A Candidate-Pathway Approach to Identify Gene-Environment Interactions: Analyses of Colon Cancer Risk and Survival
Bibliographic record
Abstract
BACKGROUND: Genetic association studies have traditionally focused on associations between individual single nucleotide polymorphisms (SNPs) and disease. Standard analysis ignores interactions between multiple SNPs and environmental exposures explaining a small portion of disease heritability: the often-cited issue of "missing heritability." METHODS: We present a novel three-step analytic framework for modeling gene-environment interactions (GEIs) between an angiogenesis candidate-gene pathway and three lifestyle exposures (dietary protein, smoking, and alcohol consumption) on colon cancer risk and survival. Logic regression was used to summarize the gene-pathway effects, and GEIs were modeled using logistic regression and Cox proportional hazards models. We analyzed data from 1541 colon cancer case patients and 1934 control subjects in the Diet, Activity and Lifestyle as a Risk Factor for Colon Cancer Study. RESULTS: We identified five statistically significant GEIs for colon cancer risk. For risk interaction, odds ratios (ORINT) and 95% confidence intervals (CIs) were FLT1(rs678714) and BMP4(rs17563) and smoking (ORINT = 1.64, 95% CI = 1.11 to 2.41 and ORINT = 1.60, 95% CI = 1.10 to 2.32, respectively); FLT1(rs2387632 OR rs9513070) and protein intake (ORINT = 1.69, 95% CI = 1.03 to 2.77); KDR(rs6838752) and TLR2(rs3804099) and alcohol (ORINT = 1.53, 95% CI = 1.10 to 2.13 and ORINT = 1.59, 95% CI = 1.05 to 2.38, respectively). Three GEIs between TNF, BMP1, and BMPR2 genes and the three exposures were statistically significant at the 5% level in relation to colon cancer survival but not after multiple-testing adjustment. CONCLUSIONS: Adopting a comprehensive biologically informed candidate-pathway approach identified GEI effects on colon cancer. Findings may have important implications for public health and personalized medicine targeting prevention and therapeutic strategies. Findings from this study need to be validated in other studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".