Genetic variants and pathways implicated in a pediatric inflammatory bowel disease cohort
Bibliographic record
Abstract
In the United States, approximately 5% of individuals with inflammatory bowel disease (IBD) are younger than 20 years old. Studies of pediatric cohorts can provide unique insights into genetic architecture of IBD, which includes Crohn's disease (CD) and ulcerative colitis (UC). Large genome-wide association studies have found more than 200 IBD-associated loci but explain a minority of disease variance for CD and UC. We sought to characterize the contribution of rare variants to disease development, comparing exome sequencing of 368 pediatric IBD patients to publicly available exome sequencing (dbGaP) and aggregate frequency data (ExAC). Using dbGaP data, we performed logistic regression for common variants and optimal unified association tests (SKAT-O) for rare, likely-deleterious variants. We further compared rare variants to ExAC counts with Fisher's exact tests. We did pathway enrichment analysis on the most significant genes from each comparison. Many variants overlapped with known IBD-associated genes (e.g. NOD2). Rare variants were enriched in CD-associated loci (p = 0.009) and showed suggestive enrichment in neutrophil function genes (p = 0.05). Pathway enrichment implicated immune-related pathways, especially cell killing and apoptosis. Variants in extracellular matrix genes also emerged as an important theme in our analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".