ezQTL

ezQTL performs interactive visualization and statistical colocalization of GWAS, QTL, and eQTL data to identify candidate causal variants, genes, and mechanisms at loci, including non-coding regions, while accounting for linkage disequilibrium (LD) patterns.

Key Features:

• Interactive Visualization: Visualizes GWAS loci and molecular QTL association signals across genomic regions.
• Data Quality Control: Performs quality control on variants to ensure accurate matching across datasets.
• LD Visualization: Displays linkage disequilibrium structure for loci under study.
• Reference Data Support: Supports data from the 1000 Genomes Project, UK Biobank, and user-provided datasets.
• Colocalization Analysis: Implements statistical colocalization using eCAVIAR and HyPrColoc to test for shared causal variants between traits.
• Batch Processing: Enables batch colocalization analyses across multiple loci and datasets.

Scientific Applications:

• Post-GWAS Investigation: Facilitates prioritization of candidate causal genes from GWAS loci through QTL colocalization.
• Genetic Basis of Disease Research: Supports integration of GWAS and molecular QTL data to elucidate genetic mechanisms of complex diseases, including cancer.

Methodology:

Performs variant quality control, visualizes genetic associations and LD using reference panels (1000 Genomes Project, UK Biobank), and applies statistical colocalization methods eCAVIAR and HyPrColoc to integrate GWAS and eQTL data, with support for batch analyses.