Browsing by Subject "Cluster Analysis"

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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.