Genome-wide association meta-analysis identifies five modifier loci of lung disease severity in cystic fibrosis
Why this work is in the frame
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Bibliographic record
Abstract
The identification of small molecules that target specific CFTR variants has ushered in a new era of treatment for cystic fibrosis (CF), yet optimal, individualized treatment of CF will require identification and targeting of disease modifiers. Here we use genome-wide association analysis to identify genetic modifiers of CF lung disease, the primary cause of mortality. Meta-analysis of 6,365 CF patients identifies five loci that display significant association with variation in lung disease. Regions on chr3q29 (MUC4/MUC20; P=3.3 × 10(-11)), chr5p15.3 (SLC9A3; P=6.8 × 10(-12)), chr6p21.3 (HLA Class II; P=1.2 × 10(-8)) and chrXq22-q23 (AGTR2/SLC6A14; P=1.8 × 10(-9)) contain genes of high biological relevance to CF pathophysiology. The fifth locus, on chr11p12-p13 (EHF/APIP; P=1.9 × 10(-10)), was previously shown to be associated with lung disease. These results provide new insights into potential targets for modulating lung disease severity in CF.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it