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<p>Polygenic scores (PGS) are prospective tools for health screening, prevention and trials, but most are trained in European genome-wide association studies and lose accuracy and calibration in non-European populations and those of mixed ancestral heritage. Multi-ancestry methods are proliferating, yet benchmarking standards lag. This review focuses on benchmark design for cross-population prediction. We show how ancestry assignment, linkage disequilibrium references, variant sets, tuning and trait architecture shape apparent performance. We propose a scorecard spanning discrimination, calibration, equity gaps and compute cost, plus stress tests in diverse cohorts and realistic simulations. Finally. we outline a future vision of living, auditable benchmarking frameworks.</p>

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Evidence standards for multi-ancestry polygenic prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月13日

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  • Blessing Oselu,
  • Itunuoluwa Isewon,
  • Jelili Oyelade,
  • Conrad O. Iyegbe

摘要

Polygenic scores (PGS) are prospective tools for health screening, prevention and trials, but most are trained in European genome-wide association studies and lose accuracy and calibration in non-European populations and those of mixed ancestral heritage. Multi-ancestry methods are proliferating, yet benchmarking standards lag. This review focuses on benchmark design for cross-population prediction. We show how ancestry assignment, linkage disequilibrium references, variant sets, tuning and trait architecture shape apparent performance. We propose a scorecard spanning discrimination, calibration, equity gaps and compute cost, plus stress tests in diverse cohorts and realistic simulations. Finally. we outline a future vision of living, auditable benchmarking frameworks.