Prediction of Ankylosing Spondylitis in the HUNT Study by a Genetic Risk Score Combining 110 Single-nucleotide Polymorphisms of Genome-wide Significance
Bibliographic record
Abstract
OBJECTIVE: The genetic component of ankylosing spondylitis (AS) development is ∼90%. Of the known heritability, ∼20% is explained by HLA-B27, and 113 identified AS-associated single-nucleotide polymorphisms (SNP) account for ∼7.4%. The objectives were to construct a weighted genetic risk score (wGRS) using currently known genome-wide susceptibility SNP, and to evaluate its predictive ability for AS in the Norwegian population-based Nord-Trøndelag Health Study (HUNT). METHODS: AS cases (n = 164) and controls (n = 49,032) were from the second (1995-1997) and third (2006-2008) waves of the HUNT study, to which the entire adult population of the northern region of Trøndelag was invited. A wGRS based on 110 SNP weighted by published OR for AS was constructed, representing each person's carriage of all risk variants. Logistic regression models including the wGRS alone or in combination with HLA-B27 carrier state and other adjustment variables (sex, age, smoking, body mass index, and hypertension) were developed. Discrimination among models was compared using area under the curve (AUC). RESULTS: The wGRS was associated with AS (OR 1.7, 95% CI 1.4-2.1), but showed low discrimination (AUC 0.62, 95% CI 0.58-0.67). HLA-B27 was significantly associated with AS (OR 50, 95% CI 32-81), showing high discrimination (AUC 0.88, 95% CI 0.85-0.90). Combining the wGRS and HLA-B27 improved prediction (AUC 0.90, 95% CI 0.87-0.92; p < 0.001 vs wGRS alone, p < 0.01 vs HLA-B27 alone). Further inclusion of adjustment variables gave a small improvement (AUC 0.91, 95% CI 0.89-0.94; p = 0.03). CONCLUSION: Prediction in a population-based setting based on all currently known AS susceptibility SNP was better than HLA-B27 carrier state alone, although the improvement was small and of uncertain clinical value.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".