A Synopsis of Exercise Genomics Research and a Vision for its Future Translation into Practice

  • Linda S. PescatelloEmail author
  • Stephen M. Roth
Part of the Molecular and Translational Medicine book series (MOLEMED)


Despite the promise of exercise genomics, work translating this knowledge into practice has proceeded slowly. The contributors to this volume are leading researchers in exercise genomics. The topics addressed by these experts include fundamental concepts (Chap. 1) and statistical and methodological considerations (Chap. 2) in exercise genomics, and the exercise genomics of physical activity (Chap. 3), type 2 diabetes mellitus (Chap. 4), body composition and obesity (Chap. 5), plasma lipoprotein-lipid and blood pressure (Chap. 6), muscle strength and size (Chap. 7), and aerobic capacity and endurance performance (Chap. 8). Chapter topics were chosen because they represent leading content areas of investigation in exercise genomics. For these reasons, this concluding chapter is written as a synopsis of the take home messages from each of the individual chapters. This chapter highlights key developments, discoveries, and challenges discussed by the author(s) of each chapter followed by the authors’ vision for the future translation of exercise genomics into practice. We conclude with a discussion of common themes that have emerged from this book.


Adenosine monophosphate deaminase 1 Adrenoreceptors Angiotensin-converting enzyme Alpha actinin 3 Allele Apolipoprotein E Blood pressure Body composition Copy number variation Dopamine receptor one Epigenetic Epistasis Exercise prescription (Ex RxGene (or Genotype) × environment interaction Genome-wide association study Hardy-Weinberg equilibrium HEealth RIsk factors exercise TrAining and GEnetics Family Study Hypoxia-inducible factor 1 alpha Linkage disequilibrium Lipid Lipoprotein Maximal oxygen consumption Minor allele frequency Nescient helix-hoop-helix 2 Nitric oxide synthase (NOS1, NOS2, NOS3Obesity Overweight Peroxisome proliferative-activated receptor-associated genes Physical activity Potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11Proteomics Quantitative trait loci Single nucleotide polymorphism RNA interference Transcription factor 7-like 2 Type 2 diabetes mellitus Uncoupling protein-associated genes 


  1. 1.
    Bray MS, Hagberg JM, Perusse L, Rankinen T, Roth SM, Wolfarth B, Bouchard C. The human gene map for performance and health-related fitness phenotypes: the 2006–2007 update. Med Sci Sports Exerc. 2009;41(1):35–73.PubMedCrossRefGoogle Scholar
  2. 2.
    Rankinen T, Roth SM, Bray MS, Loos R, Perusse L, Wolfarth B, et al. Advances in exercise, fitness, and performance genomics. Med Sci Sports Exerc. 2010;42(5):835–46.PubMedCrossRefGoogle Scholar
  3. 3.
    Hemminki K, Lorenzo Bermejo J, Forsti A. The balance between heritable and environmental aetiology of human disease. Nat Rev Genet. 2006;7(12):958–65.PubMedCrossRefGoogle Scholar
  4. 4.
    Bodmer W, Bonilla C. Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet. 2008;40(6):695–701.PubMedCrossRefGoogle Scholar
  5. 5.
    Safdar A, Abadi A, Akhtar M, Hettinga BP, Tarnopolsky MA. miRNA in the regulation of skeletal muscle adaptation to acute endurance exercise in C57Bl/6J male mice. PLoS One. 2009;4(5):e5610.PubMedCrossRefGoogle Scholar
  6. 6.
    Barrett JC, Hansoul S, Nicolae DL, Cho JH, Duerr RH, Rioux JD, et al. Genome-wide association defines more than 30 distinct susceptibility loci for Crohn’s disease. Nat Genet. 2008;40(8):955–62.PubMedCrossRefGoogle Scholar
  7. 7.
    DE Moor MH, Liu YJ, Boomsma DI, Li J, Hamilton JJ, Hottenga JJ, et al. Genome-wide association study of exercise behavior in Dutch and American adults. Med Sci Sports Exerc. 2009;41:1887–95.PubMedCrossRefGoogle Scholar
  8. 8.
    Frayling TM. Genome-wide association studies provide new insights into type 2 diabetes aetiology. Nat Rev Genet. 2007;8(9):657–62.PubMedCrossRefGoogle Scholar
  9. 9.
    Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH, Prokopenko I, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet. 2008;40(6):768–75.PubMedCrossRefGoogle Scholar
  10. 10.
    Pearson TA, Manolio TA. How to interpret a genome-wide association study. JAMA. 2008;299(11):1335–44.PubMedCrossRefGoogle Scholar
  11. 11.
    Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet. 2005;6(2):95–108.PubMedCrossRefGoogle Scholar
  12. 12.
    Hoffman EP, DuBois DC, Hoffman RI, Almon RR. Expression profiling and pharmacogenomics of muscle and muscle disease. Curr Opin Pharmacol. 2003;3(3):309–16.PubMedCrossRefGoogle Scholar
  13. 13.
    Chen YW, Nader GA, Baar KR, Fedele MJ, Hoffman EP, Esser KA. Response of rat muscle to acute resistance exercise defined by transcriptional and translational profiling. J Physiol. 2002;545(Pt 1):27–41.PubMedCrossRefGoogle Scholar
  14. 14.
    Timmons JA, Knudsen S, Rankinen T, Koch LG, Sarzynski M, Jensen T, et al. Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans. J Appl Physiol. 2010;108(6):1487–96.PubMedCrossRefGoogle Scholar
  15. 15.
    Horne BD, Anderson JL, Carlquist JF, Muhlestein JB, Renlund DG, Bair TL, et al. Generating genetic risk scores from intermediate phenotypes for use in association studies of clinically significant endpoints. Ann Hum Genet. 2005;69(Pt 2):176–86.PubMedCrossRefGoogle Scholar
  16. 16.
    Hunter DJ, Kraft P. Drinking from the fire hose – statistical issues in genomewide association studies. N Engl J Med. 2007;357(5):436–9.PubMedCrossRefGoogle Scholar
  17. 17.
    NCI-NHGRI Working Group on Replication in Association Studies, Chanock SJ, Manolio T, Boehnke M, Boerwinkle E, Hunter DJ, et al. Replicating genotype-phenotype associations. Nature. 2007;447(7145):655–60.PubMedCrossRefGoogle Scholar
  18. 18.
    Little J, Higgins JP, Ioannidis JP, Moher D, Gagnon F, von Elm E, et al. STrengthening the REporting of genetic association studies (STREGA) – an extension of the STROBE statement. Eur J Clin Invest. 2009;39(4):247–66.PubMedCrossRefGoogle Scholar
  19. 19.
    Thomas DC. Statistical methods in genetic epidemiology. Oxford: Oxford University Press; 2004.Google Scholar
  20. 20.
    Hopkins WG. A new view of statistics. Accessed 20 Jan 2011.
  21. 21.
    Devlin B, Risch N. A comparison of linkage disequilibrium measures for fine-scale mapping. Genomics. 1995;29(2):311–22.PubMedCrossRefGoogle Scholar
  22. 22.
    Silver LM. Mouse genetics: concepts and applications. New York: Oxford University Press; 1995.Google Scholar
  23. 23.
    DiPetrillo K, Wang X, Stylianou IM, Paigen B. Bioinformatics toolbox for narrowing rodent quantitative trait loci. Trends Genet. 2005;21(12):683–92.PubMedCrossRefGoogle Scholar
  24. 24.
    Flint J, Valdar W, Shifman S, Mott R. Strategies for mapping and cloning quantitative trait genes in rodents. Nat Rev Genet. 2005;6(4):271–86.PubMedCrossRefGoogle Scholar
  25. 25.
    Axtell MJ, Snyder JA, Bartel DP. Common functions for diverse small RNAs of land plants. Plant Cell. 2007;19(6):1750–69.PubMedCrossRefGoogle Scholar
  26. 26.
    Siepel A. Darwinian alchemy: human genes from noncoding DNA. Genome Res. 2009;19(10):1693–5.PubMedCrossRefGoogle Scholar
  27. 27.
    Forrest AR, Abdelhamid RF, Carninci P. Annotating non-coding transcription using functional genomics strategies. Brief Funct Genomic Proteomic. 2009;8(6):437–43.PubMedCrossRefGoogle Scholar
  28. 28.
    Booth FW, Gordon SE, Carlson CJ, Hamilton MT. Waging war on modern chronic diseases: primary prevention through exercise biology. J Appl Physiol. 2000;88(2):774–87.PubMedGoogle Scholar
  29. 29.
    Chenoweth D, Leutzinger J. The economic cost of physical inactivity and excess weight in american adults. J Phys Act Health. 2006;3(2):148–63.Google Scholar
  30. 30.
    Centers for Disease Control and Prevention (U.S.). Chronic diseases and their risk factors: the Nation’s leading causes of death. Atlanta: Department of Health and Human Services, Centers for Disease Control and Prevention; 1999.Google Scholar
  31. 31.
    Lyssenko V, Groop L. Genome-wide association study for type 2 diabetes: clinical applications. Curr Opin Lipidol. 2009;20(2):87–91.PubMedCrossRefGoogle Scholar
  32. 32.
    Maher B. Personal genomes: the case of the missing heritability. Nature. 2008;456(7218):18–21.PubMedCrossRefGoogle Scholar
  33. 33.
    Grarup N, Rose CS, Andersson EA, Andersen G, Nielsen AL, Albrechtsen A, et al. Studies of association of variants near the HHEX, CDKN2A/B, and IGF2BP2 genes with type 2 diabetes and impaired insulin release in 10, 705 Danish subjects: validation and extension of genome-wide association studies. Diabetes. 2007;56(12):3105–11.PubMedCrossRefGoogle Scholar
  34. 34.
    Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007;316(5829):1336–41.PubMedCrossRefGoogle Scholar
  35. 35.
    Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, Walters GB, et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet. 2007;39(6):770–5.PubMedCrossRefGoogle Scholar
  36. 36.
    Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007;445(7130):881–5.PubMedCrossRefGoogle Scholar
  37. 37.
    Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research, Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316(5829):1331–6.PubMedCrossRefGoogle Scholar
  38. 38.
    Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316(5829):1341–5.PubMedCrossRefGoogle Scholar
  39. 39.
    Wareham NJ, Jakes RW, Rennie KL, Mitchell J, Hennings S, Day NE. Validity and repeatability of the EPIC-norfolk physical activity questionnaire. Int J Epidemiol. 2002;31(1):168–74.PubMedCrossRefGoogle Scholar
  40. 40.
    Friedenreich CM, Courneya KS, Neilson HK, Matthews CE, Willis G, Irwin M, et al. Reliability and validity of the past year total physical activity questionnaire. Am J Epidemiol. 2006;163(10):959–70.PubMedCrossRefGoogle Scholar
  41. 41.
    Rankinen T, Zuberi A, Chagnon YC, Weisnagel SJ, Argyropoulos G, Walts B, et al. The human obesity gene map: the 2005 update. Obesity (Silver Spring). 2006;14(4):529–644.CrossRefGoogle Scholar
  42. 42.
    Bouchard C. The biological predisposition to obesity: beyond the thrifty genotype scenario. Int J Obes (Lond). 2007;31(9):1337–9.CrossRefGoogle Scholar
  43. 43.
    Bray MS. Implications of gene-behavior interactions: prevention and intervention for obesity. Obesity (Silver Spring). 2008;16 Suppl 3:S72–8.CrossRefGoogle Scholar
  44. 44.
    Feitosa MF, Rice T, North KE, Kraja A, Rankinen T, Leon AS, et al. Pleiotropic QTL on chromosome 19q13 for triglycerides and adiposity: the HERITAGE family study. Atherosclerosis. 2006;185(2):426–32.PubMedCrossRefGoogle Scholar
  45. 45.
    Feitosa MF, Borecki IB, Rankinen T, Rice T, Despres JP, Chagnon YC, et al. Evidence of QTLs on chromosomes 1q42 and 8q24 for LDL-cholesterol and apoB levels in the HERITAGE family study. J Lipid Res. 2005;46(2):281–6.PubMedCrossRefGoogle Scholar
  46. 46.
    Rankinen T, An P, Rice T, Sun G, Chagnon YC, Gagnon J, et al. Genomic scan for exercise blood pressure in the health, risk factors, exercise training and genetics (HERITAGE) family study. Hypertension. 2001;38(1):30–7.PubMedGoogle Scholar
  47. 47.
    Rice T, Rankinen T, Chagnon YC, Province MA, Perusse L, Leon AS, et al. Genomewide linkage scan of resting blood pressure: HERITAGE family study. health, risk factors, exercise training, and genetics. Hypertension. 2002;39(6):1037–43.PubMedCrossRefGoogle Scholar
  48. 48.
    Hubal MJ, Gordish-Dressman H, Thompson PD, Price TB, Hoffman EP, Angelopoulos TJ, et al. Variability in muscle size and strength gain after unilateral resistance training. Med Sci Sports Exerc. 2005;37(6):964–72.PubMedGoogle Scholar
  49. 49.
    Thomis MA, Beunen GP, Van Leemputte M, Maes HH, Blimkie CJ, Claessens AL, et al. Inheritance of static and dynamic arm strength and some of its determinants. Acta Physiol Scand. 1998;163(1):59–71.PubMedCrossRefGoogle Scholar
  50. 50.
    Thomis MA, Beunen GP, Maes HH, Blimkie CJ, Van Leemputte M, Claessens AL, et al. Strength training: importance of genetic factors. Med Sci Sports Exerc. 1998;30(5):724–31.PubMedCrossRefGoogle Scholar
  51. 51.
    Perusse L, Lortie G, Leblanc C, Tremblay A, Theriault G, Bouchard C. Genetic and environmental sources of variation in physical fitness. Ann Hum Biol. 1987;14(5):425–34.PubMedCrossRefGoogle Scholar
  52. 52.
    Tucker T, Marra M, Friedman JM. Massively parallel sequencing: the next big thing in genetic medicine. Am J Hum Genet. 2009;85(2):142–54.PubMedCrossRefGoogle Scholar
  53. 53.
    Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature. 2009;461(7261):218–23.PubMedCrossRefGoogle Scholar
  54. 54.
    Bouchard C, An P, Rice T, Skinner JS, Wilmore JH, Gagnon J, et al. Familial aggregation of VO2 max response to exercise training: results from the HERITAGE family study. J Appl Physiol. 1999;87(3):1003–8.PubMedGoogle Scholar
  55. 55.
    Klissouras V. Heritability of adaptive variation. J Appl Physiol. 1971;31(3):338–44.PubMedGoogle Scholar
  56. 56.
    Yu N, Chen FC, Ota S, Jorde LB, Pamilo P, Patthy L, et al. Larger genetic differences within Africans than between Africans and Eurasians. Genetics. 2002;161(1):269–74.PubMedGoogle Scholar
  57. 57.
    International HapMap Consortium. A haplotype map of the human genome. Nature. 2005;437(7063):1299–320.CrossRefGoogle Scholar
  58. 58.
    Bouchard C, Leon AS, Rao DC, Skinner JS, Wilmore JH, Gagnon J. The HERITAGE family study. Aims, design, and measurement protocol. Med Sci Sports Exerc. 1995;27(5):721–9.PubMedGoogle Scholar
  59. 59.
    Kostek MA, Hubal MJ, Pescatello LS. The role of genetics in developing muscle strength. Am J Lifestyle Med. in press.Google Scholar
  60. 60.
    Baldwin KM, Haddad F. Research in the exercise sciences: where we are and where do we go from here – part II. Exerc Sport Sci Rev. 2010;38(2):42–50.PubMedCrossRefGoogle Scholar
  61. 61.
    Pescatello LS. The promises and challenges of the use of genomics in the prescription of exercise for hypertension. Cur Hypertens Rev. 2010;1(6):32–4.CrossRefGoogle Scholar
  62. 62.
    Roth SM. Perspective on the future use of genomics in exercise prescription. J Appl Physiol. 2008;104(4):1243–5.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Human Performance Laboratory, Department of Kinesiology, Neag School of EducationUniversity of ConnecticutStorrsUSA

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