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<p>Polygenic scores (PGSs) quantify individual genetic susceptibility to complex diseases and can identify high-risk individuals well before clinical onset. Their clinical translation, however, requires population-based reference resources, standardized benchmarking, and accessible tools for translating individual scores into disease likelihood. In this article, we systematically evaluate 3168 PGS models, primarily from the PGS Catalog, in 473,681 FinnGen participants, placing all models on a common performance scale to enable cross-model and cross-trait comparison. For each PGS, we create ancestry-adjusted reference distributions, providing a biobank-scale resource for interpreting individual scores. We perform phenome-wide association studies for each PGS, identifying 439,070 significant phenotypic associations, demonstratin g that integrating multiple scores improves predictive performance for most complex diseases, and providing public access to 11 top-performing interactive time-to-event models. All resources are accessible through the PGS Browser (<a href="http://pgs.nchigm.org">pgs.nchigm.org</a>), which offers a population-aware framework for score interpretation and lays groundwork for the clinical application of PGSs.</p>

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PGS Browser: a public platform for personalized polygenic score analysis and interpretation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月20日

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  • Nikita Kolosov,
  • Mary P. Reeve,
  • Pietro Della Briotta Parolo,
  • Mitja I. Kurki,
  • Pietro Della Briotta Parolo,
  • Vincent Llorens,
  • Timo Petteri Sipila,
  • Adam Herman,
  • Ivan Molotkov,
  • Mervi Aavikko,
  • Samuli Ripatti,
  • Aarno Palotie,
  • Mark J. Daly,
  • Mykyta Artomov

摘要

Polygenic scores (PGSs) quantify individual genetic susceptibility to complex diseases and can identify high-risk individuals well before clinical onset. Their clinical translation, however, requires population-based reference resources, standardized benchmarking, and accessible tools for translating individual scores into disease likelihood. In this article, we systematically evaluate 3168 PGS models, primarily from the PGS Catalog, in 473,681 FinnGen participants, placing all models on a common performance scale to enable cross-model and cross-trait comparison. For each PGS, we create ancestry-adjusted reference distributions, providing a biobank-scale resource for interpreting individual scores. We perform phenome-wide association studies for each PGS, identifying 439,070 significant phenotypic associations, demonstratin g that integrating multiple scores improves predictive performance for most complex diseases, and providing public access to 11 top-performing interactive time-to-event models. All resources are accessible through the PGS Browser (pgs.nchigm.org), which offers a population-aware framework for score interpretation and lays groundwork for the clinical application of PGSs.