A Systematic Analysis of Interactions between Environmental Risk Factors and Genetic Variation in Susceptibility to Colorectal Cancer
Bibliographic record
Abstract
Abstract Background: The underlying etiology of colorectal cancer includes both genetic variation and environmental exposures. The main aim of this study was to search for interaction effects between well-established environmental risk factors and published common genetic variants exerting main effects on colorectal cancer risk. Methods: We used a two-phase approach: (i) discovery phase (2,652 incident colorectal cancer cases and 10,608 controls from UK Biobank) and (ii) validation phase (1,656 cases and 2,497 controls from the Study of Colorectal Cancer in Scotland). Interactions with nominal P < 0.05 in phase I were taken forward for validation in phase II. Furthermore, we constructed a weighted genetic risk score (GRS) of colorectal cancer risk for each individual and studied interactions between the GRS and the environmental risk factors. Results: Seventy of the 1,500 tested interactions were nominally significant in phase I. After testing these 70 interactions in phase II, an interaction between rs11903757 (2q32.3) and body mass index (BMI) was nominally significant (P = 0.02) with the same direction of effect. The rs11903757*BMI interaction was also significant (ratio of ORs = 1.26; 95% confidence interval, 1.10–1.44; Pinteraction = 6.03 × 10−4; Pheterogeneity = 0.63) in a meta-analysis combining results from both phases. No interactions were significant in phase II after accounting for multiple testing. No interactions involving the GRS were found with statistical significance. Conclusions: Limited evidence of gene–environment interactions in colorectal cancer risk was observed. There are potential modifications of the rs11903757 effect by BMI on colorectal cancer risk. Impact: Our findings might contribute to identifying subpopulations with different susceptibility to the effect of BMI on colorectal cancer risk.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".