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You searched for +publisher:"Vanderbilt University" +contributor:("Jason Moore"). Showing records 1 – 2 of 2 total matches.

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Vanderbilt University

1. White, Marquitta Jonisse. Genetics of Plasminogen Activator Inhibitor – 1: a potent biological effector of cardiovascular disease risk.

Degree: PhD, Human Genetics, 2014, Vanderbilt University

Cardiovascular disease (CVD) is an inclusive term encompassing several disorders of the circulatory system that together account for the majority of global non-communicable disease (NCD) mortality. Major thrombotic events, due in part to impaired fibrinolysis, are a unifying characteristic of several CVDs. Plasminogen activator inhibitor-1 (PAI-1) is a major regulator of fibrinolysis, and PAI-1 levels associate with CVD susceptibility and severity in several populations. The main objectives of this dissertation were to evaluate the genetic impact of common single nucleotide polymorphisms (SNPs) on inter-individual variation in PAI-1 levels in a Ghanaian population, and present a novel method to identify candidate genes for prioritization in future studies. We discovered novel associations between single variants in the arylsulfatase b (ARSB), carboxypeptidase A2 (CPA2), and leukocyte receptor cluster member 9 (LENG9) and median PAI-1 levels. Quantile regression analyses directed at the upper quartile of the PAI-1 distribution was performed to uncover novel variants with significant impact on this clinically relevant portion of the PAI-1 distribution. Upper quartile regression analyses revealed significant associations between single variants in period circadian clock 3 (PER3), a discovery that supports previous evidence of the involvement of the circadian pathway in regulation of PAI-1 levels in Caucasian populations as well as model organisms. This finding suggests that the significance of the circadian pathway as a whole may be generalizable across populations, even though gene effects may be population specific. We present a novel approach; Multi-lOcus based selection of Candidate genes (MOCA), which incorporates multi-variant association signals into the prioritization of genes for further evaluation. MOCA identified four significantly associated loci; these loci included 28 novel candidate genes for PAI-1 levels. Each MOCA identified locus was located within previously identified CVD and/or PAI-1 related quantitative trait loci (QTL). Advisors/Committee Members: Nancy J. Brown (committee member), melinda aldrich (committee member), Dana Crawford (committee member), Jason Moore (committee member), Scott M. Williams (committee member), Bingshan Li (Committee Chair).

Subjects/Keywords: population genetics; plasminogen activator inhibitor-1; cardiovascular disease

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

White, M. J. (2014). Genetics of Plasminogen Activator Inhibitor – 1: a potent biological effector of cardiovascular disease risk. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/14029

Chicago Manual of Style (16th Edition):

White, Marquitta Jonisse. “Genetics of Plasminogen Activator Inhibitor – 1: a potent biological effector of cardiovascular disease risk.” 2014. Doctoral Dissertation, Vanderbilt University. Accessed April 17, 2021. http://hdl.handle.net/1803/14029.

MLA Handbook (7th Edition):

White, Marquitta Jonisse. “Genetics of Plasminogen Activator Inhibitor – 1: a potent biological effector of cardiovascular disease risk.” 2014. Web. 17 Apr 2021.

Vancouver:

White MJ. Genetics of Plasminogen Activator Inhibitor – 1: a potent biological effector of cardiovascular disease risk. [Internet] [Doctoral dissertation]. Vanderbilt University; 2014. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/1803/14029.

Council of Science Editors:

White MJ. Genetics of Plasminogen Activator Inhibitor – 1: a potent biological effector of cardiovascular disease risk. [Doctoral Dissertation]. Vanderbilt University; 2014. Available from: http://hdl.handle.net/1803/14029


Vanderbilt University

2. Reif, David Michael. Integrated Analysis of Genetic and Proteomic Data.

Degree: PhD, Human Genetics, 2006, Vanderbilt University

Biological organisms are complex systems that dynamically integrate inputs from a multitude of physiological and environmental factors. Complex clinical outcomes arise from the concerted interactions among the myriad components of a biological system. Therefore, in addressing questions concerning the etiology of phenotypes as complex as common human disease or adverse reaction to vaccination, it is essential that the systemic nature of biology be taken into account. Analysis methods must integrate the information provided by each data type in a manner analogous to the operation of the body itself. It is hypothesized that such integrated approaches will provide a more comprehensive portrayal of the mechanisms underlying complex phenotypes and lend confidence to the biological interpretation of analytical conclusions.This dissertation concerns the development of a comprehensive analysis paradigm wherein experimental data of multiple types were analyzed jointly in the study of complex phenotypes. Flexible machine learning methods were used to integrate information that is insensitive to spatial and temporal flux (genetic polymorphisms) with information subject to dynamic changes (protein concentrations measured at multiple time points). This strategy was applied to genetic and proteomic data in both simulated and real analysis situations. Results of studies using simulated data indicated that utilizing multiple data types is beneficial when the disease model is complex and the phenotypic outcome-associated data type is unknown. The successful application to combined genetic and proteomic data from smallpox vaccine studies supported the hypothesis that such integrated approaches are analytically beneficial. Considering the rapid progress in experimental technologies able to reliably generate vast quantities of data, as well as continual improvements in cost efficiency, it is expected that datasets including multiple types of experimental information will become commonplace in the near future. It is hoped that the positive conclusions from this dissertation will help spur the adoption of an analytical approach that rightfully takes the broader physiological context of complex biological systems into account. Advisors/Committee Members: James Crowe, Jr. (committee member), Douglas Fisher (committee member), Jonathan Haines (committee member), Jason Moore (committee member), Scott Williams (Committee Chair).

Subjects/Keywords: machine learning; statistics; Systems biology; immunology; Genetic polymorphisms; Smallpox  – Vaccination  – Complications; Proteomics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Reif, D. M. (2006). Integrated Analysis of Genetic and Proteomic Data. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/14337

Chicago Manual of Style (16th Edition):

Reif, David Michael. “Integrated Analysis of Genetic and Proteomic Data.” 2006. Doctoral Dissertation, Vanderbilt University. Accessed April 17, 2021. http://hdl.handle.net/1803/14337.

MLA Handbook (7th Edition):

Reif, David Michael. “Integrated Analysis of Genetic and Proteomic Data.” 2006. Web. 17 Apr 2021.

Vancouver:

Reif DM. Integrated Analysis of Genetic and Proteomic Data. [Internet] [Doctoral dissertation]. Vanderbilt University; 2006. [cited 2021 Apr 17]. Available from: http://hdl.handle.net/1803/14337.

Council of Science Editors:

Reif DM. Integrated Analysis of Genetic and Proteomic Data. [Doctoral Dissertation]. Vanderbilt University; 2006. Available from: http://hdl.handle.net/1803/14337

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