Abstract 403: A Common Polymorphism Connects Propionyl-coa Carboxylase To Coronary Artery Disease
Bibliographic record
Abstract
Genome-Wide Association studies (GWAS) have identified more than 200 common genetic variants that cumulatively account for approximately 50% of individual risk for coronary artery disease (CAD). This information is relevant to CAD risk assessment but may also provide new insights into the underlying biology of atherosclerosis. The vast majority of these common single nucleotide polymorphisms (SNPs) are in non-coding regions of the genome and distal from protein coding genes; thus mechanistically linking GWAS identified SNPs to CAD is challenging. Here, we have investigated the relationship of rs667920, identified as a top CAD associated SNP in a recent GWAS, to CAD. Conditional analysis, using lipid traits and other phenotypes as covariates, is consistent with the hypothesis that hypercholesterolemia mediates the impact of rs667920 on CAD risk. Although rs667920 is intronic to STAG1, originally denoted as a possible risk gene, it is not associated with altered expression of STAG1 in CAD relevant tissues according to GTEx. Chromatin immunoprecipitation data indicate the presence of enhancer activity overlapping rs667920 in fibroblast and muscle cells, consistent with cis regulation Indeed, this SNP is a significant expression quantitative locus (eQTL) for a neighboring gene, PCCB, encoding the Propionyl-CoA carboxylase beta subunit, in vascular tissues and whole blood, with the common allele rs667920_T being linked to increased expression of PCCB, circulating lipids and CAD, suggesting that lower expression of PCCB may be beneficial. Although its eQTL relationship with rs667920 was considerably weaker, mendelian randomization analysis of whole blood datasets prioritized NCK1, another local gene. Additional experiments are currently underway in our laboratory to validate and complement these preliminary findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".