Whole-exome Sequence Analysis Implicates Rare Il17REL Variants in Familial and Sporadic Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Rare variants (<1%) likely contribute significantly to risk for common diseases such as inflammatory bowel disease (IBD) in specific patient subsets, such as those with high familiality. They are, however, extraordinarily challenging to identify. METHODS: To discover candidate rare variants associated with IBD, we performed whole-exome sequencing on 6 members of a pediatric-onset IBD family with multiple affected individuals. To determine whether the variants discovered in this family are also associated with nonfamilial IBD, we investigated their influence on disease in 2 large case-control (CC) series. RESULTS: We identified 2 rare variants, rs142430606 and rs200958270, both in the established IBD-susceptibility gene IL17REL, carried by all 4 affected family members and their obligate carrier parents. We then demonstrated that both variants are associated with sporadic ulcerative colitis (UC) in 2 independent data sets. For UC in CC 1: rs142430606 (odds ratio [OR] = 2.99, Padj = 0.028; minor allele frequency [MAF]cases = 0.0063, MAFcontrols = 0.0021); rs200958270 (OR = 2.61, Padj = 0.082; MAFcases = 0.0045, MAFcontrols = 0.0017). For UC in CC 2: rs142430606 (OR = 1.94, P = 0.0056; MAFcases = 0.0071, MAFcontrols = 0.0045); rs200958270 (OR = 2.08, P = 0.0028; MAFcases = 0.0071, MAFcontrols = 0.0042). CONCLUSIONS: We discover in a family and replicate in 2 CC data sets 2 rare susceptibility variants for IBD, both in IL17REL. Our results illustrate that whole-exome sequencing performed on disease-enriched families to guide association testing can be an efficient strategy for the discovery of rare disease-associated variants. We speculate that rare variants identified in families and confirmed in the general population may be important modifiers of disease risk for patients with a family history, and that genetic testing of these variants may be warranted in this patient subset.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".