A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies.
J Clin Endocrinol Metab
; 105(6)2020 06 01.
Article
en En
| MEDLINE
| ID: mdl-31917831
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, ANDMETHODS:
Leveraging the electronic health records (EHRs) of 124â 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.Palabras clave
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Base de datos:
MEDLINE
Asunto principal:
Fenotipo
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Síndrome del Ovario Poliquístico
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Algoritmos
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Herencia Multifactorial
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Estudio de Asociación del Genoma Completo
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Fenómica
Tipo de estudio:
Etiology_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Adolescent
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Aged
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Child
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Female
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Humans
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Middle aged
Idioma:
En
Revista:
J Clin Endocrinol Metab
Año:
2020
Tipo del documento:
Article