A genome-wide linkage scan identifies multiple quantitative trait loci for HDL-cholesterol levels in families with premature CAD and MI
Bibliographic record
Abstract
Plasma HDL cholesterol levels (HDL-C) are an independent predictor of coronary artery disease (CAD). We have completed a genome-wide linkage scan for HDL-C in a US cohort consisting of 388 multiplex families with premature CAD (GeneQuest). The heritability of HDL-C in GeneQuest was 0.37 with gender and age as covariates (P = 5.1 x 10(-4)). Two major quantitative trait loci (QTL) for log-transformed HDL-C adjusted for age and gender were identified onto chromosomes 7p22 and 15q25 with maximum multipoint logarithm of odds (LOD) scores of 3.76 and 6.69, respectively. Fine mapping decreased the 7p22 LOD score to a nonsignificant level of 3.09 and split the 15q25 QTL into two loci, one minor QTL on 15q22 (LOD = 2.73) that spanned the LIPC gene, and the other at 15q25 (LOD = 5.63). A family-based quantitative transmission disequilibrium test (QTDT) revealed significant association between variant rs1800588 in LIPC and HDL-C in the GeneQuest population (P = 0.0067), which may account for the minor QTL on 15q22. The 15q25 QTL is the most significant locus identified for HDL-C to date, and these results provide a framework for the ultimate identification of the underlying HDL-C variant and gene on chromosomes 15q25, which will provide insights into novel regulatory mechanisms of HDL-C metabolism.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".