Abstract 199: Identifying the Genetic Determinants of Spontaneous Coronary Artery Dissection with Whole Exome Sequencing
Bibliographic record
Abstract
Spontaneous Coronary Artery Dissection (SCAD) is a rare type of Acute Coronary Syndrome (ACS) that primarily affects individuals under 55 who do not exhibit “traditional” cardiovascular risk factors such as smoking and hypertension. Connective tissue disorders (CTDs) such as Ehlers-Danlos and Marfan syndrome and the arteriopathy fibromuscular dysplasia (FMD) appear to be more prevalent among SCAD cases than other ACS patients, however each of these disorders is associated with less than 1% of SCAD cases. Only 1.2% of SCAD cases have familial inheritance (likely due to low disease penetrance) making it difficult to identify causal mutations based on currently accepted clinical guidelines. We developed an analysis pipeline to identify potentially causal mutations without familial data based on minor allele frequencies, previously reported pathogenicity, and computational predictions of mutation intolerance. We tested our methodology on Whole Exome Sequencing data from 5 SCAD patients in the PRAXY-GENESIS cohort who had no traditional cardiovascular risk factors and were <51 years old, reasoning that these patients are the most likely to have an easily identifiable genetic cause such as a CTD. We identified strong candidate missense mutations in two patients that were verified with Sanger sequencing. One is a mutation in the FBN1 gene, which encodes the fibrillin-1 protein. Mutations in this gene can cause Marfan syndrome. The second is a novel mutation in the LEMD3 gene, which encodes the inner nuclear envelope protein Man1, an integral part of the TGF-β pathway. Deletion mutations in this gene can cause the connective tissue disorder Buschke-Ollendorff syndrome, and other mutations in the TGF-β pathway are associated with cervical artery dissection. Our results demonstrate that monogenic disorders should be considered for SCAD patients, especially for early-onset cases without risk factors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".