A polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by phenomewide association studies
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.
|Keywords||Genomic prediction, Phenome-wide association study, Polycystic ovary syndrome, Polygenic risk Score|
|Persistent URL||dx.doi.org/10.1210/clinem/dgz326, hdl.handle.net/1765/127551|
|Journal||Journal of Clinical Endocrinology and Metabolism|
Joo, Y.Y. (Yoonjung Yoonie), Actkins, K. (Ky'Era), Pacheco, J.A, Basile, A.O. (Anna O.), Carroll, R. (Robert), Crosslin, D.R, … Geoffrey Hayes, M. (M.). (2020). A polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by phenomewide association studies. Journal of Clinical Endocrinology and Metabolism, 105(6). doi:10.1210/clinem/dgz326