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Updated: Jan 14, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Spatial2GWAS: a database for linking spatial transcriptomic regions with GWAS traits.

Xi Hu¹, Aoqi Wang¹, Huan Yu¹

¹West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China.

Nucleic Acids Research

|October 22, 2025

Summary

This summary is machine-generated.

This study introduces spatial2GWAS, a database linking spatial transcriptomic regions with genome-wide association study (GWAS) traits. It reveals how tissue gene expression patterns influence complex diseases and identifies potential therapeutic targets.

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Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Spatial heterogeneity in gene expression impacts biological functions and disease pathogenesis.
Systematic associations between spatially resolved transcriptomics and phenotypes, particularly in complex diseases, are underexplored.

Purpose of the Study:

To develop spatial2GWAS, a comprehensive resource linking spatial transcriptomic (ST) regions with genome-wide association study (GWAS) traits.
To enable systematic exploration of spatial mechanisms underlying complex traits and provide insights into region-specific biological functions and therapeutic targets.

Main Methods:

Collected 1196 ST slices (human and mouse) across five technologies and 812 GWAS traits.
Identified 29,701 ST slice-GWAS trait pairs with 47,492 significant regions.
Performed functional analyses on cell type composition, gene expression, pathway activation, and cell-cell communication.

Main Results:

Distinct patterns of cell type composition, gene expression, and pathway activation were observed between trait-related and unrelated spatial regions.
Spatial2GWAS provides a user-friendly interface for visualization and advanced queries.
The database bridges ST data with high-level phenotypes, advancing understanding of tissue heterogeneity.

Conclusions:

Spatial2GWAS facilitates the systematic investigation of spatial mechanisms in complex traits.
The resource aids in understanding tissue heterogeneity's role in complex human diseases.
Identified region-specific biological functions and potential therapeutic targets.