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The Potential Role of Genetic Markers in Talent Identification and Athlete Assessment in Elite Sport

School of Health Sciences, University of Notre Dame Australia, Fremantle 6160, Australia
School of Medical and Health Sciences, Edith Cowan University, Joondalup 6027, Australia
Institute for Health Research, University of Notre Dame Australia, Fremantle 6160, Australia
Exercise Medicine Research Institute, Edith Cowan University, Joondalup 6027, Australia
Centre for Neuromuscular and Neurological Disorders, University of Western Australia, Nedlands 6009, Australia
Perron Institute for Neurological and Translational Science, Nedlands 6009, Australia
Author to whom correspondence should be addressed.
Sports 2018, 6(3), 88;
Submission received: 11 June 2018 / Revised: 25 August 2018 / Accepted: 27 August 2018 / Published: 30 August 2018


In elite sporting codes, the identification and promotion of future athletes into specialised talent pathways is heavily reliant upon objective physical, technical, and tactical characteristics, in addition to subjective coach assessments. Despite the availability of a plethora of assessments, the dependence on subjective forms of identification remain commonplace in most sporting codes. More recently, genetic markers, including several single nucleotide polymorphisms (SNPs), have been correlated with enhanced aerobic capacity, strength, and an overall increase in athletic ability. In this review, we discuss the effects of a number of candidate genes on athletic performance, across single-skilled and multifaceted sporting codes, and propose additional markers for the identification of motor skill acquisition and learning. While displaying some inconsistencies, both the ACE and ACTN3 polymorphisms appear to be more prevalent in strength and endurance sporting teams, and have been found to correlate to physical assessments. More recently, a number of polymorphisms reportedly correlating to athlete performance have gained attention, however inconsistent research design and varying sports make it difficult to ascertain the relevance to the wider sporting population. In elucidating the role of genetic markers in athleticism, existing talent identification protocols may significantly improve—and ultimately enable—targeted resourcing in junior talent pathways.

1. Introduction

It is commonly discussed in the talent identification (TID) circles whether sporting talent is “born or bred”. In a sporting context, there is a limit to the improvement an athletes’ performance can make from practice alone; inferring that ability and skill are, at least, partially inherited qualities [1]. Short of there being a single gene responsible for sporting success, it is estimated that the human genome has 10 million different single nucleotide polymorphisms (SNPs), with dozens currently linked to sporting performance and sporting success across various sporting codes [2].
The ability to maximise athlete performance and athlete potential in elite environments is constantly at the forefront of sport science. New technology and training methods are continually being tested and developed to enhance performance and elevate athletes to an elite level status. When considering athletic performance, numerous generic and sport-specific assessments are performed to identify sporting talent and to provide an indication of current athletic ability. The notable increased inclusion of skill assessments in current athletic screening and testing batteries allows the quantification of technical and tactical attributes of an athlete to determine how one may perform in competition. Specifically, the utilisation of sport-specific skill assessments, including match day statistics, are commonly used for determining player performance in Australian Football (AF), basketball, soccer, and rugby union [3,4,5,6,7,8,9,10]. These assessments allow for an evaluation on player skill performance and match day impact.
Currently, there are several methods of determining individual player achievement within a sport: team selection, career success, coach perception, and match performance within a season [11,12]. In the context of AF, athletic field tests—such as the vertical jump, 20-metre sprint, and endurance time trials—are often used in the talent identification process to acknowledge athletes with the potential to succeed in a given sport [13]. These tests reveal indications of physical capabilities that may enhance or hinder an athlete’s ability to perform within a given sport or position within a sport. Similarly, skill-based assessments have been introduced as an additional element, as a method to rank athletes on their technical abilities [14]. As these tests are often performed within a testing battery during events such as draft combines, the results from these tests give information on the skill level of an athlete, and therefore an indication of the individual work required to elevate their abilities to an elite level.
Common genetic polymorphisms (natural variations in genetic sequences) account for the variability in the expression of several key genes important in the regulation of physiological processes. Among these, single nucleotide polymorphisms (SNPs) have gained significant attention as contributors of behavioural, physiological, and cognitive variability [15]. Owing to the diverse array of desirable physical attributes in sport, several polymorphisms within the human genome have previously been highlighted. For example, the angiotensin-converting enzyme (ACE) and alpha-actinin-3 (ACTN3) genes have both been linked with elite levels of athletic performance in endurance and/or strength/power dominant sports; including long distance running and swimming, road cycling, rowing, cross-country skiing, triathlons, sprinting, volleyball, track and field jumping and throwing, weightlifting, ice hockey, wrestling, and figure skating [16,17,18,19,20]. In addition, specific homozygous genotypes in the peroxisome proliferator-activated receptor gamma co-activator 1-alpha (PPARGC1A) gene and the beta-adrenergic receptors 1/2/3 (ADRB 1/2/3) have been linked with higher maximal oxygen uptake (VO2max), better endurance performance and more favourable body mass index in long-distance runners, long-distance swimmers road cyclists, cross-country skiers, triathletes, sprinters, sprint swimmers, weight and power lifters, track and field jumpers and throwers, boxers, and kayakers [21,22,23].
Despite the intriguing nature of these studies, there has been no strong candidate genes associated with performance in skill execution within sport. Polymorphisms within the brain-derived neurotropic factor (BDNF), dopamine D2 receptor (DRD2), and catechol-O-methyltransferase (COMT) genes have all been associated with motor control and/or learning [24,25]. However, none of these genes have previously been investigated in the context of sport-specific skill analysis. In AF, using standard skill performance assessments, a relationship between player genotype and performance has been identified [5]. However, the genetic implication of these tests has only been previously investigated once, in a small cohort of sub-elite AF players, while match performance has not been investigated in a genetic capacity at all.

2. Talent Detection and Identification

All traditional athlete development pathways share a common goal, to identify and develop individuals with the greatest long-term potential for success in elite competition [13]. The common development process of many sporting codes reflects the model proposed by Williams and Reilly [26], which involves the process of athlete detection, identification, selection, and development. In this model, athletes are selected to participate in development programs based on detection (the process of unearthing potential elite performers not currently participating in the sport in question) or identification (recognising those currently in the pathway with the potential to excel) outcomes. Athlete detection and identification is critical to the athlete development process, as they typically guide the initial, and subsequent, selection of athletes into development programs.
Assessments utilised in the identification and detection process are typically considered sport specific. For example, the testing battery used at national draft combines of the Australian Football League (AFL), National Football League (NFL), and National Basketball Association (NBA) may be considered highly specific to each of the particular sports. Each sport adopts performance tests that are considered to best represent the physical demands of that particular sport [13]. Due to the multi-disciplinary nature of most team sports, testing often examines a range of physical performance measures—such as aerobic capacity, anaerobic power, and technical skill—with results used to guide higher or elite level selection [27,28,29]. However, while there are test that are specific to each sport, usually skill specific, these tests are performed in closed-environment settings and do not have the ability to assessment an athlete’s decision making skills or execute skill under pressure. The lack of testing in open-environment is a flaw in current TID testing batteries.

3. Measurements of Sporting Performance

Prior to discussing gene candidates and their potential relation to athlete performance, skill performance, and match performance, it is first important to identify and describe the current assessments commonly utilised to measure these athletic performance domains, and so too the qualities they measure.

3.1. Measurements of Athletic Performance

3.1.1. Aerobic Assessment

An athlete’s ability to recover between bouts of high intensity effort is linked to their aerobic capacity [30,31]. Aerobic capacity is most accurately assessed as a measure of maximal oxygen uptake (VO2max) [32,33], however true VO2max testing in team sport environments is uncommon due to specialist equipment requirements and cost. Due to these limitations, predicted VO2max measures are more commonly used. Many indirect measures exist, with most assessments being either timed continuous runs, or interval based tests [34,35,36,37]. Recently, there has been a transition towards the Yo-Yo Intermittent and 30:15 Intermittent Recovery tests, as the recovery periods dispersed within the tests are more sport specific. A version of the Yo-Yo test, the Yo-Yo IR2, has been shown to have a strong relationship with high-speed running and match performance (measured by possessions) during an AF game [8].

3.1.2. Strength and Power Assessments

In many sporting codes, the ability of an athlete to jump higher than their opponent may put them at a distinct competitive advantage. As such, the vertical jump (VJ) test is commonly included as a TID assessment, as a measure of explosive anaerobic power of the lower limbs [38,39,40]. To assess an athlete’s VJ capability, there are typically two different tests performed, a stationary countermovement jump and a dynamic countermovement jump. In both instances, the aim of the test is for the athlete to attain maximum vertical displacement. The dynamic vertical jump is included in the testing barrage of many sports, and is considered a more reflective test for power assessment in team sports. However, while the VJ is a good measure of anaerobic power, it does not often relate to success in Australian Football [11].
The peak force generated during the isometric mid-thigh pull (IMTP) strength test has also been associated with VJ. The peak force was significantly related to countermovement and squat jump performance in junior male and female surfers [41]. The IMTP has been used to assess collegiate level American Football players and wrestlers, and was found to be an effectual and fast technique of measuring isometric strength [42,43]. When tested in female Olympic weightlifters, a near perfect relationship between peak force during the IMTP and VJ peak power was seen [44]. Similar results were seen in former colligate level male weightlifters, as there was a strong correlation between isometric peak force and VJ performance [45]. The IMTP can also be used to monitor athletes, such as weightlifters, as it delivers information of strength and explosiveness [46]; key areas in many sports including AF.

3.1.3. Acceleration and Maximal Linear Sprinting Speed

To assess an athlete’s sprint capacity, maximum acceleration, and speed, and the ability to perform repeated high-speed efforts are commonly used in talent pathways [47,48]. Often, acceleration and maximum speed are assessed concurrently, with timing gates used to assess an athlete’s linear speed often over 20–60 m [29,40,49]. In Australian Football (AF), the 20-m sprint has been shown to be predictive of selection into underage representative and professional senior squads [27], and AFL success [11]. Such an assessment is a good indicator of acceleration [27,50] and often provides important performance information for recruiters in power-based sports, such as American football [51].

3.1.4. Muscle Fibre Composition

The structure and composition of skeletal muscle fibres has the potential to be used as a predictive measure for athletic performance. For example, slow-twitch fibres (type I) are associated with endurance events, while fast-twitch fibres (type II) are linked to short, explosive events. Type I fibres have a higher aerobic capacity and myoglobin stores, allowing for greater blood flow and improved oxygen delivery to the muscle [52]. In contrast, type II fibres breakdown ATP rapidly, resulting in a fast muscle shortening cycle, and the production of more force [53,54]. Fast twitch fibres have an abundance of glycolytic enzymes, allowing for a large anaerobic capacity [55]. As a result, type II fibres are more fatigable than type I fibres.
Another method for muscle fibre assessment involves the measurement of muscle carnosine, a muscle metabolite that cannot be easily influenced by environment, training, or diet [56,57]. Interestingly, fast-twitch fibres can have twice the amount of muscle carnosine than slow-twitch [57]. In athletes, levels of muscle carnosine are approximately 30% higher in sprinters and short-event athletes, and approximately 20% lower in endurance event athletes, compared to the control group [57]. The relative amounts of alpha-actin-3 and alpha-actin-2 can also influence muscle fibre composition [58]. Alpha-actin-3 (fast-twitch) and alpha-actin-2 (slow-twitch) have the ability to influence fibre characteristics in individual muscle [58]. The alpha-actin genotype can affect fibre-type arrangement, metabolic profile, and contractile properties [58].

3.2. Measurements of Skill Performance

The sport specific assessment of skill is an essential and commonly examined component of the TID process [14,39,40,59]. For example, in table tennis, eye-hand coordination is the first skill assessment in the TID process [60]. Similarly, there have also been strong associations between motor coordination and status level in female volleyball players, a sport where motor coordination is an indicator of an athlete’s potential [61]. However, the assessment of skill is often a subjective evaluation and therefore has an area of biased opinion [62]. Frequently, skill assessment is rated on skill-specific criteria and is graded on a Likert scale, but due to the subjectivity of the observers making the assessment, the reliability can be questionable. It must also be stated that some sport-specific skills, such as tackling in rugby league, are more difficult to assess reliably, compared to other skills [62].

3.3. Measurements of Match Performance

Match performance is a measurement of contributions to a game and is reported as game day statistics. Individually, methods of determining player achievement can be completed subjectively via umpire or coach votes, or objectively using match-based statistics [12]. Using match statistics, such as handballs and kicks, an objective measurement of match performance can be calculated for AF players [11,50]. Match statistics are accumulated in AF by a commercial analytics company (Champion Data, South Bank, Australia) with reports of 99% accuracy [63] and have been previously used in other studies involving AF [8,64]. Direct game involvement (DGI) can be used as a sum of handball, kick, mark, and tackle statistics is a game of AF, and DGI per minute (DGI/min) can be used as a measurement of game impact [12].
Often, talent identification and pre-season testing is used as an indicator of potential match performance and fitness. Aerobic time trials, which are often apart of such testing batteries and pre-season fitness measures, have previously been associated with improved match performance in sub-elite AF players [12]. In lower body power assessments, the vertical jump does not correlate with success in AF [11]; however, the 20-m sprint shows moderate positive correlations [11,27]. Coaches and high performance managers may desire a more accurate and efficient method of gauging physical capacities during testing and pre-season training, which opens the opportunity for the investigation of genetic profiles in certain sports.

4. Contribution of Genetic Variation to Athlete Strength, Power, and Endurance

The question is often posed, what makes an athlete? External influences such as early-life exposure, social-economic status, and training history influence an individual’s ability to succeed in sport. However, a player’s genetics may play an important role in determining sporting achievement, as athleticism, like many other individual characteristics, is, at least, a partially inherited trait [1]. There are allelic variants within genes that have been identified as predisposing individuals to elite endurance or elite power. The ACE and ACTN3 genes both have one homozygous variation that predisposes individuals to power and another to endurance [18,65,66,67,68,69]. While the ADRB 1/2/3 and PPARGC1A genes that influence endurance performance, one homozygous variation positively impacts endurance performance and the other negatively impacts endurance performance [21,22,23,70,71,72,73,74]. Besides athletic traits—such as endurance, strength, and power—other traits such as skill acquisition and development affect a person’s success in sport, especially team sports such as AF. There is evidence from several genes (BDNF, DRD2, COMT) that show they impact motor coordination and/or development [24,25,75].

4.1. The Role of ACE

The human ACE gene is an important part of the renin-angiotensin-aldosterone system (RAS). This system is involved in circulatory homeostasis [76], skeletal muscle growth [77] and cardiovascular function [78]. The ACE gene is located on chromosome 17 and has an insertion/deletion polymorphism of 287 base pairs, resulting from an Alu repeat sequence, shown to insert within intron 16 [79]. This produces three well-documented ACE insertion/deletion genotypes; II homozygote, DD homozygote, and ID heterozygote. Previous studies have shown the ACE polymorphism to be correlated with cardiovascular disease risk and complications [80], proliferative diabetic retinopathy [81], Alzheimer’s disease [82], and acute macular degeneration [83]. The frequency of the ACE I/D genotype shows variation amongst ethnic populations [84,85], and also correlates with athletic traits in a number of elite sporting codes. However, in simple movements such as a squat jump or countermovement jump, neither homozygous ACE genotype (DD or II) has shown correlation to performance [86].
The ACE insertion allele indicates the presence of an Alu repeat sequence within the gene. The homozygous insertion allele (I) is associated with lower plasma levels of ACE enzyme, and has been linked to the onset and progression of Alzheimer’s disease [87]. In a sporting context, the frequency of I alleles appears to increase with race distance in studies of elite Caucasian runners [18] and swimmers [68]. Therefore, the insertion ACE allele appears to be an advantageous genotype for endurance sports. Athletes with this allele tend to demonstrate an increased aerobic capacity [79,88] and better performance in a range of endurance sports [89]. However, a smaller number of contradictory studies have also demonstrated no relationship between the I allele and endurance athletes [90] or maximal oxygen consumption (VO2max) [89], a measure of aerobic capacity. Possible reasons for these contradictions include diverse group of athletes with a small percentage of endurance athletes, the examination of elite and sub-elite athletes, and environmental factors [88]. Orysiak, Zmijewski, Klusiewicz, Kaliszewski, Malczewska-Lenczowska, Gajewski, and Pokrywka [88] even suggested that the ACE gene has a minor role in endurance performance, implying that the ACE polymorphism has a greater relationship with strength and power, and the deletion allele.
The deletion allele of the ACE gene has commonly been studied in association with sporting codes dependent on strength and/or power. This particular allele is associated with circulating plasma levels of ACE, with a DD genotype resulting in significantly higher levels of ACE compared to a II genotype [91]. Elevated levels of ACE can contribute to ischaemic heart disease and other complications arising from increased activation of the angiotensin system [92]. However, the deletion genotype (DD) has also been associated with increased musculoskeletal fitness [93] in strength-related individual sports. For example, European Caucasian elite short distance swimmers [18,69], and track and field [67] athletes, were found to have increased D alleles compared to controls. In contrast, a population of Lithuanian soccer players [78] and South East Asian rugby players [79] were found to have significantly lower proportions of the D allele. This is surprising, considering that both sporting codes have elements of strength and power, suggesting ethnicity and additional factors may also be at play. Similar studies have also revealed no correlation between ACE I/D alleles and individual [67,94,95] and team [95] sports (Table 1). More recently, a multinational study investigating the ACE I/D alleles in Caucasian endurance athletes failed to identify a correlation between genotype and performance time [96].

4.2. The Role of ACTN3

A member of the actin family, actini-alpha-3 (ACTN3) is a protein highly expressed in muscle tissue, which functions to crosslink actin filaments in fast-twitch (type II) skeletal muscle fibres [97]. Expression in glycolytic skeletal muscle allows this protein to contribute towards powerful muscle by coordinating fast-twitch muscle contractions [97]. The ACTN3 gene, coding for this protein, has been thrust into the forefront of genetic studies following the identification of a nonsense polymorphism at position 577 (rs1815739), which significantly affects ACTN3 protein levels due to a premature stop codon within the ACTN3 gene [97]. The most common nucleotide at position 577 is a cytosine (C allele) encoding an arginine amino acid (arginine = R), with the alternative T allele encoding a stop codon (stop = X). Therefore, within the literature, the CC genotype is referred to as RR, whereas the TT genotype is often called XX (referred to herein as R577X). Interestingly, over a billion people worldwide are believed to carry the homozygous absent XX genotype and be deficient in this protein [97,98]. Studies examining the effect of an ACTN3 knock out in mice show alpha-actin 2 levels increase, resulting in a transition of fibre type towards fatigue resistance, as a compensatory mechanism for the absence of functional ACTN3 protein [58]. Similar to in vivo studies, athletes with an XX genotype show an increased tendency to perform endurance sports, as shown by an increased frequency of this particular allele (Table 1).
There is currently a strong association between the homozygous R allele of ACTN3 and strength/power athletic phenotypes [16,97,99,100,101]. Furthermore, there has also been evidence that a higher proportion of slow-twitch muscle fibres are related to the X allele genotype and elite endurance status [17,98,102]. In team sports, a cohort of Brazilian soccer players with the RR genotype performed significantly better at short distance sprints and jump tests compared to athletes with the RX and XX genotypes [103].

4.3. Additional Candidate Genes Implicated in Strength and Power

A number of additional genes have been identified as being strong indicators of athletic status (see Table 1). The majority of these genes encode proteins that are associated with muscle tissue or cardiovascular function [104]. The angiotensin II receptor type 2 (AGTR2) gene, associated with the RAS system, has been implicated in power sports due to its association with fast-twitch muscle fibre composition [77]. A polymorphism in this gene (rs699) has been found to occur more frequently in females (A allele) than males (C allele). Interestingly, the A allele correlates with selection to strength and power sports, when compared to control or C allele groups [77].
Similarly, a number of polymorphisms relating to genes responsible for oxygen delivery have been identified as predictors for strength and power performance. The two variations in the endothelial PAS domain protein 1 (EPAS1) gene, the hypoxia inducible factor 1 A (HIF1A) and the nitric oxide synthase 3 (NOS3) genes, have shown an association with sprint and power sports, when compared to control groups [105,106,107]. Within a group of Polish and Russian athletes, two homozygous genotypes of the EPAS1 variants were found to be under-represented in sprint and power athletes [107]. While the CC genotype of the HIF1A gene is more frequent in wrestlers and weightlifters [106] and the T allele of the NOS3 gene has been shown to be associated with, therefore beneficial to, power sports [105].

4.4. Additional Candidate Genes Implicated in Endurance and Aerobic Capacity

Despite a heavy focus on the role of the ACE insertion/deletion polymorphism, there have been a number of recent genes implicated in aerobic/endurance performance (see Table 1). The ADRB 1/2/3 genes are an example of this, and function to encode G-coupled receptors in cardiac and adipose tissue, regulating cardiac function and metabolism [73]. Specifically, a single base variation in ADRB1 (rs1801253) can drastically increase VO2max, exercise time and endurance (C allele), or cause a decrease in VO2max (G allele) [74]. Similarly, the G allele in ADRB2 (rs1042713) is strongly correlated with increased BMI and decreased VO2max [72]. Finally, a study on Spanish athletes showed a rare C allele in ADRB3 (rs4994) strongly correlated with elite endurance performance [23], making this an ideal predictive marker.
The PPARGC1A gene plays an important role in metabolism. PPARGC1A functions as an activator of oxidative phosphorylation genes, ultimately controlling glucose and lipid metabolism [70,71]. Further, it has been linked to skeletal muscle fibre formation, and the determination of fibre type [71]. A single base variation (rs8192678) in different populations can correlate to different outcomes. For example, studies show that European men homozygous for the A allele (AA genotype) have an extremely strong correlation with higher VO2max and endurance. In contrast, Chinese men that are homozygous for the G allele (GG genotype) show a strong association with higher VO2max and endurance [21,22].

5. Genetic Links to Ability and Skill in Sport

5.1. BDNF Polymorphism and Motor Skill Acquisition

Brain-derived neurotropic factor (BDNF) protein has an effect on vascular and neuronal growth, as well as brain, spinal cord, skeletal muscle development and regeneration [119,120,121,122,123]. Structural and functional changes can occur due to different SNPs in the BDNF gene [124]. The binding and internalisation of the BDNF protein can affect axonal path finding [125], differentiation, neuronal survival [126], formation and conservation of late-phase potentiation [127], hippocampus neuronal death defence [128], and dendritic trafficking regulation [129].
The BDNF Val66Met polymorphism (rs6265), found on codon 66 [75], is known to impact on the ability of humans and mice to develop motor skills [24]. Fritsch, Reis, Martinowich, Schambra, Ji, Cohen, and Lu [24] found that BDNF levels are elevated during motor skill training, when participants completed a sequential visual isometric pinch task. This could be due to the secretion of BDNF needed to increase synaptic plasticity in the motor cortex of the brain [24]. Corticospinal output of BDNF encoded by the AA genotype increased after motor training, while AG and GG genotypes showed reverse effects [130]. This suggests that the effect of the polymorphism does not occur in the basal state, but due to increased neural activity as a behavioural response [130]. The Val66Met polymorphism is also related with short term reduced motor learning and changed motor cortex plasticity [75]. For example, AG carriers exhibit poorer motor learning in simple motor tasks, such as a serial reaction time task, compared to AA genotypes [75].

5.2. Dopamine Receptors and Procedural Learning of Complex Skills

Dopamine polymorphisms are associated with many neurological and mental disorders that are characterised by changes in cognitive and emotional processes [25]. Huertas, Buhler, Echeverry-Alzate, Gimenez, and Lopez-Moreno [25] found a connection between a SNP in the DRD2 gene (rs1800497), situated in exon 7, which affects mirror drawing, a visual-motor task that requires procedural learning. From this study, C allele and T allele carriers showed a similar ability at the commencement of the task, however C allele carriers learnt more as the task continued [25]. The results were unexpected as C allele carriers are related to low striatal DRD2 availability [131] and high DRD2 binding potential within the thalamus and cortex [132]. However, there are two possible explanations for this. The low striatal density, linked with the C allele genotype, may cause a higher affinity to compensate by an optimisation of G-protein-coupled signalling, leading to more effective learning [25]. Another explanation is that in the study, both CC and TT genotypes showed an increased heart rate during the task; stress has been showed to increase striatal dopamine release [133]. The stress produced during the task may have caused compensation in the low striatal density, providing optimal learning circumstances [25].
Catechol-O-methyltransferase (COMT) is an enzyme that promotes the breakdown of dopamine, a process essential for cognitive function [134]. A COMT polymorphism at position 158 (Val158Met), resulting in either an A or G allele (rs4680), has been associated with motor control, and linked with the expression of DRD2 [135]. Individuals homozygous for the A allele (Met) have reduced COMT enzyme activity, resulting in increased dopamine activity, compared with G carriers (Val) [135]. Furthermore, pairing both homozygous AA COMT and CC DRD2 genotypes can allow for higher rates of motor learning [135]. While these genes have been linked to motor learning and coordination, it has not been investigated whether they affect sport-specific skill acquisition.

6. Ethical Considerations

It is important to consider the ethical issues that could arise from the genetic testing of potential athletes. From a public health perspective, medical genotyping is seen as presumptuous, divisive, and discriminatory [136]. Therefore, in a sporting context, the suggestion that athletes should be left out of elite training pathways based solely on their genome would likely be considered by many as unethical [137]. However, including a junior athlete in a development program based on their genotype is a more inclusive measure of TID and could allow for late-developers to receive appropriate coaching while they mature [138]. This would allow for fair selection once all the athletes have reached the same maturity as the early-developers would not have been put at an advantage based on anthropometric and physical testing from adolescent years. The genetic screening should not be used as a measure of complete athletic potential, but as a predictive tool in athlete potential [138]. Other ethical concerns that arise from the notion of genetic testing for TID purposes are the restricting of participation options, early specialisation risks, and potentially missing out on other opportunities that could provide enjoyment [139]. However, what genetic testing can be used for is guiding interventions that could reduce injury and improve an individual’s health [137,140].

7. Conclusions

Current TID processes have been utilised for years with basic practises having been expanded on to include sport-specific tasks, involving skill. With modern technology, there is potential to examine an athlete’s genetic makeup and investigate whether a particular genotype can contribute to athletic performance. The ACE and ACTN3 genes have shown strong potential to predict athletic prowess in both endurance or power and strength-based sports, while numerous other genes have shown associations with one or the other. Even more exciting is the possibility to prophesise motor control and development in relation to skill acquisition and performance in sport. Variants in the BDNF, DRD2, and COMT genes are possible candidates for future research aiming to investigate such possibilities in different sports. However, it must be stressed that the identification of athletic talent is unlikely to be the result of a small number of genetic variants, rather a complex combination of a large number and pattern of expressed genes, as well as a number of environmental conditions.
The ability to develop training programs custom made for athletes based on their known areas of need has very plausible implications. Most coaches have used the phrase “practise makes perfect” and many coaches and sport scientists will say that training time is limited for a multitude of reasons.
Research into this area can be used to determine if a particular genotype allows for athletes to better perform and react to certain training stimuli, allowing for a more well-rounded view on training specification. Further research conducted should therefore investigate how a player’s genotype may contribute to training outcomes and adaptations. A major draw to prospective athletes to a given sporting club, or institute, is their ability to cater and individualise training programs to the individual athlete. Introducing genetic testing to these programs will allow athlete specific training regimes to improve athletic, skill, and match performance.

Author Contributions

Conceptualization, Y.J. and R.A.; Investigation, Y.J.; Resources, R.A., N.H. and T.S.; Data Curation, Y.J. and R.A.; Writing-Original Draft Preparation, R.A. and Y.J.; Writing-Review & Editing, R.A., N.H., T.S.; Supervision, R.A., N.H. and T.S.; Project Administration, R.A.


This research received no external funding.


The authors would like to acknowledge the Perron Institute for Neurological and Translational Science for administrative and technical support.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Drozdovska, S.B.; Dosenko, V.E.; Ahmetov, I.I.; Ilyin, V.N. The association of gene polymorphisms with athlete status in ukrainians. Biol. Sport 2013, 30, 163–167. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Goldstein, D.B.; Cavalleri, G.L. Genomics: Understanding human diversity. Nature 2005, 437, 1241–1242. [Google Scholar] [CrossRef] [PubMed]
  3. Castellano, J.; Casamichana, D.; Lago, C. The use of match statistics that discriminate between successful and unsuccessful soccer teams. J. Hum. Kinet. 2012, 31, 139–147. [Google Scholar] [CrossRef] [PubMed]
  4. Garcia, J.; Ibanez, S.J.; De Santos, R.M.; Leite, N.; Sampaio, J. Identifying basketball performance indicators in regular season and playoff games. J. Hum. Kinet. 2013, 36, 161–168. [Google Scholar] [CrossRef] [PubMed]
  5. Jacob, Y.; Cripps, A.; Evans, T.; Chivers, P.T.; Joyce, C.; Anderton, R.S. Identification of genetic markers for skill and athleticism in sub-elite australian football players: A pilot study. J. Sports Med. Phys. Fit. 2016. [Google Scholar] [CrossRef]
  6. Lorenzo, A.; Gomez, M.A.; Ortega, E.; Ibanez, S.J.; Sampaio, J. Game related statistics which discriminate between winning and losing under-16 male basketball games. J. Sports Sci. Med. 2010, 9, 664–668. [Google Scholar] [PubMed]
  7. Mikolajec, K.; Maszczyk, A.; Zajac, T. Game indicators determining sports performance in the nba. J. Hum. Kinet. 2013, 37, 145–151. [Google Scholar] [CrossRef] [PubMed]
  8. Mooney, M.; O’Brien, B.; Cormack, S.; Coutts, A.; Berry, J.; Young, W. The relationship between physical capacity and match performance in elite australian football: A mediation approach. J. Sci. Med. Sport Sports Med. Aust. 2011, 14, 447–452. [Google Scholar] [CrossRef] [PubMed]
  9. Ortega, E.; Villarejo, D.; Palao, J.M. Differences in game statistics between winning and losing rugby teams in the six nations tournament. J. Sports Sci. Med. 2009, 8, 523–527. [Google Scholar] [PubMed]
  10. Vaz, L.; Rooyen, M.V.; Sampaio, J. Rugby game-related statistics that discriminate between winning and losing teams in irb and super twelve close games. J. Sports Sci. Med. 2010, 9, 51–55. [Google Scholar] [PubMed]
  11. Burgess, D.; Naughton, G.; Hopkins, W. Draft-camp predictors of subsequent career success in the australian football league. J. Sci. Med. Sport 2012, 15, 561–567. [Google Scholar] [CrossRef] [PubMed]
  12. Piggott, B.G.; McGuigan, M.R.; Newton, M.J. Relationship between physical capacity and match performance in semiprofessional australian rules football. J. Strength Cond. Res. 2015, 29, 478–482. [Google Scholar] [CrossRef] [PubMed]
  13. Vaeyens, R.; Lenoir, M.; Williams, A.M.; Philippaerts, R.M. Talent identification and development programmes in sport: Current models and future directions. Sports Med. 2008, 38, 703–714. [Google Scholar] [CrossRef] [PubMed]
  14. Coelho E Silva, M.J.; Figueiredo, A.J.; Moreira Carvalho, H.; Malina, R.M. Functional capacities and sport-specific skills of 14- to 15-year-old male basketball players: Size and maturity effects. Eur. J. Sport Sci. 2008, 8, 277–285. [Google Scholar] [CrossRef]
  15. Greenwood, P.M.; Parasuraman, R. Normal genetic variation, cognition, and aging. Behav. Cognit. Neurosci. Rev. 2003, 2, 278–306. [Google Scholar] [CrossRef] [PubMed]
  16. Cieszczyk, P.; Eider, J.; Ostanek, M.; Arczewska, A.; Leonska-Duniec, A.; Sawczyn, S.; Ficek, K.; Krupecki, K. Association of the actn3 r577x polymorphism in polish power-orientated athletes. J. Hum. Kinet. 2011, 28, 55–61. [Google Scholar] [CrossRef] [PubMed]
  17. Eynon, N.; Ruiz, J.R.; Femia, P.; Pushkarev, V.P.; Cieszczyk, P.; Maciejewska-Karlowska, A.; Sawczuk, M.; Dyatlov, D.A.; Lekontsev, E.V.; Kulikov, L.M.; et al. The actn3 r577x polymorphism across three groups of elite male european athletes. PLoS ONE 2012, 7, e43132. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Myerson, S.; Hemingway, H.; Budget, R.; Martin, J.; Humphries, S.; Montgomery, H. Human angiotensin i-converting enzyme gene and endurance performance. J. Appl. Physiol. 1999, 87, 1313–1316. [Google Scholar] [CrossRef] [PubMed]
  19. Papadimitriou, I.D.; Papadopoulos, C.; Kouvatsi, A.; Triantaphyllidis, C. The ace i/d polymorphism in elite greek track and field athletes. J. Sports Med. Phys. Fit. 2009, 49, 459–463. [Google Scholar]
  20. Ribas, M.R.; Oliveira Netto, Z.C.; Salgueirosa, F.; Fernandes, P.; Matos, O.D.; Bassan, J.C. Association of actn3 r577x and ace i/d polymorphisms in brazilians wrestlers. Rev. Bras. Med. Esporte 2017, 23, 469–472. [Google Scholar] [CrossRef]
  21. Eynon, N.; Meckel, Y.; Sagiv, M.; Yamin, C.; Amir, R.; Sagiv, M.; Goldhammer, E.; Duarte, J.A.; Oliveira, J. Do ppargc1a and pparalpha polymorphisms influence sprint or endurance phenotypes? Scand. J. Med. Sci. Sports 2009, 20, e145–e150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Maciejewska, A.; Sawczuk, M.; Cieszczyk, P.; Mozhayskaya, I.A.; Ahmetov, I.I. The ppargc1a gene gly482ser in polish and russian athletes. J. Sports Sci. 2012, 30, 101–113. [Google Scholar] [CrossRef] [PubMed]
  23. Santiago, C.; Ruiz, J.R.; Buxens, A.; Artieda, M.; Arteta, D.; Gonzalez-Freire, M.; Rodriguez-Romo, G.; Altmae, S.; Lao, J.I.; Gomez-Gallego, F.; et al. Trp64arg polymorphism in adrb3 gene is associated with elite endurance performance. Br. J. Sports Med. 2011, 45, 147–149. [Google Scholar] [CrossRef] [PubMed]
  24. Fritsch, B.; Reis, J.; Martinowich, K.; Schambra, H.M.; Ji, Y.; Cohen, L.G.; Lu, B. Direct current stimulation promotes bdnf-dependent synaptic plasticity: Potential implications for motor learning. Neuron 2010, 66, 198–204. [Google Scholar] [CrossRef] [PubMed]
  25. Huertas, E.; Buhler, K.M.; Echeverry-Alzate, V.; Gimenez, T.; Lopez-Moreno, J.A. C957t polymorphism of the dopamine d2 receptor gene is associated with motor learning and heart rate. Genes Brain Behav. 2012, 11, 677–683. [Google Scholar] [CrossRef] [PubMed]
  26. Williams, A.M.; Reilly, T. Talent identification and development in soccer. J. Sports Sci. 2000, 18, 657–667. [Google Scholar] [CrossRef] [PubMed]
  27. Robertson, S.; Woods, C.; Gastin, P. Predicting higher selection in elite junior australian rules football: The influence of physical performance and anthropometric attributes. J. Sci. Med. Sport. 2015, 18, 225–229. [Google Scholar] [CrossRef] [PubMed]
  28. Sierer, S.P.; Battaglini, C.L.; Mihalik, J.P.; Shields, E.W.; Tomasini, N.T. The national football league combine: Performance differences between drafted and nondrafted players entering the 2004 and 2005 drafts. J. Strength Cond. Res. 2008, 22, 6–12. [Google Scholar] [CrossRef] [PubMed]
  29. Till, K.; Cobley, S.; O’Hara, J.; Morley, D.; Chapman, C.; Cooke, C. Retrospective analysis of anthropometric and fitness characteristics associated with long-term career progression in rugby league. J. Sci. Med. Sport 2015, 18, 310–314. [Google Scholar] [CrossRef] [PubMed]
  30. Glaister, M. Multiple sprint work: Physiological responses, mechanisms of fatigue and the influence of aerobic fitness. Sports Med. 2005, 35, 757–777. [Google Scholar] [CrossRef] [PubMed]
  31. Stanula, A.; Roczniok, R.; Maszczyk, A.; Pietraszewski, P.; Zajac, A. The role of aerobic capacity in high-intensity intermittent efforts in ice-hockey. Biol. Sport 2014, 31, 193–199. [Google Scholar] [CrossRef] [PubMed]
  32. Astorino, T.A.; White, A.C. Assessment of anaerobic power to verify vo2max attainment. Clin. Physiol. Funct. Imaging 2010, 30, 294–300. [Google Scholar] [CrossRef] [PubMed]
  33. Magnan, R.E.; Kwan, B.M.; Ciccolo, J.T.; Gurney, B.; Mermier, C.M.; Bryan, A.D. Aerobic capacity testing with inactive individuals: The role of subjective experience. J. Phys. Act. Health 2013, 10, 271–279. [Google Scholar] [CrossRef] [PubMed]
  34. Bangsbo, J.; Iaia, F.M.; Krustrup, P. The yo-yo intermittent recovery test: A useful tool for evaluation of physical performance in intermittent sports. Sports Med. 2008, 38, 37–51. [Google Scholar] [CrossRef] [PubMed]
  35. Buchheit, M. The 30–15 intermittent fitness test: Accuracy for individualizing interval training of young intermittent sport players. J. Strength Cond. Res. 2008, 22, 365–374. [Google Scholar] [CrossRef] [PubMed]
  36. Grant, J.A.; Joseph, A.N.; Campagna, P.D. The prediction of vo2max: A comparison of 7 indirect tests of aerobic power. J. Strength Cond. Res. 1999, 13, 346–352. [Google Scholar] [CrossRef]
  37. Leger, L.A.; Lambert, J. A maximal multistage 20-m shuttle run test to predict vo2 max. Eur. J. Appl. Physiol. Occup. Physiol. 1982, 49, 1–12. [Google Scholar] [CrossRef] [PubMed]
  38. Manske, R.; Reiman, M. Functional performance testing for power and return to sports. Sports Health 2013, 5, 244–250. [Google Scholar] [CrossRef] [PubMed]
  39. Matthys, S.P.; Vaeyens, R.; Fransen, J.; Deprez, D.; Pion, J.; Vandendriessche, J.; Vandorpe, B.; Lenoir, M.; Philippaerts, R. A longitudinal study of multidimensional performance characteristics related to physical capacities in youth handball. J. Sports Sci. 2013, 31, 325–334. [Google Scholar] [CrossRef] [PubMed]
  40. Woods, C.T.; Raynor, A.J.; Bruce, L.; McDonald, Z.; Collier, N. Predicting playing status in junior australian football using physical and anthropometric parameters. J. Sci. Med. Sport 2015, 18, 225–229. [Google Scholar] [CrossRef] [PubMed]
  41. Secomb, J.L.; Nimphius, S.; Farley, O.R.; Lundgren, L.E.; Tran, T.T.; Sheppard, J.M. Relationships between lower-body muscle structure and, lower-body strength, explosiveness and eccentric leg stiffness in adolescent athletes. J. Sports Sci. Med. 2015, 14, 691–697. [Google Scholar] [PubMed]
  42. McGuigan, M.R.; Winchester, J.B. The relationship between isometric and dynamic strength in college football players. J. Sports Sci. Med. 2008, 7, 101–105. [Google Scholar] [PubMed]
  43. McGuigan, M.R.; Winchester, J.B.; Erickson, T. The importance of isometric maximum strength in college wrestlers. J. Sports Sci. Med. 2006, 5, 108–113. [Google Scholar] [PubMed]
  44. Haff, G.G.; Carlock, J.M.; Hartman, M.J.; Kilgore, J.L.; Kawamori, N.; Jackson, J.R.; Morris, R.T.; Sands, W.A.; Stone, M.H. Force-time curve characteristics of dynamic and isometric muscle actions of elite women olympic weightlifters. J. Strength Cond. Res. 2005, 19, 741–748. [Google Scholar] [PubMed]
  45. Kawamori, N.; Rossi, S.J.; Justice, B.D.; Haff, E.E.; Pistilli, E.E.; O’Bryant, H.S.; Stone, M.H.; Haff, G.G. Peak force and rate of force development during isometric and dynamic mid-thigh clean pulls performed at various intensities. J. Strength Cond. Res. 2006, 20, 483–491. [Google Scholar] [PubMed]
  46. Beckham, G.; Mizuguchi, S.; Carter, C.; Sato, K.; Ramsey, M.; Lamont, H.; Hornsby, G.; Haff, G.; Stone, M. Relationships of isometric mid-thigh pull variables to weightlifting performance. J. Sports Med. Phys. Fit. 2013, 53, 573–581. [Google Scholar]
  47. Little, T.; Williams, A.G. Specificity of acceleration, maximum speed, and agility in professional soccer players. J. Strength Cond. Res. 2005, 19, 76–78. [Google Scholar] [PubMed]
  48. Pyne, D.B.; Saunders, P.U.; Montgomery, P.G.; Hewitt, A.J.; Sheehan, K. Relationships between repeated sprint testing, speed, and endurance. J. Strength Cond. Res. 2008, 22, 1633–1637. [Google Scholar] [CrossRef] [PubMed]
  49. Carling, C.; le Gall, F.; Reilly, T.; Williams, A.M. Do anthropometric and fitness characteristics vary according to birth date distribution in elite youth academy soccer players? Scand. J. Med. Sci. Sports 2009, 19, 3–9. [Google Scholar] [CrossRef] [PubMed]
  50. Young, W.B.; Pryor, L. Relationship between pre-season anthropometric and fitness measures and indicators of playing performance in elite junior australian rules football. J. Sci. Med. Sport Sports Med. Aust. 2007, 10, 110–118. [Google Scholar] [CrossRef] [PubMed]
  51. Fernandez-Fernandez, J.; Ulbricht, A.; Ferrauti, A. Fitness testing of tennis players: How valuable is it? Br. J. Sports Med. 2014, 48 (Suppl. 1), i22–i31. [Google Scholar] [CrossRef]
  52. Kanatous, S.B.; Mammen, P.P. Regulation of myoglobin expression. J. Exp. Biol. 2010, 213, 2741–2747. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Canepari, M.; Pellegrino, M.A.; D’Antona, G.; Bottinelli, R. Single muscle fiber properties in aging and disuse. Scand. J. Med. Sci. Sports 2010, 20, 10–19. [Google Scholar] [CrossRef] [PubMed]
  54. Powers, S.K.; Criswell, D.; Herb, R.A.; Demirel, H.; Dodd, S. Age-related increases in diaphragmatic maximal shortening velocity. J. Appl. Physiol. 1996, 80, 445–451. [Google Scholar] [CrossRef] [PubMed]
  55. Evertsen, F.; Medbo, J.I.; Jebens, E.; Gjovaag, T.F. Effect of training on the activity of five muscle enzymes studied on elite cross-country skiers. Acta Physiol. Scand. 1999, 167, 247–257. [Google Scholar] [CrossRef] [PubMed]
  56. Baguet, A.; Everaert, I.; Achten, E.; Thomis, M.; Derave, W. The influence of sex, age and heritability on human skeletal muscle carnosine content. Amino Acids 2012, 43, 13–20. [Google Scholar] [CrossRef] [PubMed]
  57. Baguet, A.; Everaert, I.; Hespel, P.; Petrovic, M.; Achten, E.; Derave, W. A new method for non-invasive estimation of human muscle fiber type composition. PLoS ONE 2011, 6, e21956. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Seto, J.T.; Quinlan, K.G.; Lek, M.; Zheng, X.F.; Garton, F.; MacArthur, D.G.; Hogarth, M.W.; Houweling, P.J.; Gregorevic, P.; Turner, N.; et al. Actn3 genotype influences muscle performance through the regulation of calcineurin signaling. J. Clin. Investig. 2013, 123, 4255–4263. [Google Scholar] [CrossRef] [PubMed]
  59. Malina, R.M.; Cumming, S.P.; Kontos, A.P.; Eisenmann, J.C.; Ribeiro, B.; Aroso, J. Maturity-associated variation in sport-specific skills of youth soccer players aged 13-15 years. J. Sports Sci. 2005, 23, 515–522. [Google Scholar] [CrossRef] [PubMed]
  60. Faber, I.R.; Oosterveld, F.G.; Nijhuis-Van der Sanden, M.W. Does an eye-hand coordination test have added value as part of talent identification in table tennis? A validity and reproducibility study. PLoS ONE 2014, 9, e85657. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Pion, J.A.; Fransen, J.; Deprez, D.N.; Segers, V.I.; Vaeyens, R.; Philippaerts, R.M.; Lenoir, M. Stature and jumping height are required in female volleyball, but motor coordination is a key factor for future elite success. J. Strength Cond. Res. Natl.Strength Cond. Assoc. 2015, 29, 1480–1485. [Google Scholar] [CrossRef] [PubMed]
  62. Waldron, M.; Worsfold, P.; Twist, C.; Lamb, K. The reliability of tests for sport-specific skill amongst elite youth rugby league players. Eur. J. Sport Sci. 2014, 14 (Suppl. 1), S471–S477. [Google Scholar] [CrossRef]
  63. O’Shaughnessy, D.M. Possession versus position: Strategic evaluation in afl. J. Sports Sci. Med. 2006, 5, 533–540. [Google Scholar] [PubMed]
  64. Johnston, R.J.; Watsford, M.L.; Pine, M.J.; Spurrs, R.W.; Murphy, A.; Pruyn, E.C. Movement demands and match performance in professional australian football. Int. J. Sports Med. 2012, 33, 89–93. [Google Scholar] [CrossRef] [PubMed]
  65. Alvarez, R.; Terrados, N.; Ortolano, R.; Iglesias-Cubero, G.; Reguero, J.R.; Batalla, A.; Cortina, A.; Fernandez-Garcia, B.; Rodriguez, C.; Braga, S.; et al. Genetic variation in the renin-angiotensin system and athletic performance. Eur. J. Appl. Physiol. 2000, 82, 117–120. [Google Scholar] [CrossRef] [PubMed]
  66. Gayagay, G.; Yu, B.; Hambly, B.; Boston, T.; Hahn, A.; Celermajer, D.S.; Trent, R.J. Elite endurance athletes and the ace i allele—The role of genes in athletic performance. Hum. Genet. 1998, 103, 48–50. [Google Scholar] [CrossRef] [PubMed]
  67. Nazarov, I.B.; Woods, D.R.; Montgomery, H.E.; Shneider, O.V.; Kazakov, V.I.; Tomilin, N.V.; Rogozkin, V.A. The angiotensin converting enzyme i/d polymorphism in russian athletes. Eur. J. Hum. Genet. 2001, 9, 797–801. [Google Scholar] [CrossRef] [PubMed]
  68. Tsianos, G.; Sanders, J.; Dhamrait, S.; Humphries, S.; Grant, S.; Montgomery, H. The ace gene insertion/deletion polymorphism and elite endurance swimming. Eur. J. Appl. Physiol. 2004, 92, 360–362. [Google Scholar] [CrossRef] [PubMed]
  69. Woods, D.; Hickman, M.; Jamshidi, Y.; Brull, D.; Vassiliou, V.; Jones, A.; Humphries, S.; Montgomery, H. Elite swimmers and the d allele of the ace i/d polymorphism. Hum. Genet. 2001, 108, 230–232. [Google Scholar] [CrossRef] [PubMed]
  70. Charos, A.E.; Reed, B.D.; Raha, D.; Szekely, A.M.; Weissman, S.M.; Snyder, M. A highly integrated and complex ppargc1a transcription factor binding network in hepg2 cells. Genome Res. 2012, 22, 1668–1679. [Google Scholar] [CrossRef] [PubMed]
  71. Lin, J.; Wu, H.; Tarr, P.T.; Zhang, C.Y.; Wu, Z.; Boss, O.; Michael, L.F.; Puigserver, P.; Isotani, E.; Olson, E.N.; et al. Transcriptional co-activator pgc-1 alpha drives the formation of slow-twitch muscle fibres. Nature 2002, 418, 797–801. [Google Scholar] [CrossRef] [PubMed]
  72. Moore, G.E.; Shuldiner, A.R.; Zmuda, J.M.; Ferrell, R.E.; McCole, S.D.; Hagberg, J.M. Obesity gene variant and elite endurance performance. Metabolism 2001, 50, 1391–1392. [Google Scholar] [CrossRef] [PubMed]
  73. Taylor, M.R. Pharmacogenetics of the human beta-adrenergic receptors. Pharmacogenomics J. 2007, 7, 29–37. [Google Scholar] [CrossRef] [PubMed]
  74. Wagoner, L.E.; Craft, L.L.; Zengel, P.; McGuire, N.; Rathz, D.A.; Dorn, G.W., II; Liggett, S.B. Polymorphisms of the beta1-adrenergic receptor predict exercise capacity in heart failure. Am. Heart J. 2002, 144, 840–846. [Google Scholar] [CrossRef] [PubMed]
  75. Morin-Moncet, O.; Beaumont, V.; de Beaumont, L.; Lepage, J.F.; Theoret, H. Bdnf val66met polymorphism is associated with abnormal interhemispheric transfer of a newly acquired motor skill. J. Neurophysiol. 2014, 111, 2094–2102. [Google Scholar] [CrossRef] [PubMed]
  76. Puthucheary, Z.; Skipworth, J.R.; Rawal, J.; Loosemore, M.; Van Someren, K.; Montgomery, H.E. The ace gene and human performance: 12 years on. Sports Med. 2011, 41, 433–448. [Google Scholar] [CrossRef] [PubMed]
  77. Mustafina, L.J.; Naumov, V.A.; Cieszczyk, P.; Popov, D.V.; Lyubaeva, E.V.; Kostryukova, E.S.; Fedotovskaya, O.N.; Druzhevskaya, A.M.; Astratenkova, I.V.; Glotov, A.S.; et al. Agtr2 gene polymorphism is associated with muscle fibre composition, athletic status and aerobic performance. Exp. Physiol. 2014, 99, 1042–1052. [Google Scholar] [CrossRef] [PubMed]
  78. Gineviciene, V.; Jakaitiene, A.; Tubelis, L.; Kucinskas, V. Variation in the ace, ppargc1a and ppara genes in lithuanian football players. Eur. J. Sport Sci. 2014, 14 (Suppl. 1), S289–S295. [Google Scholar] [CrossRef]
  79. Goh, K.P.; Chew, K.; Koh, A.; Guan, M.; Wong, Y.S.; Sum, C.F. The relationship between ace gene id polymorphism and aerobic capacity in asian rugby players. Singap. Med. J. 2009, 50, 997–1003. [Google Scholar]
  80. Niu, T.; Chen, X.; Xu, X. Angiotensin converting enzyme gene insertion/deletion polymorphism and cardiovascular disease: Therapeutic implications. Drugs 2002, 62, 977–993. [Google Scholar] [CrossRef] [PubMed]
  81. Rabensteiner, D.; Abrahamian, H.; Irsigler, K.; Hermann, K.M.; Kiener, H.P.; Mayer, G.; Kaider, A.; Prager, R. Ace gene polymorphism and proliferative retinopathy in type 1 diabetes: Results of a case-control study. Diabetes Care 1999, 22, 1530–1535. [Google Scholar] [CrossRef] [PubMed]
  82. Miners, S.; Ashby, E.; Baig, S.; Harrison, R.; Tayler, H.; Speedy, E.; Prince, J.A.; Love, S.; Kehoe, P.G. Angiotensin-converting enzyme levels and activity in alzheimer’s disease: Differences in brain and csf ace and association with ace1 genotypes. Am. J. Transl. Res. 2009, 1, 163–177. [Google Scholar] [PubMed]
  83. Tuo, J.; Bojanowski, C.M.; Chan, C.C. Genetic factors of age-related macular degeneration. Prog. Retin. Eye Res. 2004, 23, 229–249. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Amir, O.; Amir, R.; Yamin, C.; Attias, E.; Eynon, N.; Sagiv, M.; Sagiv, M.; Meckel, Y. The ace deletion allele is associated with israeli elite endurance athletes. Exp. Physiol. 2007, 92, 881–886. [Google Scholar] [CrossRef] [PubMed]
  85. Kim, C.; Cho, J.Y.; Jeon, J.Y.; Koh, Y.G.; Kim, Y.M.; Kim, H.J.; Park, M.; Um, H.S.; Kim, C. Ace dd genotype is unfavorable to korean short-term muscle power athletes. Int. J. Sports Med. 2010, 31, 65–71. [Google Scholar] [CrossRef] [PubMed]
  86. Micheli, M.L.; Gulisano, M.; Morucci, G.; Punzi, T.; Ruggiero, M.; Ceroti, M.; Marella, M.; Castellini, E.; Pacini, S. Angiotensin-converting enzyme/vitamin d receptor gene polymorphisms and bioelectrical impedance analysis in predicting athletic performances of italian young soccer players. J. Strength Cond. Res. 2011, 25, 2084–2091. [Google Scholar] [CrossRef] [PubMed]
  87. Hassanin, O.M.; Moustafa, M.; El Masry, T.M. Association of insertion–deletion polymorphism of ace gene and alzheimer’s disease in egyptian patients. Egypt. J. Medical Hum. Genet. 2014, 15, 355–360. [Google Scholar] [CrossRef]
  88. Orysiak, J.; Zmijewski, P.; Klusiewicz, A.; Kaliszewski, P.; Malczewska-Lenczowska, J.; Gajewski, J.; Pokrywka, A. The association between ace gene variation and aerobic capacity in winter endurance disciplines. Biol. Sport 2013, 30, 249–253. [Google Scholar] [CrossRef] [PubMed]
  89. Collins, M.; Xenophontos, S.L.; Cariolou, M.A.; Mokone, G.G.; Hudson, D.E.; Anastasiades, L.; Noakes, T.D. The ace gene and endurance performance during the south african ironman triathlons. Med. Sci. Sports Exerc. 2004, 36, 1314–1320. [Google Scholar] [CrossRef] [PubMed]
  90. Shahmoradi, S.; Ahmadalipour, A.; Salehi, M. Evaluation of ace gene i/d polymorphism in iranian elite athletes. Adv. Biomed. Res. 2014, 3. [Google Scholar] [CrossRef]
  91. Rigat, B.; Hubert, C.; Alhenc-Gelas, F.; Cambien, F.; Corvol, P.; Soubrier, F. An insertion/deletion polymorphism in the angiotensin i-converting enzyme gene accounting for half the variance of serum enzyme levels. J. Clin. Investig. 1990, 86, 1343–1346. [Google Scholar] [CrossRef] [PubMed]
  92. Ma, T.K.; Kam, K.K.; Yan, B.P.; Lam, Y.Y. Renin-angiotensin-aldosterone system blockade for cardiovascular diseases: Current status. Br. J. Pharmacol. 2010, 160, 1273–1292. [Google Scholar] [CrossRef] [PubMed]
  93. Vancini, R.L.; Pesquero, J.B.; Fachina, R.J.; Andrade Mdos, S.; Borin, J.P.; Montagner, P.C.; de Lira, C.A. Genetic aspects of athletic performance: The african runners phenomenon. Open Access J. Sports Med. 2014, 5, 123–127. [Google Scholar] [PubMed]
  94. Scott, R.A.; Irving, R.; Irwin, L.; Morrison, E.; Charlton, V.; Austin, K.; Tladi, D.; Deason, M.; Headley, S.A.; Kolkhorst, F.W.; et al. Actn3 and ace genotypes in elite jamaican and us sprinters. Med. Sci. Sports Exerc. 2010, 42, 107–112. [Google Scholar] [CrossRef] [PubMed]
  95. Sessa, F.; Chetta, M.; Petito, A.; Franzetti, M.; Bafunno, V.; Pisanelli, D.; Sarno, M.; Iuso, S.; Margaglione, M. Gene polymorphisms and sport attitude in italian athletes. Genet. Test. Mol. Biomark. 2011, 15, 285–290. [Google Scholar] [CrossRef] [PubMed]
  96. Papadimitriou, I.D.; Lockey, S.J.; Voisin, S.; Herbert, A.J.; Garton, F.; Houweling, P.J.; Cieszczyk, P.; Maciejewska-Skrendo, A.; Sawczuk, M.; Massidda, M.; et al. No association between actn3 r577x and ace i/d polymorphisms and endurance running times in 698 caucasian athletes. BMC Genom. 2018, 19, 13. [Google Scholar] [CrossRef] [PubMed]
  97. Yang, N.; MacArthur, D.G.; Gulbin, J.P.; Hahn, A.G.; Beggs, A.H.; Easteal, S.; North, K. Actn3 genotype is associated with human elite athletic performance. Am. J. Hum. Genet. 2003, 73, 627–631. [Google Scholar] [CrossRef] [PubMed]
  98. Eynon, N.; Hanson, E.D.; Lucia, A.; Houweling, P.J.; Garton, F.; North, K.N.; Bishop, D.J. Genes for elite power and sprint performance: Actn3 leads the way. Sports Med. 2013, 43, 803–817. [Google Scholar] [CrossRef] [PubMed]
  99. Alfred, T.; Ben-Shlomo, Y.; Cooper, R.; Hardy, R.; Cooper, C.; Deary, I.J.; Gunnell, D.; Harris, S.E.; Kumari, M.; Martin, R.M.; et al. Actn3 genotype, athletic status, and life course physical capability: Meta-analysis of the published literature and findings from nine studies. Hum. Mutat. 2011, 32, 1008–1018. [Google Scholar] [CrossRef] [PubMed]
  100. Kim, H.; Song, K.H.; Kim, C.H. The actn3 r577x variant in sprint and strength performance. J. Exerc. Nutr. Biochem. 2014, 18, 347–353. [Google Scholar] [CrossRef] [PubMed]
  101. Puthucheary, Z.; Skipworth, J.R.; Rawal, J.; Loosemore, M.; Van Someren, K.; Montgomery, H.E. Genetic influences in sport and physical performance. Sports Med. 2011, 41, 845–859. [Google Scholar] [CrossRef] [PubMed]
  102. Ahmetov, I.I.; Druzhevskaya, A.M.; Lyubaeva, E.V.; Popov, D.V.; Vinogradova, O.L.; Williams, A.G. The dependence of preferred competitive racing distance on muscle fibre type composition and actn3 genotype in speed skaters. Exp. Physiol. 2011, 96, 1302–1310. [Google Scholar] [CrossRef] [PubMed]
  103. Pimenta, E.M.; Coelho, D.B.; Cruz, I.R.; Morandi, R.F.; Veneroso, C.E.; de Azambuja Pussieldi, G.; Carvalho, M.R.; Silami-Garcia, E.; De Paz Fernandez, J.A. The actn3 genotype in soccer players in response to acute eccentric training. Eur. J. Appl. Physiol. 2012, 112, 1495–1503. [Google Scholar] [CrossRef] [PubMed]
  104. Bouchard, C.; Rankinen, T.; Timmons, J.A. Genomics and genetics in the biology of adaptation to exercise. Compr. Physiol. 2011, 1, 1603–1648. [Google Scholar] [PubMed]
  105. Voisin, S.; Cieszczyk, P.; Pushkarev, V.P.; Dyatlov, D.A.; Vashlyayev, B.F.; Shumaylov, V.A.; Maciejewska-Karlowska, A.; Sawczuk, M.; Skuza, L.; Jastrzebski, Z.; et al. Epas1 gene variants are associated with sprint/power athletic performance in two cohorts of european athletes. BMC Genom. 2014, 15, 382. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  106. Gabbasov, R.T.; Arkhipova, A.A.; Borisova, A.V.; Hakimullina, A.M.; Kuznetsova, A.V.; Williams, A.G.; Day, S.H.; Ahmetov, I.I. The hif1a gene pro582ser polymorphism in russian strength athletes. J. Strength Cond. Res. 2013, 27, 2055–2058. [Google Scholar] [CrossRef] [PubMed]
  107. Gomez-Gallego, F.; Ruiz, J.R.; Buxens, A.; Artieda, M.; Arteta, D.; Santiago, C.; Rodriguez-Romo, G.; Lao, J.I.; Lucia, A. The -786 t/c polymorphism of the nos3 gene is associated with elite performance in power sports. Eur. J. Appl. Physiol. 2009, 107, 565–569. [Google Scholar] [CrossRef] [PubMed]
  108. Di Cagno, A.; Sapere, N.; Piazza, M.; Aquino, G.; Iuliano, E.; Intrieri, M.; Calcagno, G. Ace and agtr1 polymorphisms in elite rhythmic gymnastics. Genet. Test. Mol. Biomark. 2013, 17, 99–103. [Google Scholar] [CrossRef] [PubMed]
  109. Juffer, P.; Furrer, R.; Gonzalez-Freire, M.; Santiago, C.; Verde, Z.; Serratosa, L.; Morate, F.J.; Rubio, J.C.; Martin, M.A.; Ruiz, J.R.; et al. Genotype distributions in top-level soccer players: A role for ace? Int. J. Sports Med. 2009, 30, 387–392. [Google Scholar] [CrossRef] [PubMed]
  110. Costa, A.M.; Silva, A.J.; Garrido, N.D.; Louro, H.; de Oliveira, R.J.; Breitenfeld, L. Association between ace d allele and elite short distance swimming. Eur. J. Appl. Physiol. 2009, 106, 785–790. [Google Scholar] [CrossRef] [PubMed]
  111. Yang, N.; MacArthur, D.G.; Wolde, B.; Onywera, V.O.; Boit, M.K.; Lau, S.Y.; Wilson, R.H.; Scott, R.A.; Pitsiladis, Y.P.; North, K. The actn3 r577x polymorphism in east and west african athletes. Med. Sci. Sports Exerc. 2007, 39, 1985–1988. [Google Scholar] [CrossRef] [PubMed]
  112. Niemi, A.K.; Majamaa, K. Mitochondrial DNA and actn3 genotypes in finnish elite endurance and sprint athletes. Eur. J. Hum. Genet. 2005, 13, 965–969. [Google Scholar] [CrossRef] [PubMed]
  113. Druzhevskaya, A.M.; Ahmetov, I.I.; Astratenkova, I.V.; Rogozkin, V.A. Association of the actn3 r577x polymorphism with power athlete status in russians. Eur. J. Appl. Physiol. 2008, 103, 631–634. [Google Scholar] [CrossRef] [PubMed]
  114. Roth, S.M.; Walsh, S.; Liu, D.; Metter, E.J.; Ferrucci, L.; Hurley, B.F. The actn3 r577x nonsense allele is under-represented in elite-level strength athletes. Eur. J. Hum. Genet. 2008, 16, 391–394. [Google Scholar] [CrossRef] [PubMed]
  115. Santiago, C.; Gonzalez-Freire, M.; Serratosa, L.; Morate, F.J.; Meyer, T.; Gomez-Gallego, F.; Lucia, A. Actn3 genotype in professional soccer players. Br. J. Sports Med. 2008, 42, 71–73. [Google Scholar] [CrossRef] [PubMed]
  116. Chiu, L.L.; Wu, Y.F.; Tang, M.T.; Yu, H.C.; Hsieh, L.L.; Hsieh, S.S. Actn3 genotype and swimming performance in taiwan. Int. J. Sports Med. 2011, 32, 476–480. [Google Scholar] [CrossRef] [PubMed]
  117. Ruiz, J.R.; Fernandez del Valle, M.; Verde, Z.; Diez-Vega, I.; Santiago, C.; Yvert, T.; Rodriguez-Romo, G.; Gomez-Gallego, F.; Molina, J.J.; Lucia, A. Actn3 r577x polymorphism does not influence explosive leg muscle power in elite volleyball players. Scand. J. Med. Sci. Sports 2011, 21, e34–e41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  118. Hagberg, J.M.; Moore, G.E.; Ferrell, R.E. Specific genetic markers of endurance performance and vo2max. Exerc. Sport Sci. Rev. 2001, 29, 15–19. [Google Scholar] [CrossRef] [PubMed]
  119. Baker-Herman, T.L.; Fuller, D.D.; Bavis, R.W.; Zabka, A.G.; Golder, F.J.; Doperalski, N.J.; Johnson, R.A.; Watters, J.J.; Mitchell, G.S. Bdnf is necessary and sufficient for spinal respiratory plasticity following intermittent hypoxia. Nat. Neurosci. 2004, 7, 48–55. [Google Scholar] [CrossRef] [PubMed]
  120. Clow, C.; Jasmin, B.J. Brain-derived neurotrophic factor regulates satellite cell differentiation and skeltal muscle regeneration. Mol. Biol. Cell 2010, 21, 2182–2190. [Google Scholar] [CrossRef] [PubMed]
  121. Cunha, C.; Brambilla, R.; Thomas, K.L. A simple role for bdnf in learning and memory? Front. Mol. Neurosci. 2010, 3. [Google Scholar] [CrossRef] [PubMed]
  122. Huang, E.J.; Reichardt, L.F. Trk receptors: Roles in neuronal signal transduction. Ann. Rev. Biochem. 2003, 72, 609–642. [Google Scholar] [CrossRef] [PubMed]
  123. Poo, M.M. Neurotrophins as synaptic modulators. Nat. Rev. Neurosci. 2001, 2, 24–32. [Google Scholar] [CrossRef] [PubMed]
  124. Sanchez, M.M.; Das, D.; Taylor, J.L.; Noda, A.; Yesavage, J.A.; Salehi, A. Bdnf polymorphism predicts the rate of decline in skilled task performance and hippocampal volume in healthy individuals. Transl. Psychiatry 2011, 1, e51. [Google Scholar] [CrossRef] [PubMed]
  125. Zhang, X.; Poo, M.M. Localized synaptic potentiation by bdnf requires local protein synthesis in the developing axon. Neuron 2002, 36, 675–688. [Google Scholar] [CrossRef]
  126. Reichardt, L.F. Neurotrophin-regulated signalling pathways. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 2006, 361, 1545–1564. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  127. Korte, M.; Carroll, P.; Wolf, E.; Brem, G.; Thoenen, H.; Bonhoeffer, T. Hippocampal long-term potentiation is impaired in mice lacking brain-derived neurotrophic factor. Proc. Natl. Acad. Sci. USA 1995, 92, 8856–8860. [Google Scholar] [CrossRef] [PubMed]
  128. Pringle, A.K.; Sundstrom, L.E.; Wilde, G.J.; Williams, L.R.; Iannotti, F. Brain-derived neurotrophic factor, but not neurotrophin-3, prevents ischaemia-induced neuronal cell death in organotypic rat hippocampal slice cultures. Neurosci. Lett. 1996, 211, 203–206. [Google Scholar] [CrossRef]
  129. Nakata, H.; Nakamura, S. Brain-derived neurotrophic factor regulates ampa receptor trafficking to post-synaptic densities via ip3r and trpc calcium signaling. FEBS Lett. 2007, 581, 2047–2054. [Google Scholar] [CrossRef] [PubMed]
  130. Kleim, J.A.; Chan, S.; Pringle, E.; Schallert, K.; Procaccio, V.; Jimenez, R.; Cramer, S.C. Bdnf val66met polymorphism is associated with modified experience-dependent plasticity in human motor cortex. Nat. Neurosci. 2006, 9, 735–737. [Google Scholar] [CrossRef] [PubMed]
  131. Hirvonen, M.; Laakso, A.; Nagren, K.; Rinne, J.O.; Pohjalainen, T.; Hietala, J. C957t polymorphism of the dopamine d2 receptor (drd2) gene affects striatal drd2 availability in vivo. Mol. Psychiatry 2004, 9, 1060–1061. [Google Scholar] [CrossRef] [PubMed]
  132. Hirvonen, M.M.; Lumme, V.; Hirvonen, J.; Pesonen, U.; Nagren, K.; Vahlberg, T.; Scheinin, H.; Hietala, J. C957t polymorphism of the human dopamine d2 receptor gene predicts extrastriatal dopamine receptor availability in vivo. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2009, 33, 630–636. [Google Scholar] [CrossRef] [PubMed]
  133. Pruessner, J.C.; Champagne, F.; Meaney, M.J.; Dagher, A. Dopamine release in response to a psychological stress in humans and its relationship to early life maternal care: A positron emission tomography study using [11c]raclopride. J. Neurosci. 2004, 24, 2825–2831. [Google Scholar] [CrossRef] [PubMed]
  134. Fiocco, A.J.; Lindquist, K.; Ferrell, R.; Li, R.; Simonsick, E.M.; Nalls, M.; Harris, T.B.; Yaffe, K.; Health, A.B.C.S. Comt genotype and cognitive function: An 8-year longitudinal study in white and black elders. Neurology 2010, 74, 1296–1302. [Google Scholar] [CrossRef] [PubMed]
  135. Noohi, F.; Boyden, N.B.; Kwak, Y.; Humfleet, J.; Burke, D.T.; Muller, M.L.; Bohnen, N.I.; Seidler, R.D. Association of comt val158met and drd2 g>t genetic polymorphisms with individual differences in motor learning and performance in female young adults. J. Neurophysiol. 2014, 111, 628–640. [Google Scholar] [CrossRef] [PubMed]
  136. Wackerhage, H.; Miah, A.; Harris, R.C.; Montgomery, H.E.; Williams, A.G. Genetic research and testing in sport and exercise science: A review of the issues. J. Sports Sci. 2009, 27, 1109–1116. [Google Scholar] [CrossRef] [PubMed]
  137. Vlahovich, N.; Fricker, P.A.; Brown, M.A.; Hughes, D. Ethics of genetic testing and research in sport: A position statement from the Australian Institute of Sport. Br. J. Sports Med. 2017, 51, 5–11. [Google Scholar] [CrossRef] [PubMed]
  138. Guth, L.M.; Roth, S.M. Genetic influence on athletic performance. Curr. Opin. Pediatr. 2013, 25, 653–658. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  139. Webborn, N.; Williams, A.; McNamee, M.; Bouchard, C.; Pitsiladis, Y.; Ahmetov, I.; Ashley, E.; Byrne, N.; Camporesi, S.; Collins, M.; et al. Direct-to-consumer genetic testing for predicting sports performance and talent identification: Consensus statement. Br. J. Sports Med. 2015, 49, 1486–1491. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  140. Vlahovich, N.; Hughes, D.C.; Griffiths, L.R.; Wang, G.; Pitsiladis, Y.P.; Pigozzi, F.; Bachl, N.; Eynon, N. Genetic testing for exercise prescription and injury prevention: AIS-Athlome consortium-FIMS joint statement. BMC Genom. 2017, 18 (Suppl. 8), 818. [Google Scholar] [CrossRef] [PubMed]
Table 1. Previous studies assessing the role of genes in determining athletic performance
Table 1. Previous studies assessing the role of genes in determining athletic performance
GeneSport/DisciplineOutcome of StudyReference
ACEBasketballHigher proportion of I allele frequency than D allele amongst athletes[90]
Cross country skiing No significance reported[67]
CyclingSignificantly higher I allele frequency among long-distance Spanish, male, elite cyclists[65]
Higher proportion of D allele frequency than I allele amongst athletes[90]
Endurance sportsIncreased frequency of DD genotype in endurance athletes compared to power athletes [84]
HandballSignificantly higher I allele frequency among Spanish, male, elite handball players (national team)[65]
Power sportsDecreased frequency of DD genotype in elite Korean power athletes compared to a control group[85]
Running (long distance)Increasing I allele frequency with increasing race distance in elite British and Spanish runners[18,65]
No significance reported[96]
Running (short distance)Increased DD genotype and D allele frequency in sprinters[18,19]
No significance reported[94,95]
Rhythmic gymnasticsD allele was more frequent in elite level gymnastics compared to sub-elite athletes and controls[108]
SoccerSignificantly lower DD, greater ID genotype in Lithuanian professional soccer players[78]
Significantly higher frequency of ID genotype and lower frequency of II genotype in soccer players compared to endurance runners[109]
SwimmingSignificant association between the DD genotype and elite, short distance swimmers[69,110]
Significantly greater I allele in middle distance Russian swimmers. Increasing I allele frequency with increasing race distance in elite long distance swimmers[67,68]
No significance reported[95]
VolleyballHigher proportion of I allele frequency than D allele amongst athletes[90]
No significance reported[95]
WeightliftingEqual distribution of D and I alleles amongst athletes[90]
ACTN3Endurance sportsNo significance reported[96,111]
Higher frequency of the XX genotype in the endurance athletes[17,112]
Power sportsSignificantly lower frequencies of the XX genotype, and higher frequency of the RR genotype, compared to the control groups[16,17,100,113,114]
SoccerSignificantly higher proportion of the RR genotype than the control group[115]
SwimmingNo significance reported[95,116]
Running (short distance)No significance reported[94,95,115]
No significantly less XX genotype in sprinters. Increased frequency of RR and R allele in elite sprinters compared to control group[16,21,97,112]
VolleyballNo significance reported[95,117]
ADRB1Endurance sportsC allele is associated with increased VO2max, exercise time, and exhaustion. G allele is associated with decreased VO2max[23,74,118]
PPARG-C1AEndurance sportsEndurance athletes have a higher proportion of GG genotype, and a lower frequency of A allele [21,22]
SprintingGG genotype is associated with increased endurance ability and AA genotype may be associated with impaired aerobic capacity[21,22]

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Jacob, Y.; Spiteri, T.; Hart, N.H.; Anderton, R.S. The Potential Role of Genetic Markers in Talent Identification and Athlete Assessment in Elite Sport. Sports 2018, 6, 88.

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Jacob Y, Spiteri T, Hart NH, Anderton RS. The Potential Role of Genetic Markers in Talent Identification and Athlete Assessment in Elite Sport. Sports. 2018; 6(3):88.

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Jacob, Ysabel, Tania Spiteri, Nicolas H. Hart, and Ryan S. Anderton. 2018. "The Potential Role of Genetic Markers in Talent Identification and Athlete Assessment in Elite Sport" Sports 6, no. 3: 88.

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