The functional landscape of coding variation in the familial hypercholesterolemia gene <i>LDLR</i>
Bibliographic record
Abstract
Variants in the familial hypercholesterolemia gene LDLR —the most important genetic driver of cardiovascular disease—can raise circulating low-density lipoprotein (LDL) cholesterol concentrations and increase the risk of premature atherosclerosis. Definitive classifications are lacking for nearly half of clinically encountered LDLR missense variants, limiting interventions that reduce disease burden. We tested the impact of ~17,000 (nearly all possible) LDLR coding variants on both LDLR cell-surface abundance and LDL uptake, yielding sequence–function maps that recapitulate known biochemistry, offer functional insights, and provide evidence for interpreting clinical variants. Functional scores correlated with hyperlipidemia phenotypes in prospective human cohorts and augmented polygenic scores to improve risk inference, highlighting the potential of this resource to accelerate familial hypercholesterolemia diagnosis and improve patient outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".