A Global Test of Association between Non-Hodgkin Lymphoma and SNPs in Histone-pathway Genes
Bibliographic record
Abstract
Identifying biological pathways affecting cancer susceptibility can provide insight into prevention.In tumour cells of non-Hodgkin lymphoma (NHL), histone-pathway genes are frequent targets of somatic mutation.We analyze data from a population-based case-control study to test whether NHL is associated with SNPs in histone-pathway genes.When individual SNP associations are minor, the standard approach of testing SNPs one-at-a-time and then correcting for multiple testing has low power.Our global testing approach avoids the multiple-testing penalty by modelling random SNP effects and testing variance.We show how an approximate score statistic may be derived by writing the likelihood as an expected conditional likelihood given latent genetic values for each individual and then applying a Taylor-series approximation.The resulting statistic is applied to the NHL data and its statistical significance is evaluated using a permutation-based procedure.Our results add to growing evidence that the histone pathway plays a role in NHL.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".