Advertisement

A Novel Multilocus Genetic Model Can Predict Muscle Fibers Composition

  • Oleg BorisovEmail author
  • Nikolay Kulemin
  • Ildus Ahmetov
  • Edward Generozov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 663)

Abstract

Muscle fibers composition is determined by the specific genetic characteristics to a large extent and it is connected with various physical traits of athletes. Establishment of genetic markers for sport prediction is an important task of great scientific and practical importance. We aimed to create a relevant model for prediction of the athlete muscle fibers structure based on a complex analysis of known genetic factors. The model included 14 single nucleotide polymorphisms and was tested for 55 subjects. Based on the performed ROC analysis, the model accuracy measured as area under the receiver operating characteristic curve (AUC) was 81% for professional athletes and 73% for non-athletes. The obtained results demonstrate that the proposed models can be used for sport testing.

Keywords

Sport testing Muscle fibers composition Genetic models 

Notes

Acknowledgments

This work was supported by the Russian Science Foundation, Grant No. 17-15-01436: “Comprehensive analysis of the contribution of genetic, epigenetic and environmental factors in the individual variability of the composition of human muscle fibers”.

References

  1. 1.
    Tsianos, G.I., et al.: Associations of polymorphisms of eight muscle- or metabolism-related genes with performance in Mount Olympus marathon runners. J. Appl. Physiol. 108, 567–574 (2010). doi: 10.1152/japplphysiol.00780.2009 CrossRefGoogle Scholar
  2. 2.
    Doring, F., et al.: A common haplotype and the Pro582Ser polymorphism of the hypoxia-inducible factor-1 (HIF1A) gene in elite endurance athletes. J. Appl. Physiol. 108, 1497–1500 (2010). doi: 10.1152/japplphysiol.01165.2009 CrossRefGoogle Scholar
  3. 3.
    Kundu, S., Mihaescu, R., Meijer, C.M.C., Bakker, R., Janssens, A.C.J.W.: Estimating the predictive ability of genetic risk models in simulated data based on published results from genome-wide association studies. Front. Genet. 5, 179 (2014)CrossRefGoogle Scholar
  4. 4.
    James, K.M., et al.: Impact of direct-to-consumer predictive genomic testing on risk perception and worry among patients receiving routine care in a preventive health clinic. Mayo Clin. Proc. 86, 933–940 (2011). doi: 10.4065/mcp.2011.0190 CrossRefGoogle Scholar
  5. 5.
    Ahmetov, I.I., Vinogradova, O.L., Williams, A.G.: Gene polymorphisms and fiber-type composition of human skeletal muscle. Int J Sport Nutr Exerc. Metab. 22, 292–303 (2012)CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Ahmetov, I.I., et al.: The combined impact of metabolic gene polymorphisms on elite endurance athlete status and related phenotypes. Hum. Genet. 126, 751–761 (2009). doi: 10.1007/s00439-009-0728-4 CrossRefGoogle Scholar
  8. 8.
    Gineviciene, V., et al.: Association analysis of ACE, ACTN3 and PPARGC1A gene polymorphisms in two cohorts of European strength and power athletes. Biol. Sport 33, 199–206 (2016). doi: 10.5604/20831862.1201051 CrossRefGoogle Scholar
  9. 9.
    Gomez-Gallego, F., et al.: The C allele of the AGT Met235Thr polymorphism is associated with power sports performance. Appl. Physiol. Nutr. Metab. 34, 1108–1111 (2009). doi: 10.1139/H09-108 CrossRefGoogle Scholar
  10. 10.
    Yeager, M., et al.: Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat. Genet. 39, 645–649 (2007). doi: 10.1038/ng2022 CrossRefGoogle Scholar
  11. 11.
    Burton, P.R., et al.: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007). doi: 10.1038/nature05911 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Oleg Borisov
    • 1
    • 2
    Email author
  • Nikolay Kulemin
    • 1
  • Ildus Ahmetov
    • 1
    • 3
  • Edward Generozov
    • 1
  1. 1.Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological AgencyMoscowRussia
  2. 2.Moscow Institute of Physics and TechnologyDolgoprudnyRussia
  3. 3.Kazan State Medical UniversityKazanRussia

Personalised recommendations