Genome-Wide Linkage Scan for the Metabolic Syndrome in the HERITAGE Family Study
Bibliographic record
Abstract
The metabolic syndrome involves multiple and interactive effects of genes and environmental factors. To identify chromosomal regions encoding genes possibly predisposing to the metabolic syndrome, we performed a genome-wide scan with 456 white and 217 black participants from 204 nuclear families of the HERITAGE Family Study, using regression-based, single- and multipoint linkage analyses on 509 markers. A principal component analysis was performed on 7 metabolic syndrome-related phenotypes. Two principal components, PC1 and PC2 (55% of the variance), were used as metabolic syndrome phenotypes. ANOVA was used to quantify the familial aggregation of PC1 and PC2. Family membership contributed significantly (P < 0.0023) to the variance in PC1 (r(2) = 0.38 in whites; r(2) = 0.55 in blacks) and PC2 (r(2) = 0.51; r(2) = 0.48). In whites, promising evidence for linkage (P < 0.0023) was found for PC1 (2 markers on 10p11.2) and PC2 (a marker on 19q13.4). Suggestive evidence of linkage (0.01 > P > 0.0023) appeared for PC1 (1q41 and 9p13.1) and PC2 (2p22.3). In blacks, promising linkage was found for PC2 on 1p34.1, and suggestive linkage was found on 7q31.3 and 9q21.1. The genome-wide scan revealed evidence for quantitative trait loci on chromosomal regions that have been previously linked with individual cardiovascular disease and type 2 diabetes risk factors. Some of these chromosomal regions harbor promising potential candidate genes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".