GEO series

Expanded chromatin accessibility mapping explains genetic variation associated with complex traits in liver

GSE277774 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 189 samples Submitted 2025/04/11 Platform GPL24676

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 189 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: 189 samples: 189 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: Expanded Chromatin Accessibility Mapping Explains Genetic Variation Associated with Complex Traits in LivermedRxiv : the preprint server for health sciences · 2025

Summary (written by the submitters)

Genome-wide association studies (GWAS) have identified thousands of genomic loci associated with a variety of common, complex human traits. The contribution of genetic variants to gene expression regulation has been well studied, supporting the idea that gene expression plays a causal role at some complex trait-associated loci. However, many current studies have not comprehensively investigated the impact of genetic variation on chromatin accessibility at a large scale within a single tissue.

Read the full summary ↓

Genetic variants associated with differences in chromatin accessibility, known as chromatin accessibility quantitative trait loci (caQTLs), are major contributors to gene expression differences and GWAS signals. We assessed chromatin accessibility in 189 human liver tissue samples using ATAC-seq and identified over two million accessible chromatin regions enriched for gene regulatory characteristics. We integrated chromatin accessibility and genotype data from 175 samples and identified 14,076 caQTLs. Using publicly available blood lipid GWAS data, we found 157 loci where the colocalization of caQTLs, expression quantitative trait loci (eQTLs), and GWAS signals generated specific molecular hypotheses about causal regulatory elements, affected genes, and, in some cases, transcription factors, resolving these associations to single-nucleotide resolution. We performed a comprehensive analysis of the GWAS signals that remain without a proposed mechanism beyond liver caQTLs and eQTLs. After incorporating additional potential regulatory mechanism data, we found that approximately 26% of blood lipid GWAS signals remain without a proposed mechanism. Overall, our results demonstrate the benefits of integrating multiple datasets to improve our understanding of GWAS signals while emphasizing the need for additional experiments to fully characterize them.

Published in

Expanded Chromatin Accessibility Mapping Explains Genetic Variation Associated with Complex Traits in Liver

Wenz BM, Dudek MF, Ramdas S et al. · medRxiv : the preprint server for health sciences 2025 · PMID 41001483 · doi:10.1101/2025.09.15.25335593

This dataset

Open GSE277774 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 GSE277774_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 PRJNA1164158 and SRA study SRP534190. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41001483) ↗

Study design

189 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 189 sample titles: 189 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

GSM8529305 3782-BW-100 ATAC-seq
GSM8529306 3782-BW-101 ATAC-seq
GSM8529307 3782-BW-102 ATAC-seq
GSM8529308 3782-BW-103 ATAC-seq
GSM8529309 3782-BW-104 ATAC-seq
GSM8529310 3782-BW-105 ATAC-seq
GSM8529311 3782-BW-106 ATAC-seq
GSM8529312 3782-BW-107 ATAC-seq
GSM8529313 3782-BW-108 ATAC-seq
GSM8529314 3782-BW-109 ATAC-seq
GSM8529315 3782-BW-10 ATAC-seq
GSM8529316 3782-BW-110 ATAC-seq
GSM8529317 3782-BW-111 ATAC-seq
GSM8529318 3782-BW-112 ATAC-seq
GSM8529319 3782-BW-113 ATAC-seq
GSM8529320 3782-BW-114 ATAC-seq
GSM8529321 3782-BW-115 ATAC-seq
GSM8529322 3782-BW-116 ATAC-seq
GSM8529323 3782-BW-117 ATAC-seq
GSM8529324 3782-BW-118 ATAC-seq
GSM8529325 3782-BW-119 ATAC-seq
GSM8529326 3782-BW-11 ATAC-seq
GSM8529327 3782-BW-120 ATAC-seq
GSM8529328 3782-BW-121 ATAC-seq
GSM8529329 3782-BW-122 ATAC-seq
GSM8529330 3782-BW-123 ATAC-seq
GSM8529331 3782-BW-124 ATAC-seq
GSM8529332 3782-BW-125 ATAC-seq
GSM8529333 3782-BW-126 ATAC-seq
GSM8529334 3782-BW-127 ATAC-seq
GSM8529335 3782-BW-128 ATAC-seq
GSM8529336 3782-BW-129 ATAC-seq
GSM8529337 3782-BW-12 ATAC-seq
GSM8529338 3782-BW-130 ATAC-seq
GSM8529339 3782-BW-131 ATAC-seq
GSM8529340 3782-BW-132 ATAC-seq
GSM8529341 3782-BW-133 ATAC-seq
GSM8529342 3782-BW-134 ATAC-seq
GSM8529343 3782-BW-135 ATAC-seq
GSM8529344 3782-BW-136 ATAC-seq

+ 149 more — browse all 189 samples with per-sample file links →

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GSE244585 Single nuclei chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry [10X single-cell multiome] 32 samples shares accessibility, chromatin, genetic
GSE204911 Mapping genotypes to chromatin accessibility profiles in single cells [Genotyping GoT-ChA amplicon] 27 samples shares accessibility, chromatin, mapping
GSE215328 Pivotal Role of the NuRD Complex in Remodeling Chromatin Environment and Maintaining Genome Architecture at CTCF Sites [hESC_H9_d049_ChIP_Seq] 135 samples shares chromatin, complex
GSE287902 BPTF regulstes androgen receptor activity by enhancing chromatin accessibility and stabilizing the AR-FOXA1 interaction [Cut&Run] 40 samples shares accessibility, chromatin
GSE316389 Chromatin accessibility and gene expression profiling of primary and metastatic ER+ breast cancer [ATAC-seq] 36 samples shares accessibility, chromatin

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

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.