Tour de France Champions born or made: where do we take the genetics of performance?

Colin N. Moran*, Yannis P. Pitsiladis

*Corresponding author for this work

    Research output: Contribution to journalReview articlepeer-review

    21 Citations (Scopus)

    Abstract

    Cyclists in the Tour de France are endurance specialists. Twin and family studies have shown that approximately 50% of the variance in a number of performance-related phenotypes (whether measured at baseline, i.e., natural talent, or in response to training) including those important to cycling can be explained by genetic variation. Research into the specific genetic variants that are responsible has identified over 200 genes containing common genetic variants involved in the genetic predisposition to physical performance. However, typically these explain only a small portion of the variance, perhaps 1–2% and collectively they rarely explain anything approaching the 50% of the variance identified in the twin and family studies. Thus, there is a gap in our understanding of the relationship between heritability and performance. This gap may be bridged by investigation of rare variants or epigenetic variation or by altering study designs through increased collaborations to pool existing cohorts together. Initial findings from such efforts show promising results. This mini-review will touch on the genetics and epigenetics of sporting performance, how they relate to cyclists in the Tour de France and where best future efforts may be directed as well as discuss some preliminary research findings.

    Original languageEnglish
    Pages (from-to)1411-1419
    Number of pages9
    JournalJournal of Sports Sciences
    Volume35
    Issue number14
    DOIs
    Publication statusPublished - 18 Jul 2017

    Scopus Subject Areas

    • Orthopedics and Sports Medicine
    • Physical Therapy, Sports Therapy and Rehabilitation

    User-Defined Keywords

    • Athlome
    • cyclists
    • elite athlete cohorts
    • epigenetics
    • GAMES
    • Genetics
    • PowerGene

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