Myocardial Infarction–Associated SNP at 6p24 Interferes With MEF2 Binding and Associates With <i>PHACTR1</i> Expression Levels in Human Coronary Arteries
Bibliographic record
Abstract
OBJECTIVE: Coronary artery disease (CAD), including myocardial infarction (MI), is the main cause of death in the world. Genome-wide association studies have identified dozens of single nucleotide polymorphisms (SNPs) associated with CAD/MI. One of the most robust CAD/MI genetic associations is with intronic SNPs in the gene PHACTR1 on chromosome 6p24. How these PHACTR1 SNPs influence CAD/MI risk, and whether PHACTR1 itself is the causal gene at the locus, is currently unknown. APPROACH AND RESULTS: Using genetic fine-mapping and DNA resequencing experiments, we prioritized an intronic SNP (rs9349379) in PHACTR1 as causal variant. We showed that this variant is an expression quantitative trait locus for PHACTR1 expression in human coronary arteries. Experiments in endothelial cell extracts confirmed that alleles at rs9349379 are differentially bound by the transcription factors myocyte enhancer factor-2. We engineered a deletion of this myocyte enhancer factor-2-binding site using CRISPR/Cas9 genome-editing methodology. Heterozygous endothelial cells carrying this deletion express 35% less PHACTR1. Finally, we found no evidence that PHACTR1 expression levels are induced when stimulating human endothelial cells with vascular endothelial growth factor, tumor necrosis factor-α, or shear stress. CONCLUSIONS: Our results establish a link between intronic SNPs in PHACTR1, myocyte enhancer factor-2 binding, and transcriptional functions at the locus, PHACTR1 expression levels in coronary arteries and CAD/MI risk. Because PHACTR1 SNPs are not associated with the traditional risk factors for CAD/MI (eg, blood lipids or pressure, diabetes mellitus), our results suggest that PHACTR1 may influence CAD/MI risk through as yet unknown mechanisms in the vascular endothelium.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.004 | 0.001 |
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".