CAUSALdb

CAUSALdb integrates GWAS summary statistics and uniformly applies fine-mapping to estimate causal probabilities of genetic variants associated with complex traits and diseases.

Key Features:

• Comprehensive Data Integration: Curates 3,052 fine-mappable GWAS summary statistics across five human super-populations covering 2,629 unique traits.
• Fine-Mapping and Causal Probability Estimation: Employs three fine-mapping tools to estimate causal probabilities for all genetic variants within GWAS-significant loci and to define credible sets.
• Trait Ontology Mapping: Maps reported traits to the Medical Subject Headings (MeSH) ontology for standardized phenotype representation.
• Visualization Plots: Produces Manhattan and LocusZoom-like plots for visualization of credible sets and locus-level association signals.
• Comparative Analysis Capabilities: Enables comparison of causal relationships at variant, gene, and trait levels across studies with differing sample sizes and populations.
• Comprehensive Variant Annotations: Integrates base-wise and allele-specific functional annotations to annotate and prioritize candidate causal variants.

Scientific Applications:

• Fine-mapping causal variants: Prioritizes candidate causal variants within GWAS-significant loci for complex traits and diseases.
• Cross-study and cross-population comparison: Compares variant-, gene-, and trait-level causal signals across studies with different sample sizes and populations.
• Functional prioritization: Uses base-wise and allele-specific annotations to inform the functional interpretation of prioritized variants.

Methodology:

Integrates and curates GWAS summary statistics and uniformly applies three fine-mapping tools to estimate causal probabilities and credible sets, maps traits to MeSH, and incorporates base-wise and allele-specific functional annotations for variant-level interpretation.