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  3. Improving Polygenic Score Prediction For Underrepresented Groups Through Transfer Learning.
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  3. Improving Polygenic Score Prediction For Underrepresented Groups Through Transfer Learning.

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Improving polygenic score prediction for underrepresented groups through transfer learning.

Hao Wu1,2, Paulino Pérez-Rodríguez3, Michael Boehnke4

  • 1Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.

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|January 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces GPTL, an R package using transfer learning to improve polygenic scores (PGS) for diverse ancestries. GPTL algorithms enhance prediction accuracy, outperforming single-ancestry PGS and matching multi-ancestry methods.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Large biobanks have improved polygenic score (PGS) accuracy.
  • Existing PGS often show reduced performance in non-European ancestries due to derivation from European-ancestry data.
  • Transfer learning presents a method to enhance PGS prediction across diverse populations.

Purpose of the Study:

  • To introduce GPTL, an R package implementing transfer learning for developing polygenic scores.
  • To address the ancestry-related disparities in PGS predictive performance.
  • To provide a flexible software tool for PGS development using various data types.

Main Methods:

  • Implementation of three transfer learning approaches within the GPTL R package: gradient descent with early stopping, penalized regression, and a Bayesian method with finite-mixture priors.
  • Utilizing simulated data and real-world data from UK-Biobank and All of Us.
  • Comparison of transfer learning-based PGS with single-ancestry and multi-ancestry ensemble-based PGS.

    • Main Results:

    • PGS developed using GPTL's transfer learning algorithms consistently outperformed single-ancestry PGS.
    • In many scenarios, GPTL-based PGS achieved performance comparable to or better than multi-ancestry ensemble-based PGS.
    • The developed methods demonstrated effectiveness across both simulated and real genomic datasets.

    Conclusions:

    • GPTL offers a robust solution for developing more accurate and equitable polygenic scores across diverse ancestries.
    • Transfer learning is a powerful strategy to mitigate ancestry-related biases in genomic prediction.
    • The GPTL software package facilitates the application of advanced transfer learning techniques for personalized genomics and genetic research.