Genetic variation in severe cystic fibrosis liver disease is associated with novel mechanisms for disease pathogenesis
Bibliographic record
Abstract
BACKGROUND AND AIMS: It is not known why severe cystic fibrosis (CF) liver disease (CFLD) with portal hypertension occurs in only ~7% of people with CF. We aimed to identify genetic modifiers for severe CFLD to improve understanding of disease mechanisms. APPROACH AND RESULTS: Whole-genome sequencing was available in 4082 people with CF with pancreatic insufficiency (n = 516 with severe CFLD; n = 3566 without CFLD). We tested ~15.9 million single nucleotide polymorphisms (SNPs) for association with severe CFLD versus no-CFLD, using pre-modulator clinical phenotypes including (1) genetic variant ( SERPINA1 ; Z allele) previously associated with severe CFLD; (2) candidate SNPs (n = 205) associated with non-CF liver diseases; (3) genome-wide association study of common/rare SNPs; (4) transcriptome-wide association; and (5) gene-level and pathway analyses. The Z allele was significantly associated with severe CFLD ( p = 1.1 × 10 -4 ). No significant candidate SNPs were identified. A genome-wide association study identified genome-wide significant SNPs in 2 loci and 2 suggestive loci. These 4 loci contained genes [significant, PKD1 ( p = 8.05 × 10 -10 ) and FNBP1 ( p = 4.74 × 10 -9 ); suggestive, DUSP6 ( p = 1.51 × 10 -7 ) and ANKUB1 ( p = 4.69 × 10 -7 )] relevant to severe CFLD pathophysiology. The transcriptome-wide association identified 3 genes [ CXCR1 ( p = 1.01 × 10 -6 ) , AAMP ( p = 1.07 × 10 -6 ), and TRBV24 ( p = 1.23 × 10 -5 )] involved in hepatic inflammation and innate immunity. Gene-ranked analyses identified pathways enriched in genes linked to multiple liver pathologies. CONCLUSION: These results identify loci/genes associated with severe CFLD that point to disease mechanisms involving hepatic fibrosis, inflammation, innate immune function, vascular pathology, intracellular signaling, actin cytoskeleton and tight junction integrity and mechanisms of hepatic steatosis and insulin resistance. These discoveries will facilitate mechanistic studies and the development of therapeutics for severe CFLD.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".