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<p>Polygenic risk score (PRS) uses genetic variants to assess disease susceptibility. While PRS performance is well-studied in Europeans, its accuracy in East Asians is less explored. This study evaluated PRSs for ten diseases in the Health Examinees (HEXA) cohort (<i>n</i> = 55,870) in Korea. Single-population PRSs were constructed using PRS-CS, LDpred2, and Lassosum based on East Asian GWAS summary statistics (sample sizes: 51,442–341,204), while cross-population PRSs were developed using PRS-CSx and CT-SLEB by integrating European and East Asian GWAS data. PRS-CS consistently outperformed other single-population methods across key metrics, including the likelihood ratio test (LRT), odds ratio per standard deviation (perSD OR), net reclassification improvement (NRI), and area under the curve (AUC). Cross-population PRSs further improved predictive performance, with average increases of 1.08-fold (LRT), 1.07-fold (perSD OR), and 1.15-fold (NRI) across seven diseases with statistical significance, and a 1.01-fold improvement in AUC. Differences in <i>R</i>² between single- and cross-population PRSs were statistically significant for five diseases, showing an average increase of 1.13%. Cross-population PRSs achieved 87.8% of the predictive performance observed in European PRSs. These findings highlight the benefits of integrating European GWAS data while underscoring the need for larger East Asian datasets to improve prediction accuracy.</p>

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Assessment of polygenic risk score performance in East Asian populations for ten common diseases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月6日

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  • Hae-Un Jung,
  • Hyein Jung,
  • Eun Ju Baek,
  • Ji-One Kang,
  • Shin Young Kwon,
  • Jaeyoon You,
  • Ji Eun Lim,
  • Bermseok Oh

摘要

Polygenic risk score (PRS) uses genetic variants to assess disease susceptibility. While PRS performance is well-studied in Europeans, its accuracy in East Asians is less explored. This study evaluated PRSs for ten diseases in the Health Examinees (HEXA) cohort (n = 55,870) in Korea. Single-population PRSs were constructed using PRS-CS, LDpred2, and Lassosum based on East Asian GWAS summary statistics (sample sizes: 51,442–341,204), while cross-population PRSs were developed using PRS-CSx and CT-SLEB by integrating European and East Asian GWAS data. PRS-CS consistently outperformed other single-population methods across key metrics, including the likelihood ratio test (LRT), odds ratio per standard deviation (perSD OR), net reclassification improvement (NRI), and area under the curve (AUC). Cross-population PRSs further improved predictive performance, with average increases of 1.08-fold (LRT), 1.07-fold (perSD OR), and 1.15-fold (NRI) across seven diseases with statistical significance, and a 1.01-fold improvement in AUC. Differences in R² between single- and cross-population PRSs were statistically significant for five diseases, showing an average increase of 1.13%. Cross-population PRSs achieved 87.8% of the predictive performance observed in European PRSs. These findings highlight the benefits of integrating European GWAS data while underscoring the need for larger East Asian datasets to improve prediction accuracy.