A Machine Learning Approach to Identifying Causal Monogenic Variants in Inflammatory Bowel Disease
Notice bibliographique
Résumé
Background and AimsDiagnosis of monogenic disease is increasingly important for patient care and personalizing therapy. However, the current process is nonstandardized, expensive, and time consuming. There is currently no accepted strategy to help identify disease-causing variants in monogenic inflammatory bowel disease (IBD). The aim of the study is to develop a prioritization strategy for monogenic IBD variant discovery through detailed analysis of a whole-exome sequencing (WES) data set.MethodsAll consenting pediatric patients with IBD presenting to our tertiary care hospital during the study period were enrolled and underwent WES (n = 1005). Available family members also underwent WES. Variants were analyzed en masse using the GEMINI framework and were further annotated using data from dbNSFP, Combined Annotation Dependent Depletion, and gnomAD. Known disease-causing variants (n = 36) were used as positive controls. Machine learning algorithms were optimized and then compared to assist with identifying monogenic IBD case characteristics.ResultsInitial gene-level analysis identified 11 genes not previously linked to IBD that could potentially harbor IBD-causing variants. Machine learning algorithms identified 4 primary variant characteristics (Combined Annotation Dependent Depletion score, dbNSFP score, relationship with a known immunodeficiency gene, and alternate allele frequency), and optimal threshold values for each were determined to assist with identifying monogenic IBD variants. Based on these characteristics, an automated variant prioritization pipeline was then created that filters and prioritizes variants from >100,000 variants per patient down to a mean of 15. This pipeline is available online for all to use.ConclusionLeveraging a large WES data set, we demonstrate a statistically rigorous strategy for prioritization of variants for monogenic IBD diagnosis. Diagnosis of monogenic disease is increasingly important for patient care and personalizing therapy. However, the current process is nonstandardized, expensive, and time consuming. There is currently no accepted strategy to help identify disease-causing variants in monogenic inflammatory bowel disease (IBD). The aim of the study is to develop a prioritization strategy for monogenic IBD variant discovery through detailed analysis of a whole-exome sequencing (WES) data set. All consenting pediatric patients with IBD presenting to our tertiary care hospital during the study period were enrolled and underwent WES (n = 1005). Available family members also underwent WES. Variants were analyzed en masse using the GEMINI framework and were further annotated using data from dbNSFP, Combined Annotation Dependent Depletion, and gnomAD. Known disease-causing variants (n = 36) were used as positive controls. Machine learning algorithms were optimized and then compared to assist with identifying monogenic IBD case characteristics. Initial gene-level analysis identified 11 genes not previously linked to IBD that could potentially harbor IBD-causing variants. Machine learning algorithms identified 4 primary variant characteristics (Combined Annotation Dependent Depletion score, dbNSFP score, relationship with a known immunodeficiency gene, and alternate allele frequency), and optimal threshold values for each were determined to assist with identifying monogenic IBD variants. Based on these characteristics, an automated variant prioritization pipeline was then created that filters and prioritizes variants from >100,000 variants per patient down to a mean of 15. This pipeline is available online for all to use. Leveraging a large WES data set, we demonstrate a statistically rigorous strategy for prioritization of variants for monogenic IBD diagnosis.
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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 | 0,000 | 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 | 0,001 |
| 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 ».