The accessibility of high-throughput biology data brought a great deal of attention to disease association studies. High density maps of single nucleotide polymorphism (SNP's) as well as massive genotype data with large number of individuals and number of SNP's become publicly available. By now most analysis of the new data is undertaken by the statistics community. In this dissertation, we pursue a different line of attack on genetic susceptibility to complex disease that adheres to the computer science community with an emphasis on design rather than analytical methodology. The main goal of disease association analysis is to identify gene variations contributing to the risk of and/or susceptibility to a particular disease. There are basically two main steps in susceptibility: (i) haplotyping of the population and (ii) predicting the genetic susceptibility to diseases. Although there exist many phasing methods for step (i), phasing and missing data recovery for data representing family trios is lagging behind, and most disease association studies are based on family trios. This study is devoted to the problem of assessing accumulated information targeting to predict genotype susceptibility to complex diseases with significantly high accuracy and statistical power. The dissertation proposes two new greedy and integer linear programming based solution methods for step (i). We also proposed several universal and ad hoc methods for step (ii). The quality of susceptibility prediction algorithm has been assessed using leave-one-out and leave-many-out tests and shown to be statistically significant based on randomization tests. The prediction of disease status can also be viewed as an integrated risk factor. A combinatorial prediction complexity measure has been proposed for case/control studies. The best prediction rate achieved by the proposed algorithms is 69.5% for Crohn's disease and 61.3% for autoimmune disorder, respectively, which are significantly higher than those achieved by universal prediction methods such as Support Vector Machine (SVM) and known statistic methods.

This KwaZulu-Natal (KZN) based study investigates hypertension, glomerulonephritides and the rarity of IgA Nephropathy (IgAN) in Africans in association with the Human Leukocyte Antigen (HLA). A retrospective hypertensive study found a positive association with HLA-B40 (P c<0.05) and HLA-B15 (Pc<0.02) in Indians and Africans respectively. No association was found in Whites. A prospective study showed glomerulonephritides to be positively associated with HLA-A33 in Indians (Pc 0.049). No associations were found with glomerulonephritides in Africans and Whites. Combined Race groups show no HLA associations. HLA-A30; HLA-A34; HLA-A29; HLA-B42; HLA-B58; HLA-B70 and HLA-DR11 were extremely significantly higher in Africans compared to Indians and Whites (all P<0.0001). In conclusion, HLA-B40 and I 1LA-B15 are possible disease susceptibility markers in Indian and African hypertensives; HLA-A33 is a possible disease susceptibility marker for glomerulonephritides in Indians and alleles in linkage might be responsible for the rarity of IgAN in Africans but further studies need to be employed. / Thesis (M.Med)-University of KwaZulu-Natal, 2007.

Bibliography: leaves 119-136 / ix, 136 leaves, [9] leaves of plates : ill ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, 1986

Network biology has proven to be powerful tool for representing and analyzing complex molecular networks. It has also been successfully applied to biological field helping understand various biological processes. However, our current knowledge about the dynamics of gene networks during disease progression is rather limited. On the other hand, network construction is a prerequisite of network analysis. When the number of samples is limited, state-of-art computational methods for network construction are not robust in terms of low statistical power. In addition, molecular networks have been used extensively to improve the inference accuracy of causal coding variants, but this potential has not been investigated to the same extent for noncoding variants. To address those limitations, I first developed inference of multiple differential modules (iMDM) algorithm to study network dynamics. This method is able to identify both unique and shared modules from multiple gene networks, each of which denoting a different perturbation condition. Using iMDM algorithm, I identified different types of modules to understand heart failure progression and disease dynamics. Next, I developed a computational framework to construct condition specific transcriptional regulatory network. I also developed a computational method to rank transcription factors in the transcriptional regulatory network. Applying this framework to RNA-seq data for hematopoietic stem cell development, I successfully constructed corresponding transcriptional regulatory network and identified key transcriptional factors that play important roles. Finally, I developed Annotation of Regulatory Variants using Integrated Networks (ARVIN), a network-based algorithm, to identify causal genetic variants for diseases. By applying ARVIN to various diseases, we obtained a systems understanding of the gene circuitry that is affected by all enhancer mutations in a given disease.

Les maladies complexes comme le Cancer et la maladie d'Alzheimer sont causées par des perturbations moléculaires multiples responsables d'un comportement cellulaire pathologique.Un enjeu majeur de la médecine de précision est l'identification des perturbations moléculaires induites par les maladies complexes et les thérapies à partir de leurs conséquences sur les phénotypes cellulaire.Nous définissons un modèle des maladies complexes,appelé la reprogrammation comportementale,assimilant les perturbations moléculaires à des altérations des fonctions dynamiques locales de systèmes dynamiques discrets induisant une reprogrammation de la dynamique globale du réseau. Ce cadre de modélisation s'appuie d'une part, sur les réseaux Booléens contrôlés, qui sont des réseaux Booléens dans lesquels sont insérés des paramètres de contrôle modélisant les perturbations et, d'autre part, sur la définition de modes (Possibilité, Nécessité) permettant d'exprimer les objectifs de cette reprogrammation.A partir de ce cadre, nous démontrons que le calcul des noyaux, i.e., des ensembles minimaux d'actions permettant la reprogrammation selon un mode s'exprime comme un problème d'inférence abductive en logique propositionnelle. En nous appuyant sur les méthodes historiques de calcul d'impliquants premiers des fonctions Booléennes,nous développons deux méthodes permettant le calcul exhaustif des noyaux de la reprogrammation. Enfin, nous évaluons la pertinence du cadre de modélisation pour l'identification des perturbations responsables de la transformation d'une cellule saine en cellule cancéreuse et la découverte de cibles thérapeutiques sur un modèle du cancer du sein. Nous montrons notamment que les perturbations inférées par nos méthodes sont compatibles avec la connaissance biologique en discriminant les oncogènes des gènes suppresseurs de tumeurs et en récupérant la mutation du gène BRCA1. De plus, la méthode récupère le phénomène de létalité synthétique entre PARP1 et BRCA1, qui constitue un traitement anticancéreux optimal car il cible spécifiquement les cellules tumorales. / Complex diseases such as cancer and Alzheimer's are caused by multiple molecular perturbations responsible for pathological cellular behavior. A major challenge of precision medicine is the identification of the molecular perturbations induced by the disease and the therapies from their consequences on cell phenotypes. We define a model of complex diseases, called behavioral reprogramming, that assimilates the molecular perturbations to alterations of the dynamic local functions of discrete dynamical systems inducing a reprogramming of the global dynamics of the network. This modeling framework relies on the one hand, on Control Boolean networks, which are Boolean networks containing control parameters modeling the perturbations and, on the other hand, the definition of reprogramming modes (Possibility, Necessity) expressing the objective of the behavioral reprogramming. From this framework, we demonstrate that the computation of the cores, namely, the minimal sets of action allowing reprogramming is a problem of abductive inference in propositional logic. Using historical methods computing the prime implicants of Boolean functions, we develop two methods computing all the reprogramming cores.Finally, we evaluate the modeling framework for the identification of perturbations responsible for the transformation of a healthy cell into a cancercell and the discovery of therapeutic targets ona model of breast cancer. In particular, we showthat the perturbations inferred by our methods a recompatible with biological knowledge by discriminating oncogenes and tumor suppressor genes and by recovering the causal of the BRCA1 gene. In addition, the method recovers the synthetic lethality phenomenon between PARP1 and BRCA1 that constitutes an optimal anti-cancer treatment because it specifically targets tumor cells.

Orientador: David Corrêa Martins Júnior / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Neurociência e Cognição, 2016. / A biologia sistêmica é um campo de pesquisa interdisciplinar que estuda as complexas interações que ocorrem entre os componentes biológicos de um organismo vivo com o objetivo de entender o seu comportamento, o qual emerge a partir dessas interações. Essas interações compõem uma rede altamente complexa, cujos interagentes podem ser de diversas naturezas. Nesse contexto, as doenças complexas são caracterizadas justamente por serem poligênicas e multifatoriais, ou seja, a gênese e o desenvolvimento dessas doenças são uma consequência da interação conjunta de diversos fatores, incluindo não apenas genes, proteínas e outras moléculas, como também fatores epigenéticos e ambientais. No entanto, diferentes métodos de priorização gênica apresentam resultados (listas de genes) com baixa convergência. Assim, a comparação desses métodos é uma questão crucial. Os objetivos principais da presente dissertação foram a realização de uma extensa revisão da literatura em relação às técnicas de priorização de genes associados a doenças complexas e a comparação de algumas dessas técnicas. Foram selecionadas duas ferramentas: o WGCNA (Weighted Gene Correlation Network Analysis) e o NERI (Network-Medicine Relative Importance), ambos métodos que baseiam-se em teoria de redes complexas e co-expressão para priorização gênica, sendo que o NERI tem o diferencial de modelar as hipóteses da Network Medicine para priorização com base na integração de dados de expressão, de redes de interação proteína-proteína (PPI) e de estudos de associação. Para comparação dos resultados, foram utilizados três bancos de dados de expressão gênica relacionados a esquizofrenia. Como previsto, devido ao diferencial de integração de dados proposto pelo NERI, tal técnica resultou em listas de genes com replicação superior à obtida pelo WGCNA para os três bancos de dados em questão. Além disso a interseção entre as listas de genes priorizados de cada metodologia foi baixa, com poucos genes sendo compartilhados pelos resultados dos dois métodos. Ambas metodologias selecionaram genes com relevância biológica relacionada a esquizofrenia, incluindo grupos de genes relacionados a atividade do sistema imune (infecções, estresse), atividade do Sistema Nervoso Central (atividade sináptica, crescimento axonal) e também de embriogênese. Baseando-se nesses resultados, conclui-se que a análise de redes e a integração de dados biológicos são fundamentais para uma ferramenta apresentar resultados promissores, sobretudo no âmbito da descoberta de novos genes e suas redes de interação biológica que seriam possivelmente desconhecidas se fosse realizada apenas a análise individual de cada tipo de dado biológico disponível. / Systems Biology is an interdisciplinary research field which studies the complex interactions that occur between biological compounds of a living organism in order to understand their behavior, which emerges from these interactions. Such interactions compose a highly complex network, whose elements can be of several types. In this context, complex diseases are characterized precisely by being of polygenic and multifactorial nature, i.e., the genesis and development of these diseases are a result of the joint interaction of several factors, including not only genes, proteins and other molecules, but also epigenetic and environmental factors. However, many methods for gene prioritization present results (list of genes) with small convergence. Thus, the comparison involving those methods is a crucial issue. The main objectives of this master thesis was to perform an extensive literature review related to gene prioritization techniques associated to complex diseases and the comparison of part of these techniques. Two techniques were selected: WGCNA (Weighted Gene Correlation Network Analysis) and NERI (Network-Medicine Relative Importance), both methods based on complex networks theory and co-expression for gene prioritization, but NERI having the differential of modeling the Network Medicine hypotheses for prioritization based on integration of expression, protein-protein interaction (PPI) network and association studies. For comparison of the results, three gene expression databases related to schizophrenia were adopted. As predicted, due to the data integration proposed by NERI, such technique resulted in genes lists with superior replication for the three databases mentioned. Additionally, the intersection between the results of the genes lists prioritized by the two methodologies was small, with few genes being found in both lists. Both methods selected biologically relevant to schizophrenia, including groups of genes related to imune system activity (infections, stress), Central Nervous System activity (synaptic activity, axonal growth) and embryogenesis. From these results, it follows that network analysis and biological data integration are fundamental for a gene prioritization method to present promising results, mainly for discovery of new genes and their biological interaction networks that would possibly be unknown if only an individual analysis of each biological data available were performed.

This post-positivist research study explored the possible relationship between Chronic Fatigue and Immune Dysfunction Syndrome (CFIDS) and the presence of underlying psychological and emotional issues. An exploratory design with naturalistic methods of inquiry was utilized to investigate whether the presence, or absence, of these issues had any impact on the overall disease process.

Doenças complexas são caracterizadas por serem poligênicas e multifatoriais, o que representa um desafio em relação à busca de genes relacionados a elas. Com o advento das tecnologias de sequenciamento em larga escala do genoma e das medições de expressão gênica (transcritoma), bem como o conhecimento de interações proteína-proteína, doenças complexas têm sido sistematicamente investigadas. Particularmente, baseando-se no paradigma Network Medicine, as redes de interação proteína-proteína (PPI -- Protein-Protein Interaction) têm sido utilizadas para priorizar genes relacionados às doenças complexas segundo suas características topológicas. Entretanto, as redes PPI são afetadas pelo viés da literatura, em que as proteínas mais estudadas tendem a ter mais conexões, degradando a qualidade dos resultados. Adicionalmente, métodos que utilizam somente redes PPI fornecem apenas resultados estáticos e não-específicos, uma vez que as topologias destas redes não são específicas de uma determinada doença. Neste trabalho, desenvolvemos uma metodologia para priorizar genes e vias biológicas relacionados à uma dada doença complexa, através de uma abordagem integrativa de dados de redes PPI, transcritômica e genômica, visando aumentar a replicabilidade dos diferentes estudos e a descoberta de novos genes associados à doença. Após a integração das redes PPI com dados de expressão gênica, aplicamos as hipóteses da Network Medicine à rede resultante para conectar genes sementes (relacionados à doença, definidos a partir de estudos de associação) através de caminhos mínimos que possuam maior co-expressão entre seus genes. Dados de expressão em duas condições (controle e doença) são usados separadamente para obter duas redes, em que cada nó (gene) dessas redes é pontuado segundo fatores topológicos e de co-expressão. Baseado nesta pontuação, desenvolvemos dois escores de ranqueamento: um que prioriza genes com maior alteração entre suas pontuações em cada condição, e outro que privilegia genes com a maior soma destas pontuações. A aplicação do método a três estudos envolvendo dados de expressão de esquizofrenia recuperou com sucesso genes diferencialmente co-expressos em duas condições, e ao mesmo tempo evitou o viés da literatura. Além disso, houve uma melhoria substancial na replicação dos resultados pelo método aplicado aos três estudos, que por métodos convencionais não alcançavam replicabilidade satisfatória. / Complex diseases are characterized as being poligenic and multifactorial, so this poses a challenge regarding the search for genes related to them. With the advent of high-throughput technologies for genome sequencing and gene expression measurements (transcriptome), as well as the knowledge of protein-protein interactions, complex diseases have been sistematically investigated. Particularly, Protein-Protein Interaction (PPI) networks have been used to prioritize genes related to complex diseases according to its topological features. However, PPI networks are affected by ascertainment bias, in which the most studied proteins tend to have more connections, degrading the quality of the results. Additionally, methods using only PPI networks can provide just static and non-specific results, since the topologies of these networks are not specific of a given disease. In this work, we developed a methodology to prioritize genes and biological pathways related to a given complex disease, through an approach that integrates data from PPI networks, transcriptomics and genomics, aiming to increase replicability of different studies and to discover new genes associated to the disease. The methodology integrates PPI network and gene expression data, and then applies the Network Medicine Hypotheses to the resulting network in order to connect seed genes (obtained from association studies) through shortest paths possessing larger coexpression among their genes. Gene expression data in two conditions (control and disease) are used to obtain two networks, where each node (gene) in these networks is rated according to topological and coexpression aspects. Based on this rating, we developed two ranking scores: one that prioritizes genes with the largest alteration between their ratings in each condition, and another that favors genes with the greatest sum of these scores. The application of this method to three studies involving schizophrenia expression data successfully recovered differentially co-expressed gene in two conditions, while avoiding the ascertainment bias. Furthermore, when applied to the three studies, the method achieved a substantial improvement in replication of results, while other conventional methods did not reach a satisfactory replicability.

Neurofibromatosis type 1 patients present a high variability in their clinical expressivity. The most common manifestation is the appearance of dermal neurofibromas, benign tumors that arise in the peripheral nervous system. They appear at puberty and increase their number throughout life, with patients showing a great variation in their number, ranging from tens to thousands. The main objective of this thesis consisted in the identification of genes and variants influencing the number of dermal neurofibromas developed by NF1 patients. We centered our studies only to Schwann cells (neurofibromas develop due to a double inactivation of the NF1 gene, but only Schwann cells bear it), and the HR mechanism (HR has been found to be responsible for a high percentage of somatic NF1 inactivations in neurofibromas). In the first part of this project we characterized our cohort of 117 NF1 patients at clinical (age, sex, the number of dermal neurofibromas developed) and tumor molecular (estimating the LOH frequencies together with the identification of the mechanisms generating these LOHs) level. 23.7% of tumors showed LOH. 37% of tumors exhibited LOH due to deletion, and 63% due to HR. LOH frequencies were very variable, ranging from less than 10% to more than 50% of LOH. In addition, our studies suggested that patients with the highest rates of HR frequency showed the highest rates of nº of dNFs (with a p value close to significance). We developed the Microsatellite Multiplex PCR Analysis (MMPA) that improved and facilitated neurofibroma analysis. With this technique it was possible to obtain: data regarding the tumor sample allelic imbalance (AI) status and extension, the percentage of normal cells present in the tumor sample, the copy-number status of specific alleles of heterozygous loci showing AI and the mechanisms generating these AIs, in only one PCR reaction. The re-analysis of 29 neurofibromas showed a good agreement between the information generated by MMPA and the data generated for the same tumors by other techniques. In the second part of this project we selected candidate genes, involved in the HR mechanism, as possible modifiers of the number of dermal neurofibromas. We developed the HoReYe assay to model HR in yeast. With this technique we were able to determine the HR rate for the yeast strain BMA64. Once more yeast strains were characterized for the HR rate, the X-QTL assay would be performed to determine genetic variation responsible for high or low HR rates. In addition, due to the complexity of the HoReYe setting up, a surrogate of this technique was proposed to determine, in an easier way, the HR rate of yeast strains. In the third part of this project genetic variation of candidate genes would be analyzed by direct sequencing to identify both common and rare variants. Sanger sequencing was first used to analyze the BLM gene in 12 NF1 patients, but not variant found was affecting the protein structure. We would employ Next-generation sequencing to analyze genetic variation the 18 NF1 patients characterized. However, until now, only data from patient P027 was recovered showing 845 variants, which will be further analyzed in the near future. This thesis has established the basis to identify candidate genes related to HR rate, which will be studied in the NF1 patients previously characterized in order to identify allelic variants responsible for the number of dermal neurofibromas developed. / Els pacients amb Neurofibromatosi tipus 1 presenten una gran variabilitat en les seves manifestacions clíniques. El tret més característic és l’aparició de neurofibromes dèrmics, els quals poden aparèixer a decenes o milers en un pacient. L’objectiu principal de la present tesi ha estat identificar aquells gens i variants al•lèliques responsables del nombre de neurofibromes desenvolupats pels pacients NF1. Per a realitzar aquest treball ens hem centrat en estudiar les cèl•lules de Schwann, les portadores de la doble inactivació del gen NF1, i el mecanisme de recombinació homòloga, responsable d’un alt percentatge de les inactivacions somàtiques del gen NF1. En la primera part del treball vam caracteritzar els pacients NF1 de forma clínica, analitzant el sexe, edat i el nombre de neurofibromes desenvolupats, i molecular a nivell tumoral, determinant la presència de LOH i els mecanismes mutacionals generadors d’aquesta. Així, vam establir una prevalença de LOH als pacients d’un 23.7%, éssent el mecanisme de recombinació homòloga el més frequent, i vam obtenir una possible correlació entre tenir un elevat percentatge de recombinació homòloga generant LOHs i un elevat nombre de neurofibromes desenvolupats. A més, vam desenvolupar la tècnica de MMPA per a facilitar l’anàlisi de neurofibromes dèrmics, la qual pot ser aplicada a l’anàlisi d’altres tumors. La segona part del treball consistia en identificar gens candidats responsables del nombre de neurofibromes desenvolupats pels pacients NF1. Vam decidir utilitzar el llevat com a organisme model per a estudiar el mecanisme de recombinació homòloga, i obtenir gens candidats relacionats amb aquest mecanisme. Vam desenvolupar la tècnica HoReYe per a obtenir la taxa de recombinació homòloga en diferents soques de llevat. A més, vam idear les tècniques que s’haurien d’utilitzar posteriorment per a determinar les variants al•lèliques responsables d’aquestes taxes. En la tercera part del treball els gens candidats es van analitzar, tant per sequenciació per Sanger, com per seqüenciació de próxima generació. La intenció era trobar variants tant rares com comunes, per a no perdre cap tipus de variabilitat en l’anàlisi. En aquest treball s’han introduit les bases per a identificar, en pacients prèviament caracteritzats, els gens responsables del nombre de neurofibromes desenvolupats en pacients NF1.

<p>Leukotrienes are arachidonic acid derivatives long known for their inflammatory properties and their involvement with a number of human diseases, most notably asthma. Recently, leukotriene-based inflammation has also been implicated in atherosclerosis: ALOX5AP and LTA4H, two genes in the leukotriene biosynthesis pathway, have been associated with various cardiovascular disease (CVD) phenotypes. To assess the role of the leukotriene pathway in CVD pathogenesis, we performed genetic association studies of ALOX5AP and LTA4H in a non-familial data set of early onset coronary artery disease. Our results support a modest role for the leukotriene pathway in atherosclerosis pathogenesis, reveal important genomic interactions within the pathway, and suggest the importance of using pathway-based modeling for evaluating the genomics of atherosclerosis susceptibility. Motivated by this need, we investigated the statistical properties of a class of matrix-based statistics to assess epistasis. We simulated multiple two-variant disease models with haplotypes to gain an understanding of pathway interactions in terms of correlation patterns. Our goal was to detect an interaction between multiple disease-causing variants by means of their linkage disequlibrium (LD) patterns with other haplotype markers. The simulated models can be summarized into three categories: 1. No epistasis in the presence of marginal effects and LD; 2. Epistasis in the presence of LD and no marginal effects; and 3. Epistasis in the presence marginal effects and LD. We then assessed previously introduced single-gene methods that compare whole matrices of Single Nucleotide Polymorphism (SNP) LD between two samples. These methods include comparing two sets of principal components, a sum-of-squared-differences comparing pairwise LD, and a contrast test that controls for background LD. We also considered a partial least-square (PLS) approach for modeling gene-gene interactions. Our results indicate that these measures can be used to assess epistasis as well as marginal effects under certain disease models. Understanding and quantifying whole-gene variation and association to disease using multiple SNPs remains a difficult task. Providing a single statistical measure per gene will facilitate combining multiple types of genomic data at a gene-level and will serve as an alternative approach to assess epistasis in genome-wide association studies. The matrix-based measures can also be used in pathway ascertainment tools that require scores on a gene-level.</p> / Dissertation