In partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Bioinformatics
in the School of Biological Sciences
Margaret Brown
Defends her thesis:
Integrating genomic and multiomic data for computational analysis of gene regulation in circulating immune cells
Monday, April 15th, 2024
12:00 PM
Krone Engineered Biosystems Building (EBB), Room #4029
Zoom Link: https://gatech.zoom.us/j/96510306317
Meeting ID: 965 1030 6317
Thesis Advisor:
Dr. Greg Gibson
School of Biological Sciences
Georgia Institute of Technology
Committee Members:
Dr. I. King Jordan
School of Biological Sciences
Georgia Institute of Technology
Dr. Peng Qiu
Wallace H. Coulter Department of Biomedical Engineering
Georgia Institute of Technology
Dr. Saurabh Sinha
Wallace H. Coulter Department of Biomedical Engineering
Georgia Institute of Technology
Dr. Russ Wolfinger
Scientific Discovery and Genomics
JMP Statistical Software LLC
Abstract:
In the post-GWAS era, genetic associations with pathology have sparked interest in gene regulatory mechanisms since the majority of GWAS variants are located in noncoding regions. This idea fuels the hypothesis that trait associated variants are causal to gene expression variability. The primary question driving this thesis, is whether distinct gene regulatory mechanisms associated with genetics can be identified in circulating immune cells. First, eQTL fine mapping was performed using an all-but-one conditional analysis approach to prioritize putatively causal variants by disentangling the effects of linkage disequilibrium in peripheral blood. Identified eQTL for genes associated with inflammatory bowel disease were observed in immune cell populations, suggesting a functional relationship between genetics and gene expression variability. Next, heterogeneous gene regulatory mechanisms were observed in single nuclear multiomic data of circulating immune cells from individuals with Crohn’s disease and healthy donors. Paralleled heterogeneity was observed in both arms of the adaptative immune system, including an inflammatory signature within a subset of Crohn’s disease donors. Finally, an unprecedented approach to explain gene expression was implemented by training machine learning models on chromatin accessibility data, which demonstrated that ATAC peaks which are important for explaining gene expression are enriched with inflammatory disease GWAS variants. Altogether, this thesis highlights the genetic relevance of gene regulation in circulating immune cells for inflammatory disease and suggests that the interplay of genetics and pathology with respect to gene regulation is complex and heterogeneous among individuals.
