GPA

GPA prioritizes GWAS results by integrating pleiotropy information and annotation data through a statistical framework.

Key Features:

• Pleiotropy integration: Incorporates pleiotropy information to prioritize GWAS results.
• Annotation data integration: Integrates annotation data into the statistical framework for GWAS prioritization.
• Statistical model fitting: Provides functions for fitting the GPA statistical framework.
• Interactive visualization: Includes ShinyGPA for investigating pleiotropic architecture.

Scientific Applications:

• GWAS result prioritization: Prioritizes findings from genome-wide association studies by leveraging pleiotropy and functional annotations.
• Pleiotropic architecture investigation: Enables exploration of pleiotropic relationships across traits and diseases through interactive visualization.

Methodology:

The GPA package fits a statistical framework that integrates pleiotropy information and annotation data for GWAS prioritization.