A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies

摘要: Context As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. Objective Utilizing polygenic risk prediction, we aim identify the phenome-wide comorbidity patterns characteristic PCOS improve accurate diagnosis and preventive treatment. Design, patients, methods Leveraging electronic health records (EHRs) 124 852 individuals, developed a prediction algorithm by combining scores (PRS) component phenotypes into phenotypic score (PPRS). We evaluated its predictive capability across different ancestries perform PRS-based association study (PheWAS) assess phenomic expression heightened PCOS. Results The integrated improved average performance (pseudo-R2) for detection 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over null model European, African, multi-ancestry participants respectively. subsequent PRS-powered PheWAS identified high level shared biology between range metabolic endocrine outcomes, especially obesity diabetes: "morbid obesity", "type 2 diabetes", "hypercholesterolemia", "disorders lipid metabolism", "hypertension", "sleep apnea" reaching significance. Conclusions Our has expanded methodological utility PRS patient stratification multifactorial condition like PCOS, genetic origins. By utilizing individual genome-phenome data available from EHR, our approach also demonstrates that can provide valuable opportunities discover pleiotropic network associated pathogenesis.