Objective—: Coronary artery disease (CAD), including myocardial infarction (MI), is the main cause of death in the world. Genome-wide association studies have identified dozens of single nucleotide polymorphisms (SNPs) associated with CAD/MI. One of the most robust CAD/MI genetic associations is with intronic SNPs in the gene PHACTR1 on chromosome 6p24. How these PHACTR1 SNPs influence CAD/MI risk, and whether PHACTR1 itself is the causal gene at the locus, is currently unknown. Approach and Results—: Using genetic fine-mapping and DNA resequencing experiments, we prioritized an intronic SNP (rs9349379) in PHACTR1 as causal variant. We showed that this variant is an expression quantitative trait locus for PHACTR1 expression in human coronary arteries. Experiments in endothelial cell extracts confirmed that alleles at rs9349379 are differentially bound by the transcription factors myocyte enhancer factor-2. We engineered a deletion of this myocyte enhancer factor-2–binding site using CRISPR/Cas9 genome-editing methodology. Heterozygous endothelial cells carrying this deletion express 35% less PHACTR1 . Finally, we found no evidence that PHACTR1 expression levels are induced when stimulating human endothelial cells with vascular endothelial growth factor, tumor necrosis factor-α, or shear stress. Conclusions—: Our results establish a link between intronic SNPs in PHACTR1 , myocyte enhancer factor-2 binding, and transcriptional functions at the locus, PHACTR1 expression levels in coronary arteries and CAD/MI risk. Because PHACTR1 SNPs are not associated with the traditional risk factors for CAD/MI (eg, blood lipids or pressure, diabetes mellitus), our results suggest that PHACTR1 may influence CAD/MI risk through as yet unknown mechanisms in the vascular endothelium.

In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, when in reality only a small fraction of them may actually be perturbed. Here, using simulations, we demonstrate that the standard approach to single cell analysis fails to isolate the subset of affected case cells and their markers when either the affected subset is small, or when the strength of the perturbation is mild. To address this fundamental limitation, we introduce HiDDEN, a computational method that refines the case-control labels to accurately reflect the perturbation status of each cell. We show HiDDEN’s superior ability to recover biological signals missed by the standard analysis workflow in simulated ground truth datasets of cell type mixtures. When applied to a dataset of human multiple myeloma precursor conditions, HiDDEN recapitulates the expert manual annotation and discovers malignancy in early stage samples missed in the original analysis. When applied to a mouse model of demyelination, HiDDEN identifies an endothelial subpopulation playing a role in early stage blood-brain barrier dysfunction. We anticipate that HiDDEN should find wide usage in contexts that require the detection of subtle transcriptional changes in cell types across conditions.