Genetic analysis of common factors underlying cardiovascular disease-related traits
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease-related traits, such as body mass index (BMI), systolic blood pressure (SBP), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), and glucose levels (GLUC), have moderate to high correlations with each other. We hypothesized that there might be some common factors underlying the correlations of these traits, and attempted to identify these factors and their genetic structures. Cross-sectional measurements from the 330 extended Framingham Heart Study families were used in this study. Principal component factor analysis was applied to obtain the factors that were then analyzed using variance components linkage analysis. RESULTS: With the above six traits three factors were generated: BMI-SBP-GLUC, HDL-TG, and TC-TG. The heritabilities for these factors were 32%, 45%, and 49%, respectively. Comparing the linkage results of the factors with the results of their component traits, evidence for linkage was observed for the TC-TG factor to a locus on chromosome 2p23 with a two-point LOD score 2.73 (marker GATA8F07) and a multipoint LOD score 1.81 (at 54 cM), while the LOD scores for TC and TG did not exceed 1 at this region. CONCLUSION: Our analysis showed a locus on chromosome 2 might have a pleiotropic effect on the cardiovascular disease-related traits TC and TG.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".