bioRxivGeneticsPreprint
Decoding causal genes and programs from regulatory variants in aortic valve disease
Mewen BriendiD, Anne Rufiange, Valentine Duclos, Samuel Mathieu, Thibault Kanmacher, Dominique K. BoudreauiD, Nathalie GaudreaultiD, Victoria E. S. ArmeroiD, François Dagenais, Christian CoutureiD, Philippe JoubertiD, Sébastien ThériaultiD, Yohan BosséiD, Patrick MathieuiD
Abstract
Aortic valve disease is common, yet its regulatory mechanisms remain poorly understood. We performed multi-omic profiling of human aortic valve interstitial cells (HAVICs), identifying 11,891 allele-specific chromatin accessibility QTLs (as-caQTLs), 48% novel to this cell type. These variants were enriched in active enhancers, disrupted transcription factor (TF) motifs, particularly AP-1, TEAD and GATA families, and were validated by allele-specific TF binding assays. A fine-tuned deep DNA sequence model prioritized common and rare variants at risk loci predicted to impact chromatin accessibility. Single-cell CRISPRi perturbation of 247 variants identified cis-target genes at 55 as-caQTL elements, including loci without eQTLs. We demonstrate that common regulatory variants controlling elastin and fibrillin impact the development of the aortic valve apparatus. We provide genetic evidence and a mechanistic framework for the contribution of a reduced aortic root size to CAVD risk. Perturbations identified core cell programs led by upstream regulators AHNAK, PDIA6, and RNFT1 converging on extracellular matrix production and iron transport.
- organism: human
- assay: DNA methylation
- assay: ChIP-seq
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- approach: deep learning
- Citations
- 0
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