Additional file 1 of A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry
Bibliographic record
Abstract
Additional file 1. A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry. Supplementary Table 1: Participating studies with number of total cases and controls per study. Supplementary Table 2: Detailed information of the characteristics of the study population by study design and case-control status. Supplementary Table 3: Associations of epidemiological risk factors for overall and ER-specific subtype breast cancer risk in population-based and cohort studies. Supplementary Table 4: Stratified analysis results for genome-wide significant interaction results by categories of risk factors. Supplementary Figure 1: Quantile-Quantile (Q-Q) plots of genome-wide interaction of A) Adult height on overall breast cancer risk and B) Age at menarche on ER+ breast cancer risk. Supplementary Figure 2: Frailty-scale heritability explained by GxE interaction on overall and estrogen receptor positive breast cancer risk. Supplementary Figure 3: Regional association plot for the interaction analyses between SNP rs80018847 and adult height for overall breast cancer risk. Supplementary Figure 4: Regional association plot for the interaction analyses between SNP rs4770552 and age at menarche for ER+ breast cancer risk. Supplementary Figure 5: Power (x-axis) to detect gene-environment interaction odds ratio (y-axis) at different minor allele frequencies (0.01 to 0.5: legend below) for 1:1 unmatched case-control study for different sample sizes (N = 40,000 to 120,000 with 10,000 increment). Power calculation was performed by Quanto 1.2.4, assuming a log additive model with SNP marginal effect estimate as 1.10, marginal effect estimate of the environmental risk factor as 1.20, and a two-side alpha of 5 x 10-08. We also assumed a 15% prevalence of the environmental risk factor and 1% prevalence of the disease.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.842 | 0.092 |
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".