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Atrial fibrillation variant-to-gene prioritization through cross-ancestry eQTL and single-nucleus multiomic analyses

GSE238242 Homo sapiens (human) Single-nucleus RNA-seq 14 samples Submitted 2024/07/09 Platform GPL24676
At a glance

Single-nucleus RNA-seq · human · 14 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).
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.
Paper
Atrial fibrillation variant-to-gene prioritization through cross-ancestry eQTL and single-nucleus multiomic analysesiScience · 2024
Summary (written by the submitters)
Atrial fibrillation (AF) is the most common arrhythmia in the world, and is linked to significant morbidity and mortality. Despite advances in the treatment and management of AF, important challenges remain for patients. Human genetics can provide strong therapeutic candidates, but the identification of the causal genes and their functions is difficult.
Read the full summary ↓
Here, we apply an AF fine-mapping strategy that leverages results from a cross-ancestry genome-wide association study (GWAS), expression quantitative trait loci (eQTLs) from left atrial appendages (LAA) obtained from two cohorts with distinct ancestry (European and East Asian), and a paired RNAseq and ATACseq LAA single-nucleus assay (sn-multiome). We found that AF-associated LAA eQTLs are largely consistent across ancestries. At ten AF loci, our co-localization and fine-mapping analyses implicated 14 genes. Furthermore, by integrating our LAA sn-multiome data and other epigenomic datasets with our fine-mapping results, we identified four primary candidate causal AF variants, including rs7612445 at GNB4 and rs242557 at MAPT, for which we propose molecular mechanisms of AF-association at the cellular level. Finally, we showed that the repression of the strongest AF-associated eQTL gene, LINC01629, in human embryonic stem cell-derived cardiomyocytes using CRISPR inhibition results in the dysregulation of pathways linked to genes involved in the development of atrial tissue and the cardiac conduction system (e.g. HCN4, PITX2 and TBX5).
Published in
Atrial fibrillation variant-to-gene prioritization through cross-ancestry eQTL and single-nucleus multiomic analyses
Leblanc FJA, Jin X, Kang K et al. · iScience 2024 · PMID 39262787 · doi:10.1016/j.isci.2024.110660
This dataset
Open GSE238242 in finder → Find more human 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 GSE238242_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 PRJNA998429 and SRA study SRP451377. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39262787) ↗
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
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