A single nucleotide polymorphism genetic risk score to aid diagnosis of coeliac disease: a pilot study in clinical care
Bibliographic record
Abstract
BACKGROUND: Single nucleotide polymorphism-based genetic risk scores (GRS) model genetic risk as a continuum and can discriminate coeliac disease but have not been validated in clinic. Human leukocyte antigen (HLA) DQ gene testing is available in clinic but does not include non-HLA attributed risk and is limited by discrete risk stratification. AIMS: To accurately characterise both HLA and non-HLA coeliac disease genetic risk as a single nucleotide polymorphism-based GRS and evaluate diagnostic utility. METHODS: We developed a 42 single nucleotide polymorphism coeliac disease GRS from a European case-control study (12 041 cases vs 12 228 controls) using HLA-DQ imputation and published genome-wide association studies. We validated the GRS in UK Biobank (1237 cases) and developed direct genotyping assays. We tested the coeliac disease GRS in a pilot clinical cohort of 128 children presenting with suspected coeliac disease. RESULTS: The GRS was more discriminative of coeliac disease than HLA-DQ stratification in UK Biobank (receiver operating characteristic area under the curve [ROC-AUC] = 0.88 [95% CIs: 0.87-0.89] vs 0.82 [95% CIs: 0.80-0.83]). We demonstrated similar discrimination in the pilot clinical cohort (114 cases vs 40 controls, ROC-AUC = 0.84 [95% CIs: 0.76-0.91]). As a rule-out test, no children with coeliac disease in the clinical cohort had a GRS below 38th population centile. CONCLUSIONS: A single nucleotide polymorphism-based GRS may offer more effective and cost-efficient testing of coeliac disease genetic risk in comparison to HLA-DQ stratification. As a comparatively inexpensive test it could facilitate non-invasive coeliac disease diagnosis but needs detailed assessment in the context of other diagnostic tests and against current diagnostic algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 teacher head, 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".