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Learning high-order interactions for polygenic risk prediction.

Michela C Massi1,2, Nicola R Franco1, Andrea Manzoni1

  • 1MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy.

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|February 10, 2023
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Summary
This summary is machine-generated.

This study introduces High-order Interactions-aware Polygenic Risk Score (hiPRS), a novel method to improve genetic risk prediction by modeling complex gene interactions. hiPRS offers superior performance and interpretability compared to traditional approaches, even in challenging datasets.

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Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Precision Medicine

Background:

  • Traditional Polygenic Risk Scores (PRS) struggle to model complex, high-order, non-linear SNP-SNP interactions (epistasis).
  • The computational complexity of modeling numerous interactions limits the statistical reliability of existing PRS methods.
  • There is a need for PRS approaches that can effectively incorporate gene-gene interactions for improved phenotype prediction.

Purpose of the Study:

  • To develop a novel Polygenic Risk Score (PRS) approach, termed High-order Interactions-aware Polygenic Risk Score (hiPRS), capable of modeling high-order SNP-SNP interactions.
  • To create a computationally efficient and statistically robust method for predicting binary phenotypes by integrating genetic interactions.
  • To provide an interpretable PRS model that avoids reliance on external data like GWAS summary statistics.

Main Methods:

  • Developed hiPRS, combining frequent itemsets mining for interaction discovery and Mutual Information for interaction selection.
  • Constructed a weighted model of user-specified dimensionality for phenotype prediction.
  • Evaluated hiPRS performance through comprehensive simulations and a real-world case study on colorectal cancer patient mortality.

Main Results:

  • hiPRS demonstrated superior performance over state-of-the-art methods in both predictive accuracy and model interpretability.
  • The method proved robust to challenges such as small sample sizes, class imbalance, and noisy data.
  • An interaction-aware scoring model was successfully applied to predict mortality in colorectal cancer patients.

Conclusions:

  • hiPRS offers a significant advancement in Polygenic Risk Score methodology by effectively incorporating high-order genetic interactions.
  • The approach provides a powerful, interpretable, and robust tool for precision medicine and genetic susceptibility assessment.
  • hiPRS has potential applications in predicting disease risk and treatment outcomes, as shown in the colorectal cancer case study.