System Genetics Including Causal Inference Identify Immune Targets for Coronary Artery Disease and the Lifespan
Bibliographic record
Abstract
Background: Randomized clinical trials indicate that the immune response plays a significant role in coronary artery disease (CAD), a disorder impacting the lifespan potential. However, the identification of targets critical to the immune response in atheroma is still hampered by a lack of solid inference. Methods: Herein, we implemented a system genetics approach to identify causally associated immune targets implicated in atheroma. We leveraged genome-wide association studies to perform mapping and Mendelian randomization to assess causal associations between gene expression in blood cells with CAD and the lifespan. Expressed genes (eGenes) were prioritized in network and in single-cell expression derived from plaque immune cells. Results: Among 840 CAD-associated blood eGenes, 37 were predicted causally associated with CAD and 6 were also associated with the parental lifespan in Mendelian randomization. In multivariable Mendelian randomization, the impact of eGenes on the lifespan potential was mediated by the CAD risk. Predicted causal eGenes were central in network. FLT1 and CCR5 were identified as targets of approved drugs, whereas 22 eGenes were deemed tractable for the development of small molecules and antibodies. Analyses of plaque immune single-cell expression identified predicted causal eGenes enriched in macrophages ( GPX1 , C4orf3 ) and involved in ligand-receptor interactions ( CCR5 ). Conclusions: We identified 37 blood eGenes predicted causally associated with CAD. The predicted expression for 6 eGenes impacted the lifespan potential through the risk of CAD. Prioritization based on network, annotations, and single-cell expression identified targets deemed tractable for the development of drugs and for drug repurposing.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".