Scalable multi-ancestry functional genomics of blood traits and cardiovascular disease - PROJECT SUMMARY Cardiovascular disease (CVD) is the most common cause of mortality in the world. Genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with CVD. However, most variants do not necessarily cause the observed phenotypes, but rather are in linkage disequilibrium with the truly causal variants that influence disease pathophysiology via largely unknown molecular and cellular mechanisms. Thus, three central challenges for CVD GWAS are: 1) Identifying causal variants, 2) Understanding which cell types are most relevant for specific variants, and 3) Identifying the cis- and trans-regulatory target genes whose expression is modulated by CVD variants. Currently, there is no consensus on how best to identify relevant cell types and target genes. Furthermore, efforts to connect specific noncoding variants to target genes, such as CRISPR-based insertion of specific variants coupled with phenotypic assays have been hampered by low throughput. Here, we seek to develop scalable methods to address all three central challenges for CVD disease GWAS, while leveraging the considerable benefits of population-scale, multi-ancestry GWAS — namely, improved discovery of novel GWAS loci and increased resolution for causal variants at already known loci. Of the many cell types and organ systems underlying CVD pathophysiology, we will focus on blood-related mechanisms, which are currently understudied despite the importance of e.g., immune response and coagulation in CVD, demonstrated by prior work and our preliminary data. In Aim 1, we will perform colocalization of CVD and blood cell trait GWASes (as blood cells traits are naturally cell-type-specific) and produce a reference atlas of 3D enhancer maps for 13 blood cell types and 2 endothelial cell types in donors of diverse ancestries. In Aim 2, we will combine CRISPR screens and single-cell multiomics (STING-seq) in blood and endothelial cells to identify causal variants and target genes for CVD. We will further extend it by developing BeeSTING-seq, which combines cytosine and adenine base editors with a more flexible CRISPR enzyme to insert precise SNPs. For both STING methods, we will use a thoroughly-validated computational approach to identify cis and trans target genes and regulatory networks. Further, we will deeply validate top variants using key functional assays (electrical impedance, migration and stress response). This proposal takes an interdisciplinary approach with a team of experts in noncoding biology and high-throughput single-cell CRISPR screens (Sanjana), in genetics and systems biology (Lappalainen) and in CVD GWAS, cardiology and endothelial cell function (Gupta). Our integrated experimental and computational approach will not only reveal how genomic variation shapes CVD risk, but also develop a generalizable toolkit that leverages cutting-edge 3D genome mapping, gene editing and single-cell profiling to map gene regulatory elements, specific variants and target genes to inform future CVD therapeutics.
