A polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by phenomewide association studies

Yoonjung Yoonie Joo, Ky'Era Actkins, Jennifer A. Pacheco, Anna O. Basile, Robert Carroll, David R. Crosslin, Felix Day, Joshua C. Denny, Digna R.Velez Edwards, Hakon Hakonarson, John B. Harley, Scott J. Hebbring, Kevin Ho, Gail P. Jarvik, Michelle Jones, Tugce Karaderi, Frank D. Mentch, Cindy Meun, Bahram Namjou, Sarah PendergrassMarylyn D. Ritchie, Ian B. Stanaway, Margrit Urbanek, Theresa L. Walunas, Maureen Smith, Rex L. Chisholm, Abel N. Kho, Lea Davis, M. Geoffrey Hayes*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


Context: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated tobe unidentified in clinical practice. Objective: Utilizing polygenic risk prediction, we aim to identify the phenome-widecomorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventivetreatment.Design, Patients, and Methods: Leveraging the electronic health records (EHRs) of 124 852individuals, 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). Weevaluated its predictive capability across different ancestries and perform a PRS-based phenomewide association study (PheWAS) to assess the phenomic expression of the heightened risk ofPCOS.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 modelacross European, African, and multi-ancestry participants respectively. The subsequent PRSpowered PheWAS identified a high level of shared biology between PCOS and a range ofmetabolic 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 stratificationand risk prediction, especially in a multifactorial condition like PCOS, across different geneticorigins. By utilizing the individual genome-phenome data available from the EHR, our approachalso demonstrates that polygenic prediction by PRS can provide valuable opportunities todiscover the pleiotropic phenomic network associated with PCOS pathogenesis.Abbreviations: AA, African ancestry; ANOVA, analysis of variance; BMI, body mass index; EA,European ancestry; EHR, electronic health records; eMERGE, electronic Medical Records andGenomics Network; GWAS, genome-wide association study; IBD, identity-by-descent; ICDCM, International Classification of Diseases, Clinical Modification; LD, linkage disequilibrium;MA, multi-ancestry; MAF, minor allele frequency; NIH, National Institutes of Health; PCA,principal component analysis; PheWAS, phenome-wide association study; PCOS, polycysticovary syndrome; PPRS, polygenic and phenotypic risk score; PRS, polygenic risk score; RAF, riskallele frequency; ROC, receiving operating characteristic; SNV, single nucleotide variant.

Original languageEnglish (US)
JournalJournal of clinical endocrinology and metabolism
Issue number6
StatePublished - Jun 1 2020


  • Genomic prediction
  • Phenome-wide association study
  • Polycystic ovary syndrome
  • Polygenic risk Score

ASJC Scopus subject areas

  • Biochemistry, medical
  • Endocrinology
  • Biochemistry
  • Clinical Biochemistry
  • Endocrinology, Diabetes and Metabolism


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