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 10,000 endometriosis-associated single-nucleotide polymorphisms (SNPs) identified by previously published GWAS by Rahmioglu et al, using AlphaGenome across multiple genomic output types. Uterus-specific predictions with high-confidence effects (quantile score| [&ge;] 0.90) were grouped into major regulatory modalities. AlphaGenome-prioritized SNPs within {+/-}500 kb of known GWAS loci were classified into tiers based on the number of supported regulatory modalities, with broader support indicating stronger multilayer regulatory evidence. Effect allele frequency, linkage disequilibrium (LD), and overlap with previously published endometriosis-associated variants were also assessed. Results AlphaGenome generated uterus-specific, 147,033 high-confidence signals across 10,000 endometriosis-associated variants, spanning six regulatory modalities including gene expression, promoter activity, chromatin accessibility, transcription factor binding, histone modification, and RNA splicing. Within the 42 established endometriosis GWAS loci, AlphaGenome identified 42 alternative sub-threshold SNPs with stronger predicted uterus-specific regulatory effects than the published GWAS lead variants. Nineteen AlphaGenome-prioritized SNPs were classified as tier 1, showing support across all six regulatory modalities, compared with five GWAS lead SNPs. Linkage disequilibrium analysis identified eight tier 1 SNPs with weak-to-low LD (r<2> < 0.5) relative to the corresponding GWAS lead variants, regulating majority of genes involved in estrogen-driven proliferation and inflammatory signalling, highlighting their potential relevance to endometriosis pathogenesis. Additionally, we identified 167 genome-wide significant SNPs outside 42 published GWAS lead SNP loci including six tier 1 SNPs (rs1482061, rs7772579, rs6557140, rs2982571, rs12631337 and rs79626929), encompassing genes nearby ESR1/6q25.1, substantiating biological relevance for endometriosis pathogenesis. Conclusions AlphaGenome-based regulatory prioritization refined endometriosis-associated genome-wide association study loci by identifying variants with stronger predicted uterus-specific functional relevance. These findings provide a regulatory framework for prioritizing candidate variants and genes for downstream functional validation in endometriosis.