scGWAS

scGWAS integrates single-cell RNA sequencing (scRNA-seq) data with genome-wide association studies (GWAS) to identify genetically mediated cell-type associations of complex traits and diseases.

Key Features:

• Integration of scRNA-seq and GWAS Data: Leverages scRNA-seq datasets alongside GWAS summary data to map associations between genetic traits and specific cell types.
• Cellular Module Construction: Constructs cellular modules using average gene expression across cell types to represent disease-specific activation of biological processes.
• Null Distribution via Virtual Search: Employs virtual search processes to establish null distributions for module scores.
• Large-scale Application and Results: Applied to 40 GWAS datasets (average sample size ≈154,000) and 18 scRNA-seq datasets from nine major human/mouse tissues totaling >1 million cells, identifying 2,533 significant trait–cell type associations.
• Validation against Curated Databases: Validates module genes using disease or clinically annotated references including ClinVar, OMIM, and pLI variant annotations.
• Discovery of Trait-Specific Risk Gene Sets: Reveals that different traits associated with the same cell type are often mediated by distinct sets of risk genes and uncovers novel trait–tissue associations.

Scientific Applications:

• Cell-type-resolved genetics: Dissects genetic contributions to complex traits at the cellular level by linking GWAS signals to specific cell types using scRNA-seq expression.
• Module-level disease biology: Identifies disease-specific gene modules that suggest activated biological processes in particular cell types.
• Translational insights: Provides prioritized cell-type and module candidates to inform targeted therapeutic strategies and personalized-medicine hypotheses.

Methodology:

Integrates scRNA-seq and GWAS data, computes average gene expression per cell type to build cellular modules, uses virtual search processes to generate null distributions for module scores, tests modules for trait–cell type associations, and validates module genes using ClinVar, OMIM, and pLI annotations.