GGPA

GGPA prioritizes genome-wide association study (GWAS) results by integrating pleiotropy through a statistical framework to increase statistical power for identifying genetic variants associated with complex diseases.

Key Features:

• Graph-GPA model fitting: Provides functions for fitting the graph-GPA statistical framework to GWAS data.
• Association mapping: Implements association mapping to identify relationships between genetic variants and phenotypes.
• Phenotype graph generation: Generates a phenotype graph to visualize pleiotropic relationships among traits.

Scientific Applications:

• GWAS result prioritization: Ranks and prioritizes genetic variants from genome-wide association studies by leveraging pleiotropic effects across multiple phenotypes.
• Pleiotropic architecture investigation: Analyzes the shared genetic basis among complex diseases characterized by many genetic variants with small effects.

Methodology:

Graph-GPA employs a graphical model framework that integrates pleiotropy across multiple GWAS datasets to prioritize association results and infer phenotype relationships.