Impact of loss-of-function in angiopoietin-like 4 on the human phenome
Bibliographic record
Abstract
Abstract Background Carriers of the E40K loss-of-function variant in Angiopoietin-like 4 ( ANGPTL4 ), have lower plasma triglyceride levels as well as lower rates of coronary artery disease (CAD) and type 2 diabetes (T2D). These genetic data suggest ANGPTL4 inhibition as a potential therapeutic target for cardiometabolic diseases. However, it is unknown whether the association between E40K and human diseases is due to linkage disequilibrium confounding. The broader impact of genetic ANGPTL4 inhibition is also unknown, raising uncertainties about the safety and validity of this target. Methods To assess the impact of ANGPLT4 inhibition, we evaluated the impact of E40K and other loss-of-function variants in ANGPTL4 on a wide range of health markers and diseases.. The E40K impact was assessed in 29 publicly available genome-wide association meta-analyses of European ancestry on cardiometabolic traits and diseases as well as 1589 diseases assessed in electronic health record within FinnGen (n=309,154). To ensure that these relationships were truly causal, and not driven by other correlated variants, we used the Bayesian fine mapping algorithm CoPheScan. Results The CoPheScan posterior probability of E40K being the causal variant for triglyceride was 99.99%, validating the E40K to proxy lifelong lower activity of ANGPTL4. The E40K mutation was associated with lower risk of CAD (odds ratio [OR]=0.84, 95% CI=0.81 to 0.87, p=3.6e-21) and T2D (OR=0.91, 95% CI=0.87 to 0.95, p=2.8e-05) in GWAS meta-analyses, with results replicated in FinnGen. These significant results were also replicated using other rarer loss-of-function variants identified through whole exome sequencing in 488,278 participants of various ancestry in the UK Biobank. Using a Mendelian randomization study design, the E40K variant effect on cardiometabolic diseases was concordant with lipoprotein lipase enhancement (r=0.85), but not hepatic lipaseenhancement (r=-0.10), suggesting that ANGPTL4 effects on cardiometabolic diseases are potentially mainly mediated through lipoprotein lipase. The E40K variant did not significantly increase the risk of any of the 1589 diseases tested in FinnGen. Conclusion ANGPTL4 inhibition may represent a potentially safe and effective target for cardiometabolic diseases prevention or treatment.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".