SAGA (Simplified Association Genome-wide Analyses): a user-friendly Pipeline to Democratize Genome-Wide Association...

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with complex traits and diseases.
• Performing GWAS typically requires significant bioinformatics expertise, including data preprocessing, software installation, and scripting, posing a barrier for many researchers.

Purpose of the Study:

• To develop an automated, user-friendly pipeline for conducting robust and reproducible genome-wide association studies (GWAS).
• To lower the technical barrier for genetic association analyses, enabling broader access to complex genetic research.

Main Methods:

• SAGA is an open-source, BASH-based pipeline integrating established tools: PLINK, GMMAT, and SAIGE.
• The pipeline automates the entire GWAS workflow, from data preprocessing to association testing and visualization.
• Users only need to provide standard genotype and phenotype files after initial installation.

Main Results:

• SAGA automates preprocessing, association testing, and visualization, generating essential outputs like summary statistics, Manhattan plots, and quantile-quantile plots.
• The pipeline ensures robust and reproducible GWAS results.
• Successful implementation requires only standard genotype and phenotype data, significantly reducing user input and technical demands.

Conclusions:

• SAGA democratizes GWAS by providing an accessible, automated solution for researchers lacking extensive bioinformatics or scripting experience.
• This tool enhances the accessibility and reproducibility of complex genetic analyses, facilitating broader discovery in human genetics and disease research.