Dissertation Final

Can certain genetic
markers be incorporated
into an individual’s
training programme, in
order to improve athletic
performance?
By Zoe Skells
March 2015

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Table of Contents
1: Improving Athletic Performance ............................................................................................... 2
2: Nature vs. Nurture..................................................................................................................... 2
2.1: The 10,000 hour Theory ................................................................................................. 2
2.2: Case Study – Men’s High Jump Final, Osaka 2007......................................................... 4
3: Power Endurance Marker Genes............................................................................................... 6
3.1: ACE gene......................................................................................................................... 8
3.2: ADRB2........................................................................................................................... 10
3.3: ADRB1........................................................................................................................... 12
3.4: NRF1............................................................................................................................. 14
4: Injury Risk Genetics ................................................................................................................. 16
4.1: GDF5............................................................................................................................. 17
4.2: COL1A1......................................................................................................................... 18
5: Recovery Genetics ................................................................................................................... 21
5.1: NOS3............................................................................................................................. 24
5.2: IL-6................................................................................................................................ 25
6: Discussion................................................................................................................................ 28
References................................................................................................................................... 29

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1: Improving Athletic Performance
Athletes are continuously looking for methods to develop and enhance their performance in
order to achieve their goals. As a consequence the enhancement of athletic performance is a
constantly changing field, with ideas and procedures being continuously introduced from areas
as diverse as; sports psychology, clothing and equipment, sports nutrition and hydration,
ergogenic aids and types of training.
Currently, many athletes are seeing the benefits on their performance from the use of
nutritional advice. An example that has made the headlines is Novak Djokovic, a tennis player
from Serbia who is currently ranked number 1 in the world and a winner of multiple grand
slams. In 2010, Djokovic changed his diet based on the advice given to him by Dr Igor
Cetojevic; up until this point he was world number 3 (and had been for two years) and only
had one grand slam title to his name (ATP, 2014). Djokovic was experiencing symptoms such as
nausea, shortness of breath and fatigue, similar to the individual in Leone et al (2005) case
study. Dr Cetojevic suggested that Djokovic should have a gluten-free diet; the results of
continuing with the diet were clear, Djokovic felt more energetic and more mentally focussed
than he had ever been (Mancini et al, 2011). Furthermore, as well as just feeling better in
himself, he is now comfortably world number one and has won an additional six grand slams.
Later blood tests demonstrated that Djokovic was indeed intolerant to wheat as well as dairy
products (Newman, 2013).
2: Nature vs. Nurture
The argument of nature vs. nurture in sporting ability and performance has been well debated
over the years (Davids & Baker, 2007). Initially, it was presumed that dedication and training
alone were the factors that made an athlete successful (Kalinowski, 1985; Monsaas, 1985;
Helson et al, 1998). However, since the human genome was sequenced, scientists and
researchers have begun to uncover certain genes that are substantial markers for elevated
sporting performance. It is now widely accepted that it isn’t one or the other, but a
combination and balance between an athlete’s natural talent and their training environment
(Myburgh, 2003).
2.1: The 10,000 hour Theory
Ericsson et al (1993) introduced the concept of deliberate practice and the 10,000 hour
theory. The concept behind it is that to become an expert in a particular field an individual

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should undertake 10,000 hours of dedicated training on their specific area. Ericsson et al
(1993) carried out a retrospective study that focussed on three groups of young violinists –
with a mean age of 23.1 years old. The first group comprised of those with the potential to be
world-class soloists, the second those who were deemed to be good and the third those who
probably wouldn’t play professionally (likely to become music teachers). Their results showed
that all individuals from each of the three groups started playing at the age of 7.9 years old.
But, as they grew older the students from the first group started to practice for longer when
compared to the other two groups. By the age of 20, the performers from the first group had
totalled an average 10,000 hours of practice, individuals from the second group had totalled
8,000 hours and the students from the third group reached just over 4,000 hours. On the other
hand, Baker & Cote (2003) showed using a retrospective study that the average number of
practice hours for athletes involved in team ball sports to reach the their national team was
3,939 hours – where basketball players had completed more training hours (5,908.5)
compared to netball players who spent on average 2,260 hours in practice. This demonstrates
that an individual doesn’t necessarily have to complete 10,000 hours of deliberate practice to
become an expert.
More refined theories have since been published; Cote et al (2003) proposed that there are
three stages of sport involvement prior to the achievement of expert level performance –
sampling years, specializing years and investment years. During these stages, an individual’s
participation evolves from play-like to more structured, dedicated training sessions. The
foundation for this theory was based on a previous study carried out by Bloom (1985); he
observed that individuals go through three distinct periods to become experts in a particular
field – the early years, middles years and the later years. The early years consist of discovery
and exploration of the chosen field, which involves playing and external motivation; this stage
can last up to secondary school. The middle years are when individuals enhance their
understanding of the skills and the rules and regulations involved; they start to practice drills
and the use of tools or algorithms. Also, the individual’s motivation starts to become more
internal and usually lasts through secondary school. Finally, the later years is when individuals
start working towards becoming an expert in their chosen area, developing their own style and
interpretation. This particular stage usually commences in the later stages of secondary school
or into the start of university. This particular study focussed on six groups; Olympic swimmers,
world-class tennis players, concert pianists, sculptors, research mathematicians and research
neurologists. The individuals that participated in the study were at the top of their respective

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fields; for instance the swimmers were members of the US Olympic team and the tennis
players had all achieved a top ten world ranking.
2.2: Case Study – Men’s High Jump Final, Osaka 2007
Stefan Holm is a Swedish high jumper and was Olympic champion in 2004. Holm was a firm
believer in the 10,000 hour rule and trained religiously for two decades. For a high-jumper
Holm was rather short at just 1.80m, therefore he spent hours in the gym and many more
perfecting his technique. At the 2007 world athletic championships high jump final in Osaka
Holm was the favourite, but among his competitors was Donald Thomas from the Bahamas
who had a very different build-up to the championships. Thomas got into high jump through
pure chance after taking on a bet from a fellow university student in America. Thomas was
offered a scholarship to Auburn University, where he started to complete dedicated high jump
training. A year later, Thomas also found himself at the final of the 2007 world championships.
Both men made it to what would be the final height of 2.35m along with two other
competitors. Thomas with his unorthodox technique and very short run-up managed to clear
the height at his first attempt. Thomas became world champion while Holm had to settle for
4th
place (IAAF, 2014c).
A year later, it was shown that Thomas had abnormally long Achilles tendon for an individual
of his height, which would ultimately give him a slim advantage in an event like high jump. The
longer an Achilles tendon, the more power an athlete can gain from the ‘stretch-shortening
cycle’ (Kubo et al, 2000) (Komi, 2003) – so they can propel themselves further into the air.
Although Holm had an average length Achilles, his tendon became four times stiffer than an
average man’s through extensive training; meaning that his tendon was unusually powerful.
Strength and endurance training have been shown to elicit mechanical changes in tendons;
specifically and increase in the stiffness and the tensile strength of the tendon (Buchanan &
Marsh, 2002). However, regardless of the amount of training an individual may complete they
will not be able to change the length of their tendon significantly, only the stiffness of it.
Therefore, Thomas was always going to have a genetic advantage over someone like Holm.
Having said this, whilst Holm steadily improved his personal best over his career, Thomas even
with six more years of training couldn’t increase his personal best height above 2.35m, as
figure 1 demonstrates below.

5
Figure 1: Charting the best heights that both men achieved each year whilst competing (Adapted from
IAAF, 2014a & IAAF, 2014b).
This particular case study highlights some of the key aspects in the debate nature vs. nurture;
Thomas representing nature and Holm representing nurture at a simplistic level. It shows that
while ideal genetics gives an athlete a great foundation, training and hard-work is needed to
maintain the level as well as improving athletic performance.
2.05
2.1
2.15
2.2
2.25
2.3
2.35
2.4
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Mark
Season
Holm
Thomas

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3: Power Endurance Marker Genes
Endurance performance has been traditionally and will continue to be associated with three
key performance markers; maximal oxygen uptake, ventilatory anaerobic threshold and
running economy (Larsen, 2003). Maximal oxygen uptake is a measurement of the highest rate
at which the body can take up and utilise oxygen during prolonged and intense exercise
(Bassett & Howley, 2000) and is also known as maximal oxygen consumption or VO₂max. It is
considered as one of the key measurements of cardiovascular fitness (Taylor et al, 1955) and
unsurprisingly is highly influenced by the both the respiratory and cardiovascular systems. The
limiting factor to maximal oxygen uptake is the capacity of the cardiovascular and respiratory
systems to take in and transport oxygen, a process which is highly influenced by a variety of
factors; including alveolar ventilation, the rate of oxygen diffusion and the degree of blood
flow. Furthermore, the VO₂max value is highly dependent on the ability of exercising muscles
to receive and then utilise oxygen (Bassett & Howley, 2000).
Although a superior VO₂max is beneficial to an endurance athlete, it is vital for an individual to
maintain a high fraction of maximal oxygen uptake (%VO₂max) for a longer period of time
during exercise. An athlete with the capacity to sustain a high fraction of VO₂max for longer
postpones the metabolic acidosis that results from the accumulation of lactic acid in the
bloodstream. The ventilatory anaerobic threshold (%VO₂max) can be improved through
exercise training; these improvements are more significant than changes in VO₂max, as
VO₂max remains relatively constant regardless of the levels of training (Davies et al, 1981).
Burke et al (1994) demonstrated this with twenty-one physical education students; each
individual participated in a seven week training programme where the intensity of the exercise
increased every two weeks. At each session the subjects exercised to voluntary exhaustion.
Burke and colleagues observed a mean increase of 5.5% in VO₂max after the training
programme and a mean increase of 19% and 20.9% in ventilatory threshold and lactate
threshold, respectively; an increase in these variables results in enhanced aerobic capacity.
This study also showed a significant correlation between the ventilatory and lactate threshold,
regardless of training. Lactate production in the muscles increases in a curvilinear correlation
with increasing fractional utilisation of VO₂max; the level at which blood lactate increases
substantially is described as the individual’s lactate threshold (Ghosh, 2004). The lactate
threshold is widely considered has a key measure of endurance performance (Londeree, 1997;
Jones, 2006) and has been shown to be a better predictor compared to VO₂max in assessing
endurance performance in athletes (Coyle et al, 1988).

7
Running economy is defined as the required energy for a given velocity of submaximal
running, and is ascertained by measuring the steady-state of oxygen consumption and the
respiratory exchange ratio. It is an influential factor of endurance performance; as locomotion
during exercise induces an oxygen debt (Foster & Lucia, 2007). Di Prampero et al (1993)
observed that when individuals improved their running economy by 5%, it was accompanied
by a 3.8% increase in endurance running performance on average. Athletes with a better
running economy utilise less oxygen at the same steady-state speed when compared to
runners with poor running economy (Saunders, 2004); therefore they are considered more
efficient runners. In elite athletes with similar a VO₂max, running economy appears to be a
better marker for enhanced endurance performance (Conley & Krahenbuhl, 1980; Morgan et
al, 1989). Daniels (1985) found that runners with similar levels of VO₂max varied up to 30% in
their running economy. Figure 2 demonstrates the difference between an athlete with good
running economy and an athlete with poor running economy – both of whom were
international standard and possessed a similar VO₂max. The more efficient runner proved to
be a minute faster over 10km when compared to the individual with poor running economy.
Running economy is influenced by a number of factors including; maximal oxygen uptake,
training and technique, an individual’s biomechanics and their genetic composition (Saunders,
2004).
In recent times, it has become evident that African runners are dominant in middle to long-
distance running events (Figure 3); the East Africans, especially Kenyans have excelled in
middle-distance events (800–1500 m) and steeplechase, also alongside the Ethiopians in long-
distance races (5000m—marathon). Due to these results enquiries arise into which
mechanisms allow them to perform more efficiently in endurance based disciplines; it is
hypothesised that the athlete’s environment both during their upbringing and throughout
Figure 2: Comparison of oxygen
uptake (V˙O2) [mL/kg/min] in two
international calibre 10km runners,
one with good running economy
(subject 1) and the other with poor
running economy (subject 2)
(Saunders, 2004).

8
training, as well as their genetic composition are the foundation to the dominance observed.
Therefore, there is a need to understand which factors influence the three markers previously
mentioned.
Weston et al (2000) showed that African 10km runners had a significantly higher lactate
threshold and enhanced running economy when compared to their Caucasian counterparts in
sub-elite runners. Saltin et al (1995) have shown that there isn’t a significant difference in
maximal oxygen uptake between Kenyan and non-African athletes. This was also apparent in
untrained adolescents; Larsen et al (2003) showed that the average VO₂max in Kenyan boys
was 52.65mlKg ̄¹min ̄¹ compared with 51.7 mlKg ̄¹min ̄¹ observed in Danish adolescents
(Andersen et al, 1987). In addition Skinner et al (2001) demonstrated that there was similar
improvement in VO₂max following training in both white and black North Americans;
moreover, Weston et al (2000) showed black South African runners with similar 10km race
times to white South African runners had a lower VO₂max when compared to the white
runners. These findings suggest that whilst an increased VO₂max is important for improved
endurance performance, it is not necessarily the determining factor between elite endurance
athletes.
3.1: ACE gene
The ACE (Angiotensin 1-Converting Enzyme) gene was the first gene to be identified as a
marker for elevated performance over 15 years ago (Montgomery et al, 1998). The gene is
located on the long arm of chromosome 17 (17q23.3); it is 21Kb long and contains twenty-six
exons. The polymorphism of the ACE gene occurs at intron 16; in the insertion allele there are
287 extra base pairs, whereas in the deletion allele these base pairs are not present (Rigat et
al, 1990; Rieder et al, 1999). This particular polymorphism has been comprehensively
researched and shown to be a reliable marker of either power or endurance related
performance (Alvarez et al, 2000; Williams et al, 2000; Nazarov et al, 2001; Collins et al, 2004).
All-Time Top 20 (%)
Asia
Kenya
South America
Africa (excl. Kenya)
Europe
Figure 3: Relative distribution (%) between the
continents of the all-time top 20 performances in
middle- and long-distance running for men in the
six major distances from 800 m to marathon
including steeplechase in June 2003. Black
Americans are regarded as Africans excluding
Kenyans (Adapted from Larsen, 2003).

9
As figure 4 demonstrates this particular gene encodes for an enzyme that catalyses the
conversion of angiotensin I to angiotensin II; which is a powerful vasoconstrictor and so
restricts the blood flow to the peripheral muscles (Ehlers & Riordan, 1989). Furthermore, there
is evidence that it also breaks downs bradykinin, which is an important vasodilator and has
contrasting effects to angiotensin II (Murphey et al, 2000).
Angiotensinogen
Renin
Angiotensin I Bradykinin Arterial Vasodilation
ACE Increased Muscle Glucose Uptake
Angiotensin II Inactive Fragments
Arterial Vasoconstriction
Increased Sympathetic Activity
Figure 4: The renin angiotensin system particularly showing (red arrows) the actions of ACE on
Angiotensin I and Bradykinin (Adapted from Rosendorff, 1996; Jones et al, 2002).
In the insertion (I) allele, due to the extra base pairs this allele is associated with lower ACE
activity (Danser et al, 1995). Reduced ACE activity levels means an increase in blood flow - as
the blood vessels are less constricted; there is also more efficient mitochondrial respiration
and improved contraction in both skeletal and cardiac muscle (Jones et al, 2002). These
changes are more beneficial to endurance athletes and the insertion allele has been shown to
be present at a higher frequency in endurance swimmers (Tsianos et al, 2004), rowers
(Gayagay et al, 1998) and long-distance runners (Myerson et al, 1999).
On the other hand, the deletion (D) allele doesn’t contain the extra base pairs; therefore ACE
activity is higher in cardiac tissue (Danser et al, 1995). Individuals with the DD genotype have
higher serum ACE levels when compared to both heterozygotes and those homozygous for the
I polymorphism; the I/D polymorphism accounted for 47% of the total variance observed
(Rigat et al, 1990). Higher serum ACE levels are a result of increased ACE activity in endothelial
cells, which secrete plasma ACE. Although the precise mechanism of the control of ACE
synthesis is yet to be determined, it is thought that genetic control is at the transcriptional
level (Costerousse et al, 1993). The increase in ACE activity causes exercise induced
hypertrophy of the left ventricle and an increase in left ventricular mass (Montgomery et al,
1997; Fatini et al, 2000). Furthermore, the D allele is associated with higher VO₂ max and

10
further strength gain in skeletal muscles with training (Folland et al, 2000). This associates the
D allele with more power-based sports (Williams et al, 2000) such as short-distance swimming
(Woods et al, 2001; Costa et al, 2009). Having said this, some studies (Taylor et al, 1999;
Rankinen et al, 2000) show that there isn’t any significant association between the ACE
polymorphism and athletic performance. Amir et al (2007) found there to be an association
between the DD genotype and elite endurance athletes from Israel, which contradicts the
findings of the studies previously mentioned. One reason behind the differing results from
Rankinen et al (2000) study could be due to some of the participants; 42 individuals compete
in either Nordic combined or the biathlon. Both these sports comprise of an aerobic element –
cross-country skiing but also ski-jumping or rifle shooting respectively. Undoubtedly the
competitors for these events need a large aerobic capacity; but these sports are not pure
endurance disciplines, as they require other qualities in order to achieve success during the
ski-jumping or rifle shooting phases. Therefore, this could explain why their particular alleles
are not demonstrating similar patterns to models previously mentioned (Myerson et al, 1999;
Jones et al, 2002).
3.2: ADRB2
The adrenergic receptor 2 (ADRB2) gene belongs to the G protein-coupled receptor
superfamily and codes for beta-2 adrenergic receptor. The gene is located on chromosome five
at position q31-q32; it is just over 2Kb long and contains one exon. ADRB2 binds primarily to
epinephrine and regulates vasodilation along with ventricular function (Snyder et al, 2008). It
also plays an important role in conserving blood glucose levels via stimulating glycogenolysis
during sustained exercise (Wolfarth et al, 2007). The ADRB2 are prone to desensitisation
following prolonged stimulation; this is hypothesised to be a protective mechanism against
possible organ damage through stress from over-activity of the receptor (Snyder et al, 2008).
Stimulation of ADRB2 can activate the mechanisms that regulate cardiac growth and
remodelling; therefore, possibly influencing cardiovascular functions and structure. ADRB2 can
also be found in type I and type II alveolar cells; they play a vital role in bronchodilation and
lung fluid clearance. It was hypothesised that during prolonged, heavy exercise these
mechanisms are enhanced; leading to improved alveolar ventilation (Snyder et al, 2006b).
There are 4 different variants of the gene that have been studied. The first of these is an
arginine (Arg)-to-glycine (Gly) substitution at codon 16, where a substitution from G to A
(rs1042713; +46G/A) can occur (Snyder et al, 2008). Individuals homozygous for Arg16 are
hypothesised to have lower baseline receptor function (Snyder et al, 2006a). The Arg16 variant

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is associated with a decrease in blood pressure and an increase in VO₂max (Wolfarth et al,
2007), which are good markers for elite endurance performance; however, pulmonary
recovery to resting levels after exercise is relatively slow. Furthermore, the Arg allele has been
associated with an increased resistance to agonist-mediated desensitisation when compared
to the Gly allele (Snyder et al, 2008). Also, Snyder et al (2006c) observed that individuals who
were homozygous for Arg16 had 29% lower lymphocyte ADRB2 density when compared to
Gly16 subjects. Moreover, they found that ADRB2 density on lymphocytes was positively
correlated with cardiac function at rest in both the Arg16 and Gly16 groups. Individuals
homozygous for Gly16 have been shown to have an increased stroke volume and cardiac
output when compared to Arg variants in normal, healthy populations (Eisenach et al, 2005;
Snyder et al, 2006a). This trend was seen at rest, during exercise and following exercise during
the immediate recovery period; with both isometric (Eisenach et al, 2005) and isotonic
exercise (Snyder et al, 2006a). Moreover, Tang et al (2003) showed that individuals with the
Gly16 variant had an enhanced left ventricular function, explaining the increase in cardiac
function observed in the studies previously mentioned. In addition, individuals with the Gly16
variant observed prolonged bronchodilation following heavy exercise in comparison to the
Arg16 variants (Snyder et al, 2006b).
Furthermore, the second variant is a glutamine (Gln)-to-glutamate (Glu) substitution at codon
27, where there can be a substitution from C to G (rs1042714; +79C/G). Gln27 is associated
with fat burning, a higher VO₂max and therefore endurance. This is the opposite of Glu27,
which is associated with improved ADRB2 responsiveness and resistance to desensitisation
(Dishy et al, 2001) and much more closely linked with muscle growth and power (Ferec, 2014).
Significant linkage disequilibrium exists between the two polymorphisms described
previously; when Arg16 is present, normally at position 27 only glutamine is observed
(D’amato et al, 1998; Drysdale et al, 2000; Taylor & Kennedy, 2001). Dishy et al (2001)
propositioned that Gly16-Glu27 is the ideal haplotype combination for cardiovascular response
during short-term exercise; as this haplotype demonstrates enhanced stroke volume, cardiac
output and raised blood pressure. Sawczuk et al (2013) demonstrated this in a Polish
population; the Gly16-Glu27 was identified in 52.5% of the top-elite power athletes and 50% in
the sub-elite power athletes, compared to the elite endurance athletes and controls – 43.9%
and 41.5% respectively.

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3.3: ADRB1
This gene encodes for an adrenergic receptor; which is also a G protein-coupled receptor that
is primarily located in cardiac tissue. The 1-adrenergic receptor (ADRB1) gene is located on
chromosome ten at position q25.3; the gene is 2.8Kb long and contains one exon. In a similar
way to ADRB2, ADRB1 mediates the physiological effects of both epinephrine and
norepinephrine. Stimulation of ADRB1 increases heart rate and ventricular performance
through enhancing myocardial contractility. 1-adrenergic receptors are vital components in
the regulation of the cardiac system and specifically cardiac output; therefore, ADRB1 has the
potential to influence aerobic capacity (Mason et al, 1999; Defoor et al, 2006).
At amino acid position 389, an arginine (Arg)-to-glycine (Gly) substitution can occur
(rs1801253; +1165C>G) (Maqbool et al, 1999). Subjects with the Arg389 variant showed
greater coupling to G-proteins resulting in better activation of the effector mechanisms
(Mason et al, 1999); which could increase cardiac output by stimulating contractile activity of
the heart. In 2004, La Rosée et al (2004) supported this observation by demonstrating an
increase in cardiac contractility in response to catecholamines in subjects with the Arg389
allele. Mason et al (1999) also observed that subjects with the Gly389 variant had significantly
lower levels of agonist stimulated activity. Furthermore, as figure 5 illustrates Arg389
homozygotes had increased peak VO₂ and consequently increased exercise time in heart
failure patients (Wagoner et al, 2002). Although this particular investigation was not carried
out in healthy population, the polymorphisms observed in the ADRB1 gene occur within the
general population as regularly as they do in heart failure patients; therefore, there is still
potential for the gene to be a potential marker for endurance performance.
Figure 5: Showing the different
responses to exercise in heart failure
patients with regards to the
polymorphism at amino acid position
389. Asterisk, P=0.006; dagger,
P=0.04 versus homozygous Gly389
(Wagoner et al, 2002)

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A second polymorphism at amino acid position 49 (rs1801252; +145A/G) a serine (Ser)-to-
glycine (Gly) substitution occurs (Maqbool et al, 1999); affecting expression of the receptor.
Rathz et al (2002) observed that the Gly49 variant was more susceptible to agonist-promoted
down-regulation. Furthermore, Defoor et al (2006) demonstrated that in coronary artery
disease (CAD) patients those who were Gly49 homozygotes had a significantly higher peak
oxygen uptake at baseline when compared to heterozygotes and the homozygous Ser49
individuals. After three months of physical training, on average all three groups improved from
their initial values, but the Gly49 homozygous group still had a higher peak oxygen uptake.
Following on from this particular finding, Sawczuk et al (2012) showed that the presence of the
49Gly allele was significantly higher in endurance athletes than in the control group in a Polish
population. In addition, the Ser49Gly polymorphism is significantly associated with resting
heart rate; Ranade et al (2002) showed that individuals homozygous for the Ser49 allele had a
higher mean heart rate when compared to both heterozygotes and Gly49 homozygotes (Figure
6).
Figure 6: Cumulative distributions of heart rate, by Ser49Gly genotype. The solid line represents the
values for Ser49 homozygotes, the dotted line indicates results for Ser49Gly heterozygotes, and the
dashed line shows results for Gly49 homozygotes. The percentage of individuals with heart rates
lower than a certain value can be read off the Y-axis (Ranade et al, 2002).
In addition, haplotypes of ADRB1 have been studied; Defoor et al (2006) found that the most
common haplotype in their population was Ser49Arg389 (53.1%). Patients with the
homozygous Gly49Arg389 allele combination had significantly elevated aerobic power at
baseline when compared to individuals with the Ser49Gly389 and Ser49Arg389 homozygous
allele combinations. In agreement with this, the Gly49Arg389 haplotype was significantly over-

14
represented in the endurance athletes’ subgroup when compared to the controls (Sawczuk et
al, 2012).
Based on the physiological evidence and the related medical studies (Wagoner et al, 2002;
Defoor et al, 2006; Brodde, 2008), it is theorised that endurance athletes could benefit from
harbouring the Gly49Arg389 haplotype. As the haplotype combination enhances the
mechanisms that cause an increase in cardiac output and reduction in peripheral vascular
resistance, resulting in improved blood flow to the exercising muscles. These characteristics
are all highly influential in determining the duration and quality of performance over a
prolonged period of exertion.
3.4: NRF1
The Nuclear Respiratory Factor 1 (NRF1) gene is located on chromosome seven at position
q32; it is 145.4Kb and contains twelve exons. NRF1 stimulates the increase in skeletal muscle
mitochondria; this allows an increased capability to produce ATP as well as enhancing an
individual’s respiratory capacity (Hood, 2001; Baar, 2004). NRF1 (along with NRF2) directly
regulates the expression of several electron transport chain proteins and therefore increases
the capacity for oxidative phosphorylation; this results in a higher rate of ATP production
(especially during exercise). It has been shown that an overexpression of NRF1 is associated
with an increase in the levels of cytochrome c and GLUT4 protein (Baar et al, 2003).
Cytochrome c is a vital component of the electron transport chain in the mitochondria, where
it transports an electron; therefore, it is an important protein for the correct functioning of
aerobic respiration. An increase in the expression of GLUT4 was unexpected, as there was no
known NRF1 recognition sites; Baar et al (2003) did find a significant increase in myocyte
enhancer factor 2A (MEF2A) in NRF1 transgenic muscle, which could help to explain the
increase observed in the levels of GLUT4. The increase with GLUT4 protein levels correlated
with increase in blood glucose levels observed in the study; both increased 2 fold.
To date, three polymorphisms have been researched, two of which have shown promising
findings. There is a SNP in the 5’ untranslated region (rs6949152) where an A to G substitution
can occur. In addition, within intron 11 (rs240790) there is a C to T substitution. He et al (2008)
observed that individuals with the CC genotype (rs240790) had an increased VO₂max at
ventilatory threshold both at baseline and after training; also individuals with the AA genotype
(rs6949152) had increased VO₂max at ventilatory threshold in response to exercise training.
As well as the ventilatory threshold responses, both the homozygous CC genotype at rs240790
and AA genotype at rs6949152 are beneficial for increased lean body (He et al, 2008). Even

15
though this study found no association between NRF1 genotype and overall VO₂max, the
findings could still prove useful. In individuals that participate in endurance training, aerobic
adaptations occur; this includes improvements in the ventilatory threshold. Furthermore,
VO₂max is largely influenced by the delivery of oxygen to muscles rather than the ability of
muscle mitochondria to utilise oxygen (Wagner, 2000); therefore providing a possible
explanation for the lack of association between NRF1 and VO₂max. Furthermore, the CC
genotype is associated with more efficient running economy; which as previously mentioned is
an influential factor in enhanced endurance performance.

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4: Injury Risk Genetics
Reducing the prevalence and incidence of injuries in sport has become one of areas at the
forefront of sports medicine research. In 2001, Jacques Rogge, the president of the
International Olympic Committee (IOC) stated that ‘‘the most important goal of the IOC
Medical Commission is to protect the health of the athlete’’ (Renstrom et al, 2008). Commonly
sporting injuries come from overuse and exerting excessive force upon certain parts of the
body; many of which are caused by tendinopathies. Tendinopathy is a term that encompasses
inflammation and small tears in the tendon; these are frequently caused by overuse and
ageing. These injuries are often debilitating and can cause athletes to retire early from their
respective sports; out of nine athletes with Achilles tendinopathy (two competitive and seven
recreational athletes) included in a study (de Mos et al, 2007), three of them reduced their
sporting activities while the other six had to stop completely. The tendons usually more
vulnerable to overuse injuries are the cruciate ligaments, Achilles, posterior tibialis and rotator
cuff (Maffulli et al, 2003; Rees et al, 2006). Marshall et al (2007) showed that approximately
200,000 individuals in the USA alone suffer from anterior cruciate ligament (ACL) ruptures
annually and that the risk of ACL rupture in elite athletes is up to ten times higher when
compared to recreational athletes.
Previous studies have alluded to the possible genetic factor to the risk of developing
tendinopathy. Hakim et al (2003) estimated a heritability of 40% for tendinopathy at the
lateral epicondyle using a twin study; comprising of monozygotic twin pairs and dizygotic twin
pairs. Heritability was estimated using logistic regression analysis based on DeFries–Fulker
regression model (Sham et al, 1994). The lateral epicondyle is an important structure in the
elbow and provides attachment for ligaments and tendons. Furthermore, it is one of the
structures that can be badly damaged through overuse and is frequently associated with the
injury colloquially known as tennis elbow (Hakim et al, 2003). Harvie et al (2004) identified a
strong genetic influence over rotator cuff tears using a sibling study; where an individual was
five times more likely to develop symptoms associated with a rotator cuff tear if their sibling
had previously presented the symptoms, which could not be clarified by environmental factors
alone. The controls used in the study were the patient’s spouses, as they share similar
environmental risk factors with the patient during adulthood; whereas, the siblings
experienced the same environment during childhood. The use of controls helps to minimise
the effect of environmental factors.

17
There are currently treatments available to athletes in order to help stimulate recovery from
tendinopathy, particularly heavy load exercise (Kountouris & Cook, 2007). Bahr & Krosshaug
(2005) put forward the concept of personalised training programmes in order to reduce the
risk of injury, especially at the joints. Having said this, in order for the training to be successful
a multi-factorial approach should be undertaken to incorporate all factors that could increase
the incidence of an injury; therefore understanding genetic markers associated with the
prevalence of sporting injuries is important to the success of a personalised training
programme.
4.1: GDF5
Growth Differentiation Factor 5 is a member of the TGF- superfamily and closely associated
with the bone morphogenetic protein (BMP) family. GDF5 is located on chromosome 20 at
q11.2; the gene is 8.04Kb in length with four exons. GDF5 is involved in the regulation of the
growth and development of cartilage and bone (Chapman et al, 2008). More importantly in
this case, it plays a role in the healing of joint and soft tissue; therefore it holds an influence
over the liability of an individual becoming injured and their ability to recuperate from them. A
known SNP in the 5’ untranslated region (+140T/C; rs143383) has been identified as a possible
marker for osteoarthritis (OA) and sporting injuries – Achilles tendon pathology and anterior
cruciate ligament (ACL) damage. There is evidence to suggest that this polymorphism affects
the expression levels of GDF5 in numerous connective tissues (Egli et al, 2009) through
decreasing transcriptional activity by significantly reducing promoter activity (Miyamoto et al,
2007).
The T allele (rs143383) has been linked with a decreased expression of the GDF5 protein –
27% lower expression when compared to the C allele (Southam et al, 2007). Posthumus et al
(2010) demonstrated that an individual with the TT genotype has double the risk of acquiring
Achilles tendon pathology (Achilles tendinopathy and/or Achilles tendon rupture) in Australian
and South African populations. This agrees with Wolfman et al (1997) who first suggested a
possible role for GDF5 in ligament and tendon biology. They theorised that GDF5 (along with
GDF6 and GDF7) influence the differentiation of connective tissue precursors into soft tissue
forming cells; therefore, GDF5 could potentially aid in the repair and regeneration of ligament
and tendon injuries. Studies carried out in different populations around the globe show that
this particular SNP has relevance world-wide; both Asian (Miyamoto et al, 2007;
Tawonsawatruk et al, 2011) and European (Southam et al, 2007; Evangelou et al, 2009; Valdes
et al, 2009) cohorts have shown significant association between the SNP (rs143383, +140T/C )

18
and osteoarthritis (OA) susceptibility. Egli et al (2009) broadened the analysis of the SNP
association with OA risk to other soft tissues within the synovial joint, presenting similar
results. Their findings demonstrate that osteoarthritis risk mediated by this particular locus
isn’t just restricted to cartilage but potentially joint-wide. Pan et al (2014) carried out the
largest meta-analysis to date, compromising of 20 different studies and 23,995 subjects in
total; their findings were consistent with previous studies showing a significant association
between the T allele and OA risk. As well as statistical association of the GDF5 SNP, there is
also evidence of mechanistic involvement with this particular SNP (rs143883). Mikic et al
(2001) demonstrated that GDF5-deficient tendons contained 40% less collagen and were
therefore significantly weaker in mice; they also observed impaired tendon healing.
In contrast, Raleigh et al (2013) found no association between the SNP (rs143883) and
anterior cruciate ligament (ACL) rupture. Having said this, the data obtained could prove useful
in other aspects. As previously mentioned the SNP has been associated with Achilles tendon
pathology (Posthumus et al, 2010) and decreased collagen content in tendons (Mikic et al,
2001); possibly highlighting a vital difference between tendon and ligaments and their relation
to the predisposition of sport related injury. This indicates that alterations in the expression of
GDF5 are not as influential on ACL rupture risk in Caucasians. Although further studies should
investigate this lack of association in other ethnic groups to confirm the findings; the research
gathered could be invaluable for designing injury prevention models in the future, specific to
ligament and/or tendon injuries.
In addition, the T allele (rs143883) has also been associated with a small decrease in overall
height (Sanna et al, 2008). In Finnish and American populations, the presence of a C allele
equated to an increase in height by 0.697cm; along with an increase in height of 0.546cm per C
allele in an Italian population. In all three populations the allele frequency was between 0.4
and 0.5 for the C allele. This follows on logically from the decreased GDF5 expression observed
in the T allele, which would lead to decreased limb bone growth; therefore, resulting in
decreased stature. Currently, decreased stature is seen as a disadvantage in most sporting
disciplines, especially in sports where stride length is important.
4.2: COL1A1
Collagen Type-1 Alpha-1 is located on chromosome 17 at q21.33 and is 18,344 bases long
consisting of 51 exons. COL1A1 encodes for an important protein that makes up type 1
collagen, which is the most copious form of collagen in the body (Garcia-Giralt et al, 2002).
COL1A1 along with COL1A2 encode collagen I1 and collagen I2 polypeptides respectively;

19
these polypeptides then associate in a 2:1 ratio to establish collagen type 1, which provides
structural stability to ligaments and tendons (Khoschnau et al, 2008). Within the first intron in
the COL1A1 gene a functional Sp1 binding site polymorphism (rs1800012; -1997G/T) can occur
(Mann et al, 2001).
The T allele enhances the affinity for the transcription factor Sp1 resulting in increased
transcription and therefore higher expression of COL1A1; which increases the production of
collagen type-1 alpha-1 (Collins & Raleigh, 2009). This over production of COL1A1 hinders the
formation of collagen type 1, as the ratio is no longer 2:1; resulting in weaker bone and soft
tissue formation. Khoschnau et al (2008) found that individuals with the TT genotype were
underrepresented in the injury group, both in cruciate ligament (CL) ruptures and shoulder
dislocations in a Swedish population; by identifying only one individual with the TT genotype in
both of the groups. They found that individuals with the rare TT genotype had a reduced risk of
injury of up to 85% when compared to the GG genotype. A similar study, obtained results
consistent with Khoschnau et al (2008) findings in a South African population demonstrating
an association between the SNP and ACL ruptures (Posthumus et al, 2009a). As figure 7
demonstrates the TT genotype is not present in the ACL or Achilles rupture (RUP) group,
suggesting that the TT genotype has a role in the protection against soft tissue ruptures.
Moreover, comparable TT genotype frequencies were observed in the control subjects from
both of the studies - South African (4.7%, n = 256) and Swedish (3.7%, n = 325), this is in
agreement with the genotype frequencies reported in larger control populations (Mann et al,
2001). Whereas, the G allele is highly represented in all categories this implies that carriers of
the G allele are at an increased risk of developing ligament injuries. Ficek et al (2013)
demonstrated that the GT genotype is associated with a reduced risk for anterior cruciate
ligament rupture. Furthermore, the study showed that the TT genotype is less frequent in the
anterior cruciate ligament rupture group – although this result was not statistically significant.

20
Figure 7: The relative genotype frequencies of the functional sp1 binding site polymorphism within
intron 1 of the COL1A1 gene (a) in the South African control (CON), ACL rupture (ACL), chronic Achilles
tendiopathy (TEN), Achilles rupture (RUP); (b) Swedish control (CON), cruciate ligament rupture (CL),
shoulder dislocations (Shoulder) groups (Collins & Raleigh, 2009).
Having said this, although Posthumus et al (2009b) identified no association between the
polymorphism (rs1800012) and Achilles tendiopathy; they couldn’t exclude the possibility that
other polymorphisms within the gene were associated. Other studies have demonstrated a
connection between over expression of type 1 collagen and tendinotic tissue (Ireland et al,
2001; de Mos et al, 2007); both studies used paired tendon specimens from patients
undergoing surgery for Achillles tendinopathy. One tissue sample was from the affected
tendinotic lesion and the other from adjacent healthy tissue; moreover, de Mos et al (2007)
used three healthy controls. Ireland et al (2001) only reports an up-regulation in gene
expression if the difference from normal tissue was equal or greater than a two-fold increase.
In addition, Bell et al (2012) observed an association between the TT genotype and Genu
Recurvatum (knee hyperextension). GR is caused by ligamentous laxity, especially in the ACL
and can lead to knee OA. This finding is inconsistent with the previously discovered protective
role and currently there is no accepted explanation. One theory is that the alteration in the
ratio of collagen I1 and collagen I2 polypeptides has differing effects on ligament strength
and density when compared to bone.

21
5: Recovery Genetics
As well as identifying genes that act as direct markers for endurance and power ability; genes
that are associated with recovery from injury are also important. Currently in most sporting
disciplines the number and length of competitions and tournaments has increased significantly
over recent years; adding to the intensity of training regimes, and reducing or shortening rest
periods. It is vital that an athlete and their coaching team manage the training schedules
carefully, anticipating the goal of multiple and long term performance peaks Therefore, it is
vital that an athlete and their coaching team manage the time they have sensibly; this means
allowing adequate time for recovery.
Sporting activities frequently cause direct (mechanical) trauma to the skeletal muscles
through a number of mechanisms; including strains, contusions and in some cases lacerations.
After injury the muscle goes through three distinct phases of healing; 1) degeneration and
inflammation, 2) regeneration and 3) fibrosis – the formation of scar tissue (Figure 8) (Huard et
al, 2002). The first phase commences within minutes of the trauma and can continue for over
a week. During the phase localised swelling and necrosis of muscle tissue occurs, as well as the
infiltration of activated macrophages and T-lymphocytes into the injured tissue. This then
stimulates the secretion of substances such as cytokines to accelerate the inflammation
response (Hurme et al, 1991). The regeneration phase initiates the release of growth factors,
which stimulate the activity of satellite cells; these cells are vital components in the process of
muscle regeneration after injury. The satellite cells are located between the basal lamina and
the sarcolemma, these structures often loose integrity through mechanical trauma; as a
consequence satellite cells proliferate (Huard et al, 2002). The mechanism is localised, where
the proliferated satellite cells generate myoblasts that eventually mature into mature muscle
fibres; this process can continue for up to ten days depending on the severity of the injury
(Hurme & Kalimo, 1992). Once regeneration has reached its peak activity, the formation of scar
tissue commences (Figure 8).

22
1 2 3 4
Weeks after Injury
Figure 8: The different stages of muscle healing after muscle injury. The initial phase is muscle
degeneration and inflammation, which occurs within the first minutes and continues for up to one to
two weeks after injury. Muscle regeneration begins in the first week post-injury and peaks at two
weeks post-injury. Fibrosis commonly commences at two weeks post-injury and increases gradually
for up to four weeks post-injury (Adapted from Huard et al, 2002).
Exercise itself (other than injury) induces a number of responses from different metabolic
processes, both short-term and long-term in response to acute and chronic exercise training
(Figure 9). Short-term responses usually consist of hypertrophic growth and the provision of
substrates to modulate contractile force during acute bouts of exercise (Williams & Neufer,
1996). Hypertrophic growth involves a non-specific increase in the abundance of most protein
components in the muscle fibres; allowing the muscle a greater capacity for peak force
generation. Whereas, long-term adaptations include an increase in the transcription and
mRNA content of specific genes; increasing the synthesis rate of certain proteins involved in
metabolism. Peroxisome proliferator activated receptor co-activator 1alpha (PGC-1) is a
transcription coactivator that plays a central role in the regulation of cellular energy
metabolism; with increased transcription levels observed following exhaustive exercise
(Pilegaard et al, 2003). This finding suggests that PGC-1 may play a crucial role in coordinating
the activation of metabolic genes in human muscle in response to exercise; as PGC-1 induces
mitochondrial biogenesis through interaction with NRF-1 and PPARs (Baar, 2004).
Mitochondrial biogenesis in skeletal muscle is thought to be a consequence of the cumulative
effects of brief increases in mRNA levels that encode mitochondrial proteins after successive
exercise sessions (Williams & Neufer, 1996; Pilegaard et al, 2000). In agreement with this,
Perry et al (2010) demonstrated that mitochondrial DNA increased after 24 hours following a
third session of exercise and continued to increase; whilst PGC-1α mRNA levels increased more
than 10-fold within four hours following exercise but then returned to pre-exercise levels 24
hours into recovery. Mitochondrial biogenesis leads to an increase in the muscle’s respiratory
and ATP production capacity, enhancing the efficiency of muscular contraction. Mitochondrial
RelativeAmountofTissue
Red Area = Inflammation
Green Area = Regeneration
Blue Area = Fibrosis

23
biogenesis aids in the process of re-modelling in skeletal muscles; where myofibrils do not
enlarge but are reorganised, through selective activation or repression of genes. These
mechanisms result in a muscle that is more resistant to fatigue during prolonged periods of
repetitive contractions (Williams & Neufer, 1996). The molecular responses often remain
elevated or increase continually for a number of hours after the cessation of exercise;
furthermore, they are highly influenced by the duration and the intensity of the exercise
(Hildebrandt et al, 2003). These continued raised levels of proteins suggest that this is the
duration of time needed for molecular adaptations in skeletal muscles towards exercise to
take place. These adaptations are vital for improvement in athletic performance, as they lead
to an increase in the muscle’s capacity to produce ATP among the other improvements
previously mentioned; therefore allowing time for the molecular adaptations to occur is
beneficial to the athlete. Furthermore, the molecular recovery from exercise is affected by
other metabolic alterations in skeletal muscle; including muscle glycogen content. By reducing
the content of muscle glycogen before the start of exercise, there appears to be an elevation
of exercise-induced transcriptional activation in exercise responsive genes (Keller et al, 2001).
Figure 9: Schematic representation of changes in mRNA expression (bottom panel) and protein
content in skeletal muscle (middle panel) over time as a consequence of acute exercise and chronic
(repetitive) exercise training. Training-induced changes in protein content or enzyme function alter
metabolic responses to exercise at the level of substrate metabolism, resulting in improved exercise
performance (upper panel) (Egan & Zierath, 2013).

24
The processes of recovery are closely linked with the concept of injury susceptibility and a
recovery period is often utilised by coaches and athletes to either help repair the damaged
caused or to aid in preventing the occurrence of injury. Research into this area isn’t as
advanced as in other topics related to sports performance, but some genes have been
identified that show an association with the ability of an individual to recover from exercise.
5.1: NOS3
The NOS3 (Nitric Oxide Synthase 3) gene also known as the endothelial nitric oxide synthase
(eNOS) gene, is located on chromosome 7 (position q36); it is approximately 23.5Kb long and
contains twenty-nine exons. NOS3 synthesises nitric oxide (NO) from the amino acid L-arginine
in the endothelial cells (Ignarro, 1989). At rest, nitric oxide is continually released at small
levels from the endothelium; it aids in the maintenance of basal vascular tone and structure in
both the coronary and peripheral circulation (Maiorana et al, 2003). Nitric oxide is a potent
vasodilator with a short half-life in the blood of a few seconds (Ignarro, 1989); NO is lipid
soluble and rapidly diffuses into the tunica media, where it binds to the enzyme guanylate
cyclase (Moncada et al, 1988). The activation of soluble guanylate cyclase stimulates the
production of cyclic guanosine monophosphate (cGMP); the increase in cGMP causes the
increase in smooth muscle relaxation and therefore vascular dilation (Ignarro, 1989; Maiorana
et al, 2003). Nitric oxide is a central cellular signalling molecule and mechanical stimuli; nitric
oxide signalling is greatly involved in maintaining muscle integrity and correct signalling
systems while metabolic adaptations occur. During exercise, there is a significant increase in
muscle blood flow and vascular laminar shear stress; both these factors are important stimuli
for the adaptation in the endothelium. Stress exerted on endothelial cells stimulates up-
regulation of NOS3 activity, resulting in increased NO production and bioavailability (Haram et
al, 2008; Francescomarino et al, 2009). The bioavailability of NO depends on the sensitivity of
the target tissue as well as oxidative stress-mediated destruction of NO (Higashi & Yoshizumi
2004; Rush et al, 2005). After moderate bouts of exercise over weeks and months the
responsiveness of the endothelium-dependent vasodilator function can be enhanced; mostly
resulting from the increased expression of NOS3 (Maiorana et al, 2003). Nitric oxide is also
involved in the modulation of oxygen consumption in skeletal muscles (Wilkerson et al, 2004)
and human skeletal muscle glucose uptake during exercise (McConell et al, 2006). In addition,
NO enhances vascular dilation further by suppressing the production of potent
vasoconstrictors – including endothelin and angiotensin II; these vasoconstrictors stimulate
vascular smooth muscle cell proliferation. Therefore, if there is a reduction in NO production,

25
the activity of vasoconstrictors increases leading to heightened vessel reactivity (Duncker &
Bache, 2008).
There is a SNP at position -786T/C (rs2070744) of the NOS3 gene; individuals with the T allele
leads to higher gene transcription; individuals carrying the C allele showed a 52% reduction in
promoter activity (Nakayama et al, 1999); therefore reducing endothelial production of NO in
coronary arteries. Dosenko et al (2006) demonstrated further that the C allele was associated
with reduced levels of NOS3 mRNA, resulting in 2.1times lower NOS3 enzyme activity in
isolated platelets. The T allele has been linked with elite performance in power-based events in
Spanish (Gomez-Gallego et al, 2009) Italian (Sessa et al, 2011) and Ukrainian (Drozdovska et al,
2013) populations. As previously mentioned the C allele is associated with a decrease in
promoter activity; therefore contributing to an increase in blood pressure. Hyndman et al
(2002) observed that subjects with the CC genotype had significantly elevated systolic blood
pressure compared to the TT genotype group (P<0.05) and estimated that individuals with the
CC genotype were 2.16 times more likely to be hypertensive when compared to homozygous
TT individuals. In one study (Eynon et al, 2012) the C allele was found to be more common in
elite football players, when compared to sedentary individuals, as well as endurance and
power athletes.
In contrast, the C allele has been associated with improved aerobic capacity in hypertensive
men; as patients with the C allele demonstrated an increase of 7.6mmHg less in systolic blood
pressure from baseline after moderate exercise (60% of VO₂max) when compared to non-
exercising controls (Augeri et al, 2009). A potential explanation for this finding could be the
improvement seen in endothelial function following exercise; it is widely accepted that
improvement in vascular dilation is more enhanced in (and potentially limited to) subjects with
pre-existing endothelial dysfunction (Rush et al, 2005; Haram et al, 2008). It has been
identified that endurance athletes tend to have slightly lower blood pressure when compared
to strength trained athletes. Berge et al (2015) carried out a systematic review on studies
reporting blood pressure in athletes (both elite and recreational) and normal controls. The
group demonstrated that on average strength trained athletes’ blood pressure was
131.3mmHg, whereas in endurance trained individuals mean blood pressure was 118.6mmHg;
but overall found no significant difference in blood pressure between athletes and controls.
5.2: IL-6
The IL-6 (Interleukin-6) is multifunctional cytokine. The gene is located on chromosome seven
(position p21); it is 4.8Kb long and contains five exons. Along with other cytokines and growth

26
factors, IL-6 is an important part of the inflammation response induced through exercise; IL-6
released from contracting skeletal muscle fibres possess anti-inflammatory properties that aid
in muscle repair after acute exercise (Ruiz et al, 2010). IL-6 mRNA levels were significantly
elevated in human quadriceps muscle immediately after a marathon race when compared to
pre-exercise levels (Ostrowski et al, 1998). This finding was then replicated in rat muscle,
where IL-6 mRNA levels were elevated following electrically stimulated contractions – both
concentric and eccentric (Jonsdottir et al, 2000). Moreover, Steensberg et al (2000) observed a
gradual increase in the arterial plasma IL-6 concentration with exercise; although a more
significant increase in IL-6 concentration was recorded after three hours of exercise. The
maximal net release of IL-6 from the exercising muscle was ~100 fold higher than before the
exercise commenced. Furthermore, Helge et al (2003) found that the amount of IL-6 released
from working skeletal muscle was positively correlated to the intensity of exercise and glucose
uptake, as well as stimulating hepatic glucose production. This suggests that IL-6 improves
muscle glucose delivery and therefore has a role in glucose homeostasis during exercise
(Gleeson, 2000; Steensberg et al, 2000; Febbraio et al, 2004).
A functional SNP occurs at position -174 (rs1800795) in the promoter region of IL-6, involving
a C to G substitution first described by Fishman et al (1998). The G allele is linked with
increased transcriptional activity (Bennermo et al, 2004) and therefore higher expression of IL-
6 protein (Fishman et al, 1998). As a consequence, the G allele is associated with higher plasma
IL-6 levels in response to inflammatory stimuli (Bennermo et al, 2004) and more efficient
muscle repair following exercise (Serrano et al, 2008). On the other hand, individuals with the
CC genotype have raised muscle damage following exercise. Yamin et al (2008) observed a
strong association between the C allele and higher levels of total serum creatine kinase activity
(an indicator of skeletal muscle damage) resulting from eccentric contractions of the elbow
flexor muscles in young adults. Therefore, a link with fatigue for the duration of exercise has
been identified; in addition with the ability of an individual to recover from competitions and
training sessions. Skeletal muscle damage occurs frequently during exercise, as the working
muscles are under continual stress for the duration of the exercise. In this case the harm
caused doesn’t necessarily have a negative impact; as the damage doesn’t always lead to
muscle injury but influences muscular adaptations. It has also been suggested that the G allele
of this particular polymorphism may facilitate power performance in sport. Ruiz et al (2010)
observed that the G allele occurred more frequently in elite power athletes when compared to
both the control and elite endurance athletes. This accords well with the previous observations
of Taaffe et al (2000); who reported a negative association between the levels of IL-6 and

27
muscle strength. Furthermore, this group reported that the G allele does not appear to be
linked to endurance sports performance, as both the G allele and homozygous GG genotype
frequencies were similar between endurance and control groups.
Furthermore, Robson-Ansley et al (2004) suggested that IL-6 can impair endurance
performance through increasing the feeling of fatigue by varying central nervous system’s
serotonergic activity. They demonstrated this by administering a small dose of recombinant lL-
6 (rhlL-6) to elite male runners; this caused a significant impairment to the athletes 10km
running time trial when compared to the placebo group.

28
6: Discussion
The findings collected here could provide useful information and direction towards designing
training programmes for individual athletes. By analysing the current data in injury risk and
recovery genetics in particular, it is feasible that more efficient and personalised training plans
can be created, in order to get the best performance out of the athlete.
Both the T allele of GDF5 (+140T/C; rs143383) and the G allele of COL1A1 (rs1800012; -
1997G/T) were associated with a raised risk of injury in the soft tissues (Wolfman et al, 1997;
Collins & Raleigh, 2009; Posthumus et al, 2010). The COL1A1 G allele was shown to be
overrepresented in ACL ruptures and Achilles tendiopathy (Khoschnau et al, 2008; Posthumus
et al, 2009a); therefore individuals with the G allele should be adapting their training regime to
incorporate strength and resistance exercises. These exercises help to promote the strength
and stability of ligaments and tendons; so the structures can tolerate higher levels of stress.
Furthermore, the ACL is essential for changing direction quickly; therefore by including
exercises that improve rotational agility it can help reduce the risk of injury. In addition,
making nutritional changes can greatly enhance the reduction in injury risk; commencing a diet
promoting anti-inflammatory effects will help to compensate for the weaker soft tissue
formations observed in GDF5 T variants and COL1A1 G variants.
In addition, the IL-6 C allele (-174C/G; rs1800795) was associated with decreased IL-6 levels
and raised muscle damage following exercise (Yamin et al, 2008). As a consequence of this,
individuals with the C allele should increase their rest period to allow the muscles to recover
properly. Furthermore, it has been recommended that these individuals should periodically
check inflammatory biomarkers to regulate exercise intensity and duration (Ferec, 2014).
The field of genetics is gradually moving to the forefront of sport and exercise medicine, with
numerous potential applications; from incorporating the field into training programmes briefly
discussed here to genetic screening and gene doping. Sport and exercise genomics is becoming
a widely accepted component of the multi-disciplinary approach in sports and exercise science.
Myburgh (2003) proposed that current exercise scientists should at the minimum understand
the contributions that the fields of genetics and molecular biology offer, if not incorporate
them into their research. The margins between success and failure at the elite level of world
sport are commonly minute; with athletes utilising similar training programmes within their
sporting disciplines, there is considerable potential of genetics to have the answers for these
small but significant differences seen in elite sporting performance.

29
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