TY - JOUR

T1 - A polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by phenomewide association studies

AU - Joo, Yoonjung Yoonie

AU - Actkins, Ky'Era

AU - Pacheco, Jennifer A.

AU - Basile, Anna O.

AU - Carroll, Robert

AU - Crosslin, David R.

AU - Day, Felix

AU - Denny, Joshua C.

AU - Edwards, Digna R.Velez

AU - Hakonarson, Hakon

AU - Harley, John B.

AU - Hebbring, Scott J.

AU - Ho, Kevin

AU - Jarvik, Gail P.

AU - Jones, Michelle

AU - Karaderi, Tugce

AU - Mentch, Frank D.

AU - Meun, Cindy

AU - Namjou, Bahram

AU - Pendergrass, Sarah

AU - Ritchie, Marylyn D.

AU - Stanaway, Ian B.

AU - Urbanek, Margrit

AU - Walunas, Theresa L.

AU - Smith, Maureen

AU - Chisholm, Rex L.

AU - Kho, Abel N.

AU - Davis, Lea

AU - Geoffrey Hayes, M.

N1 - Funding Information: Financial Support: The phase III of the eMERGE Network was initiated and funded by the NHGRI through the following grants: U01HG008657 (Kaiser Permanente Washington/ University of Washington School of Medicine); U01HG008685 (Brigham and Women’s Hospital); U01HG008672 (Vanderbilt University Medical Center); U01HG008666 (Cincinnati Children’s Hospital Medical Center); U01HG006379 (Mayo Clinic); U01HG008679 (Geisinger Clinic); U01HG008680 (Columbia University Health Sciences); U01HG008684 (Children’s Hospital of Philadelphia); U01HG008673 (Northwestern University); U01HG008701 (Vanderbilt University Medical Center serving as the Coordinating Center); U01HG008676 (Partners Healthcare/Broad Institute); and U01HG008664 (Baylor College of Medicine). Publisher Copyright: © Endocrine Society 2020. All rights reserved.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Genomic prediction

KW - Phenome-wide association study

KW - Polycystic ovary syndrome

KW - Polygenic risk Score

U2 - 10.1210/clinem/dgz326

DO - 10.1210/clinem/dgz326

M3 - Journal article

C2 - 31917831

AN - SCOPUS:85085619085

VL - 105

JO - Journal of Clinical Endocrinology and Metabolism

JF - Journal of Clinical Endocrinology and Metabolism

SN - 0013-7227

IS - 6

ER -