Convergence of biomarkers and risk factor trait loci of coronary artery disease at 3p21.31 and HLA region
Bibliographic record
Abstract
Here we seek to identify molecular biomarkers that mediate the effect of risk factors on coronary artery disease (CAD). We perform a SNP-based multiomics data analysis to find biomarkers (probes) causally associated with the risk of CAD within known genomic loci for its risk factors. We identify 78 biomarkers, the majority (64%) of which are methylation probes. We detect the convergence of several CNS and lifestyle trait loci and their biomarkers at the 3p21.31 and human leukocyte antigen (HLA) regions. The 3p21.31 locus was the most populated region in the convergence of biomarkers and risk factors. In this region, we noted as the BSN gene becomes methylated the level of stomatin (STOM) in blood increases and this contributes to higher risk of CAD. In the HLA locus, we identify several methylation biomarkers associated with various CAD risk factors. SNPs in the CFB gene display a trans-regulatory impact on the GRIA4 protein level. A methylation site upstream of the APOE gene is associated with a higher protein level of S100A13 which in turn leads to higher LDL-C and greater CAD risk. We find UHRF1BP1 and ILRUN mediate the effect of obesity on CAD whereas methylation sites within NOS3 and CKM mediate the effect of their associated-risk factors on CAD. This study provides further insight into the biology of CAD and identifies a list of biomarkers that mediate the impact of risk factors on CAD. A SNP-based initiative can unite data from various fields of omics into a single network of knowledge.
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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 | 0.000 | 0.000 |
| 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".