Genetic variations in taste receptors are associated with chronic rhinosinusitis: a replication study
Bibliographic record
Abstract
BACKGROUND: Recent evidence implicates polymorphisms of the bitter taste receptor TAS2R38 as defining characteristics in respiratory innate defense that may contribute to the complex genetic and environmental interactions predisposing to chronic rhinosinusitis (CRS). The purpose of this study was to (1) verify whether identified polymorphisms associated with respiratory infection in taste receptors replicate within our existing population of patients with CRS and (2) identify other taste receptors potentially associated with CRS. METHODS: Pooling-based genomewide association studies (pGWAS) were previously performed on 2 populations of Canadian CRS patients (genetics of chronic rhinosinusitis 1, refractory CRS [GCRS1]; and genetics of chronic rhinosinusitis 2, CRS with nasal polyposis [GCRS2]) using the Illumina HumanHap 1-M chip. The pGWAS data were screened for polymorphisms in taste receptor genes. Single-nucleotide polymorphisms (SNPs) were considered replicated when the allele frequency differences were ≥10% in cases compared to controls. RESULTS: The previously identified TAS2R38 coding SNP rs10246939 (I296V) was associated with CRS in both populations. The difference in allele frequency in cases compared to control subjects was 11% in GCRS1 and 15% in GCRS2. In addition, 3 previously undescribed missense variants were associated with CRS in our populations: 1 in the TAS2R13 gene (rs1015443), and the others in the TAS2R49 gene (rs12226920, rs12226919). CONCLUSION: This study replicates previous work which showed that the coding SNP rs10246939 in the TAS2R38 gene is associated with CRS. Moreover, the results suggest that other taste receptors may be implicated in CRS. Further studies using individual genotyping and sequencing, and functional studies will provide more information about the implication of these genetic variants in CRS.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".