A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies

Abstract

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.

Genetic Susceptibility to Joint Occurrence of Polycystic Ovary Syndrome and Hashimoto’s Thyroiditis: How Far Is Our Understanding?

“…Moreover, due to the complex background of both disorders, this combination may be specific not so much to the risk of the disease itself as to the expression of its individual phenotypes. In line with this assumption, it has been recently indicated that the use of combined polygenic and phenotypic risk prediction may improve the accuracy of PCOS diagnosis ( 279 ).…”

Characterizing the Clinical and Genetic Spectrum of Polycystic Ovary Syndrome in Electronic Health Records

“…Thus, we were limited to using summary statistics from a European GWAS to build PRS both for our European- and African-descent samples, even though it is known that European-based PRS do not perform as robustly in non-European samples ( 41 ). Despite this, previous studies have shown that PCOS PRS models can detect cross-ancestry genetic risk in African and multiancestry cohorts using European-based PRS models ( 42 ). In our study, the PCOS PRS model explained little variance in case/control status (PRS pseudo- R 2 ≤ 1%), but the PCOS PRS was significantly higher among cases in the PCOS coded-strict , PCOS keyword-broad , and the PCOS keyword-strict algorithms compared to controls.…”

A scalable EHR-based approach for phenotype discovery and variant interpretation for hereditary cancer genes

“…Limitations of this study include the use of phecodes, which are phenotypes based on aggregations of related billing codes. While phecodes have been shown to be an effective tool for replicating genetic associations with EHR data [21][22][23] , they are unable to capture all phenotypes, including some unique characteristics of hereditary cancer syndromes. For example, patients with familial adenomatous polyposis typically present with numerous polyps, a condition which lacks a specific billing code.…”