| Literature DB >> 34937977 |
Bo-Young Youn1, Seong-Gyu Ko2, Jee Young Kim3.
Abstract
Each athlete's innate talent is widely recognized as one of the important contributors to achievement in athletic performance, and genetic factors determine a significant portion of talent or traits. Advances in DNA sequencing technology allow us to discover specific genetic variants contributing to these traits in sports performance. The objective of this systematic review is to identify genes that may play a significant role in the performance of elite-level combat sports athletes. Through the review of 18 full-text articles, a total of 109 different polymorphisms were investigated in 14,313 participants (2,786 combat sports athletes, 8,969 non-athlete controls, 2,558 other sports athletes). Thirteen polymorphisms showed a significant difference between elite combat athletes and the control group, and consist of 8 (PPARA rs4253778, ACTN3 rs1815739, ACE rs4646994, CKM rs8111989, MCT1 rs1049434, FTO rs9939609, GABPβ1 rs7181866 and rs8031031) oriented to athletic performance and 5 (COMT rs4680, FEV rs860573, SLC6A2 rs2242446, HTR1B rs11568817, ADRA2A rs521674) focused on psychological traits including emotional and mental traits in combat sports athletes. In addition, a recent whole genome sequencing study identified 4 polymorphisms (KIF27 rs10125715, APC rs518013, TMEM229A rs7783359, LRRN3 rs80054135) associated with reaction time in wrestlers. However, it is not clearly identified which genes are linked explicitly with elite combat sports athletes and how they affect the elite athlete's status or performance in combat sports. Hence, a greater number of candidate genes should be included in future studies to practically utilize the genetic information.Entities:
Keywords: Athletic performance; Combat sports; Genes; Genetic; Phenotype
Year: 2021 PMID: 34937977 PMCID: PMC8670794 DOI: 10.5114/biolsport.2022.102864
Source DB: PubMed Journal: Biol Sport ISSN: 0860-021X Impact factor: 4.606
FIG. 1Flow diagram of the phases of study selection during the search process based on PRISMA
Summary of studies included in this review
| References | Participants | Study design | Genes and SNPs | Results | |
|---|---|---|---|---|---|
| Performance | Cieszczyk et al. 2011. [ | Polish combat athletes (n = 60) and sedentary controls (n = 181) | Case- Control | Higher frequency of the | |
| Kikuchi et al. 2012. [ | Japanese elite wrestlers (n = 135) and college students (n = 333) | Case- Control | The combination of the | ||
| Rodriguez-Romo et al. 2013. [ | Spanish Judo athletes (n = 108) and nonathletic men (n = 343) | Case- Control | No between-groups difference in allele | ||
| Kikuchi et al. 2013. [ | Japanese wrestlers (n = 135) and healthy controls (n = 243) | Case- Control | Lower frequency of the | ||
| Olga et al. 2013. [ | Polish and Russian combat athletes (n = 159) and sedentary individuals (n = 1512) | Case- Control | G allele was significantly higher in combat athletes | ||
| Kikuchi et al. 2017. [ | Wrestlers (n = 199) and controls (n = 649) | Case-control | AA genotype of the | ||
| Ribas et al. 2017. [ | Brazilian combat athletes (n = 37) | Cohort | No difference between athletes and controls | ||
| Itaka et al. 2016. [ | Japanese judo athletes (n = 156) and controls (n = 167) | Case- Control | GG+GA genotype of the | ||
| Guilherme et al. 2017. [ | Brazilian athletes (n = 908; 328 endurance, 415 power, 165 combat) and non-athletes (n = 967) | Case- Control | The power and combat groups showed an inverse genotype distribution for | ||
| Guilherme et al. 2019. [ | Brazilian athletes (n = 677; 323 endurance, 192 power, 162 combat) and non-athletes (n = 652) Russian athletes (n = 920; 347 endurance, 228 power, 254 game, 91 combat) and non-athletes (n = 754) | Case- Control | No differences were found between Russian combat sports athletes and matched non-athletes; Increased frequency of A-allele carriers in the Brazilian combat group. | ||
| Guilherme et al. 2020. [ | Brazilian combat athletes (n = 164) and controls (n = 965) | Case- Control | G-allele in rs7181866 in 4% of the controls compared to 8% of the athletes group or 10.9% of world-class competitors; T-allele in rs8031031 in 4% of the controls compared to 9.5% of the athletes group or 11.9% of world-class competitors | ||
| Pain perception | Leźnicka et al. 2017. [ | Combat athletes (n = 214) and healthy controls (n = 395) | Case- Control | No difference between athletes and controls. | |
| Emotional or mental trait | Tartar et al. 2020. [ | Martial arts fighters (n = 21), athletes (n = 21), and control (n = 41) | Case- Control | Greater GG genotype frequency in martial art fighters | |
| Leźnicka et al. 2018. [ | Combat athletes (n = 199) and healthy controls (n = 165) | Case- Control | homozygous athletes with the G allele (GG) of | ||
| Cherepkova et al. 2019. [ | MMA fighters (n = 107), Criminals (n = 214), controls (n = 425 for | Case- Control | Combination of the | ||
| Michalowska- Sawczyn et al. 2019. [ | Polish combat athletes (n = 200) and healthy controls (n = 102) | Case- Control | No differences of genotype between cases and controls, but associated with openness and conscientiousness | ||
| Peplonska et al. 2019. [ | Elite athletes (n = 621; 212 endurance, 183 power, 226 combat) and sedentary controls (n = 672) | Case-control | AG genotype in | ||
| Psychological | Boulygina et al. 2020.23 | Tatar wrestlers (n = 20), athletes (n = 283; 101 boxing, 82 wrestling, 21 karate, 24 taekwondo, 45 volleyball, 10 table tennis), controls (n = 189) | Case-control | Whole genome sequencing | 4 alleles ( |
SNP, single nucleotide polymorphism
Allele frequencies of the single nucleotide polymorphism identified by comparison between elite combat sports athletes and controls.
| References | Candidate allele or genotype | Frequency in elite combat athletes | Frequency in controls | P-value |
|---|---|---|---|---|
| Cieszczyk et al. 2011. [ |
| 82.50% | 70.17% | 0.01 |
| Kikuchi et al. 2012. [ | 53% | 49% | 0.346 | |
|
| 65% | 48% | < 0.01 | |
| Rodriguez-Romo et al. 2013. [ | 49.6% | 56.4% | 0.077 | |
| Kikuchi et al. 2013.[ |
| 11% | 29% | 0.019 |
| Olga et al. 2013. [ |
| 41.2% | 35.6% | 0.047 |
| Kikuchi et al. 2017. [ |
| 53% | 45% | 0.037 |
| Itaka et al. 2016. [ | 87.2% | 81.4% | 0.16 | |
| 66.7% | 73.7% | 0.06 | ||
| Guilherme et al. 2019. [ |
| 44.1% | 37.2% | 0.025 |
| Guilherme et al. 2020. [ |
| 8% | 4% | 0.003 |
|
| 9.5% | 4% | 0.002 | |
| Leźnicka et al. 2017. [ | 51.9% | 48.1% | 0.286 | |
| 91.1% | 91.2% | 0.984 | ||
| Tartar et al. 2020. [ |
| 52.4% | 19.5% | 0.003 |
| Leźnicka et al. 2018. [ | 52.01% | 49.09% | 0.43 | |
| 91.46% | 91.21% | 0.91 | ||
| Michalowska-Sawczyn et al. 2019. [ | 78% | 80% | 0.413 | |
| Peplonska et al. 2019. [ |
| 3.18% | 7.44% | 0.025 |
|
| 27.43% | 31.34% | 0.011 | |
|
| 46.02% | 40.13% | 0.028 | |
|
| 25.11% | 20.94% | 0.0397 |
P-value < 0.05