Genetic Variation in Genes Encoding Airway Epithelial Potassium Channels Is Associated with Chronic Rhinosinusitis in a Pediatric Population
Bibliographic record
Abstract
BACKGROUND: Apical potassium channels regulate ion transport in airway epithelial cells and influence air surface liquid (ASL) hydration and mucociliary clearance (MCC). We sought to identify whether genetic variation within genes encoding airway potassium channels is associated with chronic rhinosinusitis (CRS). METHODS: Single nucleotide polymorphism (SNP) genotypes for selected potassium channels were derived from data generated on the Illumnia HumanHap550 BeadChip or Illumina Human610-Quad BeadChip for 828 unrelated individuals diagnosed with CRS and 5,083 unrelated healthy controls from the Children's Hospital of Philadelphia (CHOP). Statistical analysis was performed with set-based tests using PLINK, and corrected for multiple testing. RESULTS: Set-based case control analysis revealed the gene KCNMA1 was associated with CRS in our Caucasian subset of the cohort (598 CRS cases and 3,489 controls; p = 0.022, based on 10,000 permutations). In addition there was borderline evidence that the gene KCNQ5 (p = 0.0704) was associated with the trait in our African American subset of the cohort (230 CRS cases and 1,594 controls). In addition to the top significant SNPs rs2917454 and rs6907229, imputation analysis uncovered additional genetic variants in KCNMA1 and in KCNQ5 that were associated with CRS. CONCLUSIONS: We have implicated two airway epithelial potassium channels as novel susceptibility loci in contributing to the pathogenesis of CRS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".