Regulatory risk loci link disrupted androgen response to pathophysiology of Polycystic Ovary Syndrome.

ABSTRACT: A major challenge in deciphering the complex genetic landscape of Polycystic Ovary Syndrome (PCOS) lies in the limited understanding of how susceptibility loci drive molecular mechanisms across diverse phenotypes. To address this, we integrated molecular and epigenomic annotations from proposed causal cell-types and employed a deep learning (DL) framework to predict cell-type-specific regulatory effects of PCOS risk variants. Our analysis revealed that these variants affect key transcription factor (TF) binding sites, including NR4A1/2, NHLH2, FOXA1, and WT1, which regulate gonadotropin signaling, folliculogenesis, and steroidogenesis across brain and endocrine cell-types. The DL model, which showed strong concordance with reporter assay data, identified enhancer-disrupting activity in app

SUBMITTER: Srivastava J 

PROVIDER: S-EPMC11974941 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature