Prediction of colorectal cancer risk based on profiling with common genetic variants
Bibliographic record
Abstract
Abstract Increasing numbers of common genetic variants associated with colorectal cancer (CRC) have been identified. Our study aimed to determine whether risk prediction based on common genetic variants might enable stratification for CRC risk. Meta‐analysis of 11 genome‐wide association studies comprising 16 871 cases and 26 328 controls was performed to capture CRC susceptibility variants. Genetic prediction models with several candidate polygenic risk scores (PRSs) were generated from Scottish CRC case‐control studies (6478 cases and 11 043 controls) and the score with the best performance was then tested in UK Biobank (UKBB) (4800 cases and 20 287 controls). A weighted PRS of 116 CRC single nucleotide polymorphisms (wPRS 116 ) was found with the best predictive performance, reporting a c‐statistics of 0.60 and an odds ratio (OR) of 1.46 (95% confidence interval [CI] = 1.41‐1.50, per SD increase) in Scottish data set. The predictive performance of this wPRS 116 was consistently validated in UKBB data set with c‐statistics of 0.61 and an OR of 1.49 (95% CI = 1.44‐1.54, per SD increase). Modeling the levels of PRS with age and sex in the general UK population shows that employing genetic risk profiling can achieve a moderate degree of risk discrimination that could be helpful to identify a subpopulation with higher CRC risk due to genetic susceptibility.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".