OP30 Dissecting the genetic basis of clinical phenotype heterogeneity within IBD in more than 63,000 IBD patients
Bibliographic record
Abstract
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 1.de Lange KM, Moutsianas L, Lee JC, Lamb CA, Luo Y, Kennedy NA, Jostins L, Rice DL, Gutierrez-Achury J, Ji SG, Heap G, Nimmo ER, Edwards C, Henderson P, Mowat C, Sanderson J, Satsangi J, Simmons A, Wilson DC, Tremelling M, Hart A, Mathew CG, Newman WG, Parkes M, Lees CW, Uhlig H, Hawkey C, Prescott NJ, Ahmad T, Mansfield JC, Anderson CA, Barrett JC. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat Genet. 2017 Feb;49(2):256-261. doi: 10.1038/ng.3760. Epub 2017 Jan 9. 2.Zhu S, Li D, Liu J, Ge T, Cho J, Daly MJ, McGovern DPB, Ye BD, Song K, Kakuta Y, Li M, Huang H. Genetic architecture of the inflammatory bowel diseases across East Asian and European ancestries.Liu Z, Liu R, Gao H, Jung S, Gao X, Sun R, Liu X, Kim Y, Lee HS, Kawai Y, Nagasaki M, Umeno J, Tokunaga K, Kinouchi Y, Masamune A, Shi W, Shen C, Guo Z, Yuan K; FinnGen; International Inflammatory Bowel Disease Genetics Consortium; Chinese Inflammatory Bowel Disease Genetics Consortium; Nat Genet. 2023 May;55(5):796-806. doi: 10.1038/s41588-023-01384-0. Epub 2023 May 8.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".