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Related Experiment Videos

Letter to the editor: A genetic-based algorithm for personalized resistance training.

A Karanikolou1, G Wang1, Y Pitsiladis1

  • 1University of Brighton, Eastbourne BN20 7SN, United Kingdom.

Biology of Sport
|April 19, 2017
PubMed
Summary

A proposed genetic algorithm for personalized resistance training lacks supporting evidence. Future progress in sports genomics requires large-scale, collaborative research initiatives.

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Area of Science:

  • Sport and Exercise Genomics
  • Human Performance Genetics

Background:

  • A recent study proposed a genetic algorithm using 15 gene polymorphisms to predict athletic power and endurance.
  • The algorithm aimed to personalize resistance training based on genetic predispositions.

Purpose of the Study:

  • To critically evaluate the evidence supporting the proposed genetic algorithm for predicting athletic performance.
  • To outline the necessary advancements for developing reliable training and performance algorithms in sports genomics.

Main Methods:

  • Review of the study design and data presented by Jones et al.
  • Analysis of the limitations in current research for deriving predictive algorithms.

Main Results:

  • The authors found insufficient evidence to support the claims made by Jones et al. regarding their genetic algorithm.
Keywords:
AthletesAthletic performanceGenetic polymorphismPersonalised trainingTalent identification

Related Experiment Videos

  • Current study designs and data are inadequate for creating robust predictive models for training response.

    • Conclusions:

    • The proposed genetic algorithm for personalized resistance training is not supported by the provided evidence.
    • Significant advancements in sports genomics, including large-scale, multi-center collaborations and well-phenotyped cohorts, are essential for future algorithm development.