Polymorphisms in Toll-Like Receptor 4 Are Not Associated with Asthma or Atopy-related Phenotypes
Bibliographic record
Abstract
Toll-like receptor 4 (TLR4) is the principal receptor for bacterial endotoxin recognition, and functional variants in the gene confer endotoxin-hyporesponsiveness in humans. Furthermore, there is evidence that endotoxin exposure during early life is protective against the development of atopy and asthma, although this relationship remains poorly understood. It is therefore possible that genetic variation in the TLR4 locus contributes to asthma susceptibility. In this study we characterize the genetic diversity in the TLR4 locus and test for association between the common genetic variants and asthma-related phenotypes. In a cohort of 90 ethnically diverse subjects, we resequenced the TLR4 locus and identified a total of 29 single nucleotide polymorphisms. We assessed five common polymorphisms for evidence of association with asthma in two large family-based cohorts: a heterogeneous North American cohort (589 families), and a more homogenous population from northeastern Quebec, Canada (167 families). Using the transmission-disequilibrium test, we found no evidence of association for any of the polymorphisms tested, including two functional variants. Furthermore, we found no evidence for association between the TLR4 variants and four quantitative intermediate asthma- and atopy-related phenotypes. Based on these results, we found no evidence that genetic variation in TLR4 contributes to asthma susceptibility.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".