Predicting Crohn's Disease Recurrence in Post-Operative Patients through RNA Splicing Profiling and Splicing QTL Analysis

Introduction:
Crohn’s disease (CD) is an inflammatory bowel disease that affects the gastrointestinal tract. Most patients with Crohn’s disease take drugs such as anti-TNF agents and thiopurines to alleviate their symptoms. Patients who are less responsive to their initial treatment or with bowel obstruction may undergo surgery with careful medical assessment. However, surgery does not provide a cure for Crohn’s Disease and the risk of postoperative recurrence is 44-55% after 10 years1.

Aberrant RNA splicing contributes to various diseases such as spinal muscular atrophy, cancers, and Hutchinson–Gilford Progeria Syndrome2,3. The genetic variants affecting the RNA splicing event is called splicing quantitative loci (sQTL). Recent studies predicted that sQTL correlates with certain diseases and might be a major contributor to diseases like Parkinson’s disease4,5. Previous research from Gibson lab shows that CD individuals with distinct transcript profile and altered exon usage might be caused by altered splicing events, which is also called spliceopathy6.
In this project, our aim is to investigate the splicing events in post-operative patients. By comparing the splicing profile of recurrence patients with non-recurrence patients, we intend to find differential isoform usage or differential exon usage that is associated with CD recurrence. Combining alternative splicing analysis, splicing QTL, and genome-wide association study (GWAS), I will address the following questions:

1) Does alternative splicing contribute to the risk of postoperative recurrence?
2) Do male and female show the same pattern of alternative splicing in CD recurrence?
3) Which genetic loci control the differential splicing between the recurrence and non-recurrence patients?

The main objective of this project is to predict the recurrence of CD from genotype and expression data. The integration of sQTL into GWAS and RNA splicing analyses provides insights into the mechanism behind post-operative disease recurrence.

Student Name
Xie, Manke Kate
Faculty Mentor
Greg Gibson