Search for new loci and low-frequency variants influencing glioma risk by exome-array analysis
Bibliographic record
Abstract
To identify protein-altering variants (PAVs) for glioma, we analysed Illumina HumanExome BeadChip exome-array data on 1882 glioma cases and 8079 controls from three independent European populations. In addition to single-variant tests we incorporated information on the predicted functional consequences of PAVs and analysed sets of genes with a higher likelihood of having a role in glioma on the basis of the profile of somatic mutations documented by large-scale sequencing initiatives. Globally there was a strong relationship between effect size and PAVs predicted to be damaging (P=2.29 × 10(-49)); however, these variants which are most likely to impact on risk, are rare (MAF<5%). Although no single variant showed an association which was statistically significant at the genome-wide threshold a number represented promising associations - BRCA2:c.9976A>T, p.(Lys3326Ter), which has been shown to influence breast and lung cancer risk (odds ratio (OR)=2.3, P=4.00 × 10(-4) for glioblastoma (GBM)) and IDH2:c.782G>A, p.(Arg261His) (OR=3.21, P=7.67 × 10(-3), for non-GBM). Additionally, gene burden tests revealed a statistically significant association for HARS2 and risk of GBM (P=2.20 × 10(-6)). Genome scans of low-frequency PAVs represent a complementary strategy to identify disease-causing variants compared with scans based on tagSNPs. Strategies to lessen the multiple testing burden by restricting analysis to PAVs with higher priors affords an opportunity to maximise study power.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".