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Comprehensive identification of coronary artery disease-associated variants regulating vascular smooth muscle cell gene expression [CUT&RUN]

GSE279298 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 48 samples Submitted 2024/10/14 Platform GPL30173
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 48 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
48 samples: 48 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Coronary artery disease-associated variants regulate vascular smooth muscle cell gene expressionNature cardiovascular research · 2025
Summary (written by the submitters)
Coronary artery disease (CAD) is a complex disorder with genetic and environmental influences. Genome-wide association studies (GWAS) have identified over 300 genomic loci associated with disease risk. However, identifying the functional variants within these loci has been limited in large part due to linkage disequilibrium. This represents a critical step in understanding the molecular mechanisms underlying disease risk.
Read the full summary ↓
To identify and prioritize candidate causal CAD-associated variants, we performed lentivirus-based massively parallel reporter assays (lentiMPRAs) in primary vascular smooth muscle cells (SMCs), which play significant roles in atherosclerosis, the underlying cause of CAD. We tested 25892 CAD-associated variants for their allele-specific enhancer activity in quiescent and proliferative SMCs, modeling healthy and disease conditions. We identified 122 candidate variants showing significant enhancer activity and differences in reporter gene expression between risk and non-risk alleles. We also identified 23 variants showing condition-biased allelic imbalance and 41 variants showing sex-biased allelic imbalance. We further functionally characterized 25892 variants by performing CUT&RUN assays to identify variants in enhancer and promoter regions of SMCs. By integrating the results of these experiments, we identified a credible set of 49 CAD-associated variants in functionally relevant regions. Furthermore, by comparing these identified variants with our previously obtained expression quantitative trait loci (eQTL) data, 27 of these 49 variants were associated with SMC gene expression levels. Finally, we performed CRISPRi experiments on 8 variants comprising 9 variant-gene pairs, rs35976034 (MAP1S), rs4888409 (CFDP1), rs73193808 (MAP3K7CL), rs67631072 (INPP5B/FHL3), rs1651285 (SNHG18), rs17293632 (SMAD3), rs2238792 (ARVCF), rs4627080 (NRIP3), to confirm their regulatory potential of nearby gene expression. Taken together, our results comprehensively fine-map the causal variants that confer increased risk of CAD through their effects on vascular smooth muscle cells.
Published in
Coronary artery disease-associated variants regulate vascular smooth muscle cell gene expression
Barbera N, Lei L, Wallace A et al. · Nature cardiovascular research 2025 · PMID 41057608 · doi:10.1038/s44161-025-00714-1
This dataset
Open GSE279298 in finder → Find more human ChIP / ATAC / CUT&Tag datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE279298_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1171822 and SRA study SRP537966. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41057608) ↗
Study design
48 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 48 sample titles: 48 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

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