OWE-16 Development and clinical validation of a genetic risk score for coeliac disease
Bibliographic record
Abstract
Introduction Specific HLA-DQ genes predispose to coeliac disease (CD) and HLA typing is occasionally used as a rule-out test in clinic. However, CD is polygenic and genome wide association studies (GWAS) have implicated ∼40 additional genetic variants. Using single nucleotide polymorphisms (SNPs) we aimed to combine all associated variants into a genetic risk score and assess its utility as a clinical tool. Methods We used imputation to identify SNPs strongly correlated (r2>0.95) with 4 key HLA-DQ haplotypes (DQ2.5/DQ2.2/DQ7.5/DQ8) in UK Biobank. We derived HLA-DQ odds ratios from 12,041 cases and 12,228 controls (Wellcome Trust). We combined this with additional SNPs from recent GWAS to generate a coeliac genetic risk score (C-GRS). We validated the C-GRS in a population based cohort (UK Biobank) with 1237 cases identified by hospital admission codes. We genotyped the C-GRS in 161 samples from a paediatric clinic where patients had been assessed using anti-tissue transglutaminase antibodies, biopsy and HLA typing. Results The C-GRS consisted of 42 SNPs and was highly discriminative of CD in UKBiobank. The C-GRS was more discriminative than HLA stratification alone (ROC-AUC=0.88 [95%CIs:0.87–0.89] v 0.81, p<0.0001) and highly discriminative in the paediatric clinic (ROC-AUC=0.82 [95%CIs:0.75–0.90], p<0.0001). Conclusions A C-GRS can aid in identifying incident cases of CD and is more effective than HLA typing alone. Given the low costs of SNP genotyping relative to HLA typing a C-GRS could improve the availability and utility of coeliac genetic testing in CD diagnosis and in recruitment to research studies.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".