Spatial GWAS Atlas: a knowledgebase for decoding the genetic architecture of complex traits in spatial resolution

Genome-wide association studies (GWAS) have identified a large number of variants linked to complex traits and diseases, most of which lie in noncoding regions and act in a tissue- and cell type-specific manner. However, how these genetic effects are distributed within the spatial architecture of tissues remains poorly understood. Spatial transcriptomics (ST) profiles gene expression while preserving spatial coordinates, offering a powerful way to localize genetic effects within tissue architecture. Here, we present the Spatial GWAS Atlas (https://zhaolab.cpl.ac.cn/spatialgwas), the first comprehensive resource systematically integrating GWAS summary statistics with ST data to map trait-associated cells at single-cell resolution with spatial context. By leveraging 3854 curated GWAS datasets spanning diverse traits and 635 ST datasets across multiple species, tissues, and platforms, we identified extensive trait–region and trait–spot associations. The database provides keyword search, multicriteria browsing, interactive visualization, and bulk download capabilities. By linking genetic association signals to spatially resolved transcriptomics, the Spatial GWAS Atlas enables high-resolution dissection of the cellular and spatial basis of complex traits, facilitating mechanistic studies, therapeutic target discovery, and precision medicine applications.