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Review

Genes and Athletic Performance: The 2023 Update

by
Ekaterina A. Semenova
1,2,
Elliott C. R. Hall
3 and
Ildus I. Ahmetov
4,5,6,7,*
1
Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, 119435 Moscow, Russia
2
Research Institute of Physical Culture and Sport, Volga Region State University of Physical Culture, Sport and Tourism, 420138 Kazan, Russia
3
Faculty of Health Sciences and Sport, University of Stirling, Stirling FK9 4UA, UK
4
Laboratory of Genetics of Aging and Longevity, Kazan State Medical University, 420012 Kazan, Russia
5
Sports Genetics Laboratory, St Petersburg Research Institute of Physical Culture, 191040 St. Petersburg, Russia
6
Department of Physical Education, Plekhanov Russian University of Economics, 115093 Moscow, Russia
7
Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool L3 5AF, UK
*
Author to whom correspondence should be addressed.
Genes 2023, 14(6), 1235; https://doi.org/10.3390/genes14061235
Submission received: 19 May 2023 / Revised: 5 June 2023 / Accepted: 7 June 2023 / Published: 8 June 2023
(This article belongs to the Special Issue Feature Papers: Molecular Genetics and Genomics 2023)
Browse Figures
Versions Notes

Abstract

:
Phenotypes of athletic performance and exercise capacity are complex traits influenced by both genetic and environmental factors. This update on the panel of genetic markers (DNA polymorphisms) associated with athlete status summarises recent advances in sports genomics research, including findings from candidate gene and genome-wide association (GWAS) studies, meta-analyses, and findings involving larger-scale initiatives such as the UK Biobank. As of the end of May 2023, a total of 251 DNA polymorphisms have been associated with athlete status, of which 128 genetic markers were positively associated with athlete status in at least two studies (41 endurance-related, 45 power-related, and 42 strength-related). The most promising genetic markers include the AMPD1 rs17602729 C, CDKN1A rs236448 A, HFE rs1799945 G, MYBPC3 rs1052373 G, NFIA-AS2 rs1572312 C, PPARA rs4253778 G, and PPARGC1A rs8192678 G alleles for endurance; ACTN3 rs1815739 C, AMPD1 rs17602729 C, CDKN1A rs236448 C, CPNE5 rs3213537 G, GALNTL6 rs558129 T, IGF2 rs680 G, IGSF3 rs699785 A, NOS3 rs2070744 T, and TRHR rs7832552 T alleles for power; and ACTN3 rs1815739 C, AR ≥21 CAG repeats, LRPPRC rs10186876 A, MMS22L rs9320823 T, PHACTR1 rs6905419 C, and PPARG rs1801282 G alleles for strength. It should be appreciated, however, that elite performance still cannot be predicted well using only genetic testing.

1. Introduction

Athletic success is influenced by many genetically determined factors, including transcriptomic, biochemical, histological, anthropometric, physiological, and psychological traits, as well as general health status [1,2,3,4,5,6,7,8]. On average, 66% of the variance in athlete status can be explained by genetic factors [9]. The remaining variance is due to environmental factors, such as deliberate practice, nutrition, ergogenic aids, birthplace, the availability of medical and social support, and even luck (e.g., birthdate) [6,10,11,12].
Starting in the late 1990s, research began to identify DNA polymorphisms associated with predisposition to certain types of sports and exercise-related phenotypes, with initial focus on variants of the ACE, ACTN3, AMPD1, PPARA, PPARD, and PPARGC1A genes [13,14,15,16,17,18,19,20,21,22,23]. Initially, most research was conducted using the candidate gene approach [24,25,26,27,28,29], which limited progress in the discovery of new genetic markers associated with exercise- and sport-related phenotypes [30]. In addition to the fact that this approach studies only a single genetic variant in isolation, most candidate gene studies in the field of sports genomics are limited by sample size. This is a potential source of type I error (false positive findings), underpinning why replication of positive associations in independent cohorts is essential. Conversely, the genome-wide association approach is considered the most efficient study design thus far in identifying genetic markers associated with sport-related characteristics. Indeed, the application of this approach has enabled the discovery of hundreds of single nucleotide polymorphisms (SNPs) directly or indirectly associated with exercise and sport, such as height (12,111 SNPs) [31], appendicular lean mass (1059 SNPs) [32], testosterone levels (855 SNPs) [33], handgrip strength (170 SNPs) [34,35,36], sarcopenia (78 SNPs) [37], and brisk walking (70 SNPs) [38].
It is important to consider that the discovery of 12,111 independent genome-wide (p < 5 × 10−8) significant SNPs associated with height (which in combination account for 40% of phenotypic variance) required the study of 5.4 million individuals [31]. Accordingly, such findings indicate that each DNA locus is likely to explain only a very small proportion of the reported phenotypic variance (~0.0033%). This also suggests that very large samples of athletes and non-athletic controls are needed to detect a substantial number of genetic markers associated with athlete status, with some suggesting those markers be used as part of talent identification strategies. To date, five genome-wide association (GWAS) studies [39,40,41,42,43], one whole-genome sequencing (WGS) study [44], and one exome-wide association (EWAS) study [45] involving athletic cohorts have been conducted. However, due to their limited sample sizes, these investigations and subsequent replication studies have resulted in the identification of only 13 genetic markers. Six markers were associated with endurance athlete status (CDKN1A rs236448 A, GALNTL6 rs558129 C, NFIA-AS2 rs1572312 C, MYBPC3 rs1052373 G, SIRT1 rs41299232 G, and TRPM2 rs1785440 G alleles) [39,40,42,43,45,46], five with sprinter/strength athlete status (AGRN rs4074992 C, CDKN1A rs236448 C, CPNE5 rs3213537 G, NUP210 rs2280084 A, and GALNTL6 rs558129 T alleles) [41,45,47,48], and two with reaction time in wrestlers and athlete status in combat/team sports (APC rs518013 A and LRRN3 rs80054135 T alleles, respectively) [44].
To overcome the issue of small samples of elite athletes, the use of genome-wide significant (p < 5 × 10−8) markers of exercise-related phenotypes (correlated with sport-related traits) discovered in large cohorts of untrained subjects (for example, UK Biobank, FinnGen, BioBank Japan, etc.) was proposed [49,50]. This approach may significantly decrease the risk of obtaining false-positive results in the discovery of markers associated with athlete status, which is recognized as a key limitation of sports genomics studies conducted on limited sample sizes. Accordingly, the first stage of such an investigation is to list candidate genetic markers previously identified in GWAS of exercise-related phenotypes in non-athletic populations. For example, this may include SNPs associated with phenotypes of handgrip strength, brisk walking, physical activity, lean mass, forced vital capacity, haemoglobin, or levels of hormones such as testosterone and IGF1. At the second stage, microarray analysis (with imputation; covering > 10 million SNPs) is carried out in a group of athletes to test the hypothesis that favourable alleles of exercise-related phenotypes are over-represented in those athletes compared to non-athletic controls (Figure 1). This approach may equally be applied to determine whether favourable alleles could be associated with direct measures of athletic capability, such as weightlifting performance, running times, or scores recorded during competitive events. In this case, the significance threshold for the second stage is set at p < 0.05 (to reduce the likelihood of potentially important findings being overlooked).
A recent example of such an approach being implemented is the study by Guilherme et al. [49], which investigated two correlated phenotypes: brisk walking pace (using UK Biobank participants) and sprint athlete status (using elite Russian sprinters). Brisk walkers perform more physical activity, are taller, have reduced adiposity and demonstrate greater physical performance and strength versus slower walkers, with such traits also recorded more commonly in sprinters than other athletes of other disciplines. Therefore, it was hypothesized that the alleles associated with high-speed walking (discovered in untrained subjects) would also be over-represented in elite sprinters. Accordingly, 70 genetic markers of brisk walking were identified from the literature [38], of which 15 SNPs had a significantly different allele frequency when comparing sprinters with non-athletic controls [49]. The same innovative approach later identified 23 SNPs associated with strength athlete status [51] based on genome-wide significant markers for handgrip strength in a non-athletic population using the UK Biobank [34,35]. Furthermore, using a panel of 822 testosterone-related SNPs from the UK Biobank study [33], five DNA-polymorphisms associated with muscle fibre size and weightlifting performance were identified [50].
Another approach that has proven effective in addressing the possibility of false positive results in sports genomics literature is to perform replication studies in two or more independent athletic cohorts (even with small or moderate sample sizes), followed by a meta-analysis to quantify the overall effect of a polymorphism on athlete status and/or a sport- and exercise-related trait [43,52,53,54,55,56,57,58,59,60,61,62,63,64,65]. However, in some cases, replication is not possible due to the exclusivity of a polymorphism to specific populations based on their geographic ancestry. For example, the rs671 G/A polymorphism of the aldehyde dehydrogenase 2 (ALDH2) gene was associated with strength in athletes and non-athletes from the Japanese population [66,67,68]. Interestingly, the unfavourable (associated with reduced strength) rs671 A allele is not present in Europeans or South Asians (frequency 0%), but common in Chinese, Japanese, and Vietnamese populations (15–25%). This demonstrates a notable challenge seeking to replicate genomic findings in larger samples, as increasing the study sample must also consider the geographic ancestry of participants. This also highlights the possibility that the genetic determinants of some sport- and exercise-related phenotypes are restricted to certain populations, demonstrating that increasing sample size is not as straightforward as simply recruiting participants from multiple countries and/or continents.
As well as the phenotypes of athlete status or competitive performance, several recent studies have investigated a broader range of traits which may relate directly or indirectly to athletic capability. These include flexibility, coordination, cardiorespiratory fitness, spatial ability, stress resilience, mental toughness, fat loss efficiency, and cardiovascular and metabolic responses to training, amongst others [69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84]. For example, combat athletes are more likely than untrained subjects to have the warrior (COMT rs4680 GG) genotype [85], whilst chess players demonstrate an increased frequency of an allele linked to improved memory and spatial ability (KIBRA rs17070145 T) [86]. Such discoveries demonstrate the broadening nature of sports genomics in recent times, with focus expanding from the traditional domain of investigating what makes elite performers different from the general population into other domains, such as sports nutrigenetics [87,88,89,90,91,92,93,94,95,96,97,98,99,100] and areas of sports medicine, such as genomic variants associated with soft-tissue injuries and sports-related concussion [101,102,103,104,105,106,107,108,109,110,111,112,113,114].
Technological advancement has lowered the cost of conducting genomic studies, increasing accessibility to researchers who wish to investigate the genetic underpinnings of sport and exercise phenotypes. Consequently, sports genomics is a dynamic and continually developing field, making it important to regularly appraise the contribution of recent advances to the field. Therefore, the aim of the current review was to summarise recent progress in understanding the genetic determinants of athlete status, and to detail novel DNA polymorphisms that may underpin differences between individuals in their athletic potential.
At the time of writing (end of May 2023), the total number of DNA polymorphisms associated with athletic performance since the first discovery in 1998 is 251 (Figure 2). Our search for sports genomics publications was based on journals indexed in major databases (i.e., PubMed etc.) using specific key words (e.g., athletes + polymorphism/genotype etc.). However, not all articles were included in the current review due to language limitations (articles written in languages other than English must contain at least abstracts in English). In addition, papers with very small cohort (less than 25 in athletes/controls), or articles with combined groups of athletes (for example, endurance + power without separation) were not included. Abstracts of conference proceedings were not considered. In recognition of the fact that many studies in the field of sports genomics report associations based on the investigation of small sample sizes, we stipulated that only markers where statistically significant associations have been reported in at least two studies (two case-control studies and/or one case-control plus one functional study; including those presented in one article) would be included in the present review.
According to these criteria, 128 markers could be associated with athlete status (41 endurance-related, 45 power-related, and 42 strength-related) from the original 251 identified in our literature search. The most promising genetic markers (i.e., most replicated and had fewer negative or controversial findings) include AMPD1 rs17602729 C, CDKN1A rs236448 A, HFE rs1799945 G, MYBPC3 rs1052373 G, NFIA-AS2 rs1572312 C, PPARA rs4253778 G, and PPARGC1A rs8192678 G alleles for endurance; ACTN3 rs1815739 C, AMPD1 rs17602729 C, CDKN1A rs236448 C, CPNE5 rs3213537 G, GALNTL6 rs558129 T, IGF2 rs680 G, IGSF3 rs699785 A, NOS3 rs2070744 T, and TRHR rs7832552 T alleles for power; and ACTN3 rs1815739 C, AR ≥ 21 CAG repeats, LRPPRC rs10186876 A, MMS22L rs9320823 T, PHACTR1 rs6905419 C, and PPARG rs1801282 G alleles for strength. This update on the panel of genetic markers associated with athlete status covers advances in research reported in the past two years (previous online version was published in 2021 [115]). The current review also lists all known markers associated with endurance, power, or strength athlete status/performance. This article does not aim to review genetic markers associated with team (game) and combat sports, markers for which are well described elsewhere [26,61,116,117].

2. Gene Variants for Endurance Athlete Status

An individual’s endurance capacity is determined by many factors, including their muscle fibre typology, haemoglobin mass, mitochondrial biogenesis, maximal cardiac output, and maximal rate of oxygen consumption (VO2max), among others [118,119,120,121,122,123,124]. Indeed, there is evidence that these intermediate phenotypes have a substantial genetic influence, with literature indicating that genetic factors account for up to 70% of the variability in endurance-related traits [125]. Usually, genetic markers associated with endurance athlete status are determined by comparing allelic frequencies between endurance athletes (e.g., biathletes, road cyclists etc.) and controls.
To support the observed findings from endurance-related case-control studies, researchers subsequently perform functional, lab-based studies to determine the relationship between genotypes and physiological measures. Examples of measurements used to complement genomic studies include (but are not limited to) VO2max, forced expiratory volume in one second (FEV1), proportion of slow-twitch muscle fibres, recovery speed, long-distance running performance, running economy, lactate threshold, erythropoietin and haemoglobin levels, number of erythrocytes, capillary density, mitochondrial density, fat metabolism, and fatigue resistance.
Our literature search revealed that at least 41 of the 114 reported markers could be associated with endurance athlete status based on our criteria (Table 1). The most promising genetic markers for endurance athlete status include AMPD1 rs17602729 C, CDKN1A rs236448 A, HFE rs1799945 G, MYBPC3 rs1052373 G, NFIA-AS2 rs1572312 C, PPARA rs4253778 G, and PPARGC1A rs8192678 G alleles. In contrast, the other 73 markers (endurance alleles) have not passed our strict criteria: ACOXL rs13027870 G, ADRA2A 6.7kb, ADRA2A rs1800544 G, ADRB1 rs1801252 G, AGT rs699 A, BDKRB2 rs1799722 T, CAMK1D rs11257754 A, CHRNB3 rs4950 G, CLSTN2 rs2194938 A, CNDP2 rs6566810 A, COL5A1 rs71746744 AGGG, COL6A1 rs35796750 T, CPQ rs6468527 A, CYP2D6 rs3892097 G, DMT1 258 bp, EPAS1 (HIF2A) rs1867785 G, EPAS1 (HIF2A) rs11689011 T, GABPB1 rs8031031 T, GALM rs3821023 A, GNB3 rs5443 T, GRM3 rs724225 G, GSTT rs17856199 (+), IGF1R rs1464430 A, IL6 rs1800795 C, IL15RA rs2228059 A, ITPR1 rs1038639 T, ITPR1 rs2131458 T, FMNL2 rs12693407 G, KCNJ11 rs5219 C, L3MBTL4 rs17483463 T, MSTN rs11333758 D, MtDNA loci (G1, HV, L0, M*, m.11215T, m.152C, m.15518T, m.15874G, m.4343G, m.514(CA) ≤ 4, poly(C ≥ 7) stretch at m.568–573, m.16080G, m.5178C, N9, V, unfavourable: B, J2, T, L3*), NALCN-AS1 rs4772341 A, NACC2 rs4409473 C, NATD1 rs732928 G, NOS3 rs1799983 G, NOS3 (CA)n 164bp, NOS3 27bp 4B, PPARD rs2016520 C, PPARD rs1053049 T, PPARGC1A rs4697425 A, PPARGC1B rs11959820 A, PPP3CA rs3804358 C, PPP3CB rs3763679 C, SGMS1 rs884880 A, SLC2A4 rs5418 A, SOD2 rs4880 C, SPOCK1 rs1051854 T, TPK1 rs10275875 T, TTN rs10497520 T, Y-chromosome haplogroups (E*, E3*, and K*(xP); unfavourable: E3b1), and ZNF429 rs1984771 G. Most of these markers have been described in previous reviews [27,126,127] but cannot be included in our current list of endurance-associated markers until they are validated through replication by additional studies.

3. Gene Variants for Power Athlete Status

Several characteristics are positively associated with power performance, including circulating levels of testosterone, percentage and cross-sectional area of fast-twitch muscle fibres, muscle mass and strength, body and calcaneus height, muscle fascicle length, and reaction time, among others [3,238,239,240,241,242,243,244]. The heritability of power-related phenotypes has been reported in the literature to range from approximately 49 to 86% in a range of phenotypes, including jumping ability [245,246]. Typically, genetic markers associated with power athlete status are determined by comparing allelic frequencies between power athletes (e.g., 100 m runners, shot putters, arm wrestlers, etc.) and untrained subjects. To support findings from case-control studies, investigators perform genotype–phenotype studies by measuring sprint times, jump performance, muscle fibre size, muscle fibre typology, maximal strength, rate of force development, and circulatory levels of anabolic hormones such as testosterone. Our literature search revealed that at least 45 of the 95 markers reportedly associated with power athlete status met our new criteria (Table 2). The most promising of these genetic markers associated with power athlete status currently include ACTN3 rs1815739 C, AMPD1 rs17602729 C, CDKN1A rs236448 C, CPNE5 rs3213537 G, GALNTL6 rs558129 T, IGF2 rs680 G, IGSF3 rs699785 A, NOS3 rs2070744 T, and TRHR rs7832552 T alleles. In contrast, the remaining 50 genetic markers (power alleles) did not meet our strict criteria: ARHGEF28 rs17664695 G, CACNG1 rs1799938 A, CALCR rs17734766 G, CLSTN2 rs2194938 C, CNDP1 rs2887 A, CNDP1 rs2346061 C, CNDP2 rs3764509 G, COTL1 rs7458 T, CREM rs1531550 A, DMD rs939787 T, EPAS1 (HIF2A) rs1867785 G, EPAS1 (HIF2A) rs11689011 C, FOCAD rs17759424 C, GABRR1 rs282114 A, GALNT13 rs10196189 G, GPC5 rs852918 T, IGF1R rs1464430 C, IL1RN rs2234663 *2, IP6K3 rs6942022 C, MCT1 rs1049434 A, MED4 rs7337521 T, MPRIP rs6502557 A, MtDNA loci (favourable: F, m.204C, m.151T, m.15314A, Non-L/U6, unfavourable: m.16278T, m.5601T, m.4833G, m.5108C, m.7600A, m.9377G, m.13563G, m.14200C, m.14569A), MTR rs1805087 G, MTRR rs1801394 G, NOS3 rs1799983 G, NRG1 rs17721043 A, PPARGC1A rs8192678 A, PPARGC1B rs10060424 C, RC3H1 rs767053 G, SLC6A2 rs1805065 C, SUCLA2 rs10397 A, TPK1 rs10275875 C, UCP2 rs660339 C, VEGFR2 rs1870377 T, WAPL rs4934207 C, and ZNF423 rs11865138 C. The majority of these markers are reported in previous reviews [25,126,127] and should be validated in additional studies before they can meet the criteria to be included in our list of power-associated genetic variants.

4. Gene Variants for Strength Athlete Status

Performance in strength-based sports is based on multiple factors. However, the factors considered to contribute substantially to strength phenotypes include skeletal muscle hypertrophy (muscle fibre size), hyperplasia, the predominance of fast-twitch muscle fibres, a greater muscle fascicle pennation angle, improved neurological adaptation, high glycolytic capacity, and increased circulatory testosterone [297]. Importantly, evidence exists that strength athletes exhibit vastly different transcriptomic, biochemical, anthropometric, physiological, and biomechanical characteristics compared to endurance athletes and/or controls [1,4]. These differences can be explained by the presence of both deliberate environmental (training, nutrition, etc.) and genetic factors. Indeed, studies indicate that there is a strong heritability of power- and strength-related traits, where genetic factors account for up to 85% of the variation in maximal isometric, isotonic, and isokinetic strength [246]. In a recent study investigating the genetic component of severe sarcopenia (the age-related decline in skeletal muscle mass, strength, and gait speed) [37], it was found that the alleles associated with higher risk of severe sarcopenia were closely linked to tiredness, alcohol intake, smoking, time spent watching television, and a higher self-reported consumption of salt and processed meat. In contrast, alleles associated with lower risk of severe sarcopenia were positively associated with levels of serum testosterone, IGF1, and 25-hydroxyvitamin D; height; physical activity; as well as indicators of healthier dietary habits (self-reported intake of cereal, cheese, oily fish, protein, water, fruit, and vegetables). Whilst muscle strength phenotypes in the general population may be less pronounced than in strength athletes, the latter may represent an ideal population to identify genomic variants associated with skeletal muscle capacity, potentially aiding the advancement of knowledge surrounding sarcopenia and directing strategies to reduce the negative impact of age-related declines in muscle mass. In general, genetic markers associated with strength athlete status can be determined by comparing allelic frequencies between strength athletes and controls. To support these findings, scientists perform genotype–phenotype studies by measuring handgrip and isokinetic strength, powerlifting/weightlifting performance, as well as evaluating the acute and chronic responses to resistance training.
Previously, 170 DNA polymorphisms were reported to be associated with handgrip strength in three large GWASs [34,35,36]. In a follow-up study involving elite weightlifters and powerlifters, Moreland et al. [51] tested the hypothesis that alleles associated with greater handgrip strength would be over-represented in these athletes compared to controls. Accordingly, they identified 23 DNA polymorphisms that were associated with strength athlete status. Of these SNPs, the LRPPRC rs10186876, MMS22L rs9320823, and PHACTR1 rs6905419 polymorphisms were also associated with superior competitive weightlifting performance [298].
Our literature search based on our new inclusion criteria revealed at that least 42 genetic markers could be associated with strength athlete status (Table 3). The most promising genetic markers for strength athlete status include ACTN3 rs1815739 C, AR ≥ 21 CAG repeats, LRPPRC rs10186876 A, MMS22L rs9320823 T, PHACTR1 rs6905419 C, and PPARG rs1801282 G alleles.

5. Conclusions

The current review demonstrates that at least 251 genetic markers are reportedly linked to sport-related traits. However, only 128 (51%) of these markers (41 endurance-related, 45 power-related, and 42 strength-related) have been associated with athlete status in two or more studies. On the other hand, of these 128 genetic markers, the significance of 29 (22.7%) DNA polymorphisms was not replicated in at least one study, raising the possibility that a number of findings may represent false positives. It is important to consider that there may be one of several reasons why the findings of a study may not be replicated by another, including disparity of sample sizes, small sample sizes in one or more of the studies, different study designs, inconsistent classification of sporting groups or types of sport (strength, power, etc.), variability in how researchers or research groups define the term “elite athletes” (some researchers define the term “elite” as performances at the international level, others if the athlete is a prize winner in international competitions), and the ethnicity/geographical ancestry of the cohorts studied, amongst others.
As discussed previously, height remains not only the most studied exercise-related phenotype at the genetic level, but also the most studied human trait, with 12,111 associated SNPs [31]. It is estimated that the final number of height-related SNPs may reach 25,000 (with a minor allele frequency of ≥1%), but the sample size needs to be increased to approximately 100 million individuals of the same ethnicity. These values should be noteworthy and serve as a benchmark for the direction of future research in the field of sports genomics, where the current number of 251 genetic markers must be increased by a considerable magnitude in order to fully comprehend the genomic underpinnings of exercise performance, and thus to be considered as potential predictors of talent in sport. Given that effective talent identification remains a challenging task despite decades of research and strategy [322,323,324], it remains possible that the development of predictive genetic performance tests in future may be able to contribute to the advancement of this field. However, the literature currently available does not support the use of genetic testing for these purposes [325,326,327].
Whilst genomics is the among the most established molecular sub-disciplines of sport and exercise research, sport- and exercise-related DNA polymorphisms do not fully explain the heritability of athlete status. Consequently, other forms of variation, such as rare mutations [328,329] and epigenetic markers (i.e., stable and heritable changes in gene expression) [330], must be considered. Newly emerging high-throughput technologies enable the design of multi-omics approaches integrating various -omics levels (metabolomics, transcriptomics, proteomics, epigenomics, etc.) with the aim of determining how each level contributes to the biological mechanisms underpinning physical performance. For example, transcriptomic analyses have revealed the roles of both genomic and epigenomic mechanisms in modulating the transcription of genes regulated by exercise [2,331,332]. Incorporating multi-omics approaches has the potential to drastically advance the understanding of how the acute response to exercise is regulated, and consequently how chronic adaptations to exercise are mediated in the context of elite performance and/or health and wellbeing. Accordingly, future research, including collaborative multicentre GWASs and whole-genome sequencing of large athlete cohorts with further validation and replication, as well as the use of large purpose-built Biobanks, should focus on identifying genetic and other -omics markers of sport-related phenotypes and their underlying biology.
Our review does have limitations. First, we have not provided information regarding genetic markers associated with team (game) and combat sports, flexibility, coordination, personality, cognitive abilities, muscle fibre composition, skeletal muscle hypertrophy, injuries, and responses to training/supplements. These markers are well described elsewhere [4,24,26,28,37,61,74,79,115,116,117,333,334]. Second, we have not described all studies in detail (ethnicity, specific sporting disciplines, sample size, p-values etc.) given word limit. Third, some genetic markers (out of the 128 most significant) were selected based on data obtained in case-control studies only, without confirmation of functional significance (genotype–phenotype studies are therefore warranted).
In conclusion, our literature search revealed at least 251 DNA polymorphisms that could be associated with endurance, power, and strength athlete statuses. Most of these genetic markers have been discovered in studies involving Australian, Brazilian, British, Canadian, Chinese, Croatian, Czech, Ethiopian, Finnish, French, German, Greek, Hungarian, Indian, Iranian, Israeli, Italian, Jamaican, Japanese, Kenyan, Korean, Lithuanian, Polish, Qatari, Russian, Slovenian, South African, Spanish, Taiwanese, Tatar, Tunisian, Turkish, Ukrainian, and US athletes.

Author Contributions

Conceptualization, I.I.A.; formal analysis, E.A.S. and I.I.A.; writing—original draft preparation, E.A.S. and I.I.A.; writing—review and editing, E.C.R.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Genes 14 01235 g001 550
Figure 1. Case-control study designs in sports genomics. In this approach, allelic frequencies are compared between athletes and controls (e.g., endurance athletes vs. untrained subjects or endurance vs. power athletes). A case-control study may be the first step followed by a genotype–phenotype study (e.g., identification of VO2max or weightlifting performance-increasing genotypes among athletes). In some cases, studies begin with a genotype–phenotype approach, and the findings are subsequently validated by a case-control study.
Figure 1. Case-control study designs in sports genomics. In this approach, allelic frequencies are compared between athletes and controls (e.g., endurance athletes vs. untrained subjects or endurance vs. power athletes). A case-control study may be the first step followed by a genotype–phenotype study (e.g., identification of VO2max or weightlifting performance-increasing genotypes among athletes). In some cases, studies begin with a genotype–phenotype approach, and the findings are subsequently validated by a case-control study.
Genes 14 01235 g001
Genes 14 01235 g002 550
Figure 2. Sports-related genetic markers discovered between 1998 and 2023.
Figure 2. Sports-related genetic markers discovered between 1998 and 2023.
Genes 14 01235 g002
Table
Table 1. Genetic markers for endurance athlete status.
Table 1. Genetic markers for endurance athlete status.
GeneFull NameLocusPolymorphismEndurance-Related AlleleReferences
Studies with Positive ResultsStudies with Negative or Controversial Results
ACEAngiotensin I converting enzyme17q23.3Alu I/D (rs4343 A/G or rs4341 C/G)I (A or C)[14,15,16,128,129,130,131,132,133,134,135,136,137,138,139,140,141][133,142,143,144,145,146,147,148,149,150,151,152,153]
ACTN3Actinin α 311q13.1rs1815739 C/TT[17,154,155,156][152,157,158,159,160,161,162,163,164,165,166,167,168,169,170]
ADRB2Adrenoceptor β 25q31-q32rs1042713 G/AA[160,171,172][173,174]
ADRB2Adrenoceptor β 25q31-q32rs1042714 G/CC[153,175][173,174]
ADRB3Adrenoceptor β 38p11.23rs4994 A/GG[170,173]
AGTR2Angiotensin II receptor type 2Xq22-q23rs11091046 A/CC[176][177]
AQP1Aquaporin 17p14rs1049305 C/GC[178,179,180]
AMPD1Adenosine monophosphate deaminase 11p13rs17602729 C/TC[19,153,181,182,183][184]
BDKRB2Bradykinin receptor B214q32.1-q32.2+9/−9 (exon 1)–9[185,186][153,187,188,189]
CDKN1ACyclin Dependent Kinase Inhibitor 1A6p21.2rs236448 A/CA[43]
CKMCreatine kinase M-type19q13.32rs8111989 A/G A[190,191,192][133,193]
COL5A1Collagen type V α 1 chain9q34.2-q34.3rs12722 C/TT[194,195]
FTOFTO α-Ketoglutarate Dependent Dioxygenase16q12.2rs9939609 T/AT[196,197][198]
GABPB1GA binding protein transcription factor subunit β 115q21.2rs12594956 A/CA[199,200]
rs7181866 A/GG[199,201][200]
GALNTL6Polypeptide N-acetylgalactosaminyltransferase 64q34.1rs558129 T/CC[40]
GSTP1Glutathione S-transferase Pi 111q13.2rs1695 A/GG[202,203]
HFEHomeostatic iron regulator6p21.3rs1799945 C/GG[153,204,205,206,207]
HIF1AHypoxia inducible factor 1 subunit α14q23.2rs11549465 C/TC[208,209][144,210]
MCT1Monocarboxylate transporter 11p12rs1049434 A/TT[60,211,212,213,214][215]
MtDNA lociMitochondrial DNAMtDNAMtDNA haplogroupsH[161,216]
Unfavourable: K[161,216]
MYBPC3Myosin Binding Protein C311p11.2rs1052373 A/GG[42]
NFATC4Nuclear factor of activated T cells 414q11.2rs2229309 G/CG[144]
NFIA-AS2NFIA antisense RNA 21p31.3rs1572312 C/AC[39,46]
NOS3Nitric oxide synthase 37q36rs2070744 T/CT[153,217,218][219]
PPARAPeroxisome proliferator activated receptor α22q13.31rs4253778 G/CG[20,220,221,222]
PPARGC1APeroxisome proliferative activated receptor, γ, coactivator 1 α4p15.1rs8192678 G/AG[18,20,170,223][216,224,225]
PPARGC1BPeroxisome proliferative activated receptor, γ, coactivator 1 β5q32rs7732671 G/CC[144,226]
PPP3R1Protein phosphatase 3 regulatory subunit B, α2p15Promoter 5I/5D5I[144,227]
PRDM1PR/SET Domain 16q21rs10499043 C/TT[228,229]
RBFOX1RNA binding fox-1 homolog 116p13.3rs7191721 G/AG[39]
SIRT1Sirtuin 110q21.3rs41299232 C/GG[45]
SPEGStriated Muscle Enriched Protein Kinase2q35rs7564856 G/AG[230]
TFAMTranscription factor A, mitochondrial10q21rs1937 G/CC[144,231] [216]
TRPM2Transient Receptor Potential Cation Channel Subfamily M Member 221q22.3rs1785440 A/GG[45]
TSHRThyroid stimulating hormone receptor14q31rs7144481 T/CC[39]
UCP2Uncoupling protein 211q13rs660339 C/TT[130,144,232]
UCP3Uncoupling Protein 311q13rs1800849 C/TT[130,144][233]
VEGFAVascular endothelial growth factor A6p12rs2010963 G/CC[144,234,235]
VEGFR2Vascular endothelial growth factor receptor 24q11-q12rs1870377 T/AA[236,237]
Table
Table 2. Genetic markers for power athlete status.
Table 2. Genetic markers for power athlete status.
GeneFull NameLocusPolymorphismPower-Related AlleleReferences
Studies with Positive ResultsStudies with Negative or Controversial Results
ACEAngiotensin I converting enzyme17q23.3Alu I/D (rs4343 A/G or rs4341 C/G)D (G)[16,128,145,169,247,248,249,250][150,251,252,253,254]
ACVR1BActivin A type IB receptor12q13.13rs2854464 A/GA[255,256][256,257]
ACTN3Actinin α 311q13.1rs1815739 C/TC [17,161,162,168,170,250,258,259,260,261,262,263,264][159,165,254,265,266]
ADAM15ADAM Metallopeptidase Domain 151q21.3rs11264302 G/AG[49]
ADRB2Adrenoceptor β 25q31-q32rs1042713 G/AG [41,174]
rs1042714 C/GG [41,174]
AGRNAgrin1p36.33rs4074992 C/TC[45]
AGTAngiotensinogen1q42.2rs699 T/CC [41,267,268]
AGTR2Angiotensin II receptor type 2Xq22-q23rs11091046 A/CA[176,177][55]
AKAP6A-Kinase Anchoring Protein 614q12rs12883788 C/TC[49]
AMPD1Adenosine monophosphate deaminase 11p13rs17602729 C/TC [184,269,270]
AUTS2Activator of Transcription and Developmental Regulator AUTS27q11.22rs10452738 A/GA[49]
BDNFBrain derived neurotrophic factor11p14.1rs10501089 G/AA[271]
CCT3Chaperonin Containing TCP1 Subunit 31q22rs11548200 T/CT[49]
CDKN1ACyclin Dependent Kinase Inhibitor 1A6p21.2rs236448 A/CC[43]
CKMCreatine kinase, M-type19q13.32rs8111989 A/GG[65,272,273][274]
CNTFRCiliary neurotrophic factor receptor9p13.3rs41274853 C/TT[275]
CPNE5Copine V6p21.2rs3213537 G/AG[41,48]
CRTAC1Cartilage Acidic Protein 110q24.2rs2439823 A/GA[49]
CRTC1CREB Regulated Transcription Coactivator 119p13.11rs11881338 T/AA[49]
E2F3E2F Transcription Factor 36p22.3rs4134943 C/TT[49]
FHL2Four and a Half LIM Domains 22q12.2rs55680124 C/TC[49]
GALNTL6Polypeptide N-acetylgalactosaminyltransferase like 64q34.1rs558129 C/TT[47,276]
GDF5Growth Differentiation Factor 520q11.22rs143384 A/GG[49]
HIF1AHypoxia inducible factor 1 α subunit14q21-q24rs11549465 C/TT [277,278,279]
HSD17B14Hydroxysteroid 17-β dehydrogenase 1419q13.33rs7247312 A/GG[41]
IGF1Insulin like growth factor 112q23.2rs35767 C/TT[280,281]
IGF2Insulin like growth factor 211p15.5rs680 A/GG [41,281,282]
IGSF3Immunoglobulin Superfamily Member 31p13.1rs699785 G/AA[49]
IL6Interleukin 67p21rs1800795 C/GG [41,283,284]
ILRUNInflammation and Lipid Regulator with UBA-Like and NBR1-Like Domains6p21.31rs205262 A/GA[49]
MTHFRMethylenetetrahydrofolate reductase1p36.3rs1801131 A/CC[285,286]
NOS3Nitric oxide synthase 37q36rs2070744 T/CT[219,279,287]
NRXN3Neurexin 314q24.3-q31.1rs8011870 G/AG[49]
NUP210Nucleoporin 2103p25.1rs2280084 C/AC[45]
PIEZO1Piezo Type Mechanosensitive Ion Channel Component 116q24.3rs572934641 (TCC/-) E756delD[288]
PPARAPeroxisome proliferator activated receptor α22q13.31rs4253778 G/CC[159,220,289]
PPARGPeroxisome proliferator activated receptor γ3p25.2rs1801282 C/GG[279,290,291][292]
rs2920503 C/TT[49]
SLC39A8Solute Carrier Family 39 Member 84q24rs13107325 C/TC[49]
SOD2Superoxide dismutase 26q25.3rs4880 C/TC[293]
TRHRThyrotropin releasing hormone receptor8q23.1rs7832552 C/TT[65,294,295]
UBR5Ubiquitin Protein Ligase E3 Component N-Recognin 58q22.3rs10505025 G/AA[296]
rs4734621 G/AA[296]
ZNF568Zinc Finger Protein 56819q13.12rs1667369 A/CA[49]
Table
Table 3. Genetic markers for strength athlete status.
Table 3. Genetic markers for strength athlete status.
GeneFull NameLocusPolymorphismStrength-Related AlleleReferences
Studies with Positive ResultsStudies with Negative or Controversial Results
ABHD17CAbhydrolase domain containing 17C15q25.1rs7165759 G/AA[35,51]
ACEAngiotensin I converting enzyme17q23.3Alu I/D (rs4343 A/G
or rs4341 C/G)		D (G)[299,300,301,302,303,304][305]
ACTG1Actin γ 117q25.3rs6565586 T/AA[34,51]
ACTN3Actinin α 311q13.1rs1815739 C/TC[302,306,307,308][51,305,309,310]
ADCY3Adenylate cyclase 32p23.3rs10203386 T/AT[35,51]
ADPGKADP dependent glucokinase15q24.1rs4776614 C/GC[35,51]
AGTAngiotensinogen1q42.2rs699 T/CC[310,311][51]
ALDH2Aldehyde Dehydrogenase 2 Family Member12q24.12rs671 G/AG[66,67,68]
ANGPT2Angiopoietin 28p23.1rs890022 G/AA[51,312]
ARAndrogen ReceptorXq12(CAG)n≥21[313,314]
ARPP21CAMP regulated phosphoprotein 213p22.3rs1513475 T/CC[35,51]
BCDIN3DBicoid interacting 3 domain containing RNA methyltransferase12q13.12rs12367809 C/TC[35,51]
CKMCreatine kinase, M-type19q13.32rs8111989 A/GG[54,315][51,274]
CNTFRCiliary neurotrophic factor receptor9p13.3rs41274853 C/TT[275,316][51]
CRTAC1Cartilage acidic protein 110q24.2rs563296 G/AG[35,51]
DHODHDihydroorotate dehydrogenase (Quinone)16q22.2rs12599952 G/AA[35,51]
GALNTL6Polypeptide N-acetylgalactosaminyltransferase-like 64q34.1rs558129 C/TT[47]
GBE11, 4-α-glucan branching enzyme 13p12.2rs9877408 A/GA[35,51]
GBF1Golgi brefeldin A resistant guanine nucleotide exchange factor 110q24.32rs2273555 G/AA[34,317]
GLIS3GLIS Family Zinc Finger 39p24.2rs34706136 T/TGTG[50]
HIF1AHypoxia inducible factor 1 α14q21-q24rs11549465 C/TT[278,295,318][51]
IGF1Insulin-like growth factor 112q23.2rs35767 C/TT[51,280,319]
IL6Interleukin 67p21rs1800795 C/GG[51,283]
ITPR1Inositol 1, 4, 5-Triphosphate Receptor Type 13p26.1rs901850 G/TT[35,51]
KIF1BKinesin family member 1B1p36.22rs11121542 G/AG[35,51]
LRPPRCLeucine-rich pentatricopeptide repeat cassette2p21rs10186876 A/GA[34,51,298]
MLNMotilin6p21.31rs12055409 A/GG[35,317]
MMS22LMethyl methanesulfonate-sensitivity protein 22-Like6q16.1rs9320823 T/CT[35,51,298]
MTHFRMethylenetetrahydrofolate reductase1p36.3rs1801131 A/CC[51,286,298]
NPIPB6Nuclear pore complex interacting protein family member B616p12.1rs2726036 A/CA[35,51]
PHACTR1Phosphate and actin regulator 16p24.1rs6905419 C/TC[35,51,298]
PLEKHB1Pleckstrin homology domain containing B111q13.4rs7128512 A/GG[51,312]
PPARAPeroxisome proliferator activated receptor α22q13.31rs4253778 G/CC[220,320,321][51]
PPARGPeroxisome proliferator activated receptor γ3p25.2rs1801282 C/GG[51,290,291]
PPARGC1APeroxisome proliferative activated receptor, γ, coactivator 1 α4p15.2rs8192678 G/AA[51,305,308]
R3HDM1R3H domain containing 12q21.3rs6759321 G/TT[35,51]
RASGRF1Ras protein specific guanine nucleotide Releasing Factor 115q25.1rs1521624 C/AA[35,51]
RMC1Regulator of MON1-CCZ118q11.2rs303760 C/TC[35,51]
SLC39A8Solute carrier family 39 member 84q24rs13135092 A/GA[35,51]
TFAP2DTranscriptional factor AP-2 delta6p12.3rs56068671 G/TT[35,51]
ZKSCAN5Zinc finger with KRAB and SCAN domains 57q22.1rs3843540 T/CC[35,51]
ZNF608Zinc finger protein 6085q23.2rs4626333 G/AG[312,317]
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Semenova, E.A.; Hall, E.C.R.; Ahmetov, I.I. Genes and Athletic Performance: The 2023 Update. Genes 2023, 14, 1235. https://doi.org/10.3390/genes14061235

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Semenova EA, Hall ECR, Ahmetov II. Genes and Athletic Performance: The 2023 Update. Genes. 2023; 14(6):1235. https://doi.org/10.3390/genes14061235

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Semenova, Ekaterina A., Elliott C. R. Hall, and Ildus I. Ahmetov. 2023. "Genes and Athletic Performance: The 2023 Update" Genes 14, no. 6: 1235. https://doi.org/10.3390/genes14061235

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Semenova, E. A., Hall, E. C. R., & Ahmetov, I. I. (2023). Genes and Athletic Performance: The 2023 Update. Genes, 14(6), 1235. https://doi.org/10.3390/genes14061235

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