Utility of polygenic scores across diverse diseases in a hospital cohort for predictive modeling

Ting-Hsuan Sun, Chia-Chun Wang, Ting-Yuan Liu, Shih-Chang Lo, Yi-Xuan Huang, Shang-Yu Chien, Yu- De Chu, Fuu-Jen Tsai () and Kai-Cheng Hsu ()
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Ting-Hsuan Sun: China Medical University Hospital
Chia-Chun Wang: China Medical University Hospital
Ting-Yuan Liu: China Medical University Hospital
Shih-Chang Lo: China Medical University Hospital
Yi-Xuan Huang: China Medical University Hospital
Shang-Yu Chien: China Medical University Hospital
Yu- De Chu: China Medical University Hospital
Fuu-Jen Tsai: China Medical University Hospital
Kai-Cheng Hsu: China Medical University Hospital

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Polygenic scores estimate genetic susceptibility to diseases. We systematically calculated polygenic scores across 457 phenotypes using genotyping array data from China Medical University Hospital. Logistic regression models assessed polygenic scores’ ability to predict disease traits. The polygenic score model with the highest accuracy, based on maximal area under the receiver operating characteristic curve (AUC), is provided on the GeneAnaBase website of the hospital. Our findings indicate 49 phenotypes with AUC greater than 0.6, predominantly linked to endocrine and metabolic diseases. Notably, hyperplasia of the prostate exhibited the highest disease prediction ability (P value = 1.01 × 10−19, AUC = 0.874), highlighting the potential of these polygenic scores in preventive medicine and diagnosis. This study offers a comprehensive evaluation of polygenic scores performance across diverse human traits, identifying promising applications for precision medicine and personalized healthcare, thereby inspiring further research and development in this field.

Date: 2024
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DOI: 10.1038/s41467-024-47472-5

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