Role of DNA copy number variation in dyslipidemias
Bibliographic record
Abstract
PURPOSE OF REVIEW: DNA copy number variations (CNVs) are quantitative structural rearrangements that include deletions, duplications, and higher order amplifications. Because of technical limitations, the contribution of this common form of genetic variation to regulation of lipid metabolism and dyslipidemia has been underestimated. RECENT FINDINGS: Recent literature involving CNVs and dyslipidemias has focused mainly on rare CNVs causing familial hypercholesterolemia, and a common CNV polymorphism as the major determinant of lipoprotein(a) plasma concentrations. Additionally, there is tantalizing evidence of largely uninvestigated but plausible presence of CNVs underlying other dyslipidemias. We also discuss the future role of improved technologies in facilitating more economic, routine CNV assessment in dyslipidemias. SUMMARY: CNVs account for large proportion of human genetic variation and are already known to contribute to susceptibility of dyslipidemias, particularly in about 10% of familial hypercholesterolemia patients. Increasing availability of clinical next-generation sequencing and bioinformatics presents a cost-effective opportunity for novel CNV discoveries in dyslipidemias.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".