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Genetic variants within silencer elements contribute to human blood cell traits

GSE347432 Homo sapiens (human) Single-nucleus RNA-seq 20 samples Submitted 2026/09/22 Platform GPL24676
At a glance

Single-nucleus RNA-seq · human · 20 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
20 samples: 8 conditions, mostly in triplicateGroups 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)
Genome-wide association studies (GWAS) have identified numerous non-coding loci associated with human complex traits and diseases. However, assigning regulatory functions to causal variants remains challenging, historical efforts have largely focused on promoters and enhancers, often overlooking the regulatory impact of silencers. In this study, we performed high-throughput screening to characterize changes in potential silencer activities within 14,720 fine-mapped causal non-coding single-nucleotide polymorphisms (SNPs) associated with 15 diverse blood traits.
Read the full summary ↓
We have prioritized 805 blood trait-associated non-coding SNPs link to changes in putative silencer activity (425 loss-of-function and 380 gain-of-function). Using targeted mutagenesis in orthogonal episomal reporter assays and endogenous knock-in via CRISPR-Cas9 editing, we validated the allelic imbalances in silencer activity of identified silencer-disruptive variants. We demonstrated that a silencer-disruptive variant rs2857078 can impair red blood cell maturation and hemoglobin synthesis using an in vitro K562 erythroid differentiation model. To prioritize pathogenic variants within silencer elements, we trained a machine learning model using evolutionary conservation metrics and single-cell chromatin accessibility profiles across the hematopoietic hierarchy. Taken together, our study underscores the importance of investigating functions of silencer variants in post-GWAS functional studies.
This dataset
Open GSE347432 in finder → Find more human RNA-seq 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 GSE347432_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1529788 and SRA study SRP736814. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
K562 cells, lenti-ReSE-library infection, Day… ×3 K562 cells, lenti-ReSE-library infection, 1 n… ×3 HepG2 cells, lenti-ReSE-library infection, Da… ×3 HepG2 cells, lenti-ReSE-library infection, 1 … ×3 K562 cells, rs4808806 Knockin genotype CC, ×2 K562 cells, rs4808806 Knockin genotype GG, ×2 K562 cells, rs2857078 Knockin genotype AA, ×2 K562 cells, rs2857078 Knockin genotype AC, ×2

Supports a between-group comparison across 20 samples.

How we read this: 8 replicated groups read from 20 sample titles; they account for 20 of them. 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
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