Literature DB >> 24262098

Genomics of elite sporting performance: what little we know and necessary advances.

Guan Wang1, Sandosh Padmanabhan, Bernd Wolfarth, Noriyuki Fuku, Alejandro Lucia, Ildus I Ahmetov, Pawel Cieszczyk, Malcolm Collins, Nir Eynon, Vassilis Klissouras, Alun Williams, Yannis Pitsiladis.   

Abstract

Numerous reports of genetic associations with performance- and injury-related phenotypes have been published over the past three decades; these studies have employed primarily the candidate gene approach to identify genes that associate with elite performance or with variation in performance-and/or injury-related traits. Although generally with small effect sizes and heavily prone to type I statistic error, the number of candidate genetic variants that can potentially explain elite athletic status, injury predisposition, or indeed response to training will be much higher than that examined by numerous biotechnology companies. Priority should therefore be given to applying whole genome technology to sufficiently large study cohorts of world-class athletes with adequately measured phenotypes where it is possible to increase statistical power. Some of the elite athlete cohorts described in the literature might suffice, and collectively, these cohorts could be used for replication purposes. Genome-wide association studies are ongoing in some of these cohorts (i.e., Genathlete, Russian, Spanish, Japanese, United States, and Jamaican cohorts), and preliminary findings include the identification of one single nucleotide polymorphism (SNP; among more than a million SNPs analyzed) that associates with sprint performance in Japanese, American (i.e., African American), and Jamaican cohorts with a combined effect size of ~2.6 (P-value <5×10(-7)) and good concordance with endurance performance between select cohorts. Further replications of these signals in independent cohorts will be required, and any replicated SNPs will be taken forward for fine-mapping/targeted resequencing and functional studies to uncover the underlying biological mechanisms. Only after this lengthy and costly process will the true potential of genetic testing in sport be determined.
© 2013 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  ACE/ACTN3 polymorphisms; candidate gene association study; complex trait; elite performance; genome-wide association study; single nucleotide polymorphism

Mesh:

Substances:

Year:  2013        PMID: 24262098     DOI: 10.1016/B978-0-12-407703-4.00004-9

Source DB:  PubMed          Journal:  Adv Genet        ISSN: 0065-2660            Impact factor:   1.944


  15 in total

1.  Genes and Elite Marathon Running Performance: A Systematic Review.

Authors:  Hannah J Moir; Rachael Kemp; Dirk Folkerts; Owen Spendiff; Cristina Pavlidis; Elizabeth Opara
Journal:  J Sports Sci Med       Date:  2019-08-01       Impact factor: 2.988

Review 2.  Concurrent exercise training: do opposites distract?

Authors:  Vernon G Coffey; John A Hawley
Journal:  J Physiol       Date:  2016-10-09       Impact factor: 5.182

3.  Genome-wide association study identifies three novel genetic markers associated with elite endurance performance.

Authors:  Ii Ahmetov; Na Kulemin; Dv Popov; Va Naumov; Eb Akimov; Yr Bravy; Es Egorova; Aa Galeeva; Ev Generozov; Es Kostryukova; Ak Larin; Lj Mustafina; Ea Ospanova; Av Pavlenko; Lm Starnes; P Żmijewski; Dg Alexeev; Ol Vinogradova; Vm Govorun
Journal:  Biol Sport       Date:  2014-10-21       Impact factor: 2.806

4.  Sports genetics: the PPARA gene and athletes' high ability in endurance sports. A systematic review and meta-analysis.

Authors:  S Lopez-Leon; C Tuvblad; D A Forero
Journal:  Biol Sport       Date:  2015-11-19       Impact factor: 2.806

5.  Preface: genomics and biology of exercise is undergoing a paradigm shift.

Authors:  Nir Eynon; Sarah Voisin; Alejandro Lucia; Guan Wang; Yannis Pitsiladis
Journal:  BMC Genomics       Date:  2017-11-14       Impact factor: 3.969

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

Authors:  A Karanikolou; G Wang; Y Pitsiladis
Journal:  Biol Sport       Date:  2016-11-11       Impact factor: 2.806

Review 7.  Genes to predict VO2max trainability: a systematic review.

Authors:  Camilla J Williams; Mark G Williams; Nir Eynon; Kevin J Ashton; Jonathan P Little; Ulrik Wisloff; Jeff S Coombes
Journal:  BMC Genomics       Date:  2017-11-14       Impact factor: 3.969

8.  Applying personal genetic data to injury risk assessment in athletes.

Authors:  Gabrielle T Goodlin; Andrew K Roos; Thomas R Roos; Claire Hawkins; Sydney Beache; Stephen Baur; Stuart K Kim
Journal:  PLoS One       Date:  2015-04-28       Impact factor: 3.240

9.  EPAS1 gene variants are associated with sprint/power athletic performance in two cohorts of European athletes.

Authors:  Sarah Voisin; Pawel Cieszczyk; Vladimir P Pushkarev; Dmitry A Dyatlov; Boris F Vashlyayev; Vladimir A Shumaylov; Agnieszka Maciejewska-Karlowska; Marek Sawczuk; Lidia Skuza; Zbigniew Jastrzebski; David J Bishop; Nir Eynon
Journal:  BMC Genomics       Date:  2014-05-18       Impact factor: 3.969

10.  Direct-to-consumer genetic testing for predicting sports performance and talent identification: Consensus statement.

Authors:  Nick Webborn; Alun Williams; Mike McNamee; Claude Bouchard; Yannis Pitsiladis; Ildus Ahmetov; Euan Ashley; Nuala Byrne; Silvia Camporesi; Malcolm Collins; Paul Dijkstra; Nir Eynon; Noriyuki Fuku; Fleur C Garton; Nils Hoppe; Søren Holm; Jane Kaye; Vassilis Klissouras; Alejandro Lucia; Kamiel Maase; Colin Moran; Kathryn N North; Fabio Pigozzi; Guan Wang
Journal:  Br J Sports Med       Date:  2015-12       Impact factor: 13.800

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