Pleiotropic QTL on Chromosome 12q23-q24 Influences Triglyceride and High-Density Lipoprotein Cholesterol Levels: The HERITAGE Family Study
Bibliographic record
Abstract
To determine whether a common quantitative trait locus (QTL) influences the variation of fasting triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) levels, we used a bivariate multipoint linkage analysis with 654 polymorphic markers in 99 white and 101 black families. The phenotypes were investigated under two conditions: at baseline and after a 20-week exercise training intervention. A maximum genome-wide bivariate LOD score of 3.0 (p = 0.00010) was found on chromosome 12q23-q24, located within the IGF1 gene (insulin-like growth factor 1, at 107 cM) for TG and HDL-C at baseline in whites. This bivariate linkage peak is considerably higher than the univariate linkage results at the same chromosome location for either trait (for TG, LOD = 2.07, p = 0.00108; for HDL-C, LOD = 2.04, p = 0.00101). The genetic correlations between baseline TG and HDL-C levels were -0.14 for the residual and -0.33 for the QTL components. Moreover, association analysis showed that TG, HDL-C, and IGF1 are significantly associated (p = 0.04). In conclusion, these results suggest that a QTL on chromosome 12q23-q24 influences the variation of plasma TG and HDL-C levels. Further investigation should confirm whether IGF1 or another nearby gene is responsible for the concomitant variation in TG and HDL-C levels.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".