COSGAP: COntainerized Statistical Genetics Analysis Pipelines

COSGAP packages established statistical genetics software tools into Singularity containers with Python helper scripts to enable reproducible and standardized genetic data analysis across diverse computing environments.

Key Features:

• Containerized Analysis Pipelines: Established software tools are packaged into Singularity containers to standardize execution across different operating systems and environments.
• Auto-generated Analysis Scripts: Python helper scripts generate customized scripts for conducting analyses on high-performance computing facilities or personal computers.
• Federated Analysis Support: The containerized approach addresses challenges of non-shareable sensitive data, software dependencies, limited internet access, and format requirements for multi-site consortia.
• Pre-configured Statistical Genetics Methods: Containers include tools for genome-wide association studies, polygenic scoring, LD score regression, Gaussian Mixture Models, and gene-set analysis.

Scientific Applications:

• Genome-wide Association Studies: COSGAP supports GWAS analysis through containerized implementations of established software tools.
• Polygenic Scoring: The pipeline enables polygenic score calculation as part of its statistical genetics analysis capabilities.
• LD Score Regression: COSGAP includes LD score regression for estimating genetic correlations and heritability from summary statistics.
• Gaussian Mixture Models: The framework supports Gaussian Mixture Model-based analyses for statistical genetics applications.
• Gene-set Enrichment Analysis: COSGAP provides containerized tools for gene-set analysis to evaluate enrichment of genetic signals in predefined gene sets.

Methodology:

COSGAP uses Singularity containers to package established software tools, with Python helper scripts that auto-generate analysis scripts for execution on high-performance computing facilities or personal computers.