Date of Award

2017

Embargo Period

8-1-2024

Document Type

Thesis

Degree Name

Master of Biomedical Science

Department

Biometry and Epidemiology

College

College of Graduate Studies

First Advisor

Dongjun Chung

Second Advisor

Kelly Hunt

Third Advisor

Paula Ramos

Fourth Advisor

Hang Kim

Fifth Advisor

Brian Neelon

Abstract

As of this year, genome-wide association studies (GWAS) have identified over 20,000 single nucleotide polymorphisms (SNPs) associated with at least one disease or trait. Such achievements have provided various clinical and medical benefits with novel biomarkers and therapeutic targets. Recently, there has been accumulating evidence suggesting that different complex traits share common risk basis, a phenomenon known as pleiotropy. For example, 17% of genes reported in the GWAS Catalog are associated with more than one phenotype. Thus, a better understanding of pleiotropy can potentially be clinically beneficial as it may facilitate understanding of the common etiology of diseases and help improve therapies. However, effective interrogation of pleiotropic architecture still remains challenging, and it often requires employment of complicated statistical models. In order to address these challenges, we are developing ShinyGPA, an interactive and dynamic visualization toolkit for exploratory analysis of genetic studies. Specifically, ShinyGPA maps phenotypes onto two-dimensional space based on the genetic relationship among these phenotypes. In addition, ShinyGPA provides remarkable flexibility in modifying visualization to help improve user interpretations. The application of ShinyGPA to simulated data illustrates that the tuning parameter we have introduced provides a zoom functionality. Additionally, the application of ShinyGPA to GWAS datasets for 12 unique phenotypes indicates that clinically related phenotypes form clusters in the phenotype map generated by ShinyGPA. In addition, the visualization produced by ShinyGPA provides interesting hypotheses for relationships among groups of phenotypes, which require further investigation and in turn can be useful for the design of future genetic studies. We expect that ShinyGPA will be a powerful and flexible off-the-shelf tool to elucidate the genetic relationship among phenotypes, which can contribute to the development and improvement of diagnoses and therapeutics for various diseases. The R implementation of ShinyGPA is currently available at http://dongjunchung.github.io/GPA/.

Rights

All rights reserved. Copyright is held by the author.

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