GEO series

Gene-Gene Interactions Between A LMNA Variant and Common Polymorphisms Drive Early-Onset Atrial Fibrillation

GSE294948 Homo sapiens (human) RNA-seq (gene expression) 9 samples Submitted 2026/04/10 Platform GPL34281Platform GPL34284

At a glance

RNA-seq (gene expression) · human · 9 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Samples: 9 samples: 3 × W vs 3 × M vs 3 × S143P-correctedGroups 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: Gene-gene interactions between a LMNA variant and common polymorphisms drive early-onset atrial fibrillationNature communications · 2026

Summary (written by the submitters)

Atrial fibrillation (AF) is a common arrhythmia with a complex genetic basis, yet the molecular mechanisms linking rare and common variants remain unclear. Using induced pluripotent stem cell-derived atrial cardiomyocytes, we uncover a novel mechanism by which a rare pathogenic LMNA variant encoding Lamin A/C disrupts chromatin accessibility and gene regulation at AF-associated loci.

Read the full summary ↓

Specifically, reduced accessibility at an SCN5A enhancer harboring an AF-associated variant leads to reduced sodium current, conduction abnormalities, and re-entrant AF. These electrophysiological defects are rescued by CRISPR-mediated activation of the SCN5A promoter and enhancer providing the first molecular evidence of epistatic gene-gene interactions driving arrhythmia risk and mechanistically linking atrial myopathy and AF. At the population level, we demonstrate carriers of LMNA proteinaltering variants with a high polygenic risk score are at two-fold increased risk of earlyonset AF, highlighting the need to integrate rare and common variants for more accurate AF risk assessment.

Published in

Gene-gene interactions between a LMNA variant and common polymorphisms drive early-onset atrial fibrillation

Owais A, Chen H, Farooq H et al. · Nature communications 2026 · PMID 42156780 · doi:10.1038/s41467-026-73113-0

This dataset

Open GSE294948 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 GSE294948_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1252080 and SRA study SRP579433. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42156780) ↗

Study design

3 × W vs 3 × M vs 3 × S143P-corrected

Supports a between-group comparison across 9 samples.

How we read this: 3 replicated groups read from 9 sample titles; they account for 9 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

GSM8931880 W1 W
GSM8931881 W2 W
GSM8931882 W3 W
GSM8931883 M1 M
GSM8931884 M2 M
GSM8931885 M3 M
GSM9561830 S143P-corrected 1 S143P-corrected
GSM9561831 S143P-corrected 2 S143P-corrected
GSM9561832 S143P-corrected 3 S143P-corrected

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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.