A systematic evaluation of the <i>ataxia telangiectasia mutated</i> gene does not show an association with non‐Hodgkin lymphoma
Bibliographic record
Abstract
The ataxia telangiectasia mutated (ATM) gene is critical for the detection and repair of DNA double-stranded breaks. Mutations in this gene cause the autosomal recessive syndrome ataxia telangiectasia (AT), an attribute of which is an increased risk of cancer, particularly lymphoma. We have undertaken a population-based case/control study to assess the influence of genetic variation in ATM on the risk of non-Hodgkin lymphoma (NHL). A number of the subtypes that constitute NHL have in common the occurrence of specific somatic translocations that contribute to lymphomagenesis. We hypothesize that ATM function is slightly attenuated by some variants, which could reduce double-stranded break repair capacity, contributing to the occurrence of translocations and subsequent lymphomas. We sequenced the promoter and all exons of ATM in the germline DNA of 86 NHL patients and identified 79 variants. Eighteen of these variants correspond to nonsynonymous amino acid differences, 6 of which were predicted to be deleterious to protein function; these variants were all rare. Eleven common variants make up 10 haplotypes that are specified by 7 tagSNPs. Linkage disequilibrium across the ATM gene is high but incomplete. TagSNPs and the 6 putatively deleterious variants were genotyped in 798 NHL cases and 793 controls. Our results indicate that common variants of ATM do not significantly contribute to the risk of NHL in the general population. However, some rare, functionally deleterious variants may contribute to an increased risk of development of rare subtypes 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".