GEO series

Identifying severe COVID-19 risk variants modulating enhancer reporter activity in lung cells

GSE320469 Homo sapiens (human) Other 7 samples Submitted 2026/06/24 Platform GPL30173

At a glance

human · 7 samples

Organism: Homo sapiens (human)
Samples: 7 samples: 5 × A549 STARR-seq, output, vs 2 × A549 STARR-seq, input,Groups 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.

Summary (written by the submitters)

Common genetic variants contribute to risk for complex human diseases. However, despite thousands of associations, variants modulating disease risk and their functional impact remain largely unknown. This includes SARS-CoV-2 infection, where outcomes range from asymptomatic to fatal. Most host risk variants associated with COVID-19 disease, identified through genome wide association studies, are located in the non-coding genome and may function by altering gene expression in disease-relevant cells and tissues.

Read the full summary ↓

To address this at scale, we tested >4800 severe COVID-19-associated variants to determine the impact of individual variants and variant combinations on regulatory activity using Self-Transcribing Active Regulatory Region sequencing, a massively-parallel reporter assay, in a lung epithelial cell line (A549). We identify 166 variants within active sequences, of which 29 modulate activity allele-specifically. Evaluating variant combinations, we observe both additive and non-additive effects on regulatory activity. We employ state-of-the-art deep learning models to interpret allele-specific variant effects on regulatory activity and endogenous genomic features. Our work provides a set of prioritised severe COVID-19-associated variants that modulate regulatory activity in lung epithelial cells, candidate transcription factors, and candidate target genes with potential to be disease modifying.

This dataset

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Download · which file do I need?

Direct links to NCBI, 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.

All supplementary files ↗

3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE320469_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

Also filed as BioProject PRJNA1428260. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

5 × A549 STARR-seq, output, vs 2 × A549 STARR-seq, input,

Supports a between-group comparison across 7 samples.

How we read this: 2 replicated groups read from 7 sample titles; they account for 7 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

GSM9543822 A549 STARR-seq, input, rep1 A549 STARR-seq, input,
GSM9543823 A549 STARR-seq, input, rep2 A549 STARR-seq, input,
GSM9543824 A549 STARR-seq, output, rep1 A549 STARR-seq, output,
GSM9543825 A549 STARR-seq, output, rep2 A549 STARR-seq, output,
GSM9543826 A549 STARR-seq, output, rep3 A549 STARR-seq, output,
GSM9543827 A549 STARR-seq, output, rep4 A549 STARR-seq, output,
GSM9543828 A549 STARR-seq, output, rep5 A549 STARR-seq, output,

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