ArticleNucleic acids research2026
Spatial GWAS Atlas: a knowledgebase for decoding the genetic architecture of complex traits in spatial resolution.
Article in Nucleic acids research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Shared genetic basis of sarcopenia and osteoporosis: insights from GWAS and multi-omics analyses.Journal of bone and mineral metabolism · 2026Article
- The 2026 Nucleic Acids Research database issue and the online molecular biology database collection.Nucleic acids research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.