Introducing GWAStic: a user-friendly, cross-platform solution for genome-wide association studies and genomic prediction.

Abstract

Motivation: Advances in genomics have created an insistent need for accessible tools that simplify complex genetic data analysis, enabling researchers across fields to harness the power of genome-wide association studies and genomic prediction. GWAStic was developed to bridge this gap, providing an intuitive platform that combines artificial intelligence with traditional statistical methods, making sophisticated genomic analysis accessible without requiring deep expertise in statistical software.

Results: We present GWAStic, an intuitive, cross-platform desktop application designed to streamline genome-wide association studies and genomic prediction for biological and medical researchers. With a user-friendly graphical interface, GWAStic integrates machine learning and traditional statistical approaches to support genetic analysis. The application accepts inputs from standard text-based Variant Call Formats and PLINK binary files, generating clear graphical outputs, including Manhattan plots, quantile-quantile plots, and genomic prediction correlation plots to enhance data visualization and analysis.

Availability and implementation: Project page: https://github.com/snowformatics/gwastic_desktop; GWAStic documentation: https://snowformatics.gitbook.io/product-docs; PyPI: https://pypi.org/project/gwastic-desktop/.