Heritability of Lung Disease Severity in Cystic Fibrosis
Bibliographic record
Abstract
RATIONALE: Obstructive lung disease, the major cause of mortality in cystic fibrosis (CF), is poorly correlated with mutations in the disease-causing gene, indicating that other factors determine severity of lung disease. OBJECTIVES: To quantify the contribution of modifier genes to variation in CF lung disease severity. METHODS: Pulmonary function data from patients with CF living with their affected twin or sibling were converted into reference values based on both healthy and CF populations. The best measure of FEV(1) within the last year was used for cross-sectional analysis. FEV(1) measures collected over at least 4 years were used for longitudinal analysis. Genetic contribution to disease variation (i.e., heritability) was estimated in two ways: by comparing similarity of lung function in monozygous (MZ) twins (approximately 100% gene sharing) with that of dizygous (DZ) twins/siblings (approximately 50% gene sharing), and by comparing similarity of lung function measures for related siblings to similarity for all study subjects. MEASUREMENTS AND MAIN RESULTS: Forty-seven MZ twin pairs, 10 DZ twin pairs, and 231 sibling pairs (of a total of 526 patients) with CF were studied. Correlations for all measures of lung function for MZ twins (0.82-0.91, p < 0.0001) were higher than for DZ twins and siblings (0.50-0.64, p < 0.001). Heritability estimates from both methods were consistent for each measure of lung function and ranged from 0.54 to 1.0. Heritability estimates generally increased after adjustment for differences in nutritional status (measured as body mass index z-score). CONCLUSIONS: Our heritability estimates indicate substantial genetic control of variation in CF lung disease severity, independent of CFTR genotype.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".