Loci controlling plasma non-HDL and HDL cholesterol levels in a C57BL /6J × CASA /Rk intercross
Bibliographic record
Abstract
Plasma non-HDL and HDL cholesterol levels are predictors of cardiovascular diseases. We carried out a genetic cross between two laboratory inbred mouse strains, C57BL/6J and CASA/Rk, to detect loci that control the plasma levels of non-HDL and HDL cholesterol. With regard to non-HDL cholesterol, chow-fed CASA/Rk males and females had 87% and 25% higher levels, respectively, than did C57BL/6Js. The levels of non-HDL cholesterol in F1s were similar to C57BL/6J. There was no strain difference in HDL cholesterol levels. An intercross between F1s was performed, and plasma non-HDL and HDL cholesterol was measured in 185 male and 184 female mice. In both male and female F2 mice, plasma non-HDL and HDL cholesterol levels were unimodally distributed; however, in both cases the values for females were significantly lower than for males. Therefore, linkage analysis was performed with sex as a covariate. Significant linkage for non-HDL cholesterol was found on chromosome 6 at 49 cM (LOD 5.17), chromosome 4 at 55 cM (LOD 4.22), and chromosome 8 at 7 cM (LOD 3.68). Significant linkage for HDL cholesterol was found on chromosome 9 at 14 cM (LOD 7.52) and chromosome 8 at 76 cM (LOD 4.69). A significant epistatic interaction involving loci on chromosomes 2 and 5 was also observed for non-HDL cholesterol. In summary, linkage analysis in these cross-identified novel loci confirmed previously identified loci in control of plasma non-HDL and HDL cholesterol and disclosed a novel interaction in controlling non-HDL cholesterol levels in the mouse.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".