OP30 Dissecting the genetic basis of clinical phenotype heterogeneity within IBD in more than 63,000 IBD patients
Authors: Li D ,   IIBDGC Consortium
Published: January 2025

Abstract

Background
More than 200 genetic variants associated with Inflammatory Bowel Disease (IBD) susceptibility are known but few large-scale studies have been performed to examine the genetics basis of the heterogeneity of clinical phenotypes in IBD, including disease location, behavior, age of diagnosis as well as the distinction between Crohn’s Disease (CD) and Ulcerative Colitis (UC). Our aim was to examine the genetic association with IBD clinical phenotypes in the large International IBD Genetics Consortium(IIBDGC) GWAS cohorts.

Methods
Details of the sample recruitment, genotyping and quality control(QC) have been described previously. In brief, genome-wide genotyping was performed in more than 20 cohorts across the world and imputation was performed using the TopMed server. Clinical phenotype data, including CD/UC (ulcerative colitis) status, disease location, behavior as well as need for surgery, were collected following the IIBDGC core phenotype standard based on widely used Montreal classifications. Association analysis was performed using Regenie. All subjects gave informed consent after IRB approval.

Results
Currently 63,836 IBD cases (26,648 UC and 37,188 CD) were included in the analyses. In the CDvsUC comparison, 146 independent regions were identified and 20 of those have not been associated with IBD before (e.g., JAK1, Beta = -0.40, P=7.10E-9; IL12RB1, Beta = 0.0.097, P=2.06E-14). We also identified multiple novel association with disease location and behavior (e.g., COL24A1 with stricturing CD, Beta = 0.257, P=6.52E-8; MYOCD with CD disease location, Beta = 0.507, P = 7.09E-8; MSH5-SAPCD1 with extensive disease in UC, Beta=-0.465, P=6.39E-11). Variants at NOD2 and HLA are strongly associated with multiple clinical phenotypes including CDvsUC, CD disease location and behavior as well as age of onset. Further curation of the clinical phenotypes is on-going to further boost sample size and post-GWAS analyses including fine-mapping, pathway analysis and overlap with functional data is underway.

Conclusion
Using a large IBD cohort, we observed strong and novel association with IBD clinical phenotypes. This largest GWAS analysis on IBD clinical phenotypes will help to better understand the genetic basis of heterogeneity in IBD clinical phenotypes and provide insights for biomarkers and novel drug targets.

References
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