Quantitative Trait Locus on 15q for a Metabolic Syndrome Variable Derived from Factor Analysis
Bibliographic record
Abstract
The metabolic syndrome represents a cluster of cardiovascular risk factors co-occurring in the same individual. The aim of this study was to identify chromosomal regions encoding genes predisposing to the metabolic syndrome using composite factors derived from maximum likelihood-based factor analysis. Genetic data were obtained from the Quebec Family Study and included 707 subjects from 264 nuclear families. Factor analyses were performed on eight metabolic syndrome-related phenotypes including waist circumference; BMI; systolic and diastolic blood pressure; and plasma insulin, glucose, triglyceride, and high-density lipoprotein-cholesterol levels. Three factors were identified and interpreted as general metabolic syndrome, blood pressure, and blood lipids, respectively. The general metabolic syndrome factor had high factor loadings (>0.4) for all phenotypes and explained 42% of the total variance, and family membership accounted for 45.6% of the factor variance. A genome-wide linkage scan performed with this first factor revealed the existence of a quantitative trait locus on chromosome 15 (86 cM) with a logarithm of odds score of 3.15. Suggestive evidence of linkage (logarithm of odds > 1.75) was also observed on chromosomes 1p, 3p, 3q, 6q, 7p, 19q, and 21q. These quantitative trait loci may harbor genes contributing to the clustering of the metabolic syndrome-related phenotypes.
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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 | 0.000 |
| 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) | 0.004 | 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".