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.