A Systematic Approach to Analysing Gene-Gene Interactions: Polymorphisms at the Microsomal Epoxide Hydrolase <i>EPHX</i> and Glutathione <i>S</i>-transferase <i>GSTM1, GSTT1</i>, and <i>GSTP1</i> Loci and Breast Cancer Risk
Bibliographic record
Abstract
OBJECTIVE: We undertook a case-control study in an Australian Caucasian population-based sample of 1,246 cases and 664 controls to assess the roles of detoxification gene polymorphisms EPHX T>C Tyr(113)His, GSTT1 deletion, GSTM1 deletion, and GSTP1 A>G Ile(105)Val on risk of breast cancer. METHODS: We systematically addressed the main effects and possible gene-gene interactions using unconditional logistic regression to estimate odds ratios (OR) adjusted for potential confounders and using standard model building approaches based on likelihood theory. RESULTS: There was a decreased risk associated with the EPHX CC genotype [OR, 0.60; 95% confidence interval (95% CI), 0.43-0.84; P = 0.003], marginally significant evidence of increased risk with GSTM1 null genotype (OR, 1.21; 95% CI, 1.00-1.47; P = 0.05), but no association with GSTT1 null genotype (OR, 1.12; 95% CI, 0.86-1.45; P = 0.4) or GSTP1 (OR, 0.95; 95% CI, 0.82-1.10; P = 0.5) genotype. The full model with all interactions gave a significantly better fit than a main-effects-only model (P < 0.001), providing evidence for gene-gene interactions. The most parsimonious model included main effects for EPHX, GSTT1, and GSTM1; a two-way interaction between EPHX and GSTM1; and a three-way interaction between EPHX, GSTM1, and GSTT1. Predicted risks were greatest for women carrying deletions of both GSTT1 and GSTM1, with either the EPHX TC genotype (OR, 2.02; 95% CI, 1.19-3.45; P = 0.009) or EPHX CC genotype (OR, 3.54; 95% CI, 1.29-9.72; P = 0.14). CONCLUSION: Detoxification gene polymorphisms may interact with each other to result in small groups of individuals at modestly increased risk. We caution against overinterpretation and suggest that pooling of similarly large studies is needed to clarify the possible role of such complex gene-gene interactions on breast cancer risk. 2007;16(4):769-74).
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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 | 0.028 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".