No Evidence of Gene–Calcium Interactions from Genome-Wide Analysis of Colorectal Cancer Risk
Bibliographic record
Abstract
BACKGROUND: Calcium intake may reduce risk of colorectal cancer, but the mechanisms remain unclear. Studies of interaction between calcium intake and SNPs in calcium-related pathways have yielded inconsistent results. METHODS: To identify gene-calcium interactions, we tested interactions between approximately 2.7 million SNPs across the genome with self-reported calcium intake (from dietary or supplemental sources) in 9,006 colorectal cancer cases and 9,503 controls of European ancestry. To test for multiplicative interactions, we used multivariable logistic regression and defined statistical significance using the conventional genome-wide α = 5E-08. RESULTS: After accounting for multiple comparisons, there were no statistically significant SNP interactions with total, dietary, or supplemental calcium intake. CONCLUSIONS: We found no evidence of SNP interactions with calcium intake for colorectal cancer risk in a large population of 18,509 individuals. IMPACT: These results suggest that in genome-wide analysis common genetic variants do not strongly modify the association between calcium intake and colorectal cancer in European populations.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".