Genomic copy number variation and its potential role in lipoprotein and metabolic phenotypes
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review examines the role of copy number variation in the human genome as a newly recognized determinant of lipoprotein and metabolic phenotypes. RECENT FINDINGS: Much of the recent progress defining the molecular basis of lipoprotein disorders has been the result of studying genomic DNA at the single nucleotide level, for instance with nucleotide sequence analysis or genotyping to detect single nucleotide polymorphisms. Focus on single nucleotides, however, fails to capture the complete spectrum of potential genetic variability. Recent genome-wide mapping studies have demonstrated the surprising ubiquity of large-scale copy number variations in apparently healthy people, adding to the complexity of the 'normal' genome, but also emphasizing this form of genetic variation as a potential disease mechanism. The application of this understanding to the genetics of lipoprotein disorders has been rapid. For instance, the use of novel techniques to detect copy number variations, such as multiplex ligation-dependent probe amplification, has revealed many additional causative mutations in the low-density lipoprotein receptor gene in patients with familial hypercholesterolemia. SUMMARY: Copy number variations thus represent a new level of genomic variation that is both an important mechanism of monogenic lipoprotein disorders and a potential contributor to common complex lipoprotein and metabolic phenotypes in the general population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".