Date of Award

Fall 2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computational Biology and Bioinformatics

First Advisor

Zhao, Hongyu

Abstract

For the last two decades, genome-wide association studies (GWAS) have been applied to identify trait-associated genetic variants in thousands of traits and diseases. This method has guided the identification of disease-causing genes and further benefited the downstream clinical treatment and drug target identification. However, due to the study design, data limitations, and lack of result interpretability in GWAS, more and more downstream analysis and integration studies with other data types have been put forward. For example, epigenomic data such as DNase hypersensitivity, histone modifications, DNA methylation, chromatin organization and interaction have helped researchers to better identify disease-associated genetic variants. The most recent and widely data type is the expression quantitative trait loci (eQTL) data. Most identified disease- or trait-associated variants are located on non-coding variants. Researchers usually assume those variants regulate disease and trait phenotypes by affecting gene expression levels, which makes it reasonable and natural to integrate eQTL data to better identifying disease-associated genes and variants in GWAS downstream analysis. Here we studied disease mechanisms at both gene and cell type levels leveraging eQTL information in GWAS downstream analysis. Due to the importance of eQTL information, we have also explored the robustness of eQTL findings across different eQTL studies.

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