Genetic modifiers of penetrance to liver endpoints in HFE hemochromatosis: Associations in a large community cohort
Bibliographic record
Abstract
BACKGROUND: The iron overload condition hereditary hemochromatosis (HH) can cause liver cirrhosis and cancer, diabetes, and arthritis. Males homozygous for the p.C282Y missense mutation in the Homeostatin Iron Regulator (HFE) gene have greatest risk; yet, only a minority develop these conditions. We aimed to determine whether common genetic variants influencing iron levels or liver disease risk in the general population also modify clinical penetrance in HFE p.C282Y and p.H63D carriers. METHODS: We studied 1294 male and 1596 female UK Biobank HFE p.C282Y homozygous participants of European ancestry with medical records up to 14 years after baseline assessment. Polygenic scores quantified genetic effects of blood iron biomarkers and relevant diseases (identified in the general population). Analyses were also performed in other HFE p.C282Y/p.H63D genotype groups. RESULTS: In male p.C282Y homozygotes, a higher iron polygenic score increased the risk of liver fibrosis or cirrhosis diagnoses (odds ratio for the top 20% of iron polygenic score vs. the bottom 20% = 4.90: 95% confidence intervals, 1.63-14.73; p = 0.005), liver cancer, and osteoarthritis but not diabetes. A liver cirrhosis polygenic score was associated with liver cancer diagnoses. In female p.C282Y homozygotes, the osteoarthritis polygenic score was associated with increased osteoarthritis diagnoses and type-2 diabetes polygenic score with diabetes. However, the iron polygenic score was not robustly associated with diagnoses in p.C282Y female homozygotes or in other p.C282Y/p.H63D genotypes. CONCLUSIONS: HFE p.C282Y homozygote penetrance to clinical disease in a large community cohort was partly explained by common genetic variants that influence iron and risks of related diagnoses in the general population, including polygenic scores in HH screening and diagnosis, may help in estimating prognosis and treatment planning.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".