Phase 1 integrated release, March 2012) in the neighbourhood of the lead SNPs. I modified the IntRapid software to take account of imputed genotypes, and identified stronger signals of interaction after imputation at the majority of loci, where the lead SNP often had moved by hundreds of kilobases. The X-chromosome is often overlooked in GWAS of complex human traits, primarily because of the difference in the distribution of genotypes in males and females. I have extended IntRapid to allow for interactions with the X chromosome by considering males and females separately, and combining effect estimates across the sexes in a fixed-effects meta-analysis. Application to genotype data from the WTCCC failed to identify any strong signals of association with the X-chromosome, despite known epidemiological differences between the sexes for the traits considered. The novel methods developed as part of this doctoral work enable a user friendly, computationally efficient and powerful way of implementing genome-wide gene-gene interaction studies. Further work would be required to allow for more complex interaction modelling and deal with the associated computational burden, particularly when using next-generation sequencing (NGS) data which includes a much larger set of SNPs. However, IntRapid is demonstrably efficient in exhaustively searching for pairwise interactions in GWAS of complex traits, potentially leading to novel insights into the genetic architecture and biology of human disease.