Authors
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 Pendergrass, Marylyn D Ritchie, Ian B Stanaway, Margrit Urbanek, Theresa L Walunas, Maureen Smith, Rex L Chisholm, Abel N Kho, Lea Davis, M Geoffrey Hayes
Publication date
2020/6
Journal
The Journal of Clinical Endocrinology & Metabolism
Volume
105
Issue
6
Pages
1918-1936
Publisher
Oxford University Press
Description
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 …
Total citations
Scholar articles
YY Joo, KE Actkins, JA Pacheco, AO Basile, R Carroll… - The Journal of Clinical Endocrinology & Metabolism, 2020