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An ensemble penalized regression method for multi-ancestry polygenic risk prediction.

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We developed PROSPER, a novel method for multi-ancestry polygenic risk scores (PRS). PROSPER improves prediction for minority populations by integrating diverse genome-wide association studies (GWAS) data.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Advanced polygenic risk scores (PRS) aim to predict complex traits and diseases.
  • Current PRS methods often lack transferability due to training predominantly on European ancestry data.
  • This limits their utility in diverse global populations.

Purpose of the Study:

  • To introduce PROSPER (Polygenic Risk scOres based on enSemble of PEnalized Regression models), a novel method for generating multi-ancestry PRS.
  • To enhance the predictive power of PRS for minority populations by leveraging diverse genomic data.
  • To provide a computationally scalable solution for large-scale, multi-ancestry PRS analysis.

Main Methods:

  • PROSPER integrates genome-wide association studies (GWAS) summary statistics from multiple ancestries.
  • It employs a combination of LASSO and RIDGE penalized regression models.
  • An ensemble approach combines PRS derived from different penalty parameters for optimal performance.

Main Results:

  • PROSPER demonstrated substantial improvements in multi-ancestry polygenic prediction across various genetic architectures.
  • In African ancestry populations, PROSPER increased prediction R² by an average of 70% compared to PRS-CSx for continuous traits.
  • The method showed significant gains in out-of-sample predictive accuracy in real-world datasets.

Conclusions:

  • PROSPER offers a significant advancement in developing accurate and transferable polygenic risk scores across diverse ancestries.
  • The method effectively addresses the limitations of existing PRS in minority populations.
  • PROSPER is computationally efficient and scalable for large genomic datasets and multiple populations.