- Journal Details
- Format
- Journal
- eISSN
- 1899-7562
- First Published
- 13 Jan 2009
- Publication timeframe
- 5 times per year
- Languages
- English
The probability of becoming an elite athlete is influenced by genetic factors (Eynon et al., 2013). A PubMed base has publicized that at least 120 genetic markers are linked to elite athlete status (Ahmetov and Fedotovskaya, 2015). However, only few of genes have been associated with endurance performance (Ahmetov and Fedotovskaya, 2015). Among them genetic markers such as
Nitric oxide (NO) is a gaseous free radical that is the most potent endothelium-derived relaxation factor synthesized by nitric oxide synthase (NOS). Among three different forms of NOS i.e. neuronal NOS (nNOS or NOS1), inducible NOS (iNOS or NOS2), exactly endothelial NOS (eNOS or NOS3) are predominantly expressed in vascular endothelial cells. The 135 kDa functional homodimer, which function is to preclude neuronal damage by generating small volumes of NO to expand blood vessels, maintain cerebral blood, inhibit platelet aggregation and relaxation, and prevent oxidative destruction Popp et al., 1998), is encoded by the human
Bradykinin is a potent endothelium-dependent vasodilator, significantly reducing blood pressure via the bradykinin B2 receptors encoded by the
The uncoupling protein 2 (UCP2) is a member of the mitochondrial anioncarrier proteins family (MCAPs). The
Adenosine monophosphate deaminase 1 (AMPD1) is one of the significant enzymes used to utilize the energy from ATP. AMPD1 catalyzes the deamination of adenosine monophosphate (AMP) to inosine monophosphate (IMP). Deficiency of the AMPD1 is one of the most common cause of exercise-induced myopathy (Fishbein et al., 1978) and it is associated with the presence of a 34C/T transition in exon 2 (rs17602729C/T) of the
Thomaes et al. (2011) found that carriers of the X allele characterized significantly lower maximal oxygen uptake (VO2max), in details, a relative lower increase in peak VO2 after 12 weeks of endurance training. This observation was confirmed by Cięszczyk et al. (2011) and Rubio et al. (2005) in a group of 127 Polish rowers and 104 top-level Spanish male endurance athletes (cyclists and runners), respectively. However, Ginevičienė et al. (2014) reported opposite results when 84 Lithuanian athletes were compared with 260 controls, which make these observations ambiguous.
Via cleavage an angiotensin I at a particular location, the angiotensin-converting enzyme (ACE) converts this protein to angiotensin II which entails blood vessels to narrow the diameter, and results in increased blood pressure. Lower serum and tissue ACE activity is a consequence of presence of a 287 bp Alu sequence insertion fragment (I allele) rather than the absence (deletion, D allele) in a polymorphism mutation site in intron 16 of the human
I allele is overrepresented among triathletes (Shenoy et al., 2010), successful marathon runners (scoring better than 150th place) (Hruskovicova et al., 2006), long-distance (25 km) swimmers (Tsianos et al., 2004), and rowers (Cięszczyk et al., 2009).
One of the limitations of most studies mentioned above, investigating the association between a certain genotype and endurance performance, is grouping together highly specialized athletes and amateurs. This approach, while understandable given the very low number of world-class marathoners, reduces the accuracy of the phenotype. In our approach, we sought to address these limitations and provide deeper insights into the influence of the association between the
One hundred eighty healthy and experienced half marathon runners (all Polish Caucasians) volunteered to participate in this investigation. Potential participants were contacted from a group of runners that had taken part in previous investigations or they were enrolled at the registration desk of the competition in the Half Marathon in Szczecin. Inclusion criteria were as follows: sex men, age between 18 and 65 years, participating with success in the half marathon and having running experience of at least 3 years. The study was approved by the Local Ethics Committee and was performed according to the Declaration of Helsinki (20110714).
All genetic analyses were performed at the Centre for Human Structural and Functional Research, University of Szczecin. The buccal cells donated by the subjects were collected in Resuspension Solution (GenElute Mammalian Genomic DNA Miniprep Kit, Sigma, Germany) with the use of sterile foam-tipped applicators (Puritan, USA). DNA was extracted from the buccal cells using a GenElute Mammalian Genomic DNA Miniprep Kit (Sigma, Germany) according to the manufacturer’s protocol. All DNA samples were then stored in the same conditions at −25°C until subsequent processes were performed. The samples were genotyped in duplicate.
The samples were genotyped using an allelic discrimination assay with a C1000 Touch Thermal Cycler (Bio-Rad, Germany) instrument with TaqMan® probes. To discriminate
PCR amplification of the polymorphic region of the
The
Hardy-Weinberg equilibrium was checked with the Chi-square test. The allelic frequencies were calculated using genotype counts. Genotype and allele frequencies were compared between groups using the Chi-square test. For analysis of gene-gene interaction the multifactor dimensionality reduction (MDR) algorithm was used followed by entropy-based quantification of epistasis. An in-depth presentation of the MDR method can be found in Gui et al. (2013). Entropy estimates were used to construct an interaction map that showed the percentages of entropy removed (eg. Information gain) by SNPs and pairwise interactions (Moore et al., 2006). For evaluation of interaction models we used 10-fold cross-validation, where the dataset was divided into a training set (9/10 of the dataset) and a testing set (1/10 of the dataset). The cross-validation training score, cross-validation testing score, and cross validation consistency (CVC, the number of times the same model was chosen in the training set) were calculated;
Except for
Genotypes and alleles in the half-marathon runners with respect to finish time.
| Group (HWE) | II ( n = 50) | ID (n = 81) | DD (n = 49) | I | D |
|---|---|---|---|---|---|
| <100 ( | |||||
| 24 (31.6%) | 35 (46.1%) | 17 (22.4%) | 83 (54.6%) | 69 (45.4%) | |
| (n = 76) | |||||
| >100 ( | 26 (25.0%) | 46 (44.2%) | 32 (30.8%) | 98 (47.1%) | 110 (52.9%) |
| (n = 104) | |||||
| -/- (n = 32) | +/- (n = 85) | +/+ (n = 63) | - | + | |
| <100 ( | 14 (18.4%) | 40 (52.6%) | 22 (29.0%) | 68 (44.7%) | 84 (55.3%) |
| >100 ( | 18 (17.3%) | 45 (43.3%) | 41 (39.4%) | 81 (38.9%) | (61.1127%) |
| Glu/Glu (n = 88) | Glu/Asp (n = 76) | Asp/Asp (n = 16) | Glu | Asp | |
| <100 ( | 38 (50.0%) | 30 (39.5%) | 8 (10.5%) | (69.7106 %) | 46 (30.3%) |
| >100 ( | 50 (48.1%) | 46 (44.2%) | 8 (7.7%) | (70.2146 %) | 62 (29.8%) |
| CC (n = 136) | CT (n = 44) | TT (n = 0) | C | T | |
| <100 ( | 45 (59.2%) | 31 (40.8%) | 0 (0) | (79.6121 %) | 31 (20.4%) |
| >100 ( | 91 (87.5%) | 13 (12.5%) | 0 (0) | (93.8195 %) | 13 (6.3%) |
| CC (n = 58) | CT (n = 96) | TT (n = 26) | C | T | |
| <100 ( | 12 (15.8%) | 54 (71.1%) | 10 (13.2%) | 78 (51.3%) | 74 (48.7%) |
| >100 ( | 46 (44.2%) | 42 (40.4%) | 16 (15.4%) | (64.4134 %) | 74 (35.6%) |
The interaction map for the five-locus model (Figure 1) revealed two main effects independent of other
Figure 1
Interaction map using entropy-based measure of information gain. Percentages indicate the amount of entropy removed by each polymorphism and each pairwise combination of polymorphisms. Positive values (orange, red lines) reflect synergistic interaction, negative values (blue, green lines) indicate redundancy. Near zero values (brown line) indicate independence.
Figure 2
Graphical model of multilocus association between BDKRB2, AMPD1, UCP2 polymorphisms and half-marathon finish time (<100 vs >100 min). Dark-shaded areas represent <100 level of the new attribute, while light-shaded areas represent runners with finish time >100. Left bars - the number of runners <100, right bars - the number of runners >100.
Figure 3
Interaction map using entropy-based measure of information gain of the best multilocus model selected using the MDR method
Multilocus interaction models constructed using the MDR method
| Model | CV Consistency | Testing accuracy | p empirical p value based on 1000 permutations |
|---|---|---|---|
| 10 | 69.5% | 0.001 | |
| 10 | 79% | 0.001 | |
| 10 | 76.9% | 0.001 |
CV – cross-validation,
This study estimated the association between human endurance performance and variants of 5 genes previously linked to elite athlete status. We conducted an analysis of genetic markers such as ACE (I/D) (rs4340), NOS3 (Glu298Asp) (rs1799983), BDKRB2 (-9/+9) (rs5810761), UCP2 (Ala55Val) (rs660339), AMPD1 (Gln45Ter) (rs17602729) on a large, performance-homogenous cohort of elite half marathoners. These variants were chosen based on previous association studies on different populations (Ahmetov et al., 2008; Montgomery et al., 1998) and because of their impact on human variability in one or more endurance phenotypic traits (Cięszczyk et al., 2009; Holdys et al., 2013; Sawczuk et al., 2013).
Most studies analyze just one polymorphism (Gronek and Holdys, 2013) and populations of endurance athletes that are generally from aerobic-anaerobic sporting disciplines (Holdys et al., 2011, 2013) and different ethnic/geographical origins (Zarebska et al., 2017), or combined with amateurs (Wilkinson et al., 2013). We decided to compare data of Polish half marathoners with personal time less and more than 100 min.
For the AMPD1 and UCP2 variants, deviations from HWE were observed in the runners with a finish time < 100 min. This may be caused by diverse reasons (Salanti et al., 2005). In our project, the analyzed group was a selected population with selection based on a phenotype (participation in a very exhausting endurance event) that reflects aerobic endurance performance (Tsianos et al., 2010). In our study group, there were no participants with the genotype TT (XX) for AMPD1 (Gln45X) polymorphism. The study showed significant differences in AMPD1 genotypes distribution between subgroups of half marathoners with times below and above 100 min. In the < 100 min group, the genotype CT was significantly more frequent compared with the > 100 min group (
Our study also demonstrated differences in genotypes and alleles distribution of polymorphism Ala55Val of the UCP2 gene between runners. In faster half marathoners the genotype CT (Ala/Val) was most common compared to slower half marathoners wherein the genotype CT was smaller by 30%. The genotype CC in the < 100 min group was distinctly rarer compared to the > 100 min group, where the genotype CC was threefold more frequent and likewise most common (
Given the polygenic nature of endurance performance, we assessed the combined effect of genes polymorphisms on running performance. We found that the proportion of subjects with a high (4-7) number of 'endurance' alleles (ACE I, NOS3 Glu, BDKRB2 -9, UCP2 Val) was greater in the better half marathoners compared with the >100 min group (p = 0.0034). These data suggest that the likelihood of becoming an elite half marathoner partly depends on the carriage of a high number of endurance-related alleles.
In this study, we also performed the analysis of the interaction of all five genes. The five-gene model showed two main effects independent of other loci for genes AMPD1 and UCP2, which is consistent with the results presented in Table 1. In addition, we found an interaction between NOS3 and BDKRB2 (entropy removed 1.43%) in the absence of main effects. Smaller interaction effects were observed between genes NOS3 and ACE, as well as ACE and BDKR2, also in the absence of main effects between these genes. A three-gene model (BDKRB2, AMPD1, UCP2) was selected as the best one based on the highest value of testing accuracy (79%) using the MDR method (Gui et al., 2013). In this model, there was no gene NOS3, which has a significant effect on loss of interaction observed in the five-gene model. Further analysis carried out on the three-gene model did not show any interaction between any pair of 3 tested genes, while maintaining the main effect for AMPD1 and UCP2. The four-gene model comprising NOS3 was not chosen as the best one because of the 2% decreased testing accuracy as compared with the three-gene model.
Although the full model revealed some degree of interaction between NOS3 and BDKRB2 genes, the best model selected by cross validation consistency and prediction error did not include the NOS3 locus, thereby eliminating the gene-gene interaction. It should be noted that the model-free data mining method that we used in the current study can only detect (as many other tools) the statistical epistasis which does not guarantee any kind of interaction between biomolecules (biological epistasis) underlying the statistical phenomenon (Moore et al., 2006). Interestingly, Saunders et al. (2006) found a tendency for the −9/−9 BDKRB2 genotype combined with an NOS3 G allele to be overrepresented in the fastest finishing triathletes. However, the authors did not determine any biological meaning of this finding. As suggested by Oliveira-Paula et al. (2017), molecular mechanisms explaining the interactions among NOS3 and BDKRB2 could involve NOS3 activity and NO bioavailability.
In conclusion, our data suggest that the likelihood of becoming an elite half marathoner partly depends on the carriage of a high number of endurance-related alleles. Our results may help explain individual variations in human endurance performance.
Figure 1
Figure 2
Figure 3
Multilocus interaction models constructed using the MDR method
| Model | CV Consistency | Testing accuracy | p empirical p value based on 1000 permutations |
|---|---|---|---|
| 10 | 69.5% | 0.001 | |
| 10 | 79% | 0.001 | |
| 10 | 76.9% | 0.001 |
Genotypes and alleles in the half-marathon runners with respect to finish time.
| Group (HWE) | II ( n = 50) | ID (n = 81) | DD (n = 49) | I | D |
|---|---|---|---|---|---|
| <100 ( | |||||
| 24 (31.6%) | 35 (46.1%) | 17 (22.4%) | 83 (54.6%) | 69 (45.4%) | |
| (n = 76) | |||||
| >100 ( | 26 (25.0%) | 46 (44.2%) | 32 (30.8%) | 98 (47.1%) | 110 (52.9%) |
| (n = 104) | |||||
| -/- (n = 32) | +/- (n = 85) | +/+ (n = 63) | - | + | |
| <100 ( | 14 (18.4%) | 40 (52.6%) | 22 (29.0%) | 68 (44.7%) | 84 (55.3%) |
| >100 ( | 18 (17.3%) | 45 (43.3%) | 41 (39.4%) | 81 (38.9%) | (61.1127%) |
| Glu/Glu (n = 88) | Glu/Asp (n = 76) | Asp/Asp (n = 16) | Glu | Asp | |
| <100 ( | 38 (50.0%) | 30 (39.5%) | 8 (10.5%) | (69.7106 %) | 46 (30.3%) |
| >100 ( | 50 (48.1%) | 46 (44.2%) | 8 (7.7%) | (70.2146 %) | 62 (29.8%) |
| CC (n = 136) | CT (n = 44) | TT (n = 0) | C | T | |
| <100 ( | 45 (59.2%) | 31 (40.8%) | 0 (0) | (79.6121 %) | 31 (20.4%) |
| >100 ( | 91 (87.5%) | 13 (12.5%) | 0 (0) | (93.8195 %) | 13 (6.3%) |
| CC (n = 58) | CT (n = 96) | TT (n = 26) | C | T | |
| <100 ( | 12 (15.8%) | 54 (71.1%) | 10 (13.2%) | 78 (51.3%) | 74 (48.7%) |
| >100 ( | 46 (44.2%) | 42 (40.4%) | 16 (15.4%) | (64.4134 %) | 74 (35.6%) |
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