PhenoScanner

PhenoScanner aggregates results from human genetic association studies into a curated database for cross-referencing genetic variants with associations across diseases, gene expression, metabolites, proteins, and epigenetic markers.

Key Features:

• Comprehensive data: Encompasses over 150 million genetic variants and more than 65 billion phenotype associations, expanded from approximately 350 million associations in the previous version.
• Diverse association types: Includes associations with diseases and traits, gene expression levels, metabolite concentrations, protein levels, and epigenetic markers.
• Query modalities: Supports searches by genes, genomic regions, phenotypes, or specific genetic variants.
• Positional annotation: Positionally annotates all genetic variants using the Variant Effect Predictor (VEP).
• Phenotype mapping: Maps phenotypes to Experimental Factor Ontology (EFO) terms for standardized categorization.
• Linkage disequilibrium analysis: Incorporates linkage disequilibrium statistics from the 1000 Genomes project to identify phenotype associations with proxy variants.

Scientific Applications:

• Phenome scans: Enables comprehensive phenome-wide scans to cross-reference variants against a broad set of phenotypes.
• Genotype-phenotype correlation: Supports identification of novel genotype-phenotype correlations across molecular and clinical traits.
• Complex disease investigation: Facilitates investigations into mechanisms underlying complex diseases by aggregating multi-type association data.
• Molecular trait exploration: Allows exploration of associations between genetic variants and gene expression, metabolomic profiles, protein levels, and epigenetic modifications.
• Hypothesis generation and validation: Provides aggregated association evidence to generate and validate genetic hypotheses and explore biological pathways.

Methodology:

Aggregates association results from genetic association studies, positionally annotates variants using the Variant Effect Predictor, maps phenotypes to Experimental Factor Ontology terms, and incorporates linkage disequilibrium statistics from the 1000 Genomes project to identify proxy variant associations.