Genetic Modifiers of Lung Disease in Cystic Fibrosis
Bibliographic record
Abstract
BACKGROUND: Polymorphisms in genes other than the cystic fibrosis transmembrane conductance regulator (CFTR) gene may modify the severity of pulmonary disease in patients with cystic fibrosis. METHODS: We performed two studies with different patient samples. We first tested 808 patients who were homozygous for the DeltaF508 mutation and were classified as having either severe or mild lung disease, as defined by the lowest or highest quartile of forced expiratory volume in one second (FEV1), respectively, for age. We genotyped 16 polymorphisms in 10 genes reported by others as modifiers of disease severity in cystic fibrosis and tested for an association in patients with severe disease (263 patients) or mild disease (545). In the replication (second) study, we tested 498 patients, with various CFTR genotypes and a range of FEV1 values, for an association of the TGFbeta1 codon 10 CC genotype with low FEV1. RESULTS: In the initial study, significant allelic and genotypic associations with phenotype were seen only for TGFbeta1 (the gene encoding transforming growth factor beta1), particularly the -509 and codon 10 polymorphisms (with P values obtained with the use of Fisher's exact test and logistic regression ranging from 0.006 to 0.0002). The odds ratio was about 2.2 for the highest-risk TGFbeta1 genotype (codon 10 CC) in association with the phenotype for severe lung disease. The replication study confirmed the association of the TGFbeta1 codon 10 CC genotype with more severe lung disease in comparisons with the use of dichotomized FEV1 for severity status (P=0.0002) and FEV1 values directly (P=0.02). CONCLUSIONS: Genetic variation in the 5' end of TGFbeta1 or a nearby upstream region modifies disease severity in cystic fibrosis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".