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Rare variants in a PCSK9 distal-enhancer as drivers of LDL-cholesterol levels

GSE339521 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 14 samples Submitted 2026/07/23 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 14 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
14 samples: 14 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.
Summary (written by the submitters)
BACKGROUND. Discoveries of genetic causes of dyslipidemia have significantly advanced our understanding of lipoprotein metabolism and its implications in cardiometabolic diseases. However, a significant number of patients with dyslipidemia still have unidentified genetic causes. Leveraging insights from genome-wide association studies, where most common associated variants are located in non-coding regions, we propose that rare non-coding variants located in genomic regulatory regions may be responsible for dyslipidemia.
Read the full summary ↓
METHODS. Using whole-genome sequencing data from the UK Biobank (n > 200,000), we performed region-based rare-variant association analyses, focusing on liver specific genomic regulatory regions (assessed by transposase-accessible chromatin assay coupled with sequencing (ATAC-seq)), to assess whether the cumulative burden of rare genetic variants (allele frequency < 0.01) in these genomic regulatory regions was associated with LDL-cholesterol levels. To validate our findings, we used an advanced cellular model of human induced pluripotent stem cells (hiPSCs) stably expressing a CRISPR-interference system. This system enables, when combined with guide-RNAs, to target regions of interest and silence regulated genes. RESULTS. We found that the cumulative burden of rare genetic variants in 18 among 53,039 ATAC-seq peaks in human liver, is significantly associated with LDL-cholesterol. We focus our proof-of-concept study on a peak located ~17 kb upstream PCSK9, strongly associated with LDL-cholesterol (p=5.85e-52), apolipoprotein-B (p=3.82e-49) and PCSK9 plasma levels (p=1.15e-67). We showed that this regulatory region is also active in hiPSCs and hiPSC-derived liver organoids. When transfected with guide-RNAs targeting this region, we showed that CRISPRi-hiPSCs present a hypermethylation of the targeted region which in terms leads to a substantial decrease in PCSK9 gene expression (-48%), intracellular protein (-65%) and secretion (-36%) (p<0.001). CONCLUSIONS. This study identifies a non-coding genetic region upstream PCSK9 where rare variants lead to decreased PCSK9 expression and lowered LDL-cholesterol. Such findings advocate for the genetic screening of non-coding regulatory regions in patients with unidentified genetic causes of extreme dyslipidemia and open novel perspectives for genetic regulatory therapeutic intervention.
This dataset
Open GSE339521 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 GSE339521_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 PRJNA1499110 and SRA study SRP720333. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
14 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 14 sample titles: 14 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
Datasets on a similar topic

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