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SDPRX: A statistical method for cross-population prediction of complex traits.

Geyu Zhou1, Tianqi Chen2, Hongyu Zhao1

  • 1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

American Journal of Human Genetics
|December 2, 2022
PubMed
Summary

This study introduces SDPRX, a new method to improve polygenic risk score (PRS) accuracy in diverse populations. SDPRX enhances PRS transferability across ancestries by integrating genome-wide association study data from multiple groups.

Keywords:
PRScomplex traitscross-populationgenome-wide association studiespolygenic risk score

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Area of Science:

  • Genetics
  • Statistical genetics
  • Population genetics

Background:

  • Polygenic risk scores (PRS) are valuable for identifying disease susceptibility from genotypes.
  • Current PRS methods show limited transferability across diverse ancestral populations, hindering broad application.
  • Improving PRS prediction in non-European populations is crucial for equitable genomic medicine.

Purpose of the Study:

  • To develop a novel statistical method, SDPRX, for enhancing PRS prediction accuracy in non-European populations.
  • To address the challenge of limited PRS transferability across different ancestral groups.
  • To integrate genome-wide association study (GWAS) summary statistics from multiple populations effectively.

Main Methods:

  • Developed SDPRX, a statistical method integrating GWAS summary statistics from diverse populations.
  • SDPRX automatically adjusts for linkage disequilibrium differences between populations.
  • Characterizes joint distribution of variant effect sizes as null, population-specific, or shared with correlation.

Main Results:

  • SDPRX demonstrated improved prediction performance in non-European populations compared to existing methods.
  • Simulations and real-trait applications validated the efficacy of SDPRX.
  • The method effectively handles differences in linkage disequilibrium and effect size distributions across populations.

Conclusions:

  • SDPRX significantly enhances the prediction accuracy and transferability of polygenic risk scores in non-European populations.
  • This method offers a promising approach for more equitable and accurate genomic risk prediction across diverse ancestries.
  • SDPRX represents a key advancement in applying PRS to global health initiatives.