Advances in microarray and sequencing technology enable the era of personalized medicine. With increasing availability of genomic assays, clinicians have started to utilize genetics and gene expression of patients to guide clinical care. Signatures of gene expression and genetic variation in genes have been associated with disease risks and response to clinical treatment. It is therefore not difficult to envision a future where each patient will have clinical care that is optimized based on his or her genetic background and genomic profiles. However, many challenges exist towards the full realization of the potential personalized medicine. The human genome is complex and we have yet to gain a better understanding of how to associate genomic data with phenotype. First, the human genome is very complex: more than 50 million sequence variants and more than 20,000 genes have been reported. Many efforts have been devoted to genome-wide association studies (GWAS) in the last decade, associating common genetic variants with common complex traits and diseases. While many associations have been identified by genome-wide association studies, most of our phenotypic variation remains unexplained, both at the level of the variants involved and the underlying mechanism. Finally, interaction between genetics and environment presents additional layer of complexity governing phenotypic variation. Currently, there is much research developing computational methods to help associate genomic features with phenotypic variation. Modeling techniques such as machine learning have been very useful in uncovering the intricate relationships between genomics and phenotype. Despite some early successes, the performance of most models is disappointing. Many models lack robustness and predictions do not replicate. In addition, many successful models work as a black box, giving good predictions of phenotypic variation but unable to reveal the underlying mechanism. In this thesis I propose two methods addressing this challenge. First, I describe an algorithm that focuses on identifying causal genomic features of phenotype. My approach assumes genomic features predictive of phenotype are more likely to be causal. The algorithm builds models that not only accurately predict the traits, but also uncover molecular mechanisms that are responsible for these traits. . The algorithm gains its power by combining regularized linear regression, causality testing and Bayesian statistics. I demonstrate the application of the algorithm on a yeast dataset, where genotype and gene expression are used to predict drug sensitivity and elucidate the underlying mechanisms. The accuracy and robustness of the algorithm are both evaluated statistically and experimentally validated. The second part of the thesis takes on a much more complicated system: cancer. The availability of genomic and drug sensitivity data of cancer cell lines has recently been made available. The challenge here is not only the increasing complexity of the system (e.g. size of genome), but also the fundamental differences between cancers and tissues. Different cancers or tissues provide different contexts influencing regulatory networks and signaling pathways. In order to account for this, I propose a method to associate contextual genomic features with drug sensitivity. The algorithm is based on information theory, Bayesian statistics, and transfer learning. The algorithm demonstrates the importance of context specificity in predictive modeling of cancer pharmacogenomics. The two complementary algorithms highlight the challenges faced in personalized medicine and the potential solutions. This thesis detailed the results and analysis that demonstrate the importance of causality and context specificity in predictive modeling of drug response, which will be crucial for us towards bringing personalized medicine in practice.

Over the past 20 years, Genome-Wide Association Studies (GWAS) have identified thousands of variants in the genome linked to genetic diseases. However, these associations often reveal little about underlying genetic etiology, which for many phenotypes is thought to be highly heterogeneous. This work investigates statistical methods to move beyond conventional GWAS methods to both improve estimation of associations and to extract additional etiological insights from known associations, with a focus on schizophrenia. This thesis addresses the above aim through three primary topics: First, we describe DNA.Land, a web platform to crowdsource the collection of genomic data with user consent and active participation, thereby rapidly increasing sample sizes and power required for GWAS. Second, we describe methods to characterize the latent genomic contributors to heterogeneity in GWAS phenotypes. We develop a Z-score test to detect heterogeneity using correlations between variants among affected individuals, and we develop a contrastive tensor decomposition to explicitly characterize subtype-specific SNP effects independently of confounding heterogeneity such as ancestry. Using these methods we provide evidence of significant heterogeneity in GWAS cohorts for schizophrenia. Lastly, a major avenue of investigation beyond GWAS is identifying the genes through which associated SNPs mechanistically affect the presentation of phenotypes. We develop a method to improve estimation of expression quantitative trait loci by joint inference over gene expression reference data and GWAS data, incorporating insights from the liability threshold model. These methods will advance ongoing efforts to explain the complex etiology of genetic diseases as well as improve the accuracy of disease prediction models based on these insights.

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with hundreds of human traits. Yet the common variant model tested by traditional GWAS only provides an incomplete explanation for the known genetic heritability of many traits. Many divergent methods have been proposed to address the shortcomings of GWAS, including most notably the extension of association methods into rarer variants through whole exome and whole genome sequencing. GWAS methods feature numerous simplifications designed for feasibility and ease of use, as opposed to statistical rigor. Furthermore, no systematic quantification of the performance of GWAS across all traits exists. Beyond improving the utility of data that already exist, a more thorough understanding of the performance of GWAS on common variants may elucidate flaws not in the method but rather in its implementation, which may pose a continued or growing threat to the utility of rare variant association studies now underway. This thesis focuses on systematic evaluation and incremental improvement of GWAS modeling. We collect a rich dataset containing standardized association results from all GWAS conducted on quantitative human traits, finding that while the majority of published significant results in the field do not disclose sufficient information to determine whether the results are actually valid, those that do replicate precisely in concordance with their statistical power when conducted in samples of similar ancestry and reporting accurate per-locus sample sizes. We then look to the inability of effectively all existing association methods to handle missingness in genetic data, and show that adapting missingness theory from statistics can both increase power and provide a flexible framework for extending most existing tools with minimal effort. We finally undertake novel variant association in a schizophrenia cohort from a bottleneck population. We find that the study itself is confounded by nonrandom population sampling and identity-by-descent, manifesting as batch effects correlated with outcome that remain in novel variants after all sample-wide quality control. On the whole, these results emphasize both the past and present utility and reliability of the GWAS model, as well as the extent to which lessons from the GWAS era must inform genetic studies moving forward.

Partindo da concepção de que a ciência é, por definição, uma atividade coletiva, organizada localmente e através de instituições, esta dissertação realiza um estudo empírico do Centro de Estudos do Genoma Humano (CEGH), situado na Universidade de São Paulo. A pergunta mais geral do trabalho diz respeito à maneira pela qual se dá a organização social da produção de conhecimento e da produção de produtores de conhecimento em uma área específica de pesquisa a genética em um país periférico. Para isso, parte-se do processo de institucionalização da pesquisa em genética no Brasil, enfatizando os arranjos entre pesquisadores, universidade e agência de fomento em três aspectos considerados essenciais à atividade científica: padrão de financiamento, padrão disciplinar e padrão de circulação internacional de ideias e pesquisadores. A preocupação central é compreender a dinâmica da disciplina, pensada como um conjunto de processos sociais de produção de conhecimentos (e não como uma lista de descobrimentos, acumuladas por homens singulares), e demonstrar como a institucionalização da pesquisa em genética foi conformando uma tradição local de pesquisa. Essa tradição servirá como pano de fundo para compreender a incorporação das mudanças na pesquisa em genética humana passagem da genética clássica à molecular nos laboratórios que atualmente compõem o CEGH e as transformações no padrão de financiamento da pesquisa. Ao olhar para o CEGH, a partir dessa tradição científica local da qual ele é tributário, é possível descrever quais são os atuais arranjos organizacionais, as práticas de pesquisa e a divisão do trabalho que remodelam e atualizam essa tradição. Este trabalho considera o CEGH como um microcosmo social, que faz parte de um espaço disciplinar mais amplo que, por sua vez, insere-se no universo hierarquizado das áreas de conhecimento e disciplinas científicas. / Starting from the understanding that science is, by definition, a collective activity, organized locally and through institutions, this dissertation carries out an empirical study on the Human Genome Research Center (HGRC), situated in the University of São Paulo (USP). The broader question of this study regards the way through which the social organizing of production of knowledge occurs, and the production of the producers of knowledge in a specific field of research genetics in a peripheral country. For this, we begin from the process of institutionalisation of the genetics research in Brazil, emphasizing the arrangement between researchers, university and funding agencies in three aspects considered essentials in scientific activities: funding pattern, disciplinary pattern and the pattern of international circulation of ideas and researchers. The main concern is to understand the dynamics of the discipline, conceived as an ensemble of social processes in the production of knowledge (and not as a list of discoveries accumulated by singular men), and demonstrate how the institutionalization of research in genetics conformed to a local research tradition. This tradition will serve as a background to comprehend the incorporation of changes in human genetics research the passage from classical genetics to molecular biology in laboratories which nowadays integrate the HGRC and the transformations in the patterns of research funding. By observing the HGRC from the perspective of this local scientific tradition, from which this research center is tributary, it is possible to describe what are the recent organizational arrangements, such as the practices of research and the division of labor which reshaped and updated this tradition. This dissertation considers the HGRC a social microcosm, which integrates a disciplinary space which, in turn, is inserted in the hierarchical universe of the fields of knowledge and scientific disciplines.

Partindo da concepção de que a ciência é, por definição, uma atividade coletiva, organizada localmente e através de instituições, esta dissertação realiza um estudo empírico do Centro de Estudos do Genoma Humano (CEGH), situado na Universidade de São Paulo. A pergunta mais geral do trabalho diz respeito à maneira pela qual se dá a organização social da produção de conhecimento e da produção de produtores de conhecimento em uma área específica de pesquisa a genética em um país periférico. Para isso, parte-se do processo de institucionalização da pesquisa em genética no Brasil, enfatizando os arranjos entre pesquisadores, universidade e agência de fomento em três aspectos considerados essenciais à atividade científica: padrão de financiamento, padrão disciplinar e padrão de circulação internacional de ideias e pesquisadores. A preocupação central é compreender a dinâmica da disciplina, pensada como um conjunto de processos sociais de produção de conhecimentos (e não como uma lista de descobrimentos, acumuladas por homens singulares), e demonstrar como a institucionalização da pesquisa em genética foi conformando uma tradição local de pesquisa. Essa tradição servirá como pano de fundo para compreender a incorporação das mudanças na pesquisa em genética humana passagem da genética clássica à molecular nos laboratórios que atualmente compõem o CEGH e as transformações no padrão de financiamento da pesquisa. Ao olhar para o CEGH, a partir dessa tradição científica local da qual ele é tributário, é possível descrever quais são os atuais arranjos organizacionais, as práticas de pesquisa e a divisão do trabalho que remodelam e atualizam essa tradição. Este trabalho considera o CEGH como um microcosmo social, que faz parte de um espaço disciplinar mais amplo que, por sua vez, insere-se no universo hierarquizado das áreas de conhecimento e disciplinas científicas. / Starting from the understanding that science is, by definition, a collective activity, organized locally and through institutions, this dissertation carries out an empirical study on the Human Genome Research Center (HGRC), situated in the University of São Paulo (USP). The broader question of this study regards the way through which the social organizing of production of knowledge occurs, and the production of the producers of knowledge in a specific field of research genetics in a peripheral country. For this, we begin from the process of institutionalisation of the genetics research in Brazil, emphasizing the arrangement between researchers, university and funding agencies in three aspects considered essentials in scientific activities: funding pattern, disciplinary pattern and the pattern of international circulation of ideas and researchers. The main concern is to understand the dynamics of the discipline, conceived as an ensemble of social processes in the production of knowledge (and not as a list of discoveries accumulated by singular men), and demonstrate how the institutionalization of research in genetics conformed to a local research tradition. This tradition will serve as a background to comprehend the incorporation of changes in human genetics research the passage from classical genetics to molecular biology in laboratories which nowadays integrate the HGRC and the transformations in the patterns of research funding. By observing the HGRC from the perspective of this local scientific tradition, from which this research center is tributary, it is possible to describe what are the recent organizational arrangements, such as the practices of research and the division of labor which reshaped and updated this tradition. This dissertation considers the HGRC a social microcosm, which integrates a disciplinary space which, in turn, is inserted in the hierarchical universe of the fields of knowledge and scientific disciplines.

Indiana University-Purdue University Indianapolis (IUPUI) / The class IV alcohol dehydrogenase (ADH7, µ-ADH, σ-ADH) efficiently metabolizes ethanol and retinol. ADH7 is expressed mainly in the upper gastrointestinal tract with no expression in the liver unlike the other ADHs, and is implicated in various diseases including alcoholism, cancer and fetal alcohol syndrome. Genome wide studies have identified significant associations between ADH7 variants and alcoholism and cancer, but the causative variants have not been identified. Due to its association with two important metabolic pathways and various diseases, this dissertation is focused on studying ADH7 regulation and the effects of variants on this regulation using cell systems that replicate endogenous ADH7 expression. We identified elements regulating ADH7 transcription and observed differences in the effects of variants on gene expression. A7P-G and A7P-A, two promoter haplotypes differing in a single nucleotide at rs2851028, had different transcriptional activities and interacted with variants further upstream. A sequence located 12.5 kb upstream (7P10) can function as an enhancer. These complex interactions indicate that the effects of variants in the ADH7 regulatory elements depend on both sequence and cellular context, and should be considered in interpretation of the association of variants with alcoholism and cancer. The mechanisms governing the tissue-specific expression of ADH7 remain unexplained however. We identified an intergenic region (iA1C), located between ADH7 and ADH1C, having enhancer blocking activity in liver-derived HepG2 cells. This enhancer blocking function was cell- and position- dependent with no activity seen in CP-A esophageal cells. iA1C had a similar effect on the ectopic SV40 enhancer. The CCCTC-binding factor (CTCF) bound iA1C in HepG2 cells but not in CP-A cells. Our results suggest that in liver-derived cells, iA1C blocks the effects of downstream ADH enhancers and thereby contributes to the cell specificity of ADH7 expression. Thus, while genetic factors determine level of ADH7 transcriptional activity, iA1C helps determine the cell specificity of transcription.

Indiana University-Purdue University Indianapolis (IUPUI) / Juvenile Myelomonocytic Leukemia (JMML) is rare, fatal myeloproliferative disease (MPD) affecting young children, and is characterized by expansion of monocyte lineage cells and hypersensitivity to Granulocyte Macrophage-Colony Stimulating Factor (GM-CSF) stimulation. JMML is frequently associated with gain-of-function mutations in the PTPN11 gene, which encodes the protein tyrosine phosphatase, Shp2. Activating Shp2 mutations are known to promote hyperactivation of the Ras-Erk signaling pathway, but Akt is also observed to have enhanced phosphorylation, suggesting a potential role for Phosphatidylinositol-3-Kinase (PI3K)-Akt signaling in mutant Shp2-induced GM-CSF hypersensitivity and leukemogenesis. Having demonstrated that Class IA PI3K is hyperactivated in the presence of mutant Shp2 and contributes to GM-CSF hypersensitivity, I hypothesized the hematopoietic-specific Class IA PI3K catalytic subunit p110δ is a crucial mediator of mutant Shp2-induced PI3K hyperactivation and GM-CSF hypersensitivity in vitro and MPD development in vivo. I crossed gain-of-function mutant Shp2 D61Y inducible knockin mice, which develop fatal MPD, with mice expressing kinase-dead mutant p110δ D910A to evaluate p110δ’s role in mutant Shp2-induced GM-CSF hypersensitivity in vitro and MPD development in vivo. As a comparison, I also crossed Shp2 D61Y inducible knockin mice with mice bearing inducible knockout of the ubiquitously expressed Class IA PI3K catalytic subunit, p110α. I found that genetic interruption of p110δ, but not p110α, significantly reduced GM-CSF-stimulated hyperactivation of both the Ras-Erk and PI3K-Akt signaling pathways, and as a consequence, resulted in reduced GM-CSF-stimulated hyper-proliferation in vitro. Furthermore, I found that mice bearing genetic disruption of p110δ, but not p110α, in the presence of gain-of-function mutant Shp2 D61Y, had on average, smaller spleen sizes, suggesting that loss of p110δ activity reduced MPD severity in vivo. I also investigated the effects of three PI3K inhibitors with high specificity for p110δ, IC87114, GDC-0941, and GS-9820 (formerly known as CAL-120), on mutant Shp2-induced GM-CSF hypersensitivity. These inhibitors with high specificity for p110δ significantly reduced GM-CSF-stimulated hyperactivation of PI3K-Akt and Ras-Erk signaling and reduced GM-CSF-stimulated hyperproliferation in cells expressing gain-of-function Shp2 mutants. Collectively, these findings show that p110δ-dependent PI3K hyperactivation contributes to mutant Shp2-induced GM-CSF hypersensitivity and MPD development, and that p110δ represents a potential novel therapeutic target for JMML.