Gene-Targeted Analysis of Copy Number Variants Identifies 3 Novel Associations With Coronary Heart Disease Traits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it