Gene-Targeted Analysis of Copy Number Variants Identifies 3 Novel Associations With Coronary Heart Disease Traits
Bibliographic record
Abstract
BACKGROUND: Copy number variants (CNVs) are a major form of genomic variation, which may be implicated in complex disease phenotypes. However, investigation of the role of CNVs in coronary heart disease (CHD) traits has been limited. METHODS AND RESULTS: We examined the use of the cnvHap algorithm for CNV detection, using data for 2500 men from the Second Northwick Park Heart Study (NPHS-II). An Illumina custom chip, including 722 single-nucleotide polymorphisms covering 76 coronary heart disease-trait genes, was used. Common CNVs were significantly associated (at P<0.05, after correction) with coronary heart disease phenotypes in 5 genes. Novel associations of CNVs in toll-like receptor-4 with apolipoprotein AI were replicated (P<0.05) in the Whitehall II cohort (4887 subjects), whereas newly described associations of CNVs in sterol regulatory element-binding protein with apolipoprotein AI and associations of interleukin-6 signal transducer with apolipoprotein B were replicated in the data from 3546 subjects from the North Finnish Birth Cohort 1966 (P<0.05). CONCLUSIONS: This study supports the use of CNV detection algorithms such as cnvHap as potential tools for the identification of novel CNVs, some of which show significant association and replication with coronary heart disease risk phenotypes. However, the functional basis for these associations requires further substantiation.
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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 | 0.003 |
| 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".