PROJECT SUMMARY Genome-wide association studies have now discovered tens of thousands of noncoding variants associated with human diseases and traits. It has proven challenging to interpret these associations. A majority of causal variants lie in the noncoding genome and appear to affect DNA cis-regulatory elements, which control the logic of gene expression and could point us to new cell types, genes, and pathways for disease. However, we have lacked the tools needed to systematically characterize how these cis-regulatory variants and elements impact genome function and phenotype. Our team at Stanford University has now developed innovative single-cell, CRISPR mapping, and computational technologies that will enable identifying and functionally characterizing many thousands of elements and variants directly in the human genome. These tools include single-cell ATAC-seq to identify candidate elements in cells and tissues; sensitive CRISPR tiling methods to connect thousands of elements and variants to effects on gene expression and cellular phenotypes; and the ABC and BPNet models to predict how disease variants regulate gene expression. Together, these technologies suggest a new strategy to systematically connect DNA variants and elements to function and phenotype. Here we will apply these new technologies in collaboration with the NHGRI Impact of Genomic Variation on Function Consortium. We will use four cardiovascular cell types derived from human pluripotent stem cells as model systems. First, we will leverage single-cell maps of cardiac differentiation and development to select elements and risk variants for adult and children’s heart diseases likely to control cardiovascular cell function. Second, we will apply single-cell CRISPR tools to measure the effects of thousands of unbiased elements and variants on gene expression, and connect prioritized disease variants to target genes, cellular phenotypes, and tissue phenotypes. Third, we will leverage these experimental datasets to calibrate and refine computational models to build a variant-element-phenotype catalog across many human cell types and diseases. Fourth, we will enable future studies by sharing data, protocols, and software, and by conducting systematic evaluations of CRISPR technologies and computational models to connect variants to phenotypes. Together, these studies will advance our understanding of how DNA variants and elements impact genome function and demonstrate a novel strategy to leverage high-throughput genomic tools to understand biological mechanisms of human diseases.