ACTN3 rs1815739 Polymorphism and Neuromuscular Performance in Youth Football Players: Associations with Reactive Strength and Dynamic Stability

1. Introduction

The athletic performance phenotype is multifactorial due to complex interactions among genetic predisposition, biological maturation, training exposure, environmental influences, nutritional status, and psychological characteristics. Although systematic training is the main determinant of athletic development, considerable inter-individual variability in performance and training responsiveness is routinely observed even in athletes exposed to the same training programs. This variability has led to an increased interest in exercise genomics, a field that attempts to identify the genetic factors that underlie athletic performance and adaptation to exercise. Studies on twins and families have shown that many performance-related traits such as muscle strength, sprint performance, explosive power and aerobic capacity have moderate-to-high heritability, suggesting that genetics have a major role on athletic phenotypes, in addition to environmental and developmental factors. Contemporary sports genomics has therefore transitioned from a search for a single “performance gene” to understanding the polygenic architecture that supports sport-specific performance.

Among the many candidate genes that have been investigated in exercise genetics, ACTN3 has attracted the most scientific interest because of its well-documented physiological role in skeletal muscle function. The ACTN3 rs1815739 (R577X) polymorphism determines expression of α-actinin-3, a structural protein found almost exclusively in fast glycolytic (type II) muscle fibers. The polymorphism introduces a premature stop codon so individuals homozygous for the T allele (TT genotype) do not express α-actinin-3. Although α-actinin-3 deficiency does not associate with pathological conditions because of functional compensation by α-actinin-2, its absence causes measurable changes in muscle contractile properties, calcium handling and metabolic profile, resulting in a shift to a more oxidative muscle phenotype [1,2].

Since the pioneering study of Yang et al. (2003), ACTN3 quickly became one of the most investigated genes in sports genetics. In later studies it has been generally found that the functional C allele is modestly associated with sprint- and power-oriented performance, while the T allele occurs more frequently in endurance athletes [3,5]. However, these associations are not universal or deterministic. Their magnitude depends on ethnicity, sex, age, competitive level, training history and, crucially, the specific performance phenotype being examined. Therefore, the current evidence suggests that ACTN3 is one of the polygenic basis of athletic performance, rather than an independent predictor of sporting success [6].

Although many studies have been conducted on the relationship between the ACTN3 genotype and traditional performance measures, most of the previous studies have focused on sprint performance, maximal strength, vertical jump height and endurance capacity. Although these results are informative on physical performance, they do not fully describe the neuromuscular complexity of football, where successful performance depends not only on force production but also on efficient movement regulation, postural control and coordination during highly dynamic actions. Therefore, it remains unclear whether ACTN3 plays a role in movement-quality phenotypes, which are obviously important for football performance. Filling this knowledge gap is an important step towards a more holistic understanding of genotype–phenotype relationships in sports performance.

Football performance is more than classical performance standards. It refers to the ability to create explosive power with excellent coordination of movement and postural control in complex, sport-specific tasks. The RSI is a common neuromuscular parameter used in the assessment of SSC potentials and is a well-established indication of SSC function. The Reactive Strength Index (RSI) measures the neuromuscular system's capacity to swiftly transition from eccentric to concentric muscle action, represented as a ratio of jump height to ground contact time [7]. The RSI is a prevalent metric for assessing the efficacy of explosive force production and movement. This test evaluates not only jump height and contact time but also the efficacy of the stretch-shortening cycle in dynamic activities. This is especially significant in football, a sport defined by repetitive accelerations, decelerations, jumps, landings, and multi-directional motions that are essential to gameplay. This functional role aligns with prior research indicating robust correlations between RSI and sprint acceleration, change-of-direction capability, repeated sprint performance, and overall soccer efficacy, underscoring its significance as a sensitive measure of neuromuscular function [8,9]. From a physiological standpoint, a correlation between ACTN3 genotype and reactive strength is biologically feasible. The efficacy of the stretch-shortening cycle (SSC) relies on swift force generation, musculotendinous stiffness, and efficient elastic energy transfer, all of which are governed by the structure and function of α-actinin-3. In experimental models, the depletion of α-actinin-3 was shown to alter muscle contractile characteristics, calcium management, and muscle mechanics, potentially affecting explosive activities that necessitate rapid force generation. Nonetheless, results from human studies continue to exhibit variability. Numerous studies indicate improved explosive performance in individuals possessing the functional R allele; however, others have found a negligible or nonexistent correlation after controlling for significant confounding variables, including age, training status, competitive level, and biological maturation [4, 6].

Nevertheless, dynamic stability has been far less emphasized than reactive strength, despite its vital importance in football play. In match play, football players consistently execute unilateral landings, quick decelerations, cutting movements, and directional shifts that necessitate ongoing neuromuscular control for postural stability. Dynamic stability refers to the collaboration of proprioceptive, vestibular, visual, and neuromuscular systems that assist athletes in preserving body alignment throughout challenging motor tasks [10]. Unlike traditional performance evaluations that primarily examine maximum physical output, dynamic stability provides essential insights into movement quality, sensorimotor synchronization, and postural control, all of which are crucial components of football-specific performance.

ACTN3 has been extensively researched; nevertheless, there has been comparatively less emphasis on movement-quality traits than on conventional sports performance metrics. The majority of prior research has been on sprint speed, muscular strength, jump performance, or endurance capacity, while the dynamic stability produced by drift during repeated unilateral jumping activities remains largely unexplored from a scientific perspective. Consequently, it remains uncertain whether the physiological effects of ACTN3 influence not only force production but also neuromuscular modulation and movement control. Investigating this question may elucidate the conflicting genotype-performance associations observed in prior studies and enhance comprehension of the role of ACTN3 in football-specific neuromuscular performance.

Biological maturation is a crucial determinant of neuromuscular performance during adolescence and necessitates thorough examination. Growth-related changes, such as increased muscle mass, enhanced tendon stiffness, hormonal activity, neuronal activation, and intermuscular coordination, significantly influence strength, reactive capacity, and motor control [11, 12]. As a result, young athletes of identical chronological ages may exhibit significantly varied neuromuscular performance levels owing to disparities in their biological development phases. Consequently, neglecting developmental status may mask minor genetic effects and lead to conflicting research outcomes.

The current data indicate that the relationship between ACTN3 and neuromuscular function is inadequately comprehended. While the physiological function of α-actinin-3 is well established, it remains unclear whether this polymorphism influences movement-quality characteristics such as dynamic stability and reactive strength. Moreover, to our knowledge, no prior research has concurrently examined the Reactive Strength Index (RSI) and drift-derived dynamic stability within a unified analytical framework, while considering developmental age in youth football players. This signifies a substantial deficiency in the existing sports genomics literature.

The objective of this study was to analyze the association between the ACTN3 rs1815739 polymorphism and two complementary measures of football-specific neuromuscular performance, Reactive Strength Index (RSI) and drift-derived dynamic stability, in youth football players, accounting for developmental age. We expected that ACTN3 would show phenotypic specific connections with neuromuscular function rather than generic associations. We hypothesized that individuals with the functional CC genotype would demonstrate increased reactive strength and individuals with the TT genotype would exhibit improved dynamic stability. We hypothesized that developmental age would explain substantially more heterogeneity in neuromuscular results than the ACTN3 genotype, suggesting that biological maturation predominates during adolescence.

2. Results

2.1. Participant Characteristics

A total of 274 youth football players were analyzed, consisting of 166 players aged 11 to 14 years and 108 players aged 15 to 18 years. Consistent with the two developmental cohorts, the older group exhibited greater height and weight compared to the younger group (Table 1). Out of 274 players, 25 failed to fulfill all jump conditions; these individuals provided their available observations for the mixed-effects models, which can handle incomplete data without resorting to listwise deletion.

2.2. Genotype Distribution and Hardy-Weinberg Equilibrium

ACTN3 rs1815739 genotypes were accessible for the entire sample (CC = 132, CT = 84, TT = 58; allele frequencies: C = 0.635, T = 0.365). The genotype distribution significantly diverged from Hardy–Weinberg equilibrium (Haldane exact test, p<0.001), indicating a substantial deficiency of heterozygotes (observed CT = 84 vs. predicted 127.0; inbreeding coefficient f = 0.34; Table 2). The sample consisted of chosen youth football players instead of a random population sample, suggesting that this difference may indicate population substructure or selection based on performance-related loci; genotyping error cannot be ruled out due to the absence of raw genotyping quality-control metrics. Consequently, all genotype-based findings presented below should be considered experimental and taken with caution.

2.3. ACTN3 and Reactive Strength Index (RSI)

The reactive strength index was modeled based on genotype, leg, and plane, incorporating age group as a covariate and a random intercept for each participant. The estimated marginal means adhered to the proposed sequence, with the CC (RR) genotype exhibiting the highest RSI and the TT (XX) genotype the lowest (CC = 0.38, CT = 0.37, TT = 0.34 m·s⁻¹; Table 3). Nonetheless, none of the pairwise genotype comparisons attained statistical significance (CC vs. TT: estimate = 0.037, p = 0.149; all other comparisons p>0.50; Table 4). The age group was a significant predictor of RSI within the genotype model (p<0.001), suggesting that developmental stage, rather than ACTN3 genotype, was the primary source of variance in reactive strength. Consequently, the RSI exhibited a non-significant trend aligned with the anticipated effects of ACTN3 on musculotendinous stiffness; however, the genotype effect's magnitude was minimal and did not achieve statistical significance in this sample.

2.4. Dynamic Stability

The estimated marginal means for the log-transformed drift area suggested that the TT genotype exhibited the smallest drift area, indicating superior dynamic stability compared to the CC and CT genotypes (CC = 4.46, CT = 4.45, TT = 4.29 log cm²; Table 3). Similar to RSI, the pairwise comparisons were not statistically significant (CC vs. TT: estimate = 0.165, p = 0.162; CT vs. TT: p = 0.236). In conjunction with the RSI results, the two outcomes exhibited contrasting genotype gradients: the CC genotype correlated with increased reactive strength, whereas the TT genotype correlated with enhanced postural stability. This pattern aligns with the proposed trade-off in which increased α-actinin-3–associated stiffness enhances stretch-shortening performance, whereas more compliant muscle characteristics may benefit fine postural control; however, since neither gradient reached statistical significance, this dissociation should be regarded solely as hypothesis-generating. The CC genotype exhibited an approximately 18 percent greater drift area compared to TT (ratio = 1.18, 95% CI: 0.95–1.46), whereas CT demonstrated a 17 percent bigger area than TT (ratio = 1.17, 95% CI: 0.93–1.47).

2.5. Genetic Model Analyses

To guarantee that the genotype effect was not confounded by parameterization, dominant, recessive, and additive genetic models were applied to both outcomes (Table 5). All results remained non-significant, with effect estimates directionally aligned with the codominant analysis. The additive model for RSI (p = 0.064) and the recessive model for drift area (p = 0.056) approached the traditional significance level but did not achieve it; thus, they are presented solely as trends. In every model, age group consistently served as a significantly more robust predictor than genotype.

3. Discussion

This study examined the relationship between the ACTN3 rs1815739 polymorphism and two complementary measures of neuromuscular performance—Reactive Strength Index (RSI) and drift-derived dynamic stability—in youth football players, considering developmental age. The primary conclusion was that the ACTN3 genotype was not independently correlated with either neuromuscular outcome following age adjustment. Despite corrected means indicating biologically congruent tendencies, with CC carriers displaying elevated RSI values and TT carriers presenting reduced drift regions, none of these disparities achieved statistical significance. Conversely, developmental age proved to be a far more potent driver of neuromuscular performance than genotype, underscoring the preeminent role of biological maturation in adolescence.

These findings align with the current notion that athletic performance is a polygenic and multifactorial phenotype shaped by several genetic variations interacting with developmental, physiological, environmental, and training-related factors. Despite ACTN3 being one of the most thoroughly studied genes in sports genomics, emerging evidence suggests that its independent impact on athletic performance is comparatively limited and significantly influenced by the specific phenotype examined, along with variables such as age, ethnicity, competitive level, and training history [4,6]. The current findings reinforce the emerging consensus that ACTN3 should not be regarded as a definitive “performance gene,” but rather as one biological element within the intricate genetic framework influencing athletic performance.

Although there are no statistically significant genetic effects, the observed direction of the RSI results is still biologically feasible. α-Actinin-3 is mostly expressed in fast glycolytic muscle fibers, where it plays a crucial role in maintaining sarcomere integrity, facilitating lateral force transfer, and enabling rapid force generation during explosive contractions. Experimental investigations utilizing ACTN3-deficient animal models have revealed modifications in calcium management, muscle contractile characteristics, and muscle-tendon stiffness, alongside a transition towards a more oxidative phenotype [1,3,6]. These changes may diminish the mechanical effectiveness of the stretch–shortening cycle, offering a physiological rationale for the elevated RSI values noted in CC carriers. Nonetheless, as these differences did not attain statistical significance, they should be regarded as physiologically consistent patterns rather than proof of a causal genetic influence.

The current findings further corroborate earlier observations that the correlation between ACTN3 and explosive performance significantly diminishes after accounting for critical developmental and training-related variables [4,6]. Although initial candidate-gene studies often indicated significant correlations between ACTN3 genotype and sprint or jump performance, subsequent research has demonstrated that these associations are markedly diminished and frequently vanish when considering biological maturation and training exposure. Our findings align with the emerging body of research and indicate that developmental processes significantly outweigh the impact of variation at a single genetic locus on reactive strength.

It was notably observed that developmental age accounted for significantly greater heterogeneity in reactive strength compared to ACTN3 genotype. In adolescence, swift alterations in muscle structure, tendon rigidity, hormonal activity, neuronal engagement, and intermuscular coordination lead to significant enhancements in explosive ability. As a result, any genotype-related impacts are probably obscured by the far greater physiological adaptations linked to growth and maturity [11,12]. These findings underscore the necessity of integrating indices of biological development when examining genotype–phenotype correlations in young athletes and may partially elucidate the discrepancies noted in prior ACTN3 investigations.

The association between ACTN3 genotype and dynamic stability has garnered less scholarly scrutiny, unlike reactive strength. Football play relies not just on the capacity to produce explosive power but also on sustaining postural control during successive landings, swift decelerations, and multidirectional maneuvers. Thus, movement-quality phenotypes, such as drift-derived dynamic stability, may offer further insights beyond conventional athletic performance metrics [10].

Despite the absence of statistically significant genotype differences, players with the TT genotype consistently exhibited smaller drift regions compared to CC carriers, suggesting a propensity for enhanced dynamic stability. In light of the lack of statistical significance, this data should not be construed as proof of a genuine genetic effect. Nonetheless, the observed pattern aligns biologically with the established physiological effects of α-actinin-3 deficiency and thus warrants study as a hypothesis-generating discovery.

A potential reason pertains to the mechanical properties of the muscle-tendon unit. The performance of the stretch–shortening cycle is enhanced by increased musculotendinous stiffness, as greater stiffness promotes the storage and release of elastic energy during rapid eccentric–concentric movements [7, 13]. Conversely, good postural stabilization after landing may necessitate a more balanced stiffness-compliance ratio, enabling better absorption of mechanical shocks while promoting precise neuromuscular adaptations. Within this context, the comparatively reduced drift area reported among TT carriers may indicate modified muscle mechanical properties rather than enhanced athletic performance.

An alternate explanation may include variances in neuromuscular coordination. Dynamic stability depends on the continuous integration of proprioceptive, vestibular, and visual information, along with rapid motor responses. Although ACTN3 is not directly associated with central nervous system function, experimental evidence suggests that α-actinin-3 deficiency modifies muscle contractile characteristics and fatigue resistance, which may influence sensorimotor control during repetitive landing activities [2]. Nevertheless, concrete evidence linking the ACTN3 genotype to proprioception or postural regulation in humans remains limited. Therefore, these proposed mechanisms should be regarded as physiologically plausible interpretations rather than experimentally confirmed pathways.

This study possesses numerous methodological strengths. This study is one of the initial investigations to concurrently assess Reactive Strength Index (RSI) and drift-derived dynamic stability inside the same analytical framework for the ACTN3 rs1815739 polymorphism. The work broadens the field of sports genomics by analyzing both explosive performance and movement-quality attributes, moving beyond conventional performance metrics like sprint speed, jump height, and maximum strength. This multifaceted method offers a more thorough evaluation of neuromuscular function, which is especially pertinent in football, where movement control is as crucial as force generation.

A significant strength is the incorporation of developmental age in the statistical models. Adolescence is marked by significant alterations in muscle structure, tendon mechanical characteristics, hormonal activity, neuronal engagement, and intermuscular coordination, all of which greatly affect neuromuscular performance. The finding that developmental age accounted for a larger share of performance variability than ACTN3 genotype underscores the necessity of considering biological development when examining genotype–phenotype correlations in young athletes. This discovery offers a credible rationale for the conflicting correlations observed in prior ACTN3 research.

The current findings do not endorse the use of ACTN3 genotyping as a standalone method for talent discovery or player selection in youth football. While α-actinin-3 has a recognized physiological role in skeletal muscle function, its independent impact on reactive strength and dynamic stability seems limited in comparison to the effects of biological maturation and sport-specific training. Therefore, coaches and practitioners ought to maintain a focus on objective evaluations of neuromuscular function, movement quality, and technical proficiency instead of depending on singular genetic data for athlete assessment [11, 12].

Several limitations should be considered when interpreting these findings. The deviation from Hardy–Weinberg equilibrium warrants caution, as it may reflect sampling variation, genotyping error, population structure, or ascertainment effects [14]. However, similar deviations have been reported in selected athletic cohorts and interpreted as potentially consistent with genotype-related selection rather than as grounds for excluding the analyses [15,16]. In addition, only one candidate gene was examined, although athletic performance is a complex, polygenic, and multifactorial trait influenced by genetic, environmental, training, and developmental factors. Finally, the cross-sectional design prevents causal inference regarding genotype–performance relationships [17,18].

Future investigations ought to employ multidimensional and longitudinal methodologies, integrating polygenic profiles, direct assessments of biological maturation, tendon mechanical characteristics, electromyographic evaluations, and three-dimensional biomechanical studies. Such designs may elucidate whether the contrasting, but non-significant, trends found for reactive strength and dynamic stability signify genuine biological specialization or merely indicate typical sampling variability.

The aforementioned studies corroborate the increasing consensus that the impact of ACTN3 rs1815739 on athletic performance is phenotypically specific rather than universal. α-Actinin-3 is crucial for rapid muscular performance but exerts a limited independent influence on reactive strength and movement quality compared to the effects of biological maturation [19-21]. This work broadens the phenotypic scope of the sports genomics experiment by incorporating simultaneous assessment of reactive strength and drift-based dynamic stability. This establishes a foundation for future research to explore the intricate connections among genetics, neuromuscular development, and athletic performance.

4. Materials and Methods

4.1. Participants and Ethical Considerations

This study involved 278 male youth football players of Albanian origin, all associated with a professional football club in North Macedonia. Four players were excluded prior to analysis (three duplicate records and one participant with physiologically implausible values across multiple performance metrics), yielding a final sample of 274 players: 166 aged 11–14 years (youth category) and 108 aged 15–18 years (adolescent category). All athletes regularly engaged in structured training programs including technical, tactical, and conditioning exercises (focused on speed, strength, and endurance) for six days per week, with each session lasting approximately 45 to 75 minutes. Inclusion criteria required continuous participation in the club's official training program for a minimum of two years and a maximum of ten years. Prior to participation, written informed consent was obtained from both the players and their respective football clubs. The study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was granted by the Lokman Hekim University Non-Interventional Clinical Research Ethics Committee (decision number: 2025-110).

4.2. Exercise and Performance Measurement Methods

All participating athletes were already familiar with the testing procedures due to their prior exposure through regular training and evaluation routines at their respective clubs. Before performing the actual tests, each player completed both general and sport-specific warm-up exercises. Following the warm-up, a rest period of three to five minutes was provided to ensure optimal recovery prior to testing.

4.3. Reactive Strength Index (RSI)

The OptoJump Next system calculated the RSI for each leap by dividing jump height (m) by ground contact time (s), with jump height determined from flight duration. The RSI is a commonly utilized metric of stretch-shortening cycle performance, indicating the capacity to swiftly shift from eccentric to concentric muscle action (Flanagan and Comyns 2008). The mean RSI from the five jumps was utilized for analysis for each subject and sub-test.

4.4. Dynamic Stability

The Drift Protocol integrated within the OptoJump Next optical measurement system (Microgate S.R.L., Bolzano, Italy) was utilized to assess dynamic stability. The apparatus comprises two parallel optical bars equipped with infrared LEDs at ground level, which ascertain the athlete's foot position upon landing with adequate spatial resolution to track displacement along both horizontal axes.

The procedure consists of four sub-tests, each involving five successive unilateral jumps executed in position on one leg. Participants executed five jumps on the right leg and five on the left leg with their feet aligned parallel to the optical bars, thereafter doing five jumps on each leg with their feet positioned perpendicular to the bars. This configuration enables the quantification of displacement in the anterior-posterior and medio-lateral axes, respectively. Participants were directed to execute a vertical jump and to land as near as feasible to their initial position.

For each sub-test, the system documents the coordinates of each landing in relation to the initial origin, from which the mean drift (average displacement from the origin) and the standard deviation of the landing coordinates along each axis are calculated. The mean drift may approximate zero when consecutive jumps vary in opposing directions, thus creating a deceptive appearance of stability. Consequently, dynamic stability was measured as the drift area, determined by the product of the standard deviations of the landing coordinates along the two axes. A bigger drift area signifies increased dispersion of landing places from the origin, resulting in diminished dynamic stability. A standardized interlude was allotted between sub-tests.

4.5. Genetic Analysis

For genetic analyses, oral buccal swab samples were collected from each participant using sterile cotton swabs under standardized procedures. Samples were obtained for DNA extraction from players of a professional football club. Samples collected from athletes were segregated for analysis and dispatched to the laboratory center. DNA isolation from swab samples obtained from athletes was conducted with G-spin Total (Intron Biotechnology, South Korea DNA isolation kit). Swab samples kept in 500 µL of PBS were centrifuged at 13,000 rpm for 2 minutes. After centrifugation, 300 µL of supernatant was withdrawn and discarded. The remaining pellet was vortexed by adding 200 µL of CL buffer, 20 µL of proteinase K, 10 µL of RNAase. 200 µl of BL buffer was added, vortexed, and incubated at 56°C for 10 minutes. After incubation, 200 µl of ethanol was added to the tube and vortexed. The solution was completely transferred to the filtered tube (column tube). After the solution is centrifuged at 13,000 rpm for 2 min, the filtration is discarded, and the column is reinserted into the receiver tube. 700 µl of WA (Wash A) buffer is added and the solution is centrifuged at 13,000 rpm for 2 minutes, the filtration is discarded, and the column is reinserted into the receiver tube. 700 µl of WB (Wash B) buffer is added and the solution is centrifuged at 13,000 rpm for 2 minutes, the filtrate is discarded, and the filtered part is placed on the Eppendorf and 125 ml of CE (elution) Buffer is added. The solution is to be incubated for 1 minute at room temperature and centrifuged at 13,000 rpm for 1 minute. After centrifugation, the filtered portion is discarded, and pure DNA is obtained. After the isolated samples, the samples with appropriate OD 280/260 measurement values coded with the help of spectrophotometer and stored under appropriate conditions. After DNA isolation, the samples genotyped for ACTN3 rs1815739 gene polymorphisms by Real-Time PCR method. ACTN3 rs1815739 genotypes were determined using the G-spin One Total (Intron Biotechnology) Plus Retyping Kit (South Korea), with genotyping performed on the Real-Time PCR System (Applied Biosystems). Genotyping processes were completed by using 5 µL real time master mix, 3.5 µL sterile H2O, 0.5 µL assay and 1 µL (10 ng) DNA in a total of 10 µL, and FAM and VIC irradiations were used. These chemical solutions were prepared in a 0.1 ml plate, and the plate was placed in the real-time device.

4.6. Statistical Analysis

All analyses were conducted using R (version 4.4.2; R Core Team 2024). Descriptive statistics are reported as mean ± standard deviation for continuous data and as frequencies for categorical variables. The Hardy–Weinberg equilibrium of the ACTN3 rs1815739 (R577X) genotype distribution was evaluated using the Haldane exact test with the SELOME p-value, as implemented in the HardyWeinberg package; the inbreeding coefficient (f) was computed to measure the direction and extent of any deviation. Each subject underwent four jump circumstances (right and left leg × parallel and perpendicular foot orientation), resulting in observations being layered among individuals. Linear mixed-effects models were fitted utilizing the lme4 and lmerTest programs with constrained maximum likelihood estimation. The model incorporated ACTN3 genotype, leg, and movement plane as fixed effects for each outcome—reactive strength index (RSI) and drift area—along with their interactions, age group as a covariate, and a by-participant random intercept to address the non-independence of repeated measurements among athletes. The reference groups included the CC genotype, right leg, anterior–posterior plane, and the younger age group (11–14 years). Degrees of freedom and p-values for fixed effects were calculated using Satterthwaite's approximation, whereas pairwise genotype contrasts were derived with the emmeans package employing Tukey adjustment for multiple comparisons.

To guarantee that genotype effects were not masked by the selection of parameterization, three supplementary pre-established genetic models were applied for each outcome: dominant (CC vs. CT + TT), recessive (TT vs. CC + CT), and additive (number of T alleles: 0, 1, 2). All models are shown in their entirety, irrespective of statistical significance.

The drift area exhibited positive skewness and was consequently subjected to natural logarithmic transformation before analysis. The model assumptions were evaluated via visual examination of residual quantile–quantile and residual-versus-fitted plots; after transformation, the residuals exhibited an almost normal distribution with consistent variance. Values deemed physiologically implausible, as determined by pre-analysis screening, were excluded. Participants with incomplete jump data provided their available observations, as mixed-effects models can handle unbalanced data without listwise deletion. The threshold for statistical significance was established at α = 0.05.

5. Conclusions

This study aimed to examine the correlation between the ACTN3 rs1815739 polymorphism and two neuromuscular performance metrics: Reactive Strength Index (RSI) and dynamic stability evaluated using drift analysis. The study found no statistically significant genotype-dependent differences; however, the patterns revealed in the ACTN3 gene were biologically aligned with the established physiological function of α-actinin-3. Individuals possessing the CC genotype had enhanced reactive strength, while those with the TT genotype displayed superior dynamic stability.

The primary outcome of this study was that developmental age explained greater variance in reactive strength measurements than ACTN3 genotype. Consequently, biological maturation is the primary factor influencing the neuromuscular performance of young soccer players. This study advances the domain of sports genomics by examining the influence of Reactive Strength Index and drift-based dynamic stability, two neuromuscular phenotypes that have been inadequately explored in prior research concerning the impacts of the ACTN3 gene.

The current data corroborate the emerging notion that ACTN3 constitutes merely one component of a multifaceted array of factors influencing athletic performance. Additional longitudinal and multicenter studies incorporating polygenic profiling, biological maturation, biomechanical assessment, and environmental variables are necessary to investigate the influence of ACTN3 on the neuromuscular performance of soccer players.

Recommendations

Current data suggest that ACTN3 rs1815739 genotyping should not be utilized as a standalone method for talent discovery or player recruitment in youth football. The lack of significant genotype-dependent variations and the predominant effect of developmental age indicate that neuromuscular performance during adolescence is primarily regulated by biological maturation and training rather than by variances in a specific gene. Regular evaluation of the Reactive Strength Index (RSI) and drift-derived dynamic stability may furnish coaches, strength and conditioning experts, and sports scientists with more pertinent insights into neuromuscular development than a singular genetic examination. These performance-based assessments accurately reflect functional adaptations pertinent to football, hence enhancing their validity for monitoring training responses and guiding long-term player development. Despite ACTN3's limited independent predictive value, genetic data may enhance multidimensional athlete profiling when integrated with physical performance assessments, biological maturity, training history, anthropometric traits, and additional physiological factors. A combination method is anticipated to provide a more precise assessment of athletic potential than dependence on a solitary genetic marker. The present findings endorse the utilization of ACTN3 genotyping as a supplementary, rather than definitive, instrument, emphasizing that genetic information should complement a comprehensive evaluation of young athletes in football.

Limitations

The observed genotype distribution greatly deviated from Hardy-Weinberg equilibrium, mostly due to a pronounced deficiency of heterozygotes. In certain athletic cohorts, particularly when sampling is non-random or performance-based, analogous anomalies have been intermittently noted; hence, this discovery should be approached with circumspection. The genotype relationships identified in this study should be regarded as preliminary and require validation in larger independent cohorts exhibiting genotype frequencies in accordance with Hardy-Weinberg equilibrium.

Secondly, although developmental age was statistically regulated through age-group classification, biological maturation was not explicitly assessed using established indicators such as peak height velocity (PHV), maturity offset, or Tanner stage. Additional research utilizing direct assessments of maturation should enhance comprehension of genotype-phenotype correlations in young athletes, given that biological maturity significantly influences neuromuscular performance during adolescence.

This study concentrated on a singular candidate gene; however, athletic performance is recognized as a multifaceted polygenic phenotype shaped by various genetic variants interacting with training exposure, biological maturation, nutrition, recovery, psychological traits, and additional environmental factors. Subsequent investigations utilizing polygenic methodologies may enhance understanding of the genetic underpinnings of neuromuscular function. The cross-sectional design precludes any causal inferences on the relationship between ACTN3 genotype and neuromuscular performance. Longitudinal studies of athletes during growth and maturation are essential to elucidate the relationship between genetic predisposition and developmental processes, as well as long-term training adaptations.

This study simultaneously evaluated two complementary neuromuscular phenotypes; however, future research should incorporate further physiological and biomechanical assessments, including electromyography, tendon mechanical properties, muscle morphology, three-dimensional kinematic analysis, and prospective injury surveillance. Multidimensional approaches would yield a more thorough comprehension of the mechanisms connecting genetic diversity to neuromuscular function.