eng International Academy of Ecology and Environmental Sciences Ornamental and Medicinal Plants 2522-3682 2026-9-1 9 1-4 1 21 1 article WenJun Zhang 1 2 Yanhong Qi 1 2 School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; International Academy of Ecology and Environmental Sciences, Hong Kong Libraries of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China A GWAS (Genome-Wide Association Study) data fetcher via AI was developed in present study. It is a web tool that generates realistic synthetic GWAS data based on user inputs via AI APIs (DeepSeek, Google Gemini, or OpenAI GPT). It outputs three data components: (1) summary statistics: an array of SNP records (CHR, SNP, BP, A1, A2, FRQ_A1, BETA, SE, P, N, INFO), (2) phenotype data: an array of individual records (FID, IID, PHE, SEX, AGE, PC1, PC2, BATCH), and (3) metadata: a descriptive string containing genotyping platform, QC protocols, genome build, and population notes. By leveraging AI to generate realistic data, users can practice analysis without accessing restricted genetic databases, test analysis pipelines safely, learn GWAS data structure and format, and develop and validate bioinformatics tools. http://www.iaees.org/publications/journals/omp/articles/2026-9(1-4)/fetching-GWAS-data-via-AI.pdf GWAS (Genome-Wide Association Study) data fetcher Artificial Intelligence (AI) Large Language Model (LLM) JavaScript/HTML