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<p>Polygenic scores (PGS) have emerged as a promising tool for understanding the genetic underpinnings of physical fitness traits, including aerobic fitness (also called cardiorespiratory fitness), muscular fitness and adiposity, and neuromotor performance (including agility/speed-related parameters). However, the predictive utility of PGS for physical fitness traits is unclear. To address this knowledge gap, we conducted a systematic review to critically evaluate the current body of literature on the association between weighted and unweighted PGS models, and various fitness-related phenotypes. Following PRISMA guidelines, we identified 67 studies published prior to May 2025 that examined 74 weighted and 39 unweighted PGS models in relation to cardiovascular, respiratory, muscular and adiposity, and/or neuromotor performance-related parameters. We classified body composition along with muscular fitness in the same category, because of the close correlation of these parameters. Our evaluation highlights key genetic components implicated in fitness traits, the methodological heterogeneity across studies, and the limitations of current PGS models. While PGSs offer insights into the genetic architecture of physical fitness, their practical application remains constrained by population specificity, polygenic complexity, and environmental interactions. We discuss the implications for personalized interventions and future research directions to enhance the predictive utility of PGS for physical fitness.</p>

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Polygenic scores and physical fitness prediction: a systematic review

  • 期刊论文

  • 发表日期：

    2026年3月21日

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  • Farzaneh Rami,
  • Nazanin Vaziri,
  • Amanda V. Tyndall,
  • David W. Anderson,
  • Marc J. Poulin,
  • Chad A. Bousman

摘要

Polygenic scores (PGS) have emerged as a promising tool for understanding the genetic underpinnings of physical fitness traits, including aerobic fitness (also called cardiorespiratory fitness), muscular fitness and adiposity, and neuromotor performance (including agility/speed-related parameters). However, the predictive utility of PGS for physical fitness traits is unclear. To address this knowledge gap, we conducted a systematic review to critically evaluate the current body of literature on the association between weighted and unweighted PGS models, and various fitness-related phenotypes. Following PRISMA guidelines, we identified 67 studies published prior to May 2025 that examined 74 weighted and 39 unweighted PGS models in relation to cardiovascular, respiratory, muscular and adiposity, and/or neuromotor performance-related parameters. We classified body composition along with muscular fitness in the same category, because of the close correlation of these parameters. Our evaluation highlights key genetic components implicated in fitness traits, the methodological heterogeneity across studies, and the limitations of current PGS models. While PGSs offer insights into the genetic architecture of physical fitness, their practical application remains constrained by population specificity, polygenic complexity, and environmental interactions. We discuss the implications for personalized interventions and future research directions to enhance the predictive utility of PGS for physical fitness.