← BioTransfer GEO Dataset Finder
GEO series

Prediction and Characterization of Genetically-Regulated Expression of Asthma Tissues from African-Ancestry Populations

GSE301390 Homo sapiens (human) RNA-seq (gene expression) 260 samples Submitted 2025/09/09 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 260 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
260 samples: 260 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
Prediction and characterization of genetically regulated expression of asthma tissues from African-ancestry populationsThe Journal of allergy and clinical immunology · 2026
Summary (written by the submitters)
Background: Genetic control of gene expression in asthma-related tissues is not well-characterized, particularly for African-ancestry populations, limiting advancement in our understanding of the increased prevalence and severity of asthma in those populations. Objective: To create novel transcriptome prediction models for asthma tissues (nasal epithelium and CD4+ T cells) and apply them in transcriptome-wide association study to discover candidate asthma genes.
Read the full summary ↓
Methods: We developed and validated gene expression prediction databases for unstimulated CD4+ T cells and nasal epithelium using an elastic net framework. Combining these with existing prediction databases (N=51), we performed TWAS of 9,284 individuals of African-ancestry to identify tissue-specific and cross-tissue candidate genes for asthma. Results: Novel databases for CD4+ T cells and nasal epithelial gene expression prediction contain 8,351 and 10,296 genes, respectively, including four asthma loci (SCGB1A1, MUC5AC, ZNF366, LTC4S) not predictable with existing public databases. Prediction performance was comparable to existing databases and was most accurate for populations sharing ancestry with the training set (e.g. African ancestry). From transcriptome-wide association study, we identified 17 candidate causal asthma genes (adjusted P<0.1), including genes with tissue-specific (IL33 in nasal epithelium) and cross-tissue (CCNC and FBXW7) effects. Conclusions: Expression of IL33, CCNC, and FBXW7 may affect asthma risk in African ancestry populations by mediating inflammatory responses. The addition of CD4+ T cell and nasal epithelium prediction databases to the public sphere will improve ancestry representation and power to detect novel gene-trait associations from transcriptome-wide association study.
Published in
Prediction and characterization of genetically regulated expression of asthma tissues from African-ancestry populations
Slack SD, Esquinca E, Arehart CH et al. · The Journal of allergy and clinical immunology 2026 · PMID 40930298 · doi:10.1016/j.jaci.2025.07.035
This dataset
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.

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 GSE301390_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1270980 and SRA study SRP589269. Searching any of these in the dataset finder brings you back here.

Study design
260 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 260 sample titles: 260 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

+ 220 more — browse all 260 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.