Many important scientific questions can not be studied properly using a single measurement as a response. For example, many phenotypes of interest in recent clinical research may be difficult to characterize due to their inherent complexity. It may be difficult to determine the presence or absence of disease based on a single measurement, or even a few measurements, or the phenotype may only be defined based on a series of symptoms. Similarly, a set of related phenotypes or measurements may be studied together in order to detect a shared etiology. In this work, we propose methods for studying complex phenotypes of these types, where the phenotype may be characterized either longitudinally or by a diverse set of continuous, discrete, or not fully observed components. In chapter 1, we seek to identify predictors that are related to multiple components of diverse outcomes. We take up specifically the question of identifying a multiple regulator, where we seek a genetic marker that is associated with multiple biomarkers for autoimmune disease. To do this, we propose sparse multiple regulation testing (SMRT) both to estimate the relationship between a set of predictors and diverse outcomes and to provide a testing framework in which to identify which predictors are associated with multiple elements of the outcomes, while controlling error rates. In chapter 2, we seek to identify risk profiles or risk scores for diverse outcomes, where a risk profile is a linear combination of predictors. The risk profiles will be chosen to be highly correlated to latent traits underlying the outcomes. To do this, we propose semiparametric canonical correlation analysis (sCCA), an updated version of the classical canonical correlation analysis. In chapter 3, the scientific question of interest pertains directly to the progression of disease over time. We provide a testing framework in which to detect the association between a set of genetic markers and the progression of disease in the context of a GWAS. To test for this association while allowing for highly nonlinear longitudinal progression of disease, we propose functional principal variance component (FPVC) testing.

Genome-wide association studies (GWAS) are a tool of high importance to associate genetic markers, genes and genomic regions with complex phenotypes and diseases, allowing to understand in details this regulation of gene expression as well as the genes, and then develop new techniques of diagnoses and treatment of diseases. Nowadays, the main genetic marker used in GWAS is the SNP (single nucleotide polymorphism), a variation that affects only one base of the DNA, being the most common type of variation between individuals and inside the genome. Even though there are multiple techniques available for GWAS, several complex traits still have unexplained heritability. To contribute to these studies, reference genetic maps are being created, such as the HapMap and 1000 Genomes, which have common genetic variants from world wide population (including European, Asian and African populations). In the last years, two solutions adopted to solve the missing heritability are to use different types of genetic variants and include the rare and population specific markers. Copy number variation (CNV) is a structural variant which use is increasing in GWAS in the last years. This variant is characterized for the deletion or duplication of a region a DNA and its length can be from few bases pair to the whole chromosome, as in Down syndrome. In collaboration of the Heart Institute (InCor-FMUSP), this work uses the dataset from Baependi Heart Study to establish a methodology to characterized the CNVs in the Brazilian population using SNP array data and associate them with height. This project uses the genetic and phenotype data of 1,120 related samples (family structure). For CNV calling, resources from the software PennCNV are used and methodologies of preprocessing, normalization, identification and other analysis are reviewed. The characterization of CNVs include information about location, size, frequency in our population and the patterns of inheritance in trios. The association of CNVs and height is made using linear mixed models and with information of family structure. The obtained results indicate that the Brazilian population has regions with variation in the number of copies that are not in the literature. General characteristics, such as length and frequency in samples, are similar to the information found in the literature. In addition, it was observed that the transmission of CNVs could not follow the Mendelian laws, since the frequency of trios which one parent has a deletion/duplication and the offspring is normal is higher than the frequency of trios with one parent and the offspring has a deletion/duplication. This work also identified a region on chromosome 9 that could be associated to height, being that carries of a duplication in this region can have the expected height dropped by approximately 3cm. / Estudos de associação genética (do inglês, Genome-wide association studies - GWAS) são uma ferramenta fundamental para associar marcadores genéticos, genes e regiões genômicas com doenças e fenótipos complexos, permitindo compreender em mais detalhes essa rede de regulação bem como mapear genes e, com isso, desenvolver técnicas de diagnóstico e tratamento. Atualmente, a principal variante genética utilizada nos estudos de associação é o SNP (do inglês, Single Nucleotide Polymorphism), uma variação que afeta apenas uma base do DNA, sendo o tipo de variação mais comum tanto entre os indivíduos como dentro do genoma. Apesar das diferentes técnicas disponíveis para os estudos de associação, muitas doenças e traços complexos ainda possuem parte de sua herdabilidade inexplicada. Para contribuir com estes estudos, foram criados banco de dados genéticos de referência, como o HapMap e o 1000 Genomes, que possuem representantes das variantes genéticas comuns das populações mundiais (européias, asiáticas e africanas). Nos últimos anos, duas das solucões adotadas para tentar explicar a herdabilidade de doenças e fenótipos complexos correspondem a utilizar diferentes tipos de variantes genéticas e incluir variantes raras e específicas para uma determinada população. O CNV (do inglês, Copy Number Variation) é uma variante estrutural que está ganhando espaço nos estudos de associação nos últimos anos. Essa variante é caracterizada pela deleção ou duplicação de uma região do DNA que pode ser de apenas alguns pares de bases até cromossomos inteiros, como no caso da síndrome de Down. Em parceria com o Instituto do Coração (InCor-FMUSP), este trabalho utiliza os dados do projeto Corações de Baependi para estabelecer uma metodologia para caracterizar os CNVs na população brasileira a partir de dados de SNPs e associá-los com a altura. O projeto inclui dados genéticos e fenótipos de 1,120 indivíduos relacionados (estruturados em famílias). Para a detecção dos CNVs, os recursos do software PennCNV são utilizados e metodologias de processamento, normalização, identificação e análises envolvidas são revisadas. A caracterização dos CNVs obtidos inclui informações de localização, tamanho e frequência na população e padrões de herança genética em trios. A associação dos CNVs com a altura é realizada a partir de modelos lineares mistos e utilizando informações sobre a estrutura de família. Os resultados obtidos indicaram que a população brasileira contém regiões (únicas) com variação no número de cópias que não estão identificadas na literatura. Características gerais dos CNVs, como tamanho e frequência no indivíduo, foram semelhantes ao que é apontado na literatura. Também foi observado que a transmissão de CNV pode não seguir as leis mendelianas, uma vez que a frequência de trios com um dos pais com deleção/duplicação e filho normal era superior à frequência dos trios com filho portador da mesma variação.

abstract: Legionella pneumophila is a waterborne pathogen that causes Legionnaires' disease, an infection which can lead to potentially fatal pneumonia. In a culture-based technique, Legionella is detected using buffered charcoal-yeast extract (BCYE) agar supplemented with L-cysteine, Iron salt and antibiotics. These supplements provide essential and complex nutrient requirements and help in the suppression of non-target bacteria in Legionella analysis. Legionella occurs naturally in freshwater environments and for their detection; a sample is plated on solid agar media and then incubated for several days. There are many challenges in the detection of Legionella in environmental waters and the built environments. A common challenge is that a variety of environmental bacteria can be presumptively identified as Legionella using the culture-based method. In addition, proper identification of Legionella requires long incubation period (3-9 days) while antibiotics used in BCYE agar have relatively short half-life time. In order to overcome some of the challenges, Legionella has been genetically modified to express reporter genes such Green Fluorescent Protein (GFP) that can facilitate its detection in process validation studies under controlled laboratory conditions. However, such studies had limited success due to the instability of genetically modified Legionella strains. The development of a genetically modified Legionella with a much rapid growth rate (1-2 days) in simulated environmental systems (tightly-controlled water distribution system) is achieved. The mutant Legionella is engineered by transforming with a specific plasmid encoding CymR, LacZ and TetR genes. The newly engineered Legionella can grow on conventional BCYE agar media without L-Cysteine, Iron salt and only require one antibiotic (Tetracycline) to suppress the growth of other microorganisms in media. To the best of our knowledge, this is the first report of L. pneumophila strain capable of growing without L-Cysteine. We believe that this discovery would not only facilitate the study of the fate and transport of this pathogen in environmental systems, but also further our understanding of the genetics and metabolic pathways of Legionella. / Dissertation/Thesis / Masters Thesis Civil, Environmental and Sustainable Engineering 2018

Complex phenotypes are traits which are influenced by many factors, and not just a single gene, as for classical Mendelian traits. The brain, and its resultant behaviour, gives us a large subset of complex phenotypes to examine. Variation in these traits is affected by a range of different influences, both genetic and environmental, including social interactions and the effects of parents. Systems-genetics provides us with a framework in which to examine these complex traits, seeking to connect genetic variants to the phenotypes they cause, through intermediate phenotypes, such as gene expression and protein levels. This approach has been developed to exploit and analyse massive data sets generated for example in genomics and transcriptomics. In the first half of this thesis, I combine genetic linkage data from the BXD recombinant inbred mouse panel with genome-wide association data from humans to identify novel candidate genes, and use online gene annotations and functional descriptions to support these candidates. Firstly, I discovered MGST3 as a novel regulator of hippocampus size, which may be linked to neurodegenerative disorders. Secondly, I identified that CMYA5, MCTP1, TNR and RXRG are associated with mouse anxiety-like phenotypes and human bipolar disorder, and provide evidence that MCTP1, TNR and RXRG may be acting via inter-cellular signalling in the striatum. The second half of this thesis uses different cross-fostering designs between genetically variable BXD lines and the genetically uniform C57BL/6J strain to identify indirect genetic effects and the loci underlying them. With this, I have found novel loci expressed in mothers that alter offspring behaviour, novel loci expressed in offspring affecting the level of maternal care, and novel loci expressed in offspring, which alter the behaviour of their nestmates, as well as the level of maternal care they receive. Further I provide evidence of co-adaptation between maternal and offspring genotypes, and a positive indirect genetic effect of offspring on their nestmates, supportive of a role for kin selection. Finally, I demonstrate that the BXD lines can be used to investigate genes with parent-of-origin dependent expression, which have an indirect genetic effect on maternal care. In conclusion, this thesis identifies a number of novel loci, and in some cases genes, associated with complex traits. Not only are these techniques applicable to other phenotypes and other questions, but the candidates I identify can now be examined further in vitro or in vivo.

Parmi les différents acteurs impliqués dans le dogme de la biologie moléculaire, les protéines sont des unités biologiques fonctionnelles contribuant à de nombreux processus biologiques. Dans la compréhension de la relation génotype-phénotype, il est important d’étudier l'influence de gènes, ou de variants génétiques, sur des mécanismes moléculaires spécifiques, permettant d’expliquer la variance phénotypique de traits dits complexes. Dans cette thèse nous allons démontrer l’intérêt de proposer différentes stratégies bio-informatiques protéome-centrées pour l’étude de phénotypes complexes. Dans une première étude, nous mettons en avant comment l'utilisation de la génomique comparative, couplée à l'analyse de la propension d'agrégation des protéines, permet d'identifier certains groupes de protéines avec des différences significatives entre espèces dans leurs propriétés intrinsèques contribuant à la protéostase cellulaire. Ce mécanisme est proposé dans cette thèse comme hypothèse de travail pour étudier les différences d'espérance de vie chez les rongeurs: ce travail est réalisée sur deux espèces phylogénétiquement proches, le rat taupe-nu et la souris, mais possédant des différences phénotypiques dans le contexte du vieillissement. Dans une seconde étude, nous proposons une nouvelle méthodologie s'appuyant sur l'étude quantitative des réseaux d'interaction protéine-protéine afin d'identifier les déterminants génétiques qui seraient responsables de la variation de ces interactions, suite à une stimulation médicamenteuse dans une population de levures génétiquement diversifiées. Ces travaux de recherche étudient le protéome et ses interactions et permettent de proposer une abstraction originale des phénotypes complexes. / Among the different actors involved in the dogma of molecular biology, proteins are functional biological units contributing to many biological processes. In the understanding of the genotype-phenotype relationship, it is important to study the influence of genes, or genetic variants, on specific molecular mechanisms, allowing to explain the phenotypic variance of so-called complex traits. In this thesis we will demonstrate the interest of proposing different proteome-centric bioinformatics strategies for the study of complex phenotypes. In a first study, we highlight how the use of comparative genomics, coupled with the analysis of the aggregation propensity of proteins, allows to identify some groups of proteins with significant differences between species in their intrinsic properties contributing to cellular proteostasis. This mechanism is proposed in this thesis as a working hypothesis to study differences in life expectancy in rodents: this work is performed on two phylogenetically related species, the mole rat and the mouse, but with phenotypic differences in the context of aging. In a second study, we propose a new methodology based on the quantitative study of protein-protein interaction networks in order to identify the genetic determinants that would be responsible for the variation of these interactions, following a drug stimulation in a genetically diversified yeast population. This research studies the proteome and its interactions and proposes an original abstraction of complex phenotypes.