Electronic health record-based genome-wide meta-analysis provides insights on the genetic architecture of non-alcoholic fatty liver disease
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a complex disease linked to several chronic diseases. We aimed at identifying genetic variants associated with NAFLD and evaluating their functional consequences. We performed a genome-wide meta-analysis of 4 cohorts of electronic health record-documented NAFLD in participants of European ancestry (8,434 cases and 770,180 controls). We identify 5 potential susceptibility loci for NAFLD (located at or near GCKR , TR1B1 , MAU2 / TM6SF2 , APOE , and PNPLA3 ). We also report a potentially causal effect of lower LPL expression in adipose tissue on NAFLD susceptibility and an effect of the FTO genotype on NAFLD. Positive genetic correlations between NAFLD and cardiometabolic diseases and risk factors such as body fat accumulation/distribution, lipoprotein-lipid levels, insulin resistance, and coronary artery disease and negative genetic correlations with parental lifespan, socio-economic status, and acetoacetate levels are observed. This large GWAS meta-analysis reveals insights into the genetic architecture of NAFLD.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".