Supplementary Material for: Inferring Gene Network from Candidate SNP Association Studies Using a Bayesian Graphical Model: Application to a Breast Cancer Case-Control Study from Ontario
Bibliographic record
Abstract
Background/Aims: Gene network analysis can be a very valuable approach for elucidating complex dependence between functional SNPs in a candidate genetic pathway and for assessing their association with a disease of interest. Even when the number of SNPs evaluated is relatively small (<20), the number of potential gene networks induced by the SNPs can be very large and the contingency tables representing their joint distribution very sparse. Methods: In this paper, we propose a Bayesian model determination for gene network analysis using decomposable discrete graphical models combined with Reversible Jump Markov chain Monte Carlo. We show the application of this approach in a study of 13 SNPs in the DNA repair pathway and their association with breast cancer from a case-control study conducted in Ontario, Canada. Results: The strength of associations among the SNPs and between the SNPs and the disease status is evaluated by computing the posterior probability of any pair of variables. The corresponding gene network is reconstructed by retaining pair-wise associations with the highest posterior probabilities. In our real data analysis, we found evidence for a particular association between one SNP in the gene POLL and the disease status and also several interesting patterns of association between the SNPs themselves. Conclusion: This general statistical framework could serve as a basis for prioritizing genes and SNPs that play a major role in breast cancer etiology and to better understand their complex interactions in a specific genetic pathway.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.359 | 0.026 |
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".