A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies
Authors: Joo Yoonjung Yoonie , Actkins Ky’Era , Pacheco Jennifer A , Basile Anna O , Carroll Robert , Crosslin David R , Day Felix , Denny Joshua C , Velez Edwards Digna R , 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 ,   International PCOS Consortium, Day Felix , Karaderi Tugce , Jones Michelle R , Meun Cindy , He Chunyan , Drong Alex , Kraft Peter , Lin Nan , Huang Hongyan , Broer Linda , Magi Reedik , Saxena Richa , Laisk-Podar Triin , Urbanek Margrit , Hayes M Geoffrey , Thorleifsson Gudmar , Fernandez-Tajes Juan , Mahajan Anubha , Mullin Benjamin H , Stuckey Bronwyn G A , Spector Timothy D , Wilson Scott G , Goodarzi Mark O , Davis Lea , Obermeyer-Pietsch Barbara , Uitterlinden André G , Anttila Verneri , Neale Benjamin M , Jarvelin Marjo-Riitta , Fauser Bart , Kowalska Irina , Visser Jenny A , Anderson Marianne , Ong Ken , Stener-Victorin Elisabet , Ehrmann David , Legro Richard S , Salumets Andres , McCarthy Mark I , Morin-Papunen Laure , Thorsteinsdottir Unnur , Stefansson Kari , Styrkarsdottir Unnur , Perry John , Dunaif Andrea , Laven Joop , Franks Steve , Lindgren Cecilia M , Welt Corrine K 
Published: January 2020

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-R2) 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.