Exome Sequencing Implicates <i>DGKZ</i>, <i>ESRRA</i>, and <i>GXYLT1</i> for Modulating Granuloma Formation in Crohn Disease
Bibliographic record
Abstract
Non-caseating granulomas may indicate a more aggressive phenotype of Crohn disease (CD). Genetic associations of granulomatous CD (GCD) may help elucidate disease pathogenesis. Whole-exome sequencing was performed on peripheral blood-derived DNA from 17 pediatric patients with GCD and 19 with non-GCD (NGCD), and from an independent validation cohort of 44 GCD and 19 NGCD cases. PLINK (a tool set for whole-genome association and population-based linkage analyses) analysis was used to identify single nucleotide polymorphisms (SNPs) differentiating between groups, and subgroup allele frequencies were also compared to a public genomic database (gnomAD). The Combined Annotation Dependent Depletion scoring tool was used to predict deleteriousness of SNPs. Human leukocyte antigen (HLA) haplotype findings were compared to a control group (n = 8496). PLINK-based analysis between GCD and NGCD groups did not find consistently significant hits. gnomAD control comparisons, however, showed consistent subgroup associations with DGKZ , ESRRA , and GXYLT1 , genes that have been implicated in mammalian granulomatous inflammation. Our findings may guide future research and precision medicine.
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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 | 0.000 |
| 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) | 0.002 | 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".