Introduction

Athletic events can be divided into an “aerobic-type event” or an “anaerobic-type event” based on the usage of energy resources. Aerobic refers to the use of oxidative phosphorylation as the energy source during exercise (22); anaerobic refers to the use of creatine phosphate, glycolysis, and lactate metabolism as the sources of energy. Although aerobic-type athletic events are characterized by relatively prolonged low–moderate-intensity exercise (25), anaerobic events are characterized by short, intense efforts. Based on this differentiation, sports such as sprinting, weightlifting, and jumping are usually categorized together as anaerobic athletic events. However, other fitness parameters, such as power, speed, and strength, are used to further specify these sports subtypes to a greater degree of accuracy. For example, while weightlifters (WLs) rely predominantly on maximal strength to lift heavy weights, sprinters mainly emphasize speed as the predominant fitness characteristic.

Sprinting and weightlifting performance is influenced by both training and genetic predisposition, among other factors. Genetics have a large influence on muscle size and cross-sectional area, muscle fiber type (e.g., fast or slow twitch), and muscle strength (1,13,18,26,28–33). Moreover, genetics may also determine the response to training. Evidence for a genotype-environment interaction was found for the increase in 1 repetition maximum, static strength, and concentric flexion (28). One can assume that there are different variations between the genetic background of sprint performance (e.g., an emphasis on speed) and weightlifting performance (an emphasis on strength and power). In fact, genetic variants of different athletic performance aspects were previously demonstrated. ACTN3 encodes for the synthesis of α-actinin-3 in skeletal-muscle fibers. This sarcomeric protein is necessary for “explosive” powerful contraction production and is associated with speed performance (16,20). AGT encodes for angiotensinogen, a globular glycoprotein, which modulates activation of the renin-angiotensin system (RAS) and the generation of both angiotensin I (ANG I) and angiotensin II (ANG II), (8,9). Besides its cardiovascular and renal functions, the RAS modulates free radical production and the cellular synthesis of several molecules, such as cytokines, chemokines, and transcription factors (8,9). Furthermore, ANG II is a pleiotropic peptide involved in muscle growth and strength and power. Other genes, such as PPARD, encode a nuclear receptor protein involved in cellular transport, storage, and lipid metabolism (13,17) and therefore is associated with aerobic capacity.

The aim of this study was to explore genetic differences between subtypes of anaerobic-type athletic events. Therefore, we compared the prevalence of 3 genetic variants: ACTN3 R577X, AGT Met235Thr, and PPARD T/C among sprinters, WLs, and nonathletic controls. We hypothesized that both the ACTN3 and AGT genetic variants will be associated with power performance excellence, whereas the PPARD genotype will not differ between the power athletes and controls.

Methods

Experimental Approach to the Problem

Sports events that involved short intense efforts are usually categorized as anaerobic athletic activity. However, each of these events may emphasis different anaerobic domains. Although sprinting may rely mainly on speed, weightlifting relies mainly on muscle strength. The aim of this study was to discover genetic differences between subtypes of anaerobic-type athletic events. We selected 2 subgroups of anaerobic-type athletes: (a) sprinters and long jumpers (n = 71) and (b) weightlifters (n = 54). All athletes were of top national and international levels. The athletes were compared with 86 nonathletic controls.

Because both speed and strength are necessary for anaerobic performance, we chose to study 3 relevant genetic variants: ACTN3 as a genetic variance that affects mainly speed fitness characteristics, AGT as a genetic variance that affects mainly strength fitness characteristics, and as a control gene, we chose PPARD, a genetic variance that favors aerobic performance and therefore should not be part of the genetic milieu of anaerobic athletes.

Subjects

One hundred twenty-five track and field athletes (88 men and 37 women, aged 17–47), and 86 nonathletic controls (55 men and 31 women, aged 20–29) participated in the study. The characteristics of the athletes and controls are presented in Table 1. The study conforms to the Code of Ethics of the World Medical Association (approved by the ethics advisory board of Swansea University and the Institutional Review Board of the Hillel Yaffe Medical Center, Hadera, Israel) and required players to provide written informed consent before participation.

Table 1.: Data of athletes and controls.

Procedures

The track and field athletes were assigned to 2 subgroups according to their event specialty, as follows: (a) sprinters and long jumpers (S/J, major event: 100–200 m sprints and long jumps, n = 71), and (b) WLs (n = 54). All athletes were ranked among the top Israeli results in their event and competed in national- and/or international-level meets on a regular basis. Sixty-one athletes (18 sprinters, 9 jumpers, and 34 WLs) were classified as top athletes (participants and winners in international competitions, such as the World and European Championships and Olympic Games). The athletes' main-event results are presented in Table 2. The controls were not engaged in physical activity on a regular basis and were matched to the athletes by age and sex.

Genotyping

Genomic DNA was extracted from samples of peripheral venous blood according to the salting-out procedure. Genotypes were determined using the TaqMan allelic discrimination assay. The Assay-by-Design service (www.appliedbio-systems.com) was used to set up a TaqMan allelic discrimination assay for the ACTN3 (rs1815739 C1747T), AGT (rs699, Met235Thr T/C), and PPARD (rs 2016520 T294C).

ACTN3 primer sequences were as follows: forward: GCACGATCAGTTCAAGGCAAC, reverse: GCTGAGGGTGATGTAGGGATTG. Probe sequences for C1747T were as follows: forward: VIC-CGAGGCTGACCGAGAG, reverse: FAM-CCGAGGCTGACTGAGAG.

AGT primer sequences were as follows: forward: CCGTTTGTGCAGGGCCTGGCTCTCT, reverse: CAGGGTGCTGTCCACACTGGACCCC. Probe sequences for M235T were as follows: forward: VIC-CTATCGGGAGGGTTG, reverse: FAM-CTATCGGAAGGGTTG.

PPARD primer sequences were as follows: forward: CATGGTATAGCACTGCAGGAA, reverse: CTTCCTCCTGTGGCTGCTC. Probe sequences for C1747T were as follows: forward: VIC-CGAGGCTGACCGAGAG, reverse: FAM-CCGAGGCTGACTGAGAG.

The Poly Chain Reaction mixture included 5*ng genomic DNA, a 0.125-μL TaqMan assay (40*, ABI), a 2.5-μL Master mix (ABI), and 2.375-μL water. The Poly Chain Reaction was performed in 96-well PCR plates in an ABI 7300 PCR system (Applied Biosystems Inc., Foster City, CA, USA) and consisted of initial denaturation for 5 minutes at 95° C and 40 cycles with denaturation of 15 seconds at 95° C, and annealing and extension for 60 seconds at 63° C.

The results were analyzed by the ABI TaqMan 7900HT using the sequence detection system 2.22 software (Applied Biosystems Inc.).

Statistical Analyses

The SPSS statistical package, version 20.0, was used to perform all statistical evaluations (SPSS, Chicago, IL, USA). A χ2 test was used to confirm that the observed genotype frequencies were within the Hardy–Weinberg equilibrium and to compare allele and genotype frequencies between athletes and controls, as well as between athletes from different sports and from different competitive levels. If observed or expected values included a cell with a value of 5, we used Fisher's exact test to compare allele and genotype frequencies.

Results

The complete data on allele and genotype frequencies are presented in Table 3. The genotype subtype did not differ by age or sex. The ACTN3 genotype distribution was in agreement with the Hardy–Weinberg equilibrium in all groups (p = 0.18 for controls; p = 0.78 for S/J; p = 0.93 for WLs). The AGT genotype distribution was in agreement with the Hardy–Weinberg equilibrium in controls (p = 0.57) and WLs (p = 0.97). There was, however, a significant deviation from the Hardy–Weinberg equilibrium in the S/J group (p = 0.03). The PPARD genotype distribution was in agreement with the Hardy–Weinberg equilibrium in all groups (p = 0.64 for controls; p = 0.93 for S/J; p = 0.65 for WLs).

Table 3.: The ACTN3 R577X, AGT Met235Thr, and PPARD T294C genotype and allele frequencies in all groups.*†

ACTN3 R577X genotype and allele frequencies are presented in Figure 1. The ACTN3 RR genotype frequency was significantly higher among S/J (39.4%) compared with WLs (22.2%) and controls (18.6%) (p = 0.041 for S/J vs. WLs, p = 0.0038 for S/J vs. controls).

Figure 1.: ACTN3 R577X genotype and allele frequencies among athletes and controls. *χ2(1) = 9.83, p = 0.0017 for the ACTN3 genotype frequency, S/J vs. controls, *χ2(1) = 4.18, p = 0.04 for the RR genotype frequency, S/J vs. weightlifters (WLs). *χ2(1) = 8.37, p = 0.0038 for the RR genotype frequency, S/J vs. controls. **χ2(1) = 4.18, p = 0.04 for the RR genotype frequency, S/J vs. WLs. #χ2(1) = 3.28, p = 0.07 for the ACTN3 R577X allele frequency, S/J vs. WLs.

AGT Met235Thr genotype and allele frequencies are presented in Figure 2. The AGT Met235Thr genotype frequencies were significantly different among WLs compared with S/J (p = 0.0021) and were higher among WLs compared with controls. This difference almost reached statistical significance (p = 0.06). Overall, the Thr-Thr genotype was significantly higher among WLs (25.9%) compared with S/J (4.2%) and controls (12.8%) (p = 0.00046 for WLs vs. S/J, p = 0.048 for WLs vs. controls). The Thr allele frequency was significantly higher among WLs (51.9%) compared with S/J (38.0%) and controls (37.8%) (p = 0.029 for WLs vs. S/J, p = 0.021 for WLs vs. controls).

Figure 2.: AGT Met235Thr genotype and allele frequencies among athletes and controls. *χ2(2) = 6.24, p = 0.044 for the AGT genotype frequency, S/J vs. controls. *χ2(1) = 8.45, p = 0.0037 for the Thr-Thr genotype frequency, S/J vs. controls. **χ2(2) = 12.30, p = 0.0021 for the AGT genotype frequency, S/J vs. weightlifters (WLs). **χ2(1) = 12.29, p = 0.00046 for the Thr-Thr genotype frequency, S/J vs. WLs. ***χ2(1) = 3.90, p = 0.048 for the Thr-Thr genotype frequency, WLs vs. controls. #χ2(1) = 4.76, p = 0.029 for the AGT Met235Thr allele frequency, S/J vs. WLs. ##χ2(1) = 5.35, p = 0.021 for the AGT Met235Thr allele frequency, WLs vs. controls.

PPARD T294C genotype and allele frequencies are presented in Figure 3. PPARD T294C genotype and allele frequencies were not different between all groups (Figure 4).

Figure 3.: PPARD T294C genotype and allele frequencies among athletes and controls. Figure 4.: ACTN3 RR genotype, AGT Thr-Thr genotype, and PPARD CC genotype among athletes and controls. *χ2(1) = 12.29, p = 0.00046 for the Thr-Thr genotype frequency, S/J vs. weightlifters (WLs). **χ2(1) = 3.90, p = 0.048 for the Thr-Thr genotype frequency, WLs vs. controls. ***χ2(1) = 8.45, p = 0.0037 for the Thr-Thr genotype frequency, S/J vs. controls. #χ2(1) = 4.18, p = 0.041 for the RR genotype frequency, S/J vs. WLs. ##χ2(1) = 8.37, p = 0.0038 for the RR genotype frequency, S/J vs. controls.

Discussion

The main finding of this study is that the ACTN3 RR genotype, previously associated with power performance, was more prevalent among S/J and WLs compared with the controls, but was also more prevalent among S/J compared with WLs. In addition, the AGT Thr-Thr genotype, previously associated with muscle strength, was more prevalent among WLs compared with both S/J and controls. However, in contrast to our hypothesis, not only was it not higher but it was significantly lower in S/J compared with the controls. Consistent with our hypothesis, no difference in the PPARD CC genotype, previously associated with endurance performance, was found between the groups. Altogether, our results emphasize the concept that the so-called “power athlete” category is not a uniform group.

During the last 2 decades, scientific literature has accumulated on the genetic background that influences an athlete's capability to excel in one sports discipline (e.g., anaerobic) rather than in another (e.g., aerobic). In the vast majority of these studies, several sports subtypes, such as sprinting, jumping, and weightlifting, were grouped together under one heading. However, although these sports subtypes share some common features, such as a short and very intense performance, they vary in their strength and speed demands, and therefore their high-level athletes may not share a common genetic makeup.

The human ACTN3 gene encodes α-actinin-3, an actin-binding protein with a structural role at the sarcomeric Z-line in glycolytic (type II, fast-twitch) muscle fibers. α-actinin-3 plays an increasingly evident role in the regulation of muscle metabolism (5). A common genetic single-nucleotide polymorphism at codon 577 of the ACTN3 results in the replacement of an arginine (R) with a stop codon (X) (24). The R allele is a normal functional version of the gene, whereas the X allele contains a sequence change that completely stops the production of functional α-actinin-3 protein (24). The ACTN3 R577X (rs1815739) polymorphism is a well-studied functional polymorphism, and its RR genotype considered to be essential for elite sprinting and jumping performance (19). Consistent with this, the ACTN3 RR genotype in this study was more prevalent among sprinters, jumpers, and WLs compared with the control participants. In addition, we found that the ACTN3 RR genotype was more prevalent among sprinters and jumpers compared with the WLs. This may indicate that the RR genotype is indeed a speed-specific genotype whose prevalence is necessary for sprinters and jumpers who need a high level of speed to move their body rapidly from one point to another. At the same time, its absence among WLs may indicate that the contribution of this genotype to strength production to cope with heavy loads is limited. Altogether, these findings may substantiate the idea that power production is complex and its application in sports is task specific.

Although the ACTN3 R577X polymorphism is associated mainly with the speed component of power athletic performance, other genetic variants were found to be more related to the strength component. Angiotensinogen (AGT) is encoded by the AGT gene and responsible for the generation of angiotensins I and II (ANG I & II). AGT plays an important role in the activation of the RAS (8,9), a system that modulates cardiovascular and renal functions, free radical and inflammatory cytokine production (5,8), and cell growth and proliferation (7,11).

The M235T (rs699, 4072T>C) is a missense polymorphism, in which a T to C transition at position 4,072 in exon 2 results in a change of the amino acid methionine (M) to threonine (T) at residue 235 of the mature AGT. It was shown that the M235T polymorphism has a moderate effect on plasma AGT concentrations, with a 10–30% increase among C allele carriers (15). Carrying the C allele is associated with higher AGT and subsequently a higher ANG II level, a skeletal-muscle growth factor (27). The effects of the M235T polymorphism may result from the modulation of ANG II levels. Possible mechanisms responsible for the ANG II effect on muscle performance may include a direct hypertrophic effect on the skeletal muscle, and the redirection of blood flow from type-I fibers to the fast powerful type-II fibers, thereby enhancing power and strength capacity (16). These advantageous effects of ANG II were the basis for the findings of overrepresentation of the CC genotype in power athletes (14,35). We found in this study a consistent overexpression of the AGT CC genotype among WLs (25.9%) compared with sprinters (4.2%) and controls (12.8%), emphasizing the importance of a genetic predisposition for strength in weightlifting success. An interesting finding of this study was the extremely low prevalence of the AGT CC genotype among sprinters and jumpers (i.e., significantly lower than controls). However, if the AGT CC genotype leads to muscle hypertrophy and an increase in overall body mass, its absence in sprinters and jumpers makes sense, as it would reduce their relative power and ability to move rapidly in open space. However, the expected increase in muscle mass with a high prevalence of AGT CC among WLs may lead to higher muscle strength and lifting capability. Whether this implies that elite sprinters should carry a genetic advantage for speed on the “strength-speed” continuum of power performance, but rather should not carry a genetic advantage for “too much” strength, is still speculative and needs further research.

To distinguish between sprinters/jumpers and WLs based on their genetic predisposition to speed or strength, we also compared the frequency of the PPARD T294C polymorphism, known to be related to endurance performance. Peroxisome proliferator-activated receptors are a group of nuclear receptor proteins that function as transcription factors, regulating the expression of genes (23). There are 3 types of Peroxisome proliferator-activated receptors: alpha, gamma, and delta (4). PPARD is involved in cellular transport and lipid metabolism (17). The mRNA level of PPARD is increased after endurance exercise (21), and the activated form of PPARD produces an increase in oxidation enzymes, mitochondrial biogenesis, and production of specialized type I fiber contractile proteins, in response to endurance training (34). As expected, and according to our hypothesis, no significant differences in the allele and genotype frequencies were found between the sprinters/jumpers, WLs, and controls. Interestingly, the PPARD CC genotype was even lower in both sprinters/jumpers and weightlifters compared with the controls (Figure 4).

In summary, the novelty of this study is in its illumination of variations in the genetic makeup of power sports subtypes. Specifically, our results suggest that, although power is involved in both types of events, there may be a specific genetic makeup enabling an athlete to excel in speed-oriented athletic events (e.g., sprints), and a different genetic makeup that enables an athlete to excel in strength-oriented athletic events (e.g., weightlifting). This concurs with previous reports indicating that athletes practicing different sports disciplines (e.g., endurance and short-distance swimming compared with running) have different genetic polymorphisms, despite seemingly similar metabolic characteristics (2). Altogether, the results suggest that combining different disciplines in sports genetic research should not be carried out, or at least should be carried out with extreme caution. Finally, it should be noted that it is generally accepted that physical capability phenotypes are highly polygenic (4,7), and that each genetic variant makes only a limited contribution to overall heritability. One should also note that a favorable power–speed or power–strength genetic background cannot necessarily predict athletic achievement. Even if significant genotype–phenotype associations are found, it does not indicate that the carrying individual should anticipate a great career as a sprinter or WL. One has to take into account that any phenotype reflects a complex interaction of multiple factors, and that, although a favorable genetic predisposition is important, psychological and environmental factors, including training experience, equipment and facilities, nutrition, familial support, and motivational factors, are essential for top-level sports development as well.

Practical Applications

Although athletic performance is a multifactorial trait determined by both genetic and environmental factors and their interactions, a genetic profile may serve as an additional tool assisting athletes and coaches to choose a specific sport that best matches their talent. Despite seemingly similar metabolic characteristics of all power-type sports, it seems that different genetic makeup enable an athlete to excel in speed-oriented (e.g., sprints) or in strength-oriented events (e.g., weightlifting). Lower strength-related genetic polymorphism among sprinters may suggest that “too much” strength could be disadvantageous for sprinters. Combining different disciplines of seemingly similar metabolic demands in sports research and practice should be carried out with extreme caution.