Short Review On Exercise Genomics and Application

Exercise genomic applications

Section 1 – What is exercise genomics
All bodies are not made equally, genetic variations make up only 0.1% of the variations in the human genome, but that accounts for over 3,000,000 base pairs that can be different (Lynn B. Jorde, 2003). This leaves a lot of room for total variation in genes that code for important proteins involved in exercise performance. These genes can be debilitating or add advantage to a person innate physical ability. These small changes are extremely important for optimize performance in professional athletes, improvement for gym junkies and knowledge to people that wish to know more about themselves. There are genetic epidemiology studies that show human differences “physical activity level, cardiorespiratory fitness in the untrained state, cardiovascular and metabolic response to acute exercise, and responsiveness to regular exercise.” (Claude Bouchard et al, 2014) all based on statistically relevant genetic markers.
Types of genetic variations that exercise genomics are interested are single nucleotide polymorphism (SNP) which can be genotyped. Copy number variations (CNV) which are large sequences of DNA that are repeated multiple times in the genome, this amplifies that gene to be increased in expression. Variable nucleotide tandem repeats (VNTR) are when the number of CAG sequences are presented multiple times in sequence in the DNA code. Insertion and deletion polymorphism also are a big contributor to genetic difference, which remove or insert one base from the gene region. Expression is a critical observation that can be obtained through serum levels and tissue samples. Some of the genes that were proposed as relevant may have no effect on the user because of there being more expression of the gene or less. This can affect the overall performance of the person without getting debilitated from the disposition. (Linda S. Pescatello et al 2011). It is important to present that all variations in physical activity and genetics have three factors of influence: there is genetic influences, environmental influences and the interaction between the two factors (Linda S. Pescatello, et al 2011). Saying this we know that not all of the genes we inherit can entirely effected our physically abilities.

Section 2 Exercise genomic examples

2.1: Genetic muscular variations

There has been a lot of focus on the relationship between genetics and muscle type. Muscular strength and power are types of skeletal muscle cell called red/ fast twitch muscle fibers, and repeated movements over long durations are red slow twitch muscle fibers (starkebaum, 2016). 45% of muscle fiber inheritance is due to inherited genetic factors, which influence the north American population to ether have less than 35% or above 65% of type 1 muscle fibre (Simoneau JA, et al 1995). There is around 27 gene that have been statistically proven to affect muscle fibres and muscle function (information, n.d.), these genes can be viewed on table 2.3.1 genetic muscle variations in the appendix. Out of these genes AGTR2 is a major gene shown to affect type II muscle fibre ratios to ether increase power or endurance of the predisposed.
In a high-powered study looking at how the AGTR2 is affecting skeletal muscle development, metabolism and circulatory homeostasis, it found that alleles A showed a higher percentage of slow twitch fibres for C allele than A allele carriers. AGTR2 is a gene that functions as a receptor for angiotensin II which is an integral membrane protein that mediates the development and growth of healthy cells. It has also been seen to play a role in muscle growth stimulus. (NCBI, AGTR2 angiotensin II receptor type 2, n.d.). These genetic components effect 15.2% of the variation in muscle fibre composition and can be explained by the AGTR2 genotype. The paper used a cohort of 2178 Caucasians and 1220 control subjects, the frequency of the AGTR2 C allele was found to have a 62.7% increase in endurance athletes compared to power athletes. (Mustafina LJ et al.).
The first and most widely studied gene variations for muscle fibre is the ACE (angiotensin-converting enzyme). The ACE gene plays a role in tissue growth because it is an important controller of the endocrine renin angiotensin system which is key for the circulatory system, and skeletal muscle renin angiotensin system (NCBI, 2016). There is a positive link to the D allele with the ACE genotype to show an improvement for endurance performance because of the increase in slow twitch muscle fibre ratio and production (H. E. Montgomery, et al 1999).

Section 2.2: Genetic variations effecting VO2 Max

VO2 max is the maximum amount of oxygen that can be used by an athlete. There has been a great amount of contributions to the genetics of VO2 max, especially because of being one of the only exercise phenotypes presented in a genome wide associated study (GWAS). The findings showed 39 associated genes which can be view in the appendix, Table 2.3.2 VO2 max associated genes, and 21 of those genes accounted for 49% of the variation due to genetics. The strongest associations were the ACSL1 gene which accounts for a 6% difference in VO2 training response. The PRDM1 gene, GRIN3A, KCNH8, and ZIC4 genes also showed strong associations. (Claude Bouchard, et al, 2011).
In another GWAS it concluded that NFIA-AS2 rs1572312, TSHR rs7144481 and RBFOX1 rs7191721 were 3 novel genes effecting VO2 max. These genotypes all increase aerobic performance and elite endurance athlete ability. The TSHER rs7144481 C allele increased expression of TSHR gene which increased angiogenesis and/or metabolic rate, increases aerobic performance. RBFOX1 rs7191721 G allele was associated with increased muscle function for endurance activities. The NFIA-AS2 was associated with increased VO2 max levels by 24.6-48.85 increase in VO2 max levels, and was presented in almost 96% of the elite endurance athletes and only 78% of the non-elite cohort. (II Ahmetov, et al 2014).

Section 3: applications for exercise genomics

There are around 50 personalized genomics companies that genotype and make health documents that are present around the world (International society of genetic genealogy, 2016). Out of the 50 companies only 10 of them specialize in exercise genomics. Exercise genomics can be beneficial for elite athletes to improve their fitness level, starting athletes to see innate ability, and to obtain general fitness goals faster with this knowledge. Having this information can allow the users to overcome their genetic predisposition to pass limitations and get them past the plateau to greater results.
For example, one of the most common genes for these direct to consumer information tests is the ACTN3 gene. The ACTN3 genotype C:C can identify that there is better muscle use for sprinters because the alpha actinic 3 protein is fully functional compare to the T: T genotype (SNPedia, 2016). The proteins function is to bind to z-discs and analogous dense bodies to assist in the myofibrillar actin filaments in contraction (NCBI, ACTN3 gene). Understanding this disposition can let someone perform more sprint like activities compared to endurance like activities which can accelerate progress towards fitness goals, alleviate increase risk of injury, and increase ease of physical activity. This method of genotype interpretation can be duplicated for all the statistically proven exercise genomic genes.


Cawte, N. (2015). Alzhiermers disease review. Hamilton: McMaster.

Claude Bouchard, T. R. (2014, Feb 28th). Genomics and genetics in the biology of adaptation to exercise. Retrieved from HHS public access:

Claude Bouchard,corresponding author1 Mark A. Sarzynski,1 Treva K. Rice,2 William E. Kraus,3 Timothy S. Church,4 Yun Ju Sung,2 D. C. Rao,2 and Tuomo Rankinen. (2011, May). Genomic predictors of the maximal O2 uptake response to standardized exercise training programs. Retrieved from Journal of applied physiology:

genealogy, I. s. (2016, November ). List of personal genomics companies . Retrieved from ISOGG:

E. Montgomery1, R. Marshall1, H. Hemingway1, S. Myerson1, P. Clarkson2, C. Dollery2, M. Hayward2, D. E. Holliman3, M. Jubb4, M. World4, E. L. Thomas5, A. E. Brynes5, N. Saeed5, M. Barnard5, J. D. Bell5, K. Prasad6, M. Rayson7, P. J. Talmud8 & S. E. Hum. (1999, May). Human gene for physical performance. Retrieved from Nature:

Hertz L1, C. Y. (2015, July ). Effects of ketone bodies in Alzheimer’s disease in relation to neural hypometabolism, β-amyloid toxicity, and astrocyte function. Retrieved from NCBI:

II Ahmetov,corresponding author1,2,3 NA Kulemin,3,4 DV Popov,5 VA Naumov,3 EB Akimov,6 YR Bravy,5 ES Egorova,2 AA Galeeva,1 EV Generozov,3 ES Kostryukova,3 AK Larin,3 LJ Mustafina,1,2 EA Ospanova,3 AV Pavlenko,3 LM Starnes,7 P Żmijewski,8 DG Alexeev,3 OL . (2014, oct 21). Genome-wide association study identifies three novel genetic markers associated with elite endurance performance. Retrieved from NCBI:

information, N. (n.d.). Search results for genetic variations for muscle fiber . Retrieved from NCBI:

Linda S. Pescatello, Stephen M. Roth. (2011). Exercise genomics. New York: Springer.

Lynn B. Jorde, P. D. (2003, Oct 16). Genetic variation and human evolution. (Department of human genetics) Retrieved from

Maciej Gasior,a Michael A. Rogawski,a and Adam L. Hartmana,b . (2006 , sept). Neuroprotective and disease-modifying effects of the ketogenic diet. Retrieved from NCBI:

Mustafina LJ1, Naumov VA2, Cieszczyk P3, Popov DV4, Lyubaeva EV4, Kostryukova ES2, Fedotovskaya ON5, Druzhevskaya AM5, Astratenkova IV5, Glotov AS6, Alexeev DG2, Mustafina MM7, Egorova ES7, Maciejewska-Karłowska A8, Larin AK2, Generozov EV2, Nurullin RE9,. (n.d.). AGTR2 gene polymorphism is associated with muscle fibre composition, athletic status and aerobic performance. Retrieved from NCBI:

NCBI. (2016, Nov). ACE angiotensin I converting enzyme. Retrieved from

NCBI. (n.d.). ACTN3 gene. Retrieved from NCBI:

NCBI. (n.d.). AGTR2 angiotensin II receptor type 2. Retrieved from NCBI:

Simoneau JA, Bouchard C. (1995). Genetic determinism of fiber type proportion in human skeletal muscle. Retrieved from PubMed:

SNPedia. (2016, October). rs1815739. Retrieved from SNPedia:

starkebaum, G. A. (2016, November ). Types of skeletal muscle fibres. Retrieved from IvyROse holistic :

O’Connor, C. M. & Adams, J. U. (2010) Essentials of Cell Biology. Cambridge, MA: NPG Education

a Poian, A. T., El-Bacha, T. & Luz, M. R.M.P. (2010) Nutrient Utilization in Humans: Metabolism Pathways. Nature Education 3(9):11 (2013). Balance Food and Activity. Retrieved from NIH:

Institute of Medicine (2005) Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids,

Cholesterol, Protein, and Amino Acids (Macronutrients). Washington, DC: The National Academies Press. doi: 10.17226/10490.

Aus.Gov. (2016). Nutrient Reference Values. Retrieved from NHMRC:

Jéquier E. (1994) Carbohydrates as a source of energy.Am J Clin Nutr. 59: 3 682S-685S

WSU. (2016). Nutrition Basics. Retrieved from myNutrition:

Opchart, C. E. (2003). Overview of Lipid Funciton. Retrieved from Elmurst College:

Shenkin, A. (2006). Micronutrients in health and disease. Postgraduate Medical Journal, 82(971), 559– doi: 10.1136/pgmj.2006.047670

WHO. (2016). Micronutrients. Retrieved from World Health Organization:

CAN.Gov. (2016). Vitamin D Blood Levels of Canadians. Retrieved from Statistics Canada:

Nature. (2016). Metabolic Pathways. Retrieved from Nature:

Berkhout, J. Teusink, Bas & Bruggeman F.J. (2013). Gene network requirements for regulation of metabolic gene expression to a desired state. Nature Scientific Reports 3, Article number: 1417. doi:10.1038/srep01417

Clancy, S. (2008) DNA transcription. Nature Education 1(1):41

Clancy, S. & Brown, W. (2008) Translation: DNA to mRNA to Protein. Nature Education 1(1):101

Hoopes, L. (2008) Introduction to the gene expression and regulation topic room. Nature Education1(1):160

Illig, T., Gieger, C., Zhai, G., Römisch-Margl, W., Wang-Sattler, R., Prehn, C., … Suhre, K. (2010). A

genomewide perspective of genetic variation in human metabolism. Nature Genetics, 42(2), 137–141. doi:10.1038/ng.507

Scitable. (2016). SNP Definition. Retrieved from Nature:

NIH. (2016). What are single nucleotide polymorphisms (SNPs)? Retrieved from Genetics Home Reference:

Hunt R.C., Simhadri V.L., Iandoli M. & Sauna Z.E.(2014).Trends Genet. 30(7):308-21. doi: 10.1016/j.tig.2014.04.006.

Conti L.D, Baralle M. & Buratti E. (2012). Wires RNA. 4(1) : 49-60.doi : 10.1002/wrna.1140

NIH. (2016). What kinds of gene mutations are possible? Retrieved from Genetics Home Reference:

Durbin R.M. (2010). Nature 467, 1061–1073 doi:10.1038/nature09534

NIH. (2016). MTHFR Gene. Retrieved from Genetics Home Reference:

Pavlidis C., Patrinos G.P. & Katsila T. (2015). ATG 4: 50-53. Doi: 0.1016/j.atg

National Center for Biotechnology Information. PubChem Compound Database; CID=6137, (accessed Nov. 24, 2016).

NCBI. (2016). Rs1801133. Retrieved from SNPedia: htstps://

Lind, M., Jansson, J., Nilsson, T., et al. (2016). High homocysteine and low folate plasma concentrations are associated with cardiovascular events but not bleeding during warfarin treatment. Clinical Chemistry and Laboratory Medicine (CCLM), 54(12), pp. 1981-1986. doi:10.1515/cclm-2016-0092

Gov.UK. (2016). Vitamin B12 or folate deficiency anaemia – Symptoms. Retrieved from NHS Choices:

Iris F., Reinhild P.L., Anja B., Anja M.B., Heinz N., Heiner K.B & Klaus P. (2002) 5,10-Methylenetetrahydrofolate reductase genotype determines the plasma homocysteine-lowering effect of supplementation with 5-methyltetrahydrofolate or folic acid in healthy young women Am J Clin Nutr. 75: 2 275-282

Patrick, R. (2016). Genetic Report.Found My Fitness. Retrieved from

Parker RS. (1996). Absorption, metabolism, and transport of carotenoids. Faseb J, 10(5):542-51 “Lactose Intolerance – Genetics Home Reference.” U.S National Library of Medicine. U.S. National Library of Medicine, n.d. Web. 22 Nov. 2016.

Zheng ZJ, Croft JB, Giles WH, Mensah GA. Sudden Cardiac Death in the United States, 1989 to 1998. Circulation 2001;104:2158-63.

“About Celiac Disease.” Canadian Celiac Association RSS. N.p., n.d. Web. 22 Nov. 2016.

Berná, Genoveva, María Jesús Oliveras-López, Enrique Jurado-Ruíz, Juan Tejedo, Francisco Bedoya, Bernat Soria, and Franz Martín. “Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.” Nutrients. MDPI, Nov. 2014. Web. 22 Nov. 2016.

Büsch, K.; Ludvigsson, J. F.; Ekström-Smedby, K.; Ekbom, A.; Askling, J.; Neovius, M. (2014-01-01). “Nationwide prevalence of inflammatory bowel disease in Sweden: a population-based register study”Alimentary Pharmacology & Therapeutics. 39 (1): 57–68.doi:10.1111/apt.12528ISSN 1365-2036PMID 24127738

Cynthia L. Ogden, Ph.D.; Margaret D. Carroll, M.S.P.H.; Cheryl D. Fryar, M.S.P.H.; and Katherine M. Flegal, Ph.D “Prevalence of Obesity Among Adults and Youth: United States, 2011–2014.” NCHS Data Brief. N.p., n.d. Web. 22 Nov. 2016.

Molodecky, NA; Soon, IS; Rabi, DM; Ghali, WA; Ferris, M; Chernoff, G; Benchimol, EI; Panaccione, R; Ghosh, S; Barkema, HW; Kaplan, GG (Jan 2012). “Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review”. Gastroenterology. 142 (1): 46–54.e42; quiz e30.doi:10.1053/j.gastro.2011.10.001PMID 22001864.

“Phenylketonuria – Genetics Home Reference.” U.S National Library of Medicine. U.S. National Library of Medicine, n.d. Web. 22 Nov. 2016.

Pavlidis et al., 2012 C. Pavlidis, A. Karamitri, A. Barakou, D.N. Cooper, K. Poulas, S. Topouzis, G.P. Patrinos Ascertainment and critical assessment of the views of the general public and healthcare professionals on nutrigenomics in Greece