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Updated: Oct 7, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
Statistical models and computational tools for predicting complex traits and diseases
Wonil Chung1,2
1Department of Statistics and Actuarial Science, Soongsil University, Seoul 06978, Korea.
Abstract:
Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to computed the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation.
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