Back

Enhancer-targeting CRISPR screens at coronary artery disease loci suggest shared mechanisms of disease risk

Ramste, M. O. H.; Weldy, C.; Kundu, S.; Zhao, Q.; Li, D. Y.; Brand, K.; Sharma, D.; Ramste, A.; Jagoda, E.; Ray, J.; Caceres, R. D.; Galante, J.; Gschwind, A. R.; Lahtinen, N.; Nguyen, T.; Amrute, J.; Park, C. Y.; Kim, J. B.; Kaikkonen, M. U.; Stitziel, N. O.; Steinmetz, L.; Kundaje, A.; Engreitz, J. M.; Quertermous, T.

2025-09-02 genetic and genomic medicine
10.1101/2025.08.28.25334684 medRxiv
Show abstract

To systematically identify causal genetic mechanisms that confer risk for coronary artery disease (CAD) in GWAS loci, we mapped genome-wide variant-to-enhancer-to-gene (V2E2G) links in vascular smooth muscle cells (SMC). Enhancers identified by active chromatin features, and further prioritized by base-resolution deep learning models of chromatin accessibility in 108 CAD loci, were studied with CRISPRi targeting and Direct-Capture Targeted Perturb-seq (DC-TAP-seq) evaluation of 470 genes. Seventy-six V2E2G links were identified for 59 candidate CAD genes representing gene programs including epithelial-mesenchymal transformation, ubiquitination, and protein folding as well as BMP and TGFB signaling. Similar methods employed with an independent focused screen targeting one candidate locus at 9p21.3 identified 10 enhancers regulating expression of multiple genes at this location. Detailed molecular studies revealed that two enhancers mediating transcription factor binding and transcriptional regulation contribute to ancestry-specific and sex-specific risk for CAD and the surrogate biomarker vascular calcification. Together, these studies advance our identification of GWAS CAD V2E2G links across the genome, and specific mechanisms of risk at the complex 9p21.3 locus.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.