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<p>Polygenic risk scores (PRSs) are promising tools for advancing precision medicine. However, existing PRS construction methods rely on static summary statistics derived from genome-wide association studies, which are often updated at lengthy intervals. With genetic data and health outcomes continuously being generated, the current PRS training and deployment paradigm is suboptimal in maximizing prediction accuracy for incoming patients in healthcare settings. We introduce real-time PRS-CS (rtPRS-CS), which enables online, dynamic refinement and standardization of PRS as each new sample is collected. We perform extensive simulations to evaluate rtPRS-CS across various genetic architectures and training sample sizes. Leveraging quantitative traits from two large-scale biobanks, we show that rtPRS-CS can integrate massive streaming data to enhance PRS prediction over time. We further apply rtPRS-CS to 22 schizophrenia cohorts across seven Asian regions, demonstrating the clinical utility of rtPRS-CS in dynamically capturing health status changes and predicting disease risk across diverse genetic ancestries.</p>

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Real-time dynamic polygenic prediction for streaming data

  • 期刊论文

  • 发表日期：

    2025年10月29日

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  • Justin D. Tubbs,
  • Yu Chen,
  • Rui Duan,
  • Hailiang Huang,
  • Tian Ge

摘要

Polygenic risk scores (PRSs) are promising tools for advancing precision medicine. However, existing PRS construction methods rely on static summary statistics derived from genome-wide association studies, which are often updated at lengthy intervals. With genetic data and health outcomes continuously being generated, the current PRS training and deployment paradigm is suboptimal in maximizing prediction accuracy for incoming patients in healthcare settings. We introduce real-time PRS-CS (rtPRS-CS), which enables online, dynamic refinement and standardization of PRS as each new sample is collected. We perform extensive simulations to evaluate rtPRS-CS across various genetic architectures and training sample sizes. Leveraging quantitative traits from two large-scale biobanks, we show that rtPRS-CS can integrate massive streaming data to enhance PRS prediction over time. We further apply rtPRS-CS to 22 schizophrenia cohorts across seven Asian regions, demonstrating the clinical utility of rtPRS-CS in dynamically capturing health status changes and predicting disease risk across diverse genetic ancestries.