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

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) identify numerous variants linked to complex traits, predominantly in noncoding regions with tissue-specific effects.
• The spatial distribution of these genetic effects within tissue architecture is poorly understood.
• Spatial transcriptomics (ST) preserves spatial coordinates while profiling gene expression, enabling localization of genetic effects.

Purpose of the Study:

• To introduce the Spatial GWAS Atlas, a novel resource integrating GWAS summary statistics with ST data.
• To map trait-associated cells at single-cell resolution within their spatial context.
• To facilitate the dissection of the cellular and spatial basis of complex traits.

Main Methods:

• Systematic integration of 3854 curated GWAS datasets with 635 ST datasets across species, tissues, and platforms.
• Leveraging spatial transcriptomics to link genetic association signals to specific cell types and locations within tissues.
• Development of a database with search, browsing, and visualization functionalities.

Main Results:

• Identification of extensive trait-region and trait-spot associations by integrating diverse GWAS and ST data.
• Demonstration of the ability to map genetic effects to specific cells and spatial locations.
• Establishment of a comprehensive resource for exploring genotype-phenotype relationships in a spatial context.

Conclusions:

• The Spatial GWAS Atlas is the first resource to systematically integrate GWAS and ST data for mapping trait-associated cells.
• This atlas enables high-resolution dissection of the cellular and spatial underpinnings of complex traits.
• The resource supports mechanistic studies, therapeutic target discovery, and precision medicine.