학술논문
A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies
CLINICAL RESEARCH ARTICLE
CLINICAL RESEARCH ARTICLE
Document Type
Report
Author
Joo, Yoonjung Yoonie; Actkins, Ky'Era; Pacheco, Jennifer A.; Basile, Anna O.; Carroll, Robert; Crosslin, David R.; Day, Felix; Denny, Joshua C.; Edwards, Digna R. Velez; Hakonarson, Hakon; Harley, John B.; Hebbring, Scott J.; Ho, Kevin; Jarvik, Gail P.; Jones, Michelle; Karaderi, Tugce; Mentch, Frank D.; Meun, Cindy; Namjou, Bahram; Pendergrass, Sarah; Ritchie, Marylyn D.; Stanaway, Ian B.; Urbanek, Margrit; Walunas, Theresa L.; Smith, Maureen; Chisholm, Rex L.; Kho, Abel N.; Davis, Lea; Hayes, M.Geoffrey
Source
Journal of Clinical Endocrinology & Metabolism. June 2020, Vol. 105 Issue 6, p1918, 19 p.
Subject
Language
English
ISSN
0021-972X
Abstract
Polycystic ovary syndrome (PCOS) is the most common reproductive metabolic disorder, affecting 5% to 15% of reproductive age women worldwide (1). The estimated cost of diagnosing and treating American women [...]
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-R (2)) 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. Abbreviations: AA, African ancestry; ANOVA, analysis of variance; BMI, body mass index; EA, European ancestry; EHR, electronic health records; eMERGE, electronic Medical Records and Genomics Network; GWAS, genome-wide association study; IBD, identity-by-descent; ICD-CM, 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, polycystic ovary syndrome; PPRS, polygenic and phenotypic risk score; PRS, polygenic risk score; RAF, risk allele frequency; ROC, receiving operating characteristic; SNV, single nucleotide variant. (J Clin Endocrinol Metab 105: 1918-1936, 2020) Key Words: phenome-wide association study, genomic prediction, polygenic risk score, polycystic ovary syndrome
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-R (2)) 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. Abbreviations: AA, African ancestry; ANOVA, analysis of variance; BMI, body mass index; EA, European ancestry; EHR, electronic health records; eMERGE, electronic Medical Records and Genomics Network; GWAS, genome-wide association study; IBD, identity-by-descent; ICD-CM, 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, polycystic ovary syndrome; PPRS, polygenic and phenotypic risk score; PRS, polygenic risk score; RAF, risk allele frequency; ROC, receiving operating characteristic; SNV, single nucleotide variant. (J Clin Endocrinol Metab 105: 1918-1936, 2020) Key Words: phenome-wide association study, genomic prediction, polygenic risk score, polycystic ovary syndrome