Genetics of Lipid and Lipoprotein Disorders and Traits
Bibliographic record
Abstract
Abstract Purpose of Review Plasma lipids, namely cholesterol and triglyceride, and lipoproteins, such as low-density lipoprotein (LDL) and high-density lipoprotein, serve numerous physiological roles. Perturbed levels of these traits underlie monogenic dyslipidemias, a diverse group of multisystem disorders. We are on the verge of having a relatively complete picture of the human dyslipidemias and their components. Recent Findings Recent advances in genetics of plasma lipids and lipoproteins include the following: (1) expanding the range of genes causing monogenic dyslipidemias, particularly elevated LDL cholesterol; (2) appreciating the role of polygenic effects in such traits as familial hypercholesterolemia and combined hyperlipidemia; (3) accumulating a list of common variants that determine plasma lipids and lipoproteins; (4) applying exome sequencing to identify collections of rare variants determining plasma lipids and lipoproteins that via Mendelian randomization have also implicated gene products such as NPC1L1, APOC3, LDLR, APOA5, and ANGPTL4 as causal for atherosclerotic cardiovascular disease; and (5) using naturally occurring genetic variation to identify new drug targets, including inhibitors of apolipoprotein (apo) C-III, apo(a), ANGPTL3, and ANGPTL4. Summary Here, we compile this disparate range of data linking human genetic variation to plasma lipids and lipoproteins, providing a “one stop shop” for the interested reader.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.005 | 0.003 |
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".