GCPBayes pipeline: a tool for exploring pleiotropy at the gene level.

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Gene-set analysis can reveal pleiotropic genes and shared biological pathways across different diseases.
• Existing methods for cross-phenotype gene-set analysis often lack efficient pipelines for genome-scale data.
• Identifying pleiotropy is crucial for understanding complex disease mechanisms and developing targeted therapies.

Purpose of the Study:

• To develop a user-friendly computational pipeline for cross-phenotype gene-set analysis.
• To enable efficient analysis of genome-scale data for identifying pleiotropic genes.
• To provide a tool for exploring common genetic mechanisms between two traits.

Main Methods:

• A pipeline was designed to perform cross-phenotype gene-set analysis using the GCPBayes method.
• The pipeline integrates Shiny applications, Bash, and R scripts for automated analysis and visualization.
• The approach was validated using publicly available genome-wide association studies (GWAS) summary statistics for breast and ovarian cancer.

Main Results:

• The GCPBayes pipeline successfully identified known pleiotropic genes associated with breast and ovarian cancer.
• The analysis also uncovered novel pleiotropic genes and regions requiring further investigation.
• Recommendations for parameter selection were provided to optimize computational time for genome-scale data.

Conclusions:

• The developed GCPBayes pipeline offers an efficient and accessible tool for cross-phenotype gene-set analysis.
• This approach facilitates the discovery of pleiotropic genes and enhances understanding of shared disease etiologies.
• The pipeline and accompanying tutorial provide valuable resources for genetic researchers studying complex diseases.