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.

Multiomic Analysis and CRISPR Perturbation Screens Identify Endothelial Cell Programs and Novel Therapeutic Targets for Coronary Artery Disease.

Endothelial cells (EC) are an important mediator of atherosclerosis and vascular disease. Their exposure to atherogenic risk factors such as hypertension and serum cholesterol leads to endothelial dysfunction and many disease-associated processes. Identifying which of these multiple EC functions is causally related to disease risk has been challenging. There is evidence from in vivo models and human sequencing studies that dysregulation of nitric oxide production directly affects risk of coronary artery disease. Human genetics can help prioritize the other EC functions with causal relationships because germline mutations are acquired at birth and serve as a randomized test of which pathways affect disease risk. Though several coronary artery disease risk variants have been linked to EC function, this process has been slow and laborious. Unbiased analyses of EC dysfunction using multiomic approaches promise to identify the causal genetic mechanisms responsible for vascular disease. Here, we review the data from genomic, epigenomic, and transcriptomic studies that prioritize EC-specific causal pathways. New methods that CRISPR (clustered regularly interspaced short palindromic repeats) perturbation technology with genomic, epigenomic, and transcriptomic analysis promise to speed up the characterization of disease-associated genetic variation. We summarize several recent studies in ECs which use high-throughput genetic perturbation to identify disease-relevant pathways and novel mechanisms of disease. These genetically validated pathways can accelerate the identification of drug targets for the prevention and treatment of atherosclerosis.

Variant Polycomb complexes in Drosophila consistent with ancient functional diversity.

Polycomb group (PcG) mutants were first identified in Drosophila on the basis of their failure to maintain proper Hox gene repression during development. The proteins encoded by the corresponding fly genes mainly assemble into one of two discrete Polycomb repressive complexes: PRC1 or PRC2. However, biochemical analyses in mammals have revealed alternative forms of PRC2 and multiple distinct types of noncanonical or variant PRC1. Through a series of proteomic analyses, we identify analogous PRC2 and variant PRC1 complexes in Drosophila, as well as a broader repertoire of interactions implicated in early development. Our data provide strong support for the ancient diversity of PcG complexes and a framework for future analysis in a longstanding and versatile genetic system.