Microarray technology allows measurement of the expression levels of thousand of genes simultaneously. Several gene set analysis (GSA) methods are widely used for extracting useful information from microarrays, for example identifying differentially expressed pathways associated with a particular biological process or disease phenotype. Though GSA methods like Gene Set Enrichment Analysis (GSEA) are widely used for pathway analysis, these methods are solely based on statistics. Such methods can be awkward to use if knowledge of specific pathways involved in particular biological processes are the aim of the study. Here we present a novel method (Knowledge Based Gene Set Analysis: KB-GSA) which integrates knowledge about user-selected pathways that are known to be involved in specific biological processes. The method generates an easy to understand graphical visualization of the changes in expression of the genes, complemented with some common statistics about the pathway of particular interest.

<p>Microarray technology allows measurement of the expression levels of thousand of genes simultaneously. Several gene set analysis (GSA) methods are widely used for extracting useful information from microarrays, for example identifying differentially expressed pathways associated with a particular biological process or disease phenotype. Though GSA methods like Gene Set Enrichment Analysis (GSEA) are widely used for pathway analysis, these methods are solely based on statistics. Such methods can be awkward to use if knowledge of specific pathways involved in particular biological processes are the aim of the study. Here we present a novel method <strong><em>(Knowledge Based Gene Set Analysis: KB-GSA</em></strong>) which integrates knowledge about user-selected pathways that are known to be involved in specific biological processes. The method generates an easy to understand graphical visualization of the changes in expression of the genes, complemented with some common statistics about the pathway of particular interest.</p>

O diabetes mellitus do tipo 1 (DM1) é uma doença autoimune crônica, durante a qual as células beta pancreáticas, responsáveis pela secreção de insulina, são seletivamente destruídas. O desenvolvimento desta doença é uma consequência da predisposição genética combinada a fatores ambientais largamente desconhecidos e eventos estocásticos. Neste trabalho foi proposta a comparação da expressão gênica transcricional em grande escala (transcriptoma) entre amostras de pacientes de DM1 e controles, obtidas a partir de células mononucleares do sangue periférico (PBMCs). As alterações resultantes na expressão gênica causada pela doença podem ser amostradas em PBMCs, uma vez que as células imunes efetoras estão presumivelmente em equilíbrio com a população celular circulante. A fim de identificar alterações na expressão gênica, foram utilizados métodos analíticos como a tecnologia de microarrays e o cálculo do coeficiente de correlação de Pearson, sendo possível observar aumento ou diminuição na expressão gênica e também a magnitude desta mudança. Além disso, foi realizada análise de grupos gênicos (gene sets ou GSA), método baseado na significância de conjuntos gênicos pré-definidos, ao invés de genes individuais. Este procedimento é mais adequado para análise de uma doença poligênica, tal como o DM1. A análise de GSA possibilitou a seleção de genes envolvidos, por exemplo, nas seguintes vias: cascata de I-kappaB kinase/NF-kappaB, regulação da via de sinalização do receptor de TGF-ß, regulação da cascata de JAK-STAT e via de sinalização mediada por citocinas e quimiocinas, das quais podem ser identificados marcadores transcricionais. A análise imparcial do transcriptoma de PBMCs permitiu a identificação de gene sets e genes associados ao DM1, seu perfil de expressão preferencial em tipos celulares do sistema imune e seus padrões de modulação. / Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease, in which the pancreatic beta cells responsible for secretion of insulin are selectively destroyed. The development of this disease is a result of genetic predisposition combined with largely unknown environmental factors and stochastic events. In this work it was proposed to compare the large scale transcriptional gene expression (transcriptome) between samples obtained from T1DM patients and healthy controls, obtained from peripheral blood mononuclear cells (PBMCs). The resulting changes in gene expression caused by the disease can be sampled in PBMCs, as immune effector cells are presumably in equilibrium with the circulating cell population. In order to identify changes in gene expression, we used analytical methods such as microarray technology and calculating the Pearson correlation coefficient, where it was possible to observe increases or decreases in gene expression and also the magnitude of change. Furthermore, we performed a gene set analysis (GSA) method based on the significance of predefined gene sets instead of individual genes. This procedure is more suitable for analyzing a polygenic disease such as T1DM. GSA analysis enabled the selection of genes involved for example, in the following pathways: I-kappaB kinase/NF-kappaB cascade, regulation of TGF-ß receptor signaling pathway, regulation of JAK-STAT cascade and cytokine and chemokine mediated signaling pathway, from which transcriptional markers can be identified. An unbiased transcriptome analysis of PBMCs allowed the identification of gene sets and genes associated with T1DM, its preferential expression profile in cell types of the immune system and its modulation patterns.

O diabetes mellitus do tipo 1 (DM1) é uma doença autoimune crônica, durante a qual as células beta pancreáticas, responsáveis pela secreção de insulina, são seletivamente destruídas. O desenvolvimento desta doença é uma consequência da predisposição genética combinada a fatores ambientais largamente desconhecidos e eventos estocásticos. Neste trabalho foi proposta a comparação da expressão gênica transcricional em grande escala (transcriptoma) entre amostras de pacientes de DM1 e controles, obtidas a partir de células mononucleares do sangue periférico (PBMCs). As alterações resultantes na expressão gênica causada pela doença podem ser amostradas em PBMCs, uma vez que as células imunes efetoras estão presumivelmente em equilíbrio com a população celular circulante. A fim de identificar alterações na expressão gênica, foram utilizados métodos analíticos como a tecnologia de microarrays e o cálculo do coeficiente de correlação de Pearson, sendo possível observar aumento ou diminuição na expressão gênica e também a magnitude desta mudança. Além disso, foi realizada análise de grupos gênicos (gene sets ou GSA), método baseado na significância de conjuntos gênicos pré-definidos, ao invés de genes individuais. Este procedimento é mais adequado para análise de uma doença poligênica, tal como o DM1. A análise de GSA possibilitou a seleção de genes envolvidos, por exemplo, nas seguintes vias: cascata de I-kappaB kinase/NF-kappaB, regulação da via de sinalização do receptor de TGF-ß, regulação da cascata de JAK-STAT e via de sinalização mediada por citocinas e quimiocinas, das quais podem ser identificados marcadores transcricionais. A análise imparcial do transcriptoma de PBMCs permitiu a identificação de gene sets e genes associados ao DM1, seu perfil de expressão preferencial em tipos celulares do sistema imune e seus padrões de modulação. / Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease, in which the pancreatic beta cells responsible for secretion of insulin are selectively destroyed. The development of this disease is a result of genetic predisposition combined with largely unknown environmental factors and stochastic events. In this work it was proposed to compare the large scale transcriptional gene expression (transcriptome) between samples obtained from T1DM patients and healthy controls, obtained from peripheral blood mononuclear cells (PBMCs). The resulting changes in gene expression caused by the disease can be sampled in PBMCs, as immune effector cells are presumably in equilibrium with the circulating cell population. In order to identify changes in gene expression, we used analytical methods such as microarray technology and calculating the Pearson correlation coefficient, where it was possible to observe increases or decreases in gene expression and also the magnitude of change. Furthermore, we performed a gene set analysis (GSA) method based on the significance of predefined gene sets instead of individual genes. This procedure is more suitable for analyzing a polygenic disease such as T1DM. GSA analysis enabled the selection of genes involved for example, in the following pathways: I-kappaB kinase/NF-kappaB cascade, regulation of TGF-ß receptor signaling pathway, regulation of JAK-STAT cascade and cytokine and chemokine mediated signaling pathway, from which transcriptional markers can be identified. An unbiased transcriptome analysis of PBMCs allowed the identification of gene sets and genes associated with T1DM, its preferential expression profile in cell types of the immune system and its modulation patterns.

A typical gene expression data set consists of measurements of a large number of gene expressions, on a relatively small number of subjects, classified according to two or more outcomes, for example cancer or non-cancer. The identification of associations between gene expressions and outcome is a huge multiple testing problem. Early approaches to this problem involved the application of thousands of univariate tests with corrections for multiplicity. Over the past decade, numerous studies have demonstrated that analyzing gene expression data structured into predefined gene sets can produce benefits in terms of statistical power and robustness when compared to alternative approaches. This thesis presents the results of research on gene set analysis. In particular, it examines the properties of some existing methods for the analysis of gene sets. It introduces novel Bayesian methods for gene set analysis. A distinguishing feature of these methods is that the model is specified conditionally on the expression data, whereas other methods of gene set analysis and IGA generally make inferences conditionally on the outcome. Computer simulation is used to compare three common established methods for gene set analysis. In this simulation study a new procedure for the simulation of gene expression data is introduced. The simulation studies are used to identify situations in which the established methods perform poorly. The Bayesian approaches developed in this thesis apply reversible jump Markov chain Monte Carlo (RJMCMC) techniques to model gene expression effects on phenotype. The reversible jump step in the modelling procedure allows for posterior probabilities for activeness of gene set to be produced. These mixture models reverse the generally accepted conditionality and model outcome given gene expression, which is a more intuitive assumption when modelling the pathway to phenotype. It is demonstrated that the two models proposed may be superior to the established methods studied. There is considerable scope for further development of this line of research, which is appealing in terms of the use of mixture model priors that reflect the belief that a relatively small number of genes, restricted to a small number of gene sets, are associated with the outcome.

High-throughput technology is changing the face of research biology, generating an ever growing amount of large-scale data sets. With experiments utilizing next-generation gene sequencing, mass spectrometry, and various other global surveys of proteins, the task of translating the plethora of data into biology has become a daunting task. In response, functional networks have been developed as a means for integrating the data into models of proteomic organization. In these networks, proteins are linked if they are evidenced to operate together in the same function, facilitating predictions about the functions, phenotypes, and disease associations of uncharacterized genes. In this body of work, we explore different applications of this so-called "guilt-by-association" concept to predict loss-of-function phenotypes and diseases associated with genes in yeast, worm, and human. We also scrutinize certain limitations associated with the functional networks, predictive methods, and measures of performance used in our studies. Importantly, the predictive method and performance measure, if not chosen appropriately for the biological objective at hand, can largely distort the results and interpretation of a study. These findings are incorporated in the development of RIDDLE, a method for characterizing whole sets of genes. This machine learning-based method provides a measure of network distance, and thus functional association, between two sets of genes. RIDDLE may be applied to a wide range of potential applications, as we demonstrate with several biological examples, including linking microRNA-450a to ocular development and disease. In the last decade, functional networks have proven to be a useful strategy for interpreting large-scale proteomic and genomic data sets. With the continued growth of genome coverage in networks and the innovation of predictive methods, we will surely advance towards our ultimate goal of understanding the genetic changes that underlie disease. / text

PTSD is a moderately heritable disorder that causes intense and chronic suffering in many afflicted individuals. The pathogenesis of PTSD is not well understood, and genetic mechanisms are particularly elusive. Neurotransmitter systems are thought to contribute to PTSD etiology and are the targets of most pharmacotherapies used to treat PTSD, including the only two FDA approved options and a wide array of off-label options. However, the degree to which variation in genes which encode for and regulate neurotransmitter receptors increase risk of developing PTSD is unclear. Recently, large collaborative groups of PTSD genetics researchers have completed genome-wide association studies (GWAS) using massive sample sizes and have made summary statistics available for public use. In 2018, a new technique for high-powered analysis of GWAS summary statistics called GSA-SNP2 was introduced. In order to explore the relationship between PTSD and genetic variants in widely theorized molecular targets, this study applied GSA-SNP2 to manually curated neurotransmitter receptor gene-sets. Curated gene-sets included nine total "neurotransmitter receptor group" gene-sets and 45 total "receptor subtype" gene-sets. Each "neurotransmitter receptor group" gene-sets was designed to capture concentration of genetic risk factors for PTSD within genes which encode for all receptor subtypes that are activated by a given neurotransmitter. In contrast, "receptor subtype" gene-sets focused on specific subtypes and also accounted for intracellular signaling; each was designed to capture concentration of genetic risk factors for PTSD within genes which encode for specific receptor subtypes and the intracellular signaling proteins through which they exert their effects. Due to practical considerations, this work used summary statistics derived from a GWAS with far fewer participants (2,424 cases; 7,113 controls) than initially planned (23,212 cases; 151,447 controls). Prior to controlling for multiple comparisons, 7 of the investigated gene-sets reached statistical significance at the p ≤ .05 level. However, after controlling for multiple comparisons, none of the investigated gene-sets reached statistical significance. Due to limited statistical power of the current work, these results should be interpreted very cautiously. The current study is best interpreted as a preliminary study and is most informative in relation to refining study design. Implications for next steps are emphasized in discussion and nominally significant results are synthesized with the literature to demonstrate the types of research questions that might be addressed by applying a refined version of this study design to a larger sample. / Doctor of Philosophy / Though nearly all individuals will be exposed to a potentially traumatic event in their lifetime, only a small percentage will experience PTSD, which is a severe psychological disorder. Though genetics are known contribute to an individual's level of risk for developing PTSD, relatively little is known about which particular genetic differences are key. Neurotransmitter receptors are thought to contribute to the risk for PTSD and are a key aspect of medications for PTSD. However, little is known about whether genetic differences in neurotransmitter receptors contribute to risk for developing PTSD. Recently, large collaborative groups of PTSD genetics researchers have completed studies which investigate genetic risk factors from across the genome using massive sample sizes and have made the statistical output of these studies available to the public. In 2018, a new technique called GSA-SNP2 was created to help assist with efforts to analyze aspects of that statistical output that have not been previously analyzed. This study used GSA-SNP2 to analyze the degree to which groups of neurotransmitter receptor genes contribute to the risk of developing PTSD. Due to the coronavirus pandemic, the researcher did not have access to the computing power needed to analyze the initially planned data which included 23,212 individuals with PTSD and 151,447 individuals without PTSD. As a substitute, the current work is an analysis using statistical output data from a study which included 2,424 individuals with PTSD and 7,113 individuals without PTSD. Based on a level of statistical significance that is typically used in most psychological studies, seven of the investigated gene-sets contribute highly to the risk for PTSD. However, it was necessary to use a different threshold for statistical significance due to the testing of many different groups of genes. After making that adjustment, none of the investigated gene-sets reached statistical significance. Due to limited statistical power of the current work, these results should be interpreted very cautiously. The current study is best interpreted as a preliminary study and is most informative in relation to refining study design. Implications for next steps are emphasized in discussion and nominally significant results are synthesized with the literature to demonstrate the types of research questions that might be addressed by applying a refined version of this study design to a larger sample.

Artificial nighttime lighting (ANL) is known to adversely affect animals, but little is known what the consequences are to plants. Two genotypes of Populus balsamifera, a common urban tree, were used to investigate how ANL impacts plants. While the two genotypes varied in their physiological sensitivity to ANL, poorer levels of net leaf carbon assimilation compared to control samples suggested that ANL perturbed the perception of time of day for these plants. Gene set analysis on a subset of PopGenExpress microarray samples identified time of day specific processes in P. balsamifera, and a set of candidate ANL-sensitive genes were identified from these. Transcript measurements from the two genotypes revealed that ANL affects plants at the molecular level, for the diel cycling of the putative ANL-sensitive genes was perturbed. Together, these results suggest that ANL affects plants at the physiological and molecular level by perturbing their perception of time of day.

Artificial nighttime lighting (ANL) is known to adversely affect animals, but little is known what the consequences are to plants. Two genotypes of Populus balsamifera, a common urban tree, were used to investigate how ANL impacts plants. While the two genotypes varied in their physiological sensitivity to ANL, poorer levels of net leaf carbon assimilation compared to control samples suggested that ANL perturbed the perception of time of day for these plants. Gene set analysis on a subset of PopGenExpress microarray samples identified time of day specific processes in P. balsamifera, and a set of candidate ANL-sensitive genes were identified from these. Transcript measurements from the two genotypes revealed that ANL affects plants at the molecular level, for the diel cycling of the putative ANL-sensitive genes was perturbed. Together, these results suggest that ANL affects plants at the physiological and molecular level by perturbing their perception of time of day.

在現今生物醫學領域中，一重要課題為透過基因實驗所獲得的量化資料，來研究與分析基因與外顯表型變數(phenotype)的相關性。已知多數已發展的方法皆屬於單基因分析法，無法適當的考慮基因之間的相關性。本研究主要針對基因組分析(gene set analysis)問題，提出統計檢定方法來驗證特定基因組的顯著性。為了能盡其所能的捕捉整體基因組與外顯表型變數的關係，我們結合了傳統的檢定方法與分類方法，提出以隨機森林分類方法(Random Forests)的測試組分類誤差值(test error)作為檢定統計量(test statistic)，並以其排列顯著值(permutation-based p-value)來獲得統計結論。我們透過模擬研究將本研究方法和其他七種基因組分析方法做比較，可發現本方法在型一誤差率(type I error rate)和檢定力(power)上皆有優異表現。最後，我們運用本方法在數個實際基因資料組的分析上，並深入探討所獲得結果。 / Nowadays microarray data analysis has become an important issue in biomedical research. One major goal is to explore the relationship between gene expressions and some specific phenotypes. So far in literatures many developed methods are single gene-based methods, which use solely the information of individual genes and cannot appropriately take into account the relationship among genes. This research focuses on the gene set analysis, which carries out the statistical test for the significance of a set of genes to a phenotype. In order to capture the relationship between a gene set and the phenotype, we propose the use of performance of a complex classifier in the statistical test: The test error rate of a Random Forests classification is adopted as the test statistic, and the statistical conclusion is drawn according to its permutation-based p-value. We compare our test with other seven existing gene set analyses through simulation studies. It’s found that our method has leading performance in terms of having a controlled type I error rate and a high power. Finally, this method is applied in several real examples and brief discussions on the results are provided.