Multi-Ethnic Genetic Association Study of Carotid Intima-Media Thickness Using a Targeted Cardiovascular SNP Microarray
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Identification of subclinical atherosclerosis by ultrasonographic measurement of carotid intima-media thickness (IMT) is a validated tool, in conjunction with traditional risk factors, for clinical assessment of cardiovascular disease risk. IMT has also been recognized as a quantitative measure of cardiovascular disease progression in asymptomatic individuals, and many candidate gene association studies have attempted to identify genetic variants associated with interindividual differences in IMT with limited success. We sought to test the association between subclinical atherosclerosis measured by IMT and approximately 50,000 SNPs, densely mapping approximately 2100 genes found on the gene-centric Illumina cardiovascular disease beadchip in a multi-ethnic population-based sample. METHODS: IMT was measured by B-mode ultrasound and DNA was collected from a population-based sample of South Asian (n=328), Chinese (n=302), and European Caucasian (n=268) participants. Genetic association was measured using multivariate linear regression including adjustment for covariates. RESULTS: The most robust association across all models tested was observed for a SNP (rs3791398) in histone deacetylase 4 (HDAC4; P=1.8e-5 to P=3.6e-5), while another strong association signal was observed with natriuretic peptide receptor a/guanylate cyclase A (NPR1) (rs10082235, P=5.4e-5). Seven of 13 previously reported functional candidate genes contained a SNP that was marginally associated (0.01 < P < or = 0.05). CONCLUSION: This initial multi-ethnic high-density association study of carotid IMT suggests some novel loci requiring further evaluation in follow-up studies.
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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) | 0.000 | 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) | 0.002 | 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".