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

J Clin Endocrinol Metab. 2020 Jun 1;105(6):1918-1936. doi: 10.1210/clinem/dgz326.


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 to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment.

Design, patients, and methods: Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS.

Results: The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity", "type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension", and "sleep apnea" reaching phenome-wide significance.

Conclusions: Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome-phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.

Keywords: genomic prediction; phenome-wide association study; polycystic ovary syndrome; polygenic risk score.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adolescent
  • Aged
  • Algorithms*
  • Case-Control Studies
  • Child
  • Electronic Health Records
  • Female
  • Follow-Up Studies
  • Genetic Predisposition to Disease
  • Genome-Wide Association Study*
  • Humans
  • Middle Aged
  • Multifactorial Inheritance / genetics*
  • Phenomics / methods*
  • Phenotype*
  • Polycystic Ovary Syndrome / diagnosis*
  • Polycystic Ovary Syndrome / epidemiology
  • Polycystic Ovary Syndrome / genetics
  • Prognosis
  • Risk Factors