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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".