Lück Stefanie, Scholz Uwe, Douchkov Dimitar
Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, D-06466 Seeland, Germany.
Bioinform Adv. 2024 Nov 12;4(1):vbae177. doi: 10.1093/bioadv/vbae177. eCollection 2024.
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.
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.
Project page: https://github.com/snowformatics/gwastic_desktop; GWAStic documentation: https://snowformatics.gitbook.io/product-docs; PyPI: https://pypi.org/project/gwastic-desktop/.
基因组学的进展使得人们迫切需要能够简化复杂遗传数据分析的易用工具，从而使各个领域的研究人员能够利用全基因组关联研究和基因组预测的强大功能。开发GWAStic就是为了填补这一空白，它提供了一个直观的平台，将人工智能与传统统计方法相结合，无需深入掌握统计软件专业知识就能进行复杂的基因组分析。
我们展示了GWAStic，这是一个直观的跨平台桌面应用程序，旨在简化生物和医学研究人员的全基因组关联研究和基因组预测。GWAStic拥有用户友好的图形界面，集成了机器学习和传统统计方法以支持遗传分析。该应用程序接受基于标准文本的变异调用格式和PLINK二进制文件的输入，生成清晰的图形输出，包括曼哈顿图、分位数-分位数图和基因组预测相关图，以增强数据可视化和分析。
