Browsing by Subject "Whole Genome Sequencing"

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    Resolving the major dyslipidemia phenotypes and genetic risk factors for familial hyperlipidemia in Miniature Schnauzers
    (2022-09) Tate, Nicole
    Hyperlipidemia is common in the Miniature Schnauzer breed, especially those over the age of 10. Hyperlipidemia is defined as an increased concentration of lipids (i.e., triglycerides and/or cholesterol) in the blood. Hyperlipidemia predisposes Miniature Schnauzers to severe consequences such as pancreatitis, gallbladder mucoceles, and glomerular proteinuria. However, the underlying molecular derangements and cause remains unresolved. It is suspected that hyperlipidemia in Miniature Schnauzers is due to an underlying genetic risk factor(s). Additionally, the varied responses to management strategies in the breed, suggests a potential for multiple subtypes. Thus, the goal of this thesis was to identify the spectrum of dyslipidemia subtypes and ascertain the metabolic and genetic risk factors underlying hyperlipidemia in Miniature Schnauzers. The possibility of multiple dyslipidemia subtypes within the breed was evaluated using lipoprotein profile data and unsupervised hierarchical cluster analysis. The results support the hypothesis that multiple dyslipidemia subtypes exist in Miniature Schnauzers and that the major distinguishing factor between the subtypes may be differences in low-density lipoproteins. Additional studies are warranted to confirm the range and number of distinct lipoprotein profiles within this breed. The lipidome and metabolome of Miniature Schnauzers with moderate-to-severe primary hyperlipidemia were compared to those from Miniature Schnauzers with normal serum triglyceride concentrations to elucidate the underlying pathophysiological processes of hyperlipidemia in the breed. Differences in the lipidome and metabolome were identified between the two groups. The differentiating lipid and metabolite species suggest involvement and/or disruption of the pathways and products of glycerolipid, glycerophospholipid, glycosphingolipid, and fatty acid metabolism. The results of this study provide insights into the underlying pathways. However, it is still unknown whether these pathways are causal of hyperlipidemia or if the disturbances are in response to elevated triglyceride (TG) concentrations. This thesis also used unsupervised hierarchical cluster analysis to compare the lipidome and metabolome of Miniature Schnauzers dogs with normal serum triglyceride concentrations, mild triglyceride elevations, moderate-to-severe triglyceride elevations, and triglyceride elevations due to endocrinopathies (i.e., secondary hyperlipidemia). The most notable finding being that Miniature Schnauzers with mild HTG cannot be definitively classified as having primary HTG, as their lipid disturbances do not reliably differ from dogs with NTG. Whole genome sequencing (WGS) of eight Miniature Schnauzers with primary hyperlipidemia was screened for risk variants in six HTG candidate genes: LPL, APOC2, APOA5, GPIHBP1, LMF1, and APOE. A monogenic cause for primary hyperlipidemia in the breed was not identified in the evaluated candidate genes. Two variants passed the filtering criteria, a deletion in the TATA box of APOE and a missense variant in GPIHBP1. While the two variants did not have sufficient evidence to support a strong impact, neither can be ruled out as contributors to the disease. These findings, and the growing data on dyslipidemia subtypes in Miniature Schnauzers, suggest that hyperlipidemia in the breed is likely a polygenic or complex trait. Finally, a key challenge in genetic studies is the prioritization of identified variants. Many in silico tools have been developed to use features of amino acids and proteins to determine if a variant is likely pathogenic. However, these methods are typically trained using human variants and have not been validated for use in non-human species. Thus, this thesis evaluates the performance of eight tools for pathogenicity prediction of missense variants (MutPred2, PANTHER, PhD-SNP, PolyPhen2-HumDiv, PolyPhen2-HumVar, Provean, SIFT, and SNPs&GO) for use in the dog and horse. The findings of this study suggest that these methods can be effectively used in veterinary species.
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    Using Bioinformatics and Data-mining Techniques to Transform Whole Genome Sequencing Data and Survey Data of Mycobacterial Pathogens to Epidemiological Inferences
    (2020-05) Wang, Yuanyuan
    Like scanning barcodes on shipping labels, Whole Genome Sequencing (WGS) of pathogen genomes isolated from infected hosts provides the ultimate resolution to track the spread of disease. This is possible because transmission events are recorded in the mutations of pathogen genomes: the mutations are passed down through the chain of transmission between infected hosts. In other words, hosts sharing similar nucleotide sequences can be linked to investigate their contact history. Understanding how diseases spread at a local scale is important because disease control and surveillance strategies need to adapt to the heterogeneity in disease risk at different locations. In the local epidemiological investigations, however, a key challenge is to differentiate individuals with genetically similar WGS patterns. In this thesis, we discuss using both genomic and behavioral epidemiology to understand the local spread of two Mycobacterial pathogens, Mycobacterium bovis (M. bovis) and Mycobacterium avium subsp. paratuberculosis (Map). Both pathogens cause chronic non-treatable infections in cattle but lead to different disease scenarios: a regional outbreak of M. bovis in Minnesota, and persistent infections of Map among four U.S cattle herds in Minnesota, New York, Pennsylvania, and Vermont. First, we present two novel methods to computationally identify samples infected with multiple strains. The first method is a highly scalable tool for fast screening mixtures samples in outbreak datasets. The method generates artificial admixtures by averaging the principal component coordinates of each sample to detect true admixtures in close proximity to the artificial ones. The second method is developed for endemic diseases with persistent strains circulating and evolving in a local environment. The method utilizes temporal Non-negative Matrix Factorization to derive an evolving panel of template strains from time-series WGS data. Then, we discuss the use of dimension reduction techniques to improve visualizations of the population structure from genomic data at a local scale. Our results show that t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) can effectively reduce the noise from outliers in WGS data. Finally, we showcase the importance of incorporating behavioral data to the policymaking of infectious diseases. Specifically, we designed a factorial survey to examine how slaughtering policy and government indemnity of bovine tuberculosis can impact the purchasing behavior of cattle producers. Collectively, our interdisciplinary research integrated WGS bioinformatics, data-mining techniques and behavior epidemiology to understand the local spread of mycobacterial diseases. Our admixture detection methods highlighted the role of rare but informative heterozygous variants in recovering genealogical relationships between infected hosts. In addition, our qualitative comparison analysis demonstrated the use of dimension reduction techniques to improve resolution of visualizing herd-level population structures. Last but not least, our factorial survey revealed the complexity of human risk perception in the context of infectious diseases.