Contribution of genetic variants in the development of familial premature coronary artery disease in a cohort of cardiac patients
Bibliographic record
Abstract
Coronary artery disease (CAD), the most prevalent cardiovascular disease, is the leading cause of death worldwide. Heritable factors play a significant role in the pathogenesis of CAD. It has been proposed that approximately one-third of patients with CAD have a positive family history, and individuals with such history are at ~1.5-fold increased risk of CAD in their lifespans. Accordingly, the long-recognized familial clustering of CAD is a strong risk factor for this disease. Our study aimed to identify candidate genetic variants contributing to CAD by studying a cohort of 60 large Iranian families with at least two members in different generations afflicted with premature CAD (PCAD), defined as established disease at ≤45 years in men and ≤55 years in women. Exome sequencing was performed for a subset of the affected individuals, followed by prioritization and Sanger sequencing of candidate variants in all available family members. Subsequently, apparently healthy carriers of potential risk variants underwent coronary computed tomography angiography (CCTA), followed by co-segregation analysis of the combined data. Putative causal variants were identified in seven genes, ABCG8, CD36, CYP27A1, PIK3C2G, RASSF9, RYR2, and ZFYVE21, co-segregating with familial PCAD in seven unrelated families. Among these, PIK3C2G, RASSF9, and ZFYVE21 are novel candidate CAD susceptibility genes. Our findings indicate that rare variants in genes identified in this study are involved in CAD development.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".