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

In a recent paper entitled "A genetic-based algorithm for personalized resistance training", Jones et al. [1] presented an algorithm of 15 performance-associated gene polymorphisms that they propose can determine an athlete's training response by predicting power and endurance potenti...

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Published inBiology of sport Vol. 1; no. 1; pp. 31 - 33
Main Authors Karanikolou, A, Wang, G, Pitsiladis, Y
Format Journal Article
LanguageEnglish
Published Poland Termedia Publishing House 01.01.2017
Institute of Sport in Warsaw
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ISSN0860-021X
2083-1862
2083-1862
DOI10.5114/biolsport.2017.63385

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Abstract In a recent paper entitled "A genetic-based algorithm for personalized resistance training", Jones et al. [1] presented an algorithm of 15 performance-associated gene polymorphisms that they propose can determine an athlete's training response by predicting power and endurance potential. However, from the design of their studies and the data provided, there is no evidence to support these authors' assertions. Progress towards such a significant development in the field of sport and exercise genomics will require a paradigm shift in line with recent recommendations for international collaborations such as the Athlome Project (see www.athlomeconsortium.org). Large-scale initiatives, involving numerous multi-centre and well-phenotyped exercise training and elite performance cohorts, will be necessary before attempting to derive and replicate training and/or performance algorithms.
AbstractList In a recent paper entitled “A genetic-based algorithm for personalized resistance training”, Jones et al. [1] presented an algorithm of 15 performance-associated gene polymorphisms that they propose can determine an athlete’s training response by predicting power and endurance potential. However, from the design of their studies and the data provided, there is no evidence to support these authors’ assertions. Progress towards such a significant development in the field of sport and exercise genomics will require a paradigm shift in line with recent recommendations for international collaborations such as the Athlome Project (see www.athlomeconsortium.org). Large-scale initiatives, involving numerous multi-centre and well-phenotyped exercise training and elite performance cohorts, will be necessary before attempting to derive and replicate training and/or performance algorithms.
In a recent paper entitled "A genetic-based algorithm for personalized resistance training", Jones et al. [1] presented an algorithm of 15 performance-associated gene polymorphisms that they propose can determine an athlete's training response by predicting power and endurance potential. However, from the design of their studies and the data provided, there is no evidence to support these authors' assertions. Progress towards such a significant development in the field of sport and exercise genomics will require a paradigm shift in line with recent recommendations for international collaborations such as the Athlome Project (see www.athlomeconsortium.org). Large-scale initiatives, involving numerous multi-centre and well-phenotyped exercise training and elite performance cohorts, will be necessary before attempting to derive and replicate training and/or performance algorithms.In a recent paper entitled "A genetic-based algorithm for personalized resistance training", Jones et al. [1] presented an algorithm of 15 performance-associated gene polymorphisms that they propose can determine an athlete's training response by predicting power and endurance potential. However, from the design of their studies and the data provided, there is no evidence to support these authors' assertions. Progress towards such a significant development in the field of sport and exercise genomics will require a paradigm shift in line with recent recommendations for international collaborations such as the Athlome Project (see www.athlomeconsortium.org). Large-scale initiatives, involving numerous multi-centre and well-phenotyped exercise training and elite performance cohorts, will be necessary before attempting to derive and replicate training and/or performance algorithms.
Author Wang, G
Pitsiladis, Y
Karanikolou, A
AuthorAffiliation University of Brighton, Eastbourne BN20 7SN, United Kingdom
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Keywords Talent identification
Personalised training
Athletes
Athletic performance
Genetic polymorphism
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StartPage 31
SubjectTerms Algorithms
Athletes
Athletic performance
Gene polymorphism
Genetic polymorphism
Letter to the Editor
Personalised training
Physical training
Strength training
Talent identification
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Title Letter to the editor: A genetic-based algorithm for personalized resistance training
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