Abstract MP57: Pathway and Network Analysis of GWAS reveals Novel Candidate Causal Links between Key Biological Processes and Coronary Artery Disease
Bibliographic record
Abstract
Genome-wide association (GWA) studies have identified multiple genetic variants affecting the risk of coronary artery disease (CAD). However, for most of these variants the causal biological mechanisms remain unclear. By integrating GWA data with prior biological knowledge about pathways and functional networks, we sought to obtain novel insights into the causal processes of CAD. Using the iGSEA4GWAS analysis tool and the Reactome pathway database, we carried out a two-stage gene set enrichment analysis strategy. From a discovery cohort of seven large GWAS data sets for CAD (n=9,889 cases, 11,089 controls), nominally significant gene-sets were tested for replication in a meta-analysis of nine additional studies (n=15,502 cases, 55,730 controls) that were part of the CARDIoGRAM Consortium. A total of 32 of the 639 pathways tested representing 22 distinct biological processes showed convincing association with CAD (replication p< 0.05). After adjusting for redundancies by removing pathways with 50% or greater identity with other pathways, 19 pathways representing 9 biological processes remained including those relevant to Notch signaling, extracellular matrix integrity, innate immunity, and lipid metabolism. Network analysis of 751 unique genes within replicated pathways further revealed several interconnected functional modules representing novel associations (semaphorin regulated axonal guidance pathway) as well as confirmatory known processes (lipid metabolism). Our analyses point to potentially novel causal associations between CAD and several biological processes, many of which were not previously linked to CAD. These findings improve our understanding of the biological basis of CAD and highlight potential novel therapeutic targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".