OBJECTIVES:To investigate associations between genetic variants within COLGALT1, COL1A1, COL3A1, COL5A1, KDR, MIR608, MMP3, NID1, TIMP2 and VEGFA and injury history in elite male rugby athletes. DESIGN:A case-control genetic association study was conducted on 184 elite male rugby athletes. METHODS:Participants were genotyped for 13 genetic polymorphisms previously associated with soft tissue injury using standard PCR assays. Injury data were collected via a self-reported injury-history questionnaire. Single-locus association and Total Genotype Score (TGS) analyses were conducted using χ2 tests. In addition, multifactor dimensionality reduction and inferred haplotype analysis were used to identify genetic interactions. RESULTS:The TT genotype of MMP3 rs679620 was underrepresented in the non-injured ligament group compared to the ligament sprain and ligament rupture groups (10 %, 32 %, 25 %; P < 0.04, respectively). The T allele of MMP3 rs679620 was overrepresented in the non-injured tendon group compared to the tendinopathy group (50 %, 38 %; P < 0.02). The proportion of C allele carriers of COL5A1 rs12722 was higher in the tendon rupture group than the non-injured tendon group (96 %, 75 %; P < 0.02). Furthermore, the T-C inferred haplotype frequency of COL5A1 rs12722 and COL5A1 rs3196378 was higher in the tendon rupture, ligament sprain and total injured athlete groups compared to their respective non-injured groups (P < 0.02). CONCLUSIONS:This study is the first to identify associations between MMP3 rs679620 and COL5A1 rs12722 and soft-tissue injury history in elite male rugby athletes. These findings support the growing evidence that soft-tissue injury could be influenced by an athlete's genetic predisposition.

Objectives: The aims of this study were to: i) identify performance indicators associated with match outcomes in the United Rugby Championship; ii) compare the efficacy of isolated and relative datasets to predict match out-come; and iii) investigate whether reduced statistical models can reproduce predictive accuracy.Design: Retrospective analysis of key performance indicators in the United Rugby Championship.Methods: Twenty-seven performance indicators were selected from 96 matches (2020-21 United Rugby Cham-pionship). Random forest classification was completed on isolated and relative datasets, using a binary match outcome (win/lose). Maximum relevance and minimum redundancy performance indicator selection was utilised to reduce models. In addition, models were tested on 53 matches from the 2021-22 season to ascertain prediction accuracy.Results: Within the 2020-21 datasets, the full models correctly classified 83% of match performances for the rel-ative dataset and 64% for isolated data, the equivalent reduced models classified 85% and 66% respectively. The reduced relative model successfully predicted 90% of match performances in the 21-22 season, highlighting that five performance indicators were significant: kicks from hand, metres made, clean breaks, turnovers con-ceded and scrum penalties.Conclusions: Relative performance indicators were more effective in predicting match outcomes than isolated data. Reducing features used in random forest classification did not degrade prediction accuracy, whilst also sim-plifying interpretation for practitioners. Increased kicks from hand, metres made, and clean breaks compared to the opposition, as well as fewer scrum penalties and turnovers conceded were all indicators of winning match outcomes within the United Rugby Championship.(c) 2022 The Authors. Published by Elsevier Ltd on behalf of Sports Medicine Australia. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Objective: Concussions are common match injuries in elite rugby, and reports exist of reduced cognitive function and long-term health consequences that can interrupt or end a playing career and produce continued ill health. The aim of this study was to investigate the association between elite rugby status and 8 concussion-associated risk polymorphisms. We hypothesized that concussion-associated risk genotypes and alleles would be underrepresented in elite rugby athletes compared with nonathletes. Design: A case–control genetic association study. Setting: Institutional (university). Participants: Elite White male rugby athletes [n = 668, mean (SD) height 1.85 (0.07) m, mass 102 (12) kg, and age 29 (7) years] and 1015 nonathlete White men and women (48% men). Interventions: Genotype was the independent variable, obtained by PCR of genomic DNA using TaqMan probes. Main Outcome Measure: Elite athlete status with groups compared using χ 2 and odds ratio (OR). Results: The COMT rs4680 Met/Met (AA) genotype, Met allele possession, and Met allele frequency were lower in rugby athletes (24.8%, 74.6%, and 49.7%, respectively) than nonathletes (30.2%, 77.6%, and 54.0%; P < 0.05). The Val/Val (GG) genotype was more common in elite rugby athletes than nonathletes (OR 1.39, 95% confidence interval 1.04-1.86). No other polymorphism was associated with elite athlete status. Conclusions: Elite rugby athlete status is associated with COMT rs4680 genotype that, acting pleiotropically, could affect stress resilience and behavioral traits during competition, concussion risk, and/or recovery from concussion. Consequently, assessing COMT rs4680 genotype might aid future individualized management of concussion risk among athletes.

Due to the high-velocity collision-based nature of elite rugby league and union, the risk of sustaining a concussion is high. Occurrence of and outcomes following a concussion are probably affected by the interaction of multiple genes in a polygenic manner. This study investigated whether suspected concussion-associated polygenic profiles of elite rugby athletes differed from non-athletes and between rugby union forwards and backs. We hypothesised that a total genotype score (TGS) using eight concussion-associated polymorphisms would be higher in elite rugby athletes than non-athletes, indicating selection for protection against incurring or suffering prolonged effects of, concussion in the relatively high-risk environment of competitive rugby. In addition, multifactor dimensionality reduction was used to identify genetic interactions. Contrary to our hypothesis, TGS did not differ between elite rugby athletes and non-athletes (p ≥ 0.065), nor between rugby union forwards and backs (p = 0.668). Accordingly, the TGS could not discriminate between elite rugby athletes and non-athletes (AUC ~0.5), suggesting that, for the eight polymorphisms investigated, elite rugby athletes do not have a more ‘preferable’ concussion-associated polygenic profile than non-athletes. However, the COMT (rs4680) and MAPT (rs10445337) GC allele combination was more common in rugby athletes (31.7%; p < 0.001) and rugby union athletes (31.8%; p < 0.001) than non-athletes (24.5%). Our results thus suggest a genetic interaction between COMT (rs4680) and MAPT (rs10445337) assists rugby athletes in achieving elite status. These findings need exploration vis-à-vis sport-related concussion injury data and could have implications for the management of inter-individual differences in concussion risk.

This study investigated whether anthropometric and physical abilities explained variance in match collision performance among international female rugby union players. Physical performance and anthropometric data for fifty-one international female rugby union players, and collision actions categorised as "effort" or "performance" variables, from 20 international matches, were analysed using partial least squares regression. Among forwards, variance in carries/min was explained (R2 = .22) by a combination of; body mass, skinfolds, acceleration momentum and negative associations with mean aerobic speed and single-leg isometric squat relative force (SLISO/kgBM). Variance in collision dominance among forwards was explained (R2 = .21) by lower skinfolds and higher acceleration momentum, while tackles/min (R2 = .19) were explained by greater jumping power and single-leg isometric squat (SLISO). Among backs, variance in tackles/min (R2 = .54) was explained by greater bench press, SLISO and SLISO/kgBM. Variance in collision dominance among backs was explained (R2 = .23) by negative and positive associations with body mass and SLISO/kgBM, respectively. These findings suggest the development of physical characteristics, such as body mass and composition, strength and power contribute towards successful collision actions among international female rugby union players. The contribution of different physical characteristics towards collision events is dependent on position, and whether the collision event is categorised by "performance" or "effort". It is suggested that physical training programmes should reflect this level of specificity.HighlightsAmong elite female rugby union forwards, acceleration momentum, body mass and skinfolds are positively associated with winning collisions and carrying the ball into contact more frequently, whilst tackle frequency is positively associated with relative leg strength and power output.Among elite female backs, the ability to win collisions is positively associated with relative leg power output, and negatively associated with body mass. Tackle frequency is associated with maximum upper- and lower-body strength in this group.Physical characteristics account for some of the variability in collision performance, but interpretation of these findings should consider that factors such as technique during collision events may account for a larger proportion of total variance.Sports science practitioners can improve collision performance, to varying degrees, by enhancing specific gross physical characteristics, according to a player's position and the tactical role they are expected to fulfil.

There is growing evidence of genetic contributions to tendon and ligament pathologies. Given the high incidence and severity of tendon and ligament injuries in elite rugby, we studied whether 13 gene polymorphisms previously associated with tendon/ligament injury were associated with elite athlete status. Participants from the RugbyGene project were 663 elite Caucasian male rugby athletes (RA) (mean (standard deviation) height 1.85 (0.07) m, mass 101 (12) kg, age 29 (7) yr), including 558 rugby union athletes (RU) and 105 rugby league athletes. Non-athletes (NA) were 909 Caucasian men and women (56% female; height 1.70 (0.10) m, mass 72 (13) kg, age 41 (23) yr). Genotypes were determined using TaqMan probes and groups compared using Χ2 and odds ratio (OR). COLGALT1 rs8090 AA genotype was more frequent in RA (27%) than NA (23%; P = 0.006). COL3A1 rs1800255 A allele was more frequent in RA (26%) than NA (23%) due to a greater frequency of GA genotype (39% vs 33%). For MIR608 rs4919510, RA had 1.7 times the odds of carrying the CC genotype compared to NA. MMP3 rs591058 TT genotype was less common in RA (25.1%) than NA (31.2%; P < 0.04). For NID1 rs4660148, RA had 1.6 times the odds of carrying the TT genotype compared to NA. It appears that elite rugby athletes have an inherited advantage that contributes to their elite status, possibly via resistance to soft tissue injury. These data may, in future, assist personalised management of injury risk amongst athletes.Highlights The elite rugby athletes we studied had differing genetic characteristics to non-athletes regarding genetic variants previously associated with soft-tissue injury risk.COLGALT1 rs8090, COL3A1 rs1800255, MIR608 rs4919510, MMP3 rs591058 and NID1 rs4660148 were all associated with elite status in rugby.We propose that elite rugby athletes might possess an inherited resistance to soft tissue injury, which has enabled them to achieve elite status despite exposure to the high-risk environment of elite rugby.

Part 1 of this genetic association series highlighted several genetic variants independently associated with elite status in rugby. However, it is highly likely that the genetic influence on elite status is polygenic due to the interaction of multiple genes. Therefore, the aim of the present study was to investigate whether polygenic profiles of elite rugby athletes differed from non-athletes utilising 13 genetic polymorphisms previously associated with tendon/ligament injury. Total genotype score (TGS) was calculated and multifactor dimensionality reduction (MDR) was used to calculate SNP-SNP epistasis interactions. Based on our elite rugby data from Part 1, mean TGS was significantly higher in elite rugby athletes (52.1 ± 10.7) than non-athletes (48.7 ± 10.8). There were more elite rugby athletes (54%) within the upper TGS quartile, and fewer (46%) within the lower quartile, compared to non-athletes (31% and 69%, respectively; P = 5·10-5), and the TGS was able to distinguish between elite rugby athletes and non-athletes (area under the curve = 0.59; 95% confidence interval 0.55-0.63; P = 9·10-7). Furthermore, MDR identified a three-SNP model of COL5A1 rs12722, COL5A1 rs3196378 and MIR608 rs4919510 that was best able to predict elite athlete status, with a greater frequency of the CC-CC-CC genotype combination in elite rugby athletes (9.8%) than non-athletes (5.3%). We propose that elite rugby athletes possess "preferable" musculoskeletal soft-tissue injury-associated polygenic profiles that have helped them achieve success in the high injury risk environment of rugby. These data may, in future, have implications for the individual management of musculoskeletal soft-tissue injury.HighlightsElite rugby athletes have preferable polygenic profiles to non-athletes in terms of genetic variants previously associated with musculoskeletal soft-tissue injury.The total genotype score was able to distinguish between elite rugby athletes and non-athletes.COL5A1 rs12722, COL5A1 rs3196378 and MIR608 rs4919510 produced the best model for predicting elite athlete status.We propose that elite rugby athletes may have an inherited advantage to achieving elite status due to an increased resistance to soft-tissue injury.

PURPOSEGenetic polymorphisms have been associated with the adaptation to training in maximal oxygen uptake (V˙O2max). However, the genotype distribution of selected polymorphisms in athletic cohorts is unknown, with their influence on performance characteristics also undetermined. This study investigated whether the genotype distributions of 3 polymorphisms previously associated with V˙O2max training adaptation are associated with elite athlete status and performance characteristics in runners and rugby athletes, competitors for whom aerobic metabolism is important.METHODSGenomic DNA was collected from 732 men including 165 long-distance runners, 212 elite rugby union athletes, and 355 nonathletes. Genotype and allele frequencies of PRDM1 rs10499043 C/T, GRIN3A rs1535628 G/A, and KCNH8 rs4973706 T/C were compared between athletes and nonathletes. Personal-best marathon times in runners, as well as in-game performance variables and playing position, of rugby athletes were analyzed according to genotype.RESULTSRunners with PRDM1 T alleles recorded marathon times ∼3 minutes faster than CC homozygotes (02:27:55 [00:07:32] h vs 02:31:03 [00:08:24] h, P = .023). Rugby athletes had 1.57 times greater odds of possessing the KCNH8 TT genotype than nonathletes (65.5% vs 54.7%, χ2 = 6.494, P = .013). No other associations were identified.CONCLUSIONSThis study is the first to demonstrate that polymorphisms previously associated with V˙O2max training adaptations in nonathletes are also associated with marathon performance (PRDM1) and elite rugby union status (KCNH8). The genotypes and alleles previously associated with superior endurance-training adaptation appear to be advantageous in long-distance running and achieving elite status in rugby union.

Brazier, J, Antrobus, M, Stebbings, GK, Day, SH, Callus, P, Erskine, RM, Bennett, MA, Kilduff, LP, and Williams, AG. Anthropometric and physiological characteristics of elite male rugby athletes. J Strength Cond Res 34(6): 1790-1801, 2020-This is the first article to review the anthropometric and physiological characteristics required for elite rugby performance within both rugby union (RU) and rugby league (RL). Anthropometric characteristics such as height and body mass, and physiological characteristics such as speed and muscular strength, have previously been advocated as key discriminators of playing level within rugby. This review aimed to identify the key anthropometric and physiological properties required for elite performance in rugby, distinguishing between RU and RL, forwards and backs and competitive levels. There are differences between competitive standards such that, at the elite level, athletes are heaviest (RU forwards similar to 111 kg, backs similar to 93 kg; RL forwards similar to 103 kg, backs similar to 90 kg) with lowest % body fat (RU forwards similar to 15%, backs similar to 12%; RL forwards similar to 14%, backs similar to 11%), they have most fat-free mass and are strongest (back squat: RU forwards similar to 176 kg, backs similar to 157 kg; RL forwards similar to 188 kg, backs similar to 168 kg; bench press: RU forwards similar to 131 kg, backs similar to 118 kg; RL forwards similar to 122 kg, backs similar to 113 kg) and fastest (10 m: RU forwards similar to 1.87 seconds, backs similar to 1.77 seconds; 10 m: RL forwards similar to 1.9 seconds, backs similar to 1.83 seconds). We also have unpublished data that indicate contemporary RU athletes have less body fat and are stronger and faster than the published data suggest. Regardless, well-developed speed, agility, lower-body power, and strength characteristics are vital for elite performance, probably reflect both environmental (training, diet, etc.) and genetic factors, distinguish between competitive levels, and are therefore important determinants of elite status in rugby.

The COVID-19 pandemic in 2020 has resulted in widespread training disruption in many sports. Some athletes have access to facilities and equipment, while others have limited or no access, severely limiting their training practices. A primary concern is that the maintenance of key physical qualities (e. g. strength, power, high-speed running ability, acceleration, deceleration and change of direction), game-specific contact skills (e. g. tackling) and decision-making ability, are challenged, impacting performance and injury risk on resumption of training and competition. In extended periods of reduced training, without targeted intervention, changes in body composition and function can be profound. However, there are strategies that can dramatically mitigate potential losses, including resistance training to failure with lighter loads, plyometric training, exposure to high-speed running to ensure appropriate hamstring conditioning, and nutritional intervention. Athletes may require psychological support given the challenges associated with isolation and a change in regular training routine. While training restrictions may result in a decrease in some physical and psychological qualities, athletes can return in a positive state following an enforced period of rest and recovery. On return to training, the focus should be on progression of all aspects of training, taking into account the status of individual athletes.

ABSTRACTThe primary aim of this paper was to produce a model that predicts outcome in the group‐phase of the 2015 Rugby World Cup and to determine the relevance and importance of performance indicators (PIs) that are significant in predicting outcome. A secondary aim investigated whether this model accurately predicted match outcome in the knockout‐phase of the competition. Data was the PIs from the 40 group‐phase games of the 2015 RWC. Given the binary outcome (win/lose), a random forest classification model was built using the data sets. The outcome of the knockout‐phase was predicted using this model and accuracy of prediction of the model from the group‐phase. The model indicated that thirteen PIs were significant in predicting match outcome in the group‐phase and provided accurate prediction of match outcome in the knockout‐phase. These PIs were tackle‐ratio, clean breaks, average carry, lineouts won, penalties conceded, missed tackles, lineouts won in the opposition 22, defenders beaten, metres carried, kicks from hand, lineout success, penalties in opposition 22 m and scrums won. For the group‐phase matches tackle ratio, clean breaks and average carry were accurate standalone predictors of match outcome and respectively predicted 75%, 70% and 73% of match outcomes. The model based on the group‐phase predicted correctly 7 from 8 (87.5%) knockout‐phase matches. In the knockout‐phase clean breaks predicted 7 from 8 outcomes, whilst tackle ratio and average carry predicted 6 from 8 outcomes.

Objectives: The primary aim of this study was to examine whether accuracy of rugby union match prediction outcomes differed dependent on the method of data analysis (i.e., isolated vs. descriptively converted or relative data). A secondary aim was to then use the most appropriate method to investigate the performance indicators (PI's) most relevant to match outcome. Methods: Data was 16 PI's from 127 matches across the 2016-17 English Premiership rugby season. Given the binary outcome (win/lose), a random forest classification model was built using these data sets. Predictive ability of the models was further assessed by predicting outcomes from data sets of 72 matches across the 2017-18 season. Results: The relative data model attained a balanced prediction rate of 80% (95% CI - 75-85%) for 2016-17 data, whereas the isolated data model only achieved 64% (95% CI - 58-70%). In addition, the relative data model correctly predicted 76% (95% CI - 68-84%) of the 2017-18 data, compared with 70% (95% CI - 63-77%) for the isolated data model. From the relative data model, 10 PI's had significant relationships with game outcome: kicks from hand, clean breaks, average carry distance, penalties conceded when the opposition have the ball, turnovers conceded, total metres carried, defenders beaten, ratio of tackles missed to tackles made, total missed tackles, and turnovers won. Conclusions: Outcomes of Premiership rugby matches are better predicted when relative data sets are utilised. Basic open-field abilities based around an effective kicking game, ball carrying abilities, and not conceding penalties when the opposition are in possession are the most relevant predictors of success. (C) 2018 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

The assessment of competitive movement demands in team sports has traditionally relied upon global positioning system (GPS) analyses presented as fixed-time epochs (e.g., 5-40 min). More recently, presenting game data as a rolling average has become prevalent due to concerns over a loss of sampling resolution associated with the windowing of data over fixed periods. Accordingly, this study compared rolling average (ROLL) and fixed-time (FIXED) epochs for quantifying the peak movement demands of international rugby union match-play as a function of playing position. Elite players from three different squads (n = 119) were monitored using 10 Hz GPS during 36 matches played in the 2014-2017 seasons. Players categorised broadly as forwards and backs, and then by positional sub-group (FR: front row, SR: second row, BR: back row, HB: half back, MF: midfield, B3: back three) were monitored during match-play for peak values of high-speed running (>5 m·s-1; HSR) and relative distance covered (m·min-1) over 60-300 s using two types of sample-epoch (ROLL, FIXED). Irrespective of the method used, as the epoch length increased, values for the intensity of running actions decreased (e.g., For the backs using the ROLL method, distance covered decreased from 177.4 ± 20.6 m·min-1 in the 60 s epoch to 107.5 ± 13.3 m·min-1 for the 300 s epoch). For the team as a whole, and irrespective of position, estimates of fixed effects indicated significant between-method differences across all time-points for both relative distance covered and HSR. Movement demands were underestimated consistently by FIXED versus ROLL with differences being most pronounced using 60 s epochs (95% CI HSR: -6.05 to -4.70 m·min-1, 95% CI distance: -18.45 to -16.43 m·min-1). For all HSR time epochs except one, all backs groups increased more (p < 0.01) from FIXED to ROLL than the forward groups. Linear mixed modelling of ROLL data highlighted that for HSR (except 60 s epoch), SR was the only group not significantly different to FR. For relative distance covered all other position groups were greater than the FR (p < 0.05). The FIXED method underestimated both relative distance (~11%) and HSR values (up to ~20%) compared to the ROLL method. These differences were exaggerated for the HSR variable in the backs position who covered the greatest HSR distance; highlighting important consideration for those implementing the FIXED method of analysis. The data provides coaches with a worst-case scenario reference on the running demands required for periods of 60-300 s in length. This information offers novel insight into game demands and can be used to inform the design of training games to increase specificity of preparation for the most demanding phases of matches.

The use of physical tests to profile physical capabilities, and provide training direction to athletes is common practice. Likewise, in professional team sports, notational analysis codes the key contributions of each player during competition. Limited studies have however investigated relationships between physical capabilities and key performance indicators (KPIs) of rugby union match-play. Elite professional players, categorised as forwards (n = 15) or backs (n = 14), from an international rugby union squad (n = 29) undertook assessments of isometric mid-thigh pull (IMTP), bilateral and unilateral countermovement jumps (CMJ) and drop jumps (DJ; from 40 and 20 cm, respectively), and assessment of acceleration (10 m), a 5 m weighted sled drive, and a Yo-Yo intermittent recovery test level 1 (Yo-Yo IRTL1). Game statistics of the same players from 92 matches (~23 matches per player) during the 2014-15 season were analysed for effort and performance-based metrics. For forwards, Yo-Yo IRTL1 correlated significantly with; number of tackles made (r = 0.717), first three players at a ruck in both attack (r = 0.568) and defence (r = 0.581), number of effective rucks (r = 0.630), total possessions (r = 0.522), passes made (r = 0.651), percentage of carries over the gainline (r = 0.610), effective ruck success (r = 0.600), tackle success (r = 0.540), and the number of turnovers made (r = 0.518). Drop jump performance in forwards was associated with; the number of clean breaks (r = 0.558), dominant collisions (r = 0.589), and offloads (r = 0.594). For backs, the sled-drive test correlated with; number of carries (r = -0.751), first three players at an attacking ruck (r = -0.613), effective attacking rucks (r = -0.584), number of dominant collisions (r = -0.792) and offloads (r = -0.814). Likewise, for backs, IMTP peak force was related to; the number of possessions (r = 0.793), passes made (r = 0.792), effective attacking ruck percentage (r = 0.628), and the number of offloads (r = 0.621) whilst relative peak force correlated with; the percentage of carries over the gainline (r = 0.533), percent tackle success (r = 0.603) and effective attacking ruck percentage (r = 0.584). Regression analyses highlighted that only a small number of variables (i.e., carries, tackles, attacking and defensive first three at ruck) returned practically achievable changes (<20%) in physical qualities. In spite of this, and while leaving scope identification of further physical and/or performance predictors, greater strength, power and intermittent running performance were positively related to match-derived KPIs during competition. This may provide a basis for better integrating the strategies used by physical and technical performance-focused coaching staff to improve key performance indicators, and thus match performance, of rugby union players.

27 We aimed to quantify the ACE I/D and ACTN3 R577X (rs1815739) genetic variants in elite rugby 28 athletes (rugby union and league), compare genotype frequencies to controls and between 29 playing positions. The rugby athlete cohort consisted of 507 Caucasian men, including 431 rugby 30 union athletes that for some analyses were divided into backs and forwards and into specific 31 positional groups: front five, back row, half backs, centers and back three. Controls were 710 Caucasian 32 men and women. Real-time PCR of genomic DNA was used to determine genotypes using 33 TaqMan probes and groups were compared using Chi-square and odds ratio (OR) statistics. 34 Correction of p-values for multiple comparisons was according to Benjamini-Hochberg. There 35 was no difference in ACE I/D genotype between groups. ACTN3 XX genotype tended to be 36 underrepresented in rugby union backs (15.7%) compared to forwards (24.8%; P=0.06). 37 Interestingly, the 69 back three players (wings and full backs) in rugby union included only six XX genotype 38 individuals (8.7%), with the R allele more common in the back three (68.8%) than controls (58.0%; χ=6.672, 39 P=0.04; OR=1.60) and forwards (47.5%; χ=11.768, P=0.01; OR=2.00). Association of ACTN3 R577X with 40 playing position in elite rugby union athletes suggests inherited fatigue resistance is more prevalent in 41 forwards while inherited sprint ability is more prevalent in backs, especially wings and full backs. These 42 results also demonstrate the advantage of focusing genetic studies on a large cohort within a single sport, 43 especially when intra-sport positional differences exist, instead of combining several sports with varied 44 demands and athlete characteristics. 45

Background: FTO gene variants have been associated with obesity phenotypes in sedentary and obese populations, but rarely with skeletal muscle and elite athlete phenotypes.Methods: In 1089 participants, comprising 530 elite rugby athletes and 559 non-athletes, DNA was collected and genotyped for the FTO rs9939609 variant using real-time PCR. In a subgroup of non-resistance trained individuals (NT; n = 120), we also assessed structural and functional skeletal muscle phenotypes using dual energy x-ray absorptiometry, ultrasound and isokinetic dynamometry. In a subgroup of rugby athletes (n=77), we assessed muscle power during a countermovement jump.Results: In NT, TT genotype and T allele carriers had greater total body (4.8% and 4.1%) and total appendicular lean mass (LM; 3.0% and 2.1%) compared to AA genotype, with greater arm LM (0.8%) in T allele carriers and leg LM (2.1%) for TT, compared to AA genotype. Furthermore, the T allele was more common (94%) in selected elite rugby union athletes (back three and centre players) who are most reliant on LM rather than total body mass for success, compared to other rugby athletes (82%; P = 0.01, OR = 3.34) and controls (84%; P = 0.03, OR = 2.88). Accordingly, these athletes had greater peak power relative to body mass than other rugby athletes (14%; P = 2 x 10(-6)).Conclusion: Collectively, these results suggest that the T allele is associated with increased LM and elite athletic success. This has implications for athletic populations, as well as conditions characterised by low LM such as sarcopenia and cachexia.

Two common single nucleotide polymorphisms within the COL5A1 gene (SNPs; rs12722 C/T and rs3196378 C/A) have previously been associated with tendon and ligament pathologies. Given the high incidence of tendon and ligament injuries in elite rugby athletes, we hypothesised that both SNPs would be associated with career success.

We aimed to quantify the ACE I/D and ACTN3 R577X (rs1815739) genetic variants in elite rugby athletes (rugby union and league) and compare genotype frequencies to controls and between playing positions. The rugby athlete cohort consisted of 507 Caucasian men, including 431 rugby union athletes that for some analyses were divided into backs and forwards and into specific positional groups: front five, back row, half backs, centers, and back three. Controls were 710 Caucasian men and women. Real-time PCR of genomic DNA was used to determine genotypes using TaqMan probes and groups were compared using χ2and odds ratio (OR) statistics. Correction of P values for multiple comparisons was according to Benjamini-Hochberg. There was no difference in ACE I/D genotype between groups. ACTN3 XX genotype tended to be underrepresented in rugby union backs (15.7%) compared with forwards (24.8%, P = 0.06). Interestingly, the 69 back three players (wings and full backs) in rugby union included only six XX genotype individuals (8.7%), with the R allele more common in the back three (68.8%) than controls (58.0%; χ2= 6.672, P = 0.04; OR = 1.60) and forwards (47.5%; χ2= 11.768, P = 0.01; OR = 2.00). Association of ACTN3 R577X with playing position in elite rugby union athletes suggests inherited fatigue resistance is more prevalent in forwards, while inherited sprint ability is more prevalent in backs, especially wings and full backs. These results also demonstrate the advantage of focusing genetic studies on a large cohort within a single sport, especially when intrasport positional differences exist, instead of combining several sports with varied demands and athlete characteristics.

Abstract Speirs, DE, Bennett, MA, Finn, CV, and Turner, AP. Unilateral vs. bilateral squat training for strength, sprints, and agility in academy rugby players. J Strength Cond Res 30(2): 386–392, 2016—The purpose of this study was to investigate the effects of a 5-week lower-limb unilateral or bilateral strength program on measures of strength, sprinting, and change of direction speed. Eighteen academy rugby players (18.1 ± 0.5 years, 97.4 ± 11.3 kg, 183.7 ± 11.3 cm) were randomly assigned to either a unilateral (UNI) or bilateral (BI) group. The UNI group squatted exclusively with the rear elevated split squat (RESS), whereas the BI group trained only with the bilateral back squat (BS). Both groups trained at a relative percentage of the respective 1 repetition maximum (1RM) twice weekly over a 5-week period. Subjects were assessed at baseline and postintervention for 1RM BS, 1RM RESS, 10-m sprint, 40-m sprint, and pro-agility. There was a significant main effect of time for 1RM BS (F1,16 = 86.5, p < 0.001), ES (0.84 < Cohen d < 0.92), 1RM RESS (F1,16 = 133.0, p < 0.001), ES (0.89 < Cohen d < 0.94), 40-m sprint (F1,16 = 14.4, p = 0.002), ES (0.47 < Cohen d < 0.67) and pro-agility (F1,16 = 55.9, p < 0.001), ES (0.77 < Cohen d < 0.89), but not 10-m sprints (F1,16 = 2.69, p = 0.121), ES (0.14 < Cohen d < 0.38). No significant interactions between group and time were observed for any of the dependent variables. This is the first study to suggest that BI and UNI training interventions may be equally efficacious in improving measures of lower-body strength, 40-m speed, and change of direction in academy level rugby players.