Browsing by Author "Haznadar, Majda"

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    A Computationally Efficient and Statistically Powerful Framework for Searching High-order Epistasis with Systematic Pruning and Gene-set Constraints
    (2010-06-21) Fang, Gang; Haznadar, Majda; Wang, Wen; Steinbach, Michael; Van Ness, Brian; Kumar, Vipin
    This paper has not yet been submitted.
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    Construction and Functional Analysis of Human Genetic Interaction Networks with Genome-wide Association Data
    (2011-01-18) Fang, Gang; Wang, Wen; Paunic, Vanja; Oatley, Benjamin; Haznadar, Majda; Steinbach, Michael; Van Ness, Brian; Myers, Chad L.; Kumar, Vipin
    Motivation: Genetic interaction measures how different genes collectively contribute to a phenotype, and can reveal functional compensation and buffering between pathways under genetic perturbations. Recently, genome-wide investigation for genetic interactions has revealed genetic interaction networks that provide novel insights both when analyzed independently and when integrated with other functional genomic datasets. For higher eukaryotes such as human, the above reverse-genetics approaches are not straightforward since the phenotypes of interest for higher eukaryotes such as disease onset or survival, are difficult to study in a cell based assay. Results: In this paper, we propose a general framework for constructing and analyzing human genetic interaction networks from genome-wide single nucleotide polymorphism (SNP) datasets used for case-control studies on complex diseases. Specifically, we propose a general approach with three major steps: (1) estimating SNP-SNP genetic interactions, (2) identifying linkage disequilibrium (LD) blocks and mapping SNP-SNP interactions to LD block-block interactions, and (3) functional mapping for LD blocks. We performed two sets of functional analyses for each of the six case-control SNP datasets used in the paper, and demonstrated that (i) genes in LD blocks showing similar interaction profiles tend to be functionally related, and (ii) the network can be used to discover pairs of compensatory gene modules (between-pathway models) in their joint association with a disease phenotype. The proposed framework should provide novel insights beyond existing approaches that either ignore interactions between SNPs or model different SNP-SNP pairs with genetic interactions separately. Furthermore, our study provides evidence that some of the core properties of genetic interaction networks based on reverse genetics in model organisms like yeast are also present in genetic interactions revealed by natural variation in human populations. Availability: Supplementary material http://vk.cs.umn.edu/humanGI
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    Single nucleotide polymorphisms in cancer related genes lead to inter-individual response to prognosis, disease risk and environmental agent metabolism in multiple myeloma and lung cancer.
    (2010-08) Haznadar, Majda
    Multiple myeloma is a chronic disease for which there is presently no cure. Because of the significant genetic heterogeneity in this disease and the fact that it is rare, it has been difficult to study genetic variations that contribute to disease risk and clinical outcomes. Nevertheless, there are apoptotic and oncogenic signaling pathways that constitute common themes in genetic deregulation leading to myeloma. Therefore, we biologically guided single nucleotide (SNP) association studies by pre-identifying important pathways. We managed this by designing a targeted SNP chip panel containing genes in functional groups crucial to various cancer processes, especially to myeloma, and applied it to SNP association studies represented in this thesis. Lung cancer is one of the most common cancers in the world. It is a leading cause of cancer death in men and women in the United States. Cigarette smoking causes most lung cancers. Therefore, it is one of the rare diseases for which there is a known environmental exposure. The occurrence of lung cancer is thus attributed to a complex interplay of genetic factors and environmental exposure. Since many polymorphic genetic variations produce proteins with increased, decreased or a complete loss of enzymatic activity, they are relevant factors in the gene--environment interplay. The first part of this thesis explores impact of interindividual variations resulting in variable bone disease, prognosis (progression-free survival) and disease risk in myeloma. In the bone disease association study, we demonstrated that there are genetic variants in genes important in the inflammatory response, Wnt signaling, and in growth factors previously linked to etiology of myeloma. The novelty of this study is in combining gene expression profile of DKK1 with a SNP profile that resulted in a better prediction of bone disease. We then investigated genetic variants in relation to progression-free survival and risk in myeloma. This study resulted in the identification of polymorphisms in genes involved in drug metabolism and detoxification, immunity, DNA repair and signaling cascades important to multiple myeloma (MM) risk and survival. This was done by using novel combinatorial search algorithms that can robustly identify markers that associate with the studied outcomes, and decrease false discovery rates. The second part of this thesis explores impact of interindividual variations on the metabolism of tobacco-smoke carcinogens. Variations in genes involved in tobacco-smoke carcinogen metabolism can result in variable amounts of harmful DNA adducts that can ultimately lead to cancer. We first demonstrated that there is a variation in CYP1B1 gene, previously shown to result in decreased cellular protein levels. This polymorphism appears to function as a protection from lung cancer at low levels of exposure, whereas it loses its protectiveness at high exposure levels. This was important to unraveling gene--environment interactions, especially relevant in the initiation of lung cancer. We then demonstrated that there are SNP-SNP interactions that associate with lung cancer risk by applying a novel data mining combinatorial search algorithm. This approach will serve as a useful tool to more robustly study SNP associations with outcomes of interest, while minimizing the false discovery rate. Our findings will help aid in further understanding etiology of myeloma and lung cancer. The novel computational method we helped to develop and first applied will serve as an important tool in further identifying and validating genetic variability that leads to differential response to disease risk and outcomes.