Faculty, Staff and Student Publications

Language

English

Publication Date

1-6-2026

Journal

Nucleic Acids Research

DOI

10.1093/nar/gkaf1047

PMID

41123022

PMCID

PMC12807712

PubMedCentral® Posted Date

10-22-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Spatial heterogeneity of gene expression within tissue regions has a critical influence on biological functions, thereby affecting disease pathogenesis. However, systematic associations between spatially resolved transcriptomes and phenotypes, especially in complex diseases, remain underexplored. Here, we developed spatial2GWAS (http://www.spatial2gwas.cn), a comprehensive resource linking spatial transcriptomic (ST) regions with GWAS traits. In the database, we collected 1196 ST slices (human and mouse) from five technologies and 812 GWAS traits spanning 18 phenotype categories and identified 29 701 ST slice-GWAS trait pairs containing 47 492 significant regions. Functional analyses reveal distinct patterns of cell type composition, gene expression, GO/KEGG pathway activation, and cell-cell communication direction between trait-related and unrelated spatial regions. The database provides a user-friendly interface for visualization of spatial regions and GWAS trait associations, supporting advanced queries by slice and GWAS information, genes co-expressed with GWAS trait-associated genes, and spatial regions. Spatial2GWAS aims to enable systematic exploration of spatial mechanisms underlying complex traits and offer insights into region-specific biological functions and potential therapeutic targets. This database bridges ST and high-level phenotypes, advancing the understanding of tissue heterogeneity in complex human diseases.

Keywords

Humans, Genome-Wide Association Study, Mice, Animals, Databases, Genetic, Transcriptome, Phenotype, Quantitative Trait Loci, Gene Expression Profiling

Published Open-Access

yes

Additional Files
gkaf1047figgra1.jpg (80 kB)
Graphical Abstract
Download

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.