Genes that cause severe liver disease in children also influence risk and severity of common liver conditions in adults
Bibliographic record
Abstract
Abstract Background and aims Rare, pathogenic variants can cause severe liver disease, requiring transplantation in childhood, but it is unclear how common variants in the same genes affect adults. Here, we aimed to establish population-level genetic evidence for whether ’monogenic’ diseases are associated with liver injury in adulthood. Methods We identified 99 genes where pathological mutations cause significant liver disease in children. For each, we used data from over 1.8 million adults to identify associations with biomarkers of liver injury. Observations were validated in multiple cohorts of adults with clinical liver disease and transcriptomics. Finally, we illustrated the importance of the JAG1-NOTCH pathway on the ductular reaction using immunohistochemistry. Results Most genes (56% (55/99)) had at least ’moderate’ evidence of association with liver-related traits at a population level. We identified 82 genome-wide (p<5x10 -8 ) associations with markers of liver injury in 41% (41/99) of genes. Loss of function variants in these genes had a ten-fold greater effect on liver enzymes and well-established variants in PNPLA3 had a three-fold greater effect. Variants in ABCC2 , ASL , BCS1L , HFE , and SERPINA1 were linked with presence of clinical liver disease in adults. Aggregated effects of 35 variants as polygenic risk score (PRS) was associated with 0.6% lower prevalence of MASLD between highest and lowest PRS groups. Transcriptional expression of 30% of genes was associated with severity of MASLD. Expression of JAG1-NOTCH2 pathway was associated with severity of PSC. JAG1 and NOTCH2 were expressed in injured bile ducts but not adjacent unaffected ducts. Conclusions Onset and severity of liver disease in adulthood is influenced by genes that also cause severe monogenic liver disease in children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".