Multi-ancestry genetic analysis of gene regulation in coronary arteries prioritizes disease risk loci

Single-cell RNA sequencing reveals sex differences in the subcellular composition and associated gene-regulatory network activity of human carotid plaques

Carotid stenosis causes ischemic stroke in both sexes, but the clinical presentation and plaque characteristics differ. Here we run deep single-cell sequencing of 7,690 human carotid plaque cells from male and female patients. While we found no sex differences in major cell types, we identified a predominance of the osteogenic phenotype in smooth muscle cells, immunomodulating macrophages (MPs) and endothelial cells (ECs) undergoing endothelial-to-mesenchymal transition in females. In males, we found smooth muscle cells with the chondrocytic phenotype, MPs involved in tissue remodeling and ECs with angiogenic activity. Sex-biased subcellular clusters were integrated with tissue-specific gene-regulatory networks (GRNs) from the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task study. We identified GRN195 involved in angiogenesis and T cell-mediated cytotoxicity in male ECs, while in females, we found GRN33 and GRN122 related to TREM2-/TREM1+ MPs and endothelial-to-mesenchymal transition. The impact of GRN195 on EC proliferation in males was functionally validated, providing evidence for potential therapy targets for atherosclerosis that are sex specific.

Using omics data and genome editing methods to decipher GWAS loci associated with coronary artery disease

Coronary artery disease (CAD) is due to atherosclerosis, a pathophysiological process that involves several cell-types and results in the accumulation of lipid-rich plaque that disrupt the normal blood flow through the coronary arteries to the heart. Genome-wide association studies have identified 1000s of genetic variants robustly associated with CAD or its traditional risk factors (e.g. blood pressure, blood lipids, type 2 diabetes, smoking). However, gaining biological insights from these genetic discoveries remain challenging because of linkage disequilibrium and the difficulty to interpret the functions of non-coding regulatory elements in the human genome. In this review, we present different statistical methods (e.g. Mendelian randomization) and molecular datasets (e.g. expression or protein quantitative trait loci) that have helped connect CAD-associated variants with genes, biological pathways, and cell-types or tissues. We emphasize that these various strategies make predictions, which need to be validated in orthologous systems. We discuss specific examples where the integration of omics data with GWAS results has prioritized causal CAD variants and genes. Finally, we review how targeted and genome-wide genome editing experiments using the CRISPR/Cas9 toolbox have been used to characterize new CAD genes in human cells. Researchers now have the statistical and bioinformatic methods, the molecular datasets, and the experimental tools to dissect comprehensively the loci that contribute to CAD risk in humans.

Convergence of coronary artery disease genes onto endothelial cell programs

Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.