OP30 Dissecting the genetic basis of clinical phenotype heterogeneity within IBD in more than 63,000 IBD patients
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».