Recent success in the discovery of coronary artery disease genes
Bibliographic record
Abstract
For more than 50 years, epidemiological studies have indicated that genetic predisposition accounts for approximately 50% of the susceptibility to coronary artery disease (CAD) and its sequelae, including myocardial infarction. Since common diseases such as CAD are caused by multiple genes, the age-old method of linkage analysis used to map monogenic Mendelian disorders in families unfortunately lacks the required sensitivity. The technology to identify genes predisposing individuals to CAD and other common diseases did not become available until 2005. This technology provided computerized arrays containing hundreds of thousands of DNA markers in the form of single-nucleotide polymorphisms (SNPs). This made it possible to pursue an unbiased approach referred to as genome-wide association studies. The first gene for CAD was simultaneously identified by 2 independent groups in 2007. In a very short interval, a total of 23 loci were mapped that were linked to increased risk for CAD. The results of these studies confirm that CAD is caused by multiple genes, each contributing minimal risk. The most exciting and novel findings are that these loci do not act through known risk factors for CAD and that the loci are more likely to be in DNA regions that regulate transcription rather than being in coding regions for protein.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".