Introduction

Team handball is a dynamic sport requiring players to execute a variety of movements, including sprinting, jumping, throwing, and rapid changes in direction, which demand high levels of strength, speed, endurance, and coordination1. Handball is played extensively at amateur, semi-professional, and professional levels and has been featured as an Olympic sport since 19722. These physical and technical demands, combined with the sport’s intermittent nature, challenge researchers to identify the factors that contribute to performance3. During a handball game, players engage in movements of varying intensity, ranging from standing and walking to jogging, moderate running, sprinting, and rapid directional changes in forward, sideways, and backward motions. This complexity makes it challenging to pinpoint the factors that influence performance, given the sport’s multifaceted and dynamic nature4.

While environmental and training variables play a significant role, genetic predispositions are increasingly recognized as critical determinants of athletic potential5. Understanding the genetic factors influencing performance in handball can aid in talent identification and the development of targeted training strategies6. Genotypic traits are recognized as key factors distinguishing elite athletes from non-athletes and sub-elite athletes7. Research on the impact of polymorphisms on sports performance emphasizes that these traits are shaped by the combined influence of multiple genes, affecting not only athletic capabilities but also the body’s response to training stimuli8.

Among the genetic markers associated with athletic performance, three key polymorphisms have gained prominence: ACTN3 (R577X, rs1815739; c.1858 C > T; p.R577X), which affects α-actinin-3 protein expression in fast-twitch muscle fibers; BDKRB2 (−9/+9, rs5810761), an insertion/deletion polymorphism in the bradykinin B2 receptor gene involved in vasodilation; and AGT (M235T, rs699; c.803T > C, p.Met268Thr), which influences angiotensinogen levels. The ACTN3 gene encodes α-actinin-3, a protein found in fast-twitch muscle fibers associated with power and speed9. The R577X polymorphism in this gene results in the absence (XX genotype) or presence (RR genotype) of this protein, impacting strength and sprinting abilities10. For instance, ACTN3 (R577X) has been shown to be an influencing factor in the performance of Brazilian swimmers11. Similarly, variants in the BDKRB2 (−9/+9) gene influence bradykinin receptor activity, which modulates vasodilation and muscle oxygenation12. Research has indicated that the − 9 allele is linked to enhanced metabolic efficiency in skeletal muscle and improved physical performance during endurance training13,14. Meanwhile, the AGT (M235T) gene encodes angiotensinogen, a precursor in the renin-angiotensin system, crucial for vascular tone and sodium balance and its C allele has been linked to enhanced performance in power-based sports15. Therefore, understanding these polymorphisms in handball players may offer insights into the genetic factors contributing to the multifaceted demands of this sport.

Despite the growing research on the genetic basis of athleticism, studies focusing specifically on handball athletes remain limited. Investigating the association between these polymorphisms and performance traits in junior players can provide insights into the genetic underpinnings of success in this sport. Moreover, understanding these relationships may contribute to the development of personalized training and talent identification programs tailored to the genetic profiles of athletes. Therefore, this study aims to evaluate the association between ACTN3 (R577X), BDKRB2 (−9/+9), and AGT (M235T) polymorphisms and physical performance in Brazilian junior male handball players. By comparing genotypic distributions and assessing their influence on performance metrics, we seek to clarify the role of these genetic markers in handball and contribute to the broader understanding of genetics in sports performance.

Methods

Participants

The study included 91 male junior handball athletes (174.4 ± 5.6 cm in height, 69.4 ± 14.8 kg in weight, 16.4 ± 0.7 years old, with 3.25 ± 2.3 years of handball experience) and a control group of 74 male healthy, age-matched non-athletes. Athletes were selected intentionally and non-randomly, based on their fulfillment of specific criteria: engagement in systematic training six days a week (including competitive matches) for at least six months and participation in events sanctioned by the Brazilian Handball Confederation. Individuals who experienced discomfort during testing or did not provide informed consent were excluded. The study adhered to the principles of the Declaration of Helsinki and received approval from the Research Ethics Committee in 2022 under protocol number 5.492.842. All participants and their legal guardians were informed about the study procedures and provided written informed consent, in compliance with Resolution 466/2012 of the National Health Council.

Anthropometric measurements

Height was recorded using a wall-mounted stadiometer (Avanutri, Rio de Janeiro, Brazil) with a precision of 0.1 cm, while body weight was measured using a digital scale (Welmy, model W300, Brazil) with a precision of 0.1 kg. All measurements were taken with participants barefoot and wearing minimal clothing.

Genotyping

Buccal cell collection was performed using sterile swabs, which were air-dried and stored at −40 °C until processing. For DNA extraction, each swab was placed in a 1.5 mL microcentrifuge tube containing 300 µL of 5% Chelex® 100 resin solution, prepared in sterile Milli-Q water. Then, 10 µg/mL of proteinase K (Bio-Rad Laboratories, USA) was added to the sample, followed by incubation at 56 °C for 30 min to allow cell lysis. The sample was then heated at 95 °C for 10 min to inactivate proteinase K and release DNA into the solution. After cooling to room temperature, the tube was centrifuged at 10,000 × g for 5 min, and the supernatant containing genomic DNA was carefully transferred to a new microcentrifuge tube. The extracted DNA was stored at −80 °C until further analysis. The purity and concentration of the DNA were assessed using a NanoDrop® ND-1000 spectrophotometer (Thermo Fisher Scientific, USA), measuring absorbance ratios at 260/280 nm and 260/230 nm to ensure sufficient quality for PCR.

The ACTN3 (R577X) polymorphism was genotyped using the allelic discrimination method via quantitative polymerase chain reaction (PCR) with TaqMan Assays on the QuantStudio™ 6 Flex Real-Time PCR System (Foster City, CA, USA), according to manufacturer instructions (Applied Biosystems, Carlsbad, CA, USA). Each reaction utilized a total volume of 5 µL of the genotyping mixture, consisting of 2.5 µL of GTXpress Master Mix (TaqMan™ Universal Master Mix II, with UNG), 0.125 µL of assay mix (40×), 1.375 µL of distilled water, and 1 µL of genomic DNA (10 ng/µL). The qPCR protocol included an initial denaturation step at 95 °C for 10 min, followed by 40 cycles at 94 °C for 15 s, and a final extension at 60 °C for 60 s. After amplification, the equipment analyzed the ACTN3 gene to determine the presence of the R577X polymorphism.

BDKRB2 presence or absence (+ 9/−9) was identified by PCR using specific primers (forward: 5’-TCCAGCTCTGGCTTCTGG-3’, reverse: 5’-AGTCGCTCCCTGGTACTGC-3’). The reaction was conducted in a total volume of 10 µL, consisting of 1.5 mM MgCl₂, 0.75 nM of each dNTP, 4 pM of each primer, 0.5 U of Taq DNA polymerase, and 1 µL of genomic DNA (30–50 ng). The reaction was conducted in a total volume of 12.5 µL, consisting of 6.25 µL of GoTaq® Green Master Mix (Promega, Madison, USA), 0.5 µL of each forward and reverse primer, 0.62 µL of dimethyl sulfoxide, 4.62 µL of nuclease-free water (Promega, Madison, USA), and 0.5 µL of genomic DNA (30–50 ng).The protocol began with an initial denaturation at 94 °C for 5 min, followed by 35 amplification cycles, including denaturation at 94 °C for 30 s, annealing at 62 °C for 1 min, and elongation at 72 °C for 30 s, concluding with a final extension at 72 °C for 10 min. PCR resulted in 89 bp (+ 9/+9) and 80 bp (−9/−9) fragment analyzed on a 4% agarose gel stained with SYBR® Safe DNA gel stain (Invitrogen).

The AGT gene was genotyped using specific primers (forward: 5′-CAGGGTGCTGTCCACACTGGACCCC-3′, reverse: 5′-CCGTTTGTGCAGGGCCTGGCTCTCT-3′). DNA amplification was performed using PCR, and individuals were classified as homozygotes or heterozygotes accordingly. The reaction mixture included 12.5 µL, consisting of 6.25 µL of GoTaq® Green Master Mix (Promega, Madison, USA), 0.5 µL of each forward and reverse primer, 5.24 µL of nuclease-free water (Promega, Madison, USA), and 1 µL of genomic DNA (30–50 ng) The PCR conditions were as follows: initial denaturation at 95 8 C for 5 min; 35 cycles at 95 8 C for 30 s, 61 8 C for 30 s, and 72 8 C for 1 min; and a final extension at 72 8 C for 5 min. Fragments with T nucleotide and with M nucleotide had 266 bp and 303 bp, respectively (rs699), analyzed on a 3% agarose gel stained with SYBR® Safe DNA gel stain (Invitrogen).

Lower limbs’ strength

The Standing Long Jump test was used to assess lower-limb muscular strength. Participants positioned both feet behind a designated line, jumped as far as possible, and landed on both feet. The distance from the starting line to the nearest heel was measured using a tape measure. Each participant was given two attempts, and the longest jump was recorded for analysis.

Upper limbs’ strength

The seated Medicine Ball Throw test was used to assess upper limb strength. Participants used a 2-kilogram medicine ball, coated with magnesium carbonate (gymnastics chalk), which left an imprint on the floor after each throw, allowing for easy measurement of the throw distance. Participants sat on the floor with their legs extended and their back, shoulders, and head resting against a wall. Holding the medicine ball with both hands, they positioned their upper arms at a 90-degree angle with flexed elbows. They were instructed to throw the ball as far as possible while maintaining contact with the wall. Each participant had two attempts, and the longest throw was recorded for analysis.

Statistical analysis

The genotypic frequencies of the athletes and controls were compared via the Chi-square (χ2) test. 2 × 2 tables were used to compare the allelic frequency between groups for each polymorphism. Hardy-Weinberg equilibrium was evaluated between all participants16. Data normality and homogeneity of variance was tested using Shapiro-Wilk and Levene tests, respectively. One-Way analysis of variance (ANOVA) was used to analyze differences in physical performance between the groups, with Bonferroni’s post hoc test to determine which measurements were significantly different. Independent samples t-tests were conducted to determine differences in anthropometric and physical indicators between athletes and controls. A post hoc power analysis was conducted using GPower (version 3.1.9.7) to evaluate whether the study sample size was sufficient to detect significant differences in genotype distributions and physical performance outcomes. Effect sizes (ES) were calculated using Cramer’s V for Chi-square tests, partial eta-squared (η²) for ANOVA, and Cohen’s d for t-tests17. Observed power values were computed with an alpha level of 0.05 and reported for key comparisons. The significance level adopted for the statistical procedures was p ≤ 0.05. The statistical analysis was performed using the Statistical Package for Social Sciences (Version 20.0; SPSS, Inc., Chicago, IL, USA).

Results

Table 1 displays the genotypic distribution and allelic frequencies for the ACTN3 (R577X), BDKRB2 (−9/+9), and AGT (M235T) polymorphisms. All genotypes were confirmed to be in Hardy-Weinberg equilibrium (p > 0.05). No significant differences were found in genotypic or allelic distributions for any of the polymorphisms. The post hoc power analysis for these comparisons revealed an observed power between 0.05 and 0.23, suggesting that a larger sample may be required to detect smaller genetic effects.

Table 1 Chi-square test comparing the genotypic and allelic frequencies of ACTN3, BDKRB2, and AGT polymorphisms between athletes and controls.
Full size table

The results regarding physical performance considering each genotype can be seen in Table 2. Among ACTN3 (R577X) genotypes, RX carriers demonstrated slightly superior performance in standing long jump and medicine ball throw compared to RR and XX groups. BDKRB2 (−9/+9) genotypes showed minimal variations, with slightly higher performance in standing long jump for the + 9 + 9 and − 9 + 9 groups. Regarding AGT (M235T), MM athletes presented superior standing long jump performance and TT athletes performed better in the medicine ball throw test. However, no significant differences in performance metrics were observed with observed power values ranging from 0.12 to 0.99.

Table 2 Physical performance characteristics of all athletes in relation to ACTN3, BDKRB2, and AGT genotypes.
Full size table

Table 3 presents an analysis considering dominant, recessive, and overdominant models, which revealed no significant differences in physical performance traits for most genotypes. However, ACTN3 (R577X) RR carriers outperformed other groups in the medicine ball throw test (p = 0.049, d = 0.48). The post hoc power analysis for this comparison indicated an observed power of 0.83, suggesting statistical confidence in this result.

Table 3 Physical characteristics of all athletes in relation to ACTN3, BDKRB2, and AGT genotypes considering the dominant, recessive, and over dominant models.
Full size table

Discussion

This study aimed to investigate the relationship between ACTN3 (R577X), BDKRB2 (−9/+9), and AGT (M235T) polymorphisms and physical performance in Brazilian junior male handball players. The main finding of our study was that ACTN3 (R577X) RR carriers exhibited superior upper limbs strength, as demonstrated by the medicine ball throw test, highlighting a potential influence of this genotype on strength-related performance. While no significant associations were found for BDKRB2 (−9/+9) or AGT (M235T) polymorphisms with physical performance metrics, trends suggested a link between the BDKRB2 + 9 + 9 and strength phenotypes. Additionally, the findings of this study indicate that the ACTN3 (R577X), BDKRB2 (−9/+9), and AGT (M235T) polymorphisms do not show significant differences in genotypic or allelic frequencies between Brazilian junior male handball players and non-athletic controls. This suggests that, in this cohort, these genetic markers alone may not differentiate athletes from non-athletes. The lack of differences in allelic or genotype frequencies may be attributed to the highly admixed nature of the Brazilian population, which could dilute genetic distinctions between athletic and non-athletic groups18.

The results from Table 3 reveal a significant association between the ACTN3 (R577X) RR genotype and performance in the medicine ball throw test, suggesting that the presence of the α-actinin-3 protein may enhance upper limbs strength in junior male handball players. This finding aligns with existing literature, which links the R allele to greater strength and power in activities requiring explosive force19,20, with this allele being more frequent among Brazilian power athletes8. The superior performance of RR carriers in this test highlights the potential role of the ACTN3 (R577X) gene in developing strength-based skills essential for handball, such as throwing and pushing. Although the RX and XX genotypes did not show significant differences in this performance metric, the trend supports the hypothesis that the absence of α-actinin-3 (XX genotype) may limit maximal strength output21. These results emphasize the importance of ACTN3 (R577X) in power-oriented athletic tasks and suggest its potential utility as a genetic marker for identifying and developing talent in strength-demanding sports like handball.

Regarding the BDKRB2 (−9/+9) gene, the − 9 allele has been proposed to enhance skeletal muscle contraction efficiency and glucose uptake in skeletal muscles, potentially improving endurance performance22, and supporting success in distance events among elite track athletes14. Additionally, Saunders et al.13 reported a connection between the − 9–9 genotype and the performance outcomes of 701 male participants in an Ironman Triathlon. However, we did not find an association between this gene and the physical performance tests in Brazilian junior handball athletes. Additionally, we did not find statistical difference in allelic or genotypic frequency for the BDKRB2 (−9/+9) when comparing athletes and controls. It is important to consider the small sample size and the genetic heterogeneity of the population in this study. Similarly, Grenda et al.23 investigated the relationship between swimming performance and the − 9/+9 (rs5810761) polymorphism in the BDKRB2 gene among 157 highly trained Polish swimmers. The analysis revealed no significant differences in genotype or allele frequencies between long-distance swimmers and either the overall group of swimmers or the control group. Furthermore, Zmijewski et al.24 also investigated the effect of BDKRB2 (−9/+9) in Polish swimmers, with no significant effect found on swimming performance. These findings suggest that the influence of the BDKRB2 polymorphism on athletic performance may be sport-specific and more relevant to endurance-based activities than to sports like handball or swimming.

In our study, no association was found between the AGT gene and physical performance tests, nor were there significant differences in allelic or genotypic frequencies between athletes and controls. The M allele of the M235T (rs699) polymorphism in the AGT gene is linked to elevated levels of angiotensin II and has been associated with enhanced performance in power and strength-based sports25. In this sense, a study analyzed the AGT (M235T) polymorphism in 119 nonathletic controls, 100 world-class endurance athletes, and 63 power athletes, all male and of Caucasian descent for at least three generations. The MM genotype was significantly more prevalent in power athletes (34.9%) compared to controls and endurance athletes (both 16%) (p = 0.008 and p= 0.005, respectively)15. More recently, Remmel et al.26 assessed the prevalence genetic polymorphisms associated with power performance, including AGT (M235T), in 137 track and field athletes. The authors found that decathletes had a higher prevalence of the MM genotype, associated with strength performance, and the TT genotype compared to both sprinters and jumpers, as well as long-distance runners. These findings suggest that the influence of the AGT (M235T) polymorphism on athletic performance may vary depending on the sport’s specific physical demands and the population studied.

The studies involving candidate polymorphisms in handball athletes found in the literature have focused on several polymorphisms, including Cytochrome P450 1A2 (CYP1A2), adenosine A2A receptor (ADORA2A)27, angiotensin-converting enzyme (ACE)28,29,30,31, and others32. We found only two studies related to ACTN3 (R577X) polymorphisms in handball athletes30,33. Gutierrez-Vargas et al.30 investigated the potential connection between ACTN3 (R577X) gene expression and changes in muscle mechanical and functional properties in 30 youth players from Costa Rica’s first-division handball league. Consistent with our findings, their study concluded that the ACTN3 (R577X) did not significantly influence neuromuscular capacities, as assessed through the countermovement jump test. Similarly, Andrade-Mayorga et al.33 investigated the genotypic and allelic distribution of ACTN3 (R577X) polymorphisms in Chilean university athletes across multiple sports, including handball. While their study provided valuable insights into the genetic profiles of athletes, it lacked a comparison group of non-athletes and did not evaluate the relationship between ACTN3 (R577X) polymorphisms and physical performance metrics. This absence of performance-related data limits the applicability of their findings to understanding how the ACTN3 (R577X) polymorphism influences specific athletic traits. Our study adds to this body of knowledge by focusing specifically on junior male handball players and investigating the association between ACTN3 (R577X) genotypes and performance in sport-specific tests. The significant association observed between the ACTN3 (R577X) RR genotype and upper limb strength, as measured by the medicine ball throw, highlights a potential link between α-actinin-3 expression and strength-related athletic performance. In contrast, no relationship was identified between ACTN3 (R577X) and lower limb strength, as assessed by the standing long jump.

Despite the well-documented roles of ACTN3 (R577X), BDKRB2 (−9/+9), and AGT (M235T) polymorphisms in physical performance5, the present study did not find strong associations between these polymorphisms and performance traits in junior handball players, except for ACTN3 (R577X) RR carriers, who demonstrated superior performance in the medicine ball throw test. The RR genotype, for instance, has been linked to power and sprint performance, but handball-specific movements involve multi-directional agility, which was not assessed by the tests used in our investigation. Similarly, BDKRB2 (−9/+9) influences muscle oxygenation, which may be more relevant to endurance capacity than the short-duration power assessments conducted in this study. Additionally, AGT (M235T) is associated with muscle hypertrophy and vascular control, but its influence might be more pronounced in maximal strength tests rather than the submaximal efforts examined.

One limitation of this study is the relatively small sample size, which may reduce the statistical power to detect significant associations between genetic polymorphisms and physical performance. Additionally, the focus on a single cohort of junior male handball players limits the generalizability of the findings to other populations, such as female athletes or players from different regions or sports. Another limitation is the reliance on only a few physical performance tests (standing long jump and medicine ball throw), which may not fully capture the range of athletic abilities relevant to handball. Future studies should include larger and more diverse sample sizes, incorporating a wider range of performance metrics, including agility, speed, and endurance tests. Finally, the presence of diverse ancestral backgrounds within the sample could contribute to genetic heterogeneity, reducing the ability to detect associations. While our study did not employ ancestry-informative markers or principal component analysis to control for population stratification, future research should consider these approaches to account for potential confounding effects and enhance the accuracy of genetic associations.

Conclusion

This study evaluated the association between ACTN3 (R577X), BDKRB2 (−9/+9), and AGT (M235T) polymorphisms and strength parameters in Brazilian junior handball players. While no significant differences were observed in genotypic or allelic frequencies between athletes and controls, a trend was identified in the ACTN3 (R577X) RR genotype for greater upper-body strength, as measured by the medicine ball throw. These findings suggest that genetic factors may influence specific physical traits, although their impact on overall performance in Brazilian handball players requires further investigation.