Date of Award

12-2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Program

Biomedical Sciences

Track

Microbiology, Immunology, and Biochemistry

Research Advisor

Claire L. Simpson, PhD

Committee

David Ashbrook, PhD; Steven Brant, MD; Chester Brown, PhD; Robert L. Davis, MD

Keywords

Crohn's Disease, endophenotype, Inflammatory Bowel Disease, machine learning, multi-omic, Ulcerative Colitis

Abstract

Inflammatory bowel disease (IBD) is a disease that is classified into two subtypes: ulcerative colitis (UC) and Crohn’s disease (CD). Symptoms can range from mild discomfort to requiring surgical intervention and affects approximately 1-in-200 adults in America alone, with global incidence rates increasing. While many treatments exist for IBD, perhaps the main reason for the lack of a cure is that there are many different pathogeneses that all lead to a very similar expression of symptoms. Over 240 IBD loci have been identified to date, yet the causative allele that drives the association has only been identified in ~60 of these 240. Even with the known IBD loci, it is estimated that the known heritability for CD and UC is minimal at 13% and 8%, respectively. Genetic risk research across many populations around the world show different rates, or even complete absence, of known risk loci identified is Caucasian populations. This wide irregularity in genetic risk across populations gives reason for additional genomic research both within and outside of previously focused populations. In this study, we approach two novel methods for the discovery of additional risk loci in IBD. One method utilized multi-omic data (e.g. whole exome sequencing, methylation, and RNA-seq) and machine learning approaches in a discordant/concordant sib-pair study. The machine learning methods used were iClusterPlus and MoCluster. The iClusterPlus analysis yielded four “top priority” genes with GBP2 as a novel discovery in IBD. MoCluster yielded 10 “top priority” genes with CSK as a novel discovery in IBD. Additionally, the MoCluster analysis identified five KEGG pathways as having strong relation with IBD: "Platelet activation"; "Viral protein interaction with cytokine and cytokine receptor"; "NF-kappa B signaling pathway"; "Ferroptosis"; and "Epithelial cell signaling in Helicobacter pylori infection". A second method utilized the largest African American population of IBD patients to date in a dense endophenotype study. Using a stratified analysis to test for endophenotype-independent risk loci yielded five genes of interest. Three of the genes, TNFSF8, HLA-DQB1, and CHRNA4, were shown to increase risk of disease and two of the genes, ADCY7 and LSAMP, were found to be protective. In this study, I have identified novel genes and pathways for further IBD research. These methods have the potential to greatly increase the known genetic causes for IBD and other diseases by expanding the classifications of disease and utilizing these distinctions. An increased number of genetic loci with known, specific disease progressions will enable physicians with enhanced targeted screening capabilities, leading to an increase in quality of life for affected patients.

Declaration of Authorship

Declaration of Authorship is included in the supplemental files.

ORCID

https://orcid.org/0000-0002-0845-8687

DOI

10.21007/etd.cghs.2022.0610

Additional Files
2022-029-Stiemke-DOA.pdf (104 kB)
Declaration of Authorship
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