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On polygenic risk scores for complex traits prediction.

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Polygenic risk scores (PRS) predict complex traits using genome-wide association studies (GWAS). This study reveals optimal PRS feature selection depends on trait architecture and sample size, explaining prediction accuracy gaps.

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UK Biobankgenome-wide association studiespolygenic risk scoressparsity-free high-dimensional prediction

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Area of Science:

  • Genetics and Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Polygenic risk scores (PRS) are crucial for predicting complex traits from genome-wide association studies (GWAS).
  • Understanding PRS statistical properties in high-dimensional settings is essential for accurate genetic prediction.
  • A gap often exists between in-sample heritability estimates and out-of-sample PRS prediction accuracy.

Purpose of the Study:

  • To derive asymptotic results for out-of-sample prediction R-squared of PRS.
  • To investigate optimal feature selection strategies for constructing PRS.
  • To understand the influence of genetic architecture and sample size on PRS prediction.

Main Methods:

  • Derivation of asymptotic results for PRS out-of-sample R-squared.
  • Analysis of feature selection methods, including p-value thresholds.
  • Numerical illustrations using intensive simulations and UK Biobank data for 33 complex traits.

Main Results:

  • Asymptotic results explain the discrepancy between in-sample and out-of-sample PRS accuracy.
  • Optimal PRS feature selection is contingent on the trait's genetic architecture and the training GWAS sample size (m/n ratio).
  • Stringent feature selection can hinder PRS prediction for highly polygenic traits with large m/n ratios.

Conclusions:

  • The study provides theoretical insights into PRS prediction accuracy and its determinants.
  • Optimal PRS construction requires careful consideration of feature selection based on genetic architecture and sample size.
  • Findings aid in improving PRS utility for complex trait prediction in diverse populations.