Decoding the regulatory genetic architecture of endometriosis using AlphaGenome

Background Endometriosis is a complex, estrogen-dependent disease with a strong genetic component. Although genome-wide association studies (GWAS) have identified multiple susceptibility loci, most associated variants reside in noncoding regions, limiting biological interpretation and causal gene identification. Moreover, GWAS gene prioritization is limited by incomplete tissue-specific annotation coverage (e.g., GTEx, ENCODE, fine-mapping, Mendelian randomization, and network-based methods). We therefore applied the AlphaGenome artificial intelligence framework to prioritize endometriosis-associated variants based on predicted uterus-specific regulatory effects. Methods We analysed the top