Bibliographic record
Abstract
Despite successes in identifying genetic contributors to common metabolic phenotypes, only part of the heritable component of these traits has thus far been explained. Copy number variation (CNV) is likely to be responsible for some of the unexplained variation. As observed with single nucleotide changes, it is probable that both rare and common CNVs will contribute to susceptibility to metabolic disease. For instance, CNVs in the LDLR gene underlie a substantial portion of disease in patients with heterozygous familial hypercholesterolemia. As well, a common CNV in LPA encoding apolipoprotein(a) is the primary determinant of plasma lipoprotein(a) concentrations, a risk factor for atherosclerosis. Recent efforts to map CNVs in control populations have defined their size, frequency and distribution. Many of the identified CNVs overlap genes with important functions in metabolic pathways. The overlap of CNVs that were found in control datasets with functional candidate genes or genes with previous evidence of association with metabolic syndrome presents an important subset for future CNV association studies. Finally, we describe an approach to search for CNVs in a rare high-penetrance metabolic disorder, namely lipodystrophy. As methods to identify CNVs increase in precision and accuracy, the prospect of identifying their role in both rare Mendelian and common complex metabolic phenotypes will become a reality.
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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 | 0.002 | 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 | 0.002 |
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".