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Association studies have identified dozens of genetic variants linked to training responses and sport-related traits. However, no intervention studies utilizing the idea of personalised training based on athlete's genetic profile have been conducted. Here we propose an algorithm that allows achieving greater results in response to high- or low-intensity resistance training programs by predicting athlete's potential for the development of power and endurance qualities with the panel of 15 performance-associated gene polymorphisms. To develop and validate such an algorithm we performed two studies in independent cohorts of male athletes (study 1: athletes from different sports (n=28); study 2: soccer players (n=39)). In both studies athletes completed an eight-week high- or low-intensity resistance training program, which either matched or mismatched their individual genotype. Two variables of explosive power and aerobic fitness, as measured by the countermovement jump (CMJ) and aerobic 3-min cycle test (Aero3) were assessed pre and post 8 weeks of resistance training. In study 1, the athletes from the matched groups (i.e. high-intensity trained with power genotype or low-intensity trained with endurance genotype) significantly increased results in CMJ (P=0.0005) and Aero3 (P=0.0004). Whereas, athletes from the mismatched group (i.e. high-intensity trained with endurance genotype or lowintensity trained with power genotype) demonstrated non-significant improvements in CMJ (P=0.175) and less prominent results in Aero3 (P=0.0134). In study 2, soccer players from the matched group also demonstrated significantly greater (P<0.0001) performance changes in both tests compared to the mismatched group. Among non- or low responders of both studies, 82% of athletes (both for CMJ and Aero3) were from the mismatched group (P<0.0001). Our results indicate that matching the individual's genotype with the appropriate training modality leads to more effective resistance training. The developed algorithm may be used to guide individualised resistance-training interventions.
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Biology of Sport, Vol. 33 No2, 2016 117
Genes and personalized training
INTRODUCTION
Resistance exercise training is widely used to enhance general tness
and athletic potential/capacity across many sporting disciplines in-
cluding power, strength and endurance events [1, 2]. When prop-
erly performed and combined with adequate nutrition, resistance
training leads to increases in strength, power, speed, muscle size,
local muscular endurance, coordination, and exibility and reductions
in body fat and blood pressure [3].
Effective resistance exercise prescription involves manipulation
of several variables specic to the targeted goals, such as intensity
or load per repetition (i.e. percentage of one repetition maxi-
A genetic-based algorithm for personalized resistance training
AUTHORS: Jones N1, Kiely J2, Suraci B3, Collins DJ2, de Lorenzo D4,5, Pickering C6, Grimaldi KA6
1 DNA Sports Performance Ltd, Manchester, UK
2 Institute of Coaching and Performance, University of Central Lancashire, Preston, UK
3 Suraci Consultancy, Portsmouth, UK
4 Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, CEXS-UPF-PRBB, Barcelona,
Catalonia, Spain
5 Centro de Estudios en Genómica y Nutrición-CESGEN, Parc Cientíc i Tecnològic Agroalimentari de Lleida-
PCiTAL, Lleida, Catalonia, Spain
6 Exercise and Nutritional Genomics Research Centre, DNAFit Ltd, London, UK
ABSTRACT: Association studies have identied dozens of genetic variants linked to training responses and
sport-related traits. However, no intervention studies utilizing the idea of personalised training based on athlete’s
genetic prole have been conducted. Here we propose an algorithm that allows achieving greater results in
response to high- or low-intensity resistance training programs by predicting athlete’s potential for the development
of power and endurance qualities with the panel of 15 performance-associated gene polymorphisms. To develop
and validate such an algorithm we performed two studies in independent cohorts of male athletes (study 1:
athletes from different sports (n=28); study 2: soccer players (n=39)). In both studies athletes completed an
eight-week high- or low-intensity resistance training program, which either matched or mismatched their
individual genotype. Two variables of explosive power and aerobic tness, as measured by the countermovement
jump (CMJ) and aerobic 3-min cycle test (Aero3) were assessed pre and post 8 weeks of resistance training. In
study 1, the athletes from the matched groups (i.e. high-intensity trained with power genotype or low-intensity
trained with endurance genotype) signicantly increased results in CMJ (P=0.0005) and Aero3 (P=0.0004).
Whereas, athletes from the mismatched group (i.e. high-intensity trained with endurance genotype or low-
intensity trained with power genotype) demonstrated non-signicant improvements in CMJ (P=0.175) and less
prominent results in Aero3 (P=0.0134). In study 2, soccer players from the matched group also demonstrated
signicantly greater (P<0.0001) performance changes in both tests compared to the mismatched group. Among
non- or low responders of both studies, 82% of athletes (both for CMJ and Aero3) were from the mismatched
group (P<0.0001). Our results indicate that matching the individual’s genotype with the appropriate training
modality leads to more effective resistance training. The developed algorithm may be used to guide individualised
resistance-training interventions.
CITATION:
Jones N, Kiely J, Suraci B et al. A genetic-based algorithm for personalized resistance training.
Biol Sport. 2016;33(2):117–126.
Received: 2016-02-29; Reviewed: 2016-03-06; Re-submitted: 2016-03-07; Accepted: 2016-03-08; Published: 2015-04-01.
mum(1RM)), volume (total number of sets and repetitions), train-
ing frequency, muscle action (concentric vs. eccentric), rest intervals
between sets, repetition velocity and others [3, 4]. Furthermore,
resistance training can be categorized into two common types: low-
intensity (~30% of 1 RM and high repetitions) and high-intensity
(~70% of 1 RM and low repetitions) resistance training. Low-in-
tensity resistance training is effective for increasing absolute local
muscular endurance [5], explosive power [6, 7] and preferential
hypertrophy of slow-twitch muscle bres [8, 9], while high-intensi-
ty training (also known as classic strength training) leads to in-
Original Paper Biol. Sport 2016;33:117-126
DOI: 10.5604/20831862.1198209
Key words:
DNA
Polymorphism
Genotype
Personalized training
Power
Endurance
Corresponding author:
Nicholas Jones
DNA Sports Performance Ltd,
Manchester, UK
E-mail: nicholasjones@
dna-sports-performance.com
- - - - -
118
Jones N et al.
creases in absolute strength [3] and the hypertrophy of all types of
muscle bres [10, 11].
There is a large variability in both muscle size and strength gains
in response to resistance training between individuals [4]. In a large
study of 585 subjects, Hubal et al. [12] have shown that men and
women exhibited wide ranges of strength gain (1 RM: 0 to +250%)
and skeletal muscle hypertrophy (cross-sectional area: -2 to +59%)
in response to 12 weeks of resistance training, indicating individual
training responses may vary widely dependent on factors such as
genetic heritage. Accordingly, the level of adaptation experienced by
each individual will be dependent on the interaction between spe-
cic training performed and genotype. Indeed, there is a general
consensus that resistance training programs should be individualized,
but little information exists to accurately discern how best to person-
alize training program design to maximize outcomes [3, 4, 12, 13].
Muscle ber composition is a heritable (~45%) trait [14], with
large variability between individuals. For example, slow-twitch(TypeI)
content of vastus lateralis ranges from 5-90%. This variability, in
turn, may determine individual’s potential to perform different types
of resistance training. Accordingly, data show that Type I muscle
Gene Full name Functions and associated phenotypes Polymorphism Endurance or power
related allele
References
ACE Angiotensin I
converting enzyme
Regulates circulatory homeostasis through
the synthesis of vasoconstrictor angiotensin II
and the degradation of vasodilator kinins.
Alu I/D
(rs4646994)
Endurance: I
Power: D
[20, 21]
ACTN3 α-actinin-3 Stabilizes the muscle contractile apparatus in
fast-twitch muscle bres.
Arg577Ter
(rs1815739 C/T)
Endurance: 577Ter
(T)
Power: Arg577 (C)
[20, 22]
ADRB2 β-2 adrenoreceptor Plays a pivotal role in the regulation of the
cardiac, pulmonary, vascular, endocrine and
central nervous system.
Gly16Arg
(rs1042713 G/A)
Endurance: 16Arg (A) [23, 24]
Gln27Glu
(rs1042714 C/G)
Endurance: Gln27 (C) [25]
AGT Angiotensinogen Angiotensinogen is an essential component
of the renin-angiotensin system that regulates
vascular resistance and sodium homeostasis,
and thus determining blood pressure.
Met235Thr
(rs699 T/C)
Power: 235Thr (C) [26, 27]
BDKRB2 Bradykinin receptor
B2
Involved in the endothelium-dependent
vasodilation.
rs1799722 C/T Endurance: T [24]
COL5A1 Collagen, type V, α1 Encodes the pro-α1 chain of type V collagen,
the rate-limiting component of the of type V
collagen trimer assembly.
rs12722 C/T
(BstUI)
Endurance: T [28, 29]
CRP C-reactive protein,
pentraxin-related
Involved in several host defense related
functions based on its ability to recognize
damaged cells and to initiate their elimination
in the blood.
rs1205 A/G Endurance: A [30, 31]
GABPB1
(NRF2)
GA binding protein
transcription factor,
β subunit 1 (nuclear
respiratory factor 2)
Encodes a transcriptional regulator of
genes involved in activation of cytochrome
oxidase expression and nuclear control of
mitochondrial function.
rs7181866 A/G Endurance: G [32, 33]
IL6 Interleukin-6 IL-6 is a pleiotropic cytokine expressed in
immune and muscle cells. Involved in a
wide variety of biological functions, including
regulation of differentiation, proliferation and
survival of target cells.
-174 C/G
(rs1800795)
Power: G [34, 35]
PPARA Peroxisome
proliferator-activated
receptor α
Regulates liver, heart and skeletal muscle
lipid metabolism, glucose homeostasis,
mitochondrial biogenesis, cardiac
hypertrophy.
rs4253778 G/C Endurance: G
Power: C
[36, 37]
PPARGC1A Peroxisome
proliferator-
activated receptor γ
coactivator 1 α
Regulates fatty acid oxidation, glucose
utilization, mitochondrial biogenesis,
thermogenesis, angiogenesis, formation of
muscle bers.
Gly482Ser
(rs8192678 G/A)
Endurance: Gly482 (G) [38, 39]
TRHR Thyrotropin-
releasing hormone
receptor
Stimulates the release of thyroxine, which is
important in developing skeletal muscle.
rs16892496 A/C Power (muscle
mass): C
[40]
VDR Vitamin D receptor Involved in sustaining normocalcemia by
inhibiting the production of parathyroid
hormone and has effects on bone and
skeletal muscle biology.
BsmI A/G
(rs1544410)
Power: A [41, 42]
VEGFA Vascular endothelial
growth factor A
Growth factor active in angiogenesis,
vasculogenesis and endothelial cell growth.
rs2010963 G/C Endurance: C [43, 44]
TABLE 1.
List of genetic variants analysed by DNAFit Peak Performance Algorithm™
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Biology of Sport, Vol. 33 No2, 2016
119
Genes and personalized training
bres have high resistance to fatigue and are thus suited for low-
intensity resistance or aerobic (endurance) training, IIA bres are
better suited for medium-term anaerobic exercise, and type IIX
bres are adapted for high-intensity (power and strength) exer-
cise
[8, 13, 15]. It should be noted that although muscle bre
composition is an informative biomarker, muscle biopsies are
highly invasive. Subsequently, the potential value of non-invasive
exercise prescription tools, such as genetic proling, seems worthy
of investigation.
Association studies have linked dozens of genetic variants to train-
ing responses and sport-related traits, such as strength, skeletal
muscle mass, recovery ability and muscle bre composition [16-19].
However, no intervention studies prescribing training on the basis of
a genetic prole of athletes have been carried out. Here we evaluate
an algorithm that facilitates training prescription by using a panel of
15 gene polymorphisms associated with physical performance and
muscle-specic traits to predict an athlete’s potential for development
of power and/or endurance qualities (Table 1). These polymorphisms
are located within the genes involved in the regulation of muscle
bre type composition and muscle size, cytoskeletal function, mus-
cle damage protection, metabolism, circulatory homeostasis, mito-
chondrial biogenesis, thermogenesis and angiogenesis.
The aim of the present work therefore was to test, in two inde-
pendent studies, the hypothesis that genetically matched athletes
(i.e. high-intensity trained with power genotype or low-intensity
trained with endurance genotype) show greater improvements in
explosive power (countermovement jump) and aerobic tness (aero-
bic 3-min cycle test) in response to high- or low-intensity resistance
training compared to mismatched athletes (i.e. high-intensity trained
with endurance genotype or low-intensity trained with power geno-
type).
MATERIALS AND METHODS
Study participants.
In Study 1, 55 Caucasian male University ath-
letes, all aged 18-20 years, volunteered for the study, and 28 of them
(height 180.7 ± 1.5 cm, weight 77.0 ± 2.1 kg) successfully com-
pleted it (27 athletes had not completed all aspects of the study due
to either injury or illness). Each participant was a member of rst or
second team, actively competing in British Universities and Colleges
Sports (BUCS) leagues. The athletes competed in squash (n = 1),
swimming (n = 7), running (n = 1), ski/snowboard (n = 4), soccer
(n = 1), lacrosse (n = 2), badminton (n = 1), motorsport (n = 1),
cycling (n = 4), cricket (n = 2), volleyball (n = 1), fencing (n = 1)
and rugby union (n = 2).
In study 2, 68 male soccer players, all aged 16-19 years, volun-
teered to participate in the study, and 39 of them (height 176.1 ±
1.0 cm, weight 68.9 ± 1.5 kg) successfully completed it (29 par-
ticipants were withdrawn from the study due to non-adherence of
set training volumes over the 8 weeks, or injury). Each subject was
a member of college soccer academy who actively competed in BUCS
leagues.
Ethical approval
The two-stage study was approved by the University of Central Lan-
cashire Ethics Committee according to the Declaration of Helsinki.
Each participant gave written informed consent after procedures were
fully explained. Each participant was free to withdraw from the stud-
ies at anytime.
Study design
Study design utilised a time series trial as explained by Batterham
and Hopkins [45]. Participants of both studies were randomly allo-
cated to an eight-week high- or low-intensity resistance-training
program, after undergoing performance tests for both explosive
power and endurance. Participants transitioned from their normal
training plan to the designed 8-week intervention followed by an
eight-week wash-out period. The study was double blinded, in that
all were unaware of their ‘genetic potential status’, as determined by
the DNAFit Peak Performance Algorithm™. This also included the
lead investigator who coached the participants during the 8 weeks
of resistance training.
Prior to involvement in the study, all participants had undertaken
weekly strength and conditioning programs, supervised by an ac-
credited strength and conditioning coach, for a minimum of six months
and maximum of two and half years. These sessions took place in a
free weights facility where technique and adherence was closely
monitored at all times. Participants engaged in a minimum of one,
and maximum of two (preferentially), sessions per week. No other
form of resistance training was undertaken during this time, and
participants were actively partaking in other sport-specic training
sessions and competitive games in parallel to the intervention. The
investigator selected the same exercises for both groups: deadlift,
pulldowns, front squat to 90 degrees, dumbbell at press, step ups
to medium high box and vertical jump single effort.
Each group self-selected training loads for each session, were
monitored for progressive increases in perceived exertion, using a
modied Borg scale, and loads were recorded to ensure progression.
The only differences between the training programs were volume
modications. The high-intensity resistance training program con-
sisted of ten sets of two reps over the eight-week study. This gave a
total volume of one hundred and twenty reps per session. The low-
intensity resistance training program consisted of three sets of ten
reps for rst two weeks, three sets of fteens reps for the next three
weeks and three sets of twenty for the last three weeks. This gave a
total volume of one hundred and eighty reps in the rst two weeks,
two hundred and seventy in the next three weeks and three hundred
and sixty reps in the last three weeks.
Physiological measurements
All participants undertook a pre- and post-test measure of explosive
power and aerobic tness (endurance performance); namely, a coun-
termovement jump (CMJ) and Aerobic 3-min Cycle test (Aero3), us-
ing a Optojump (Microgate, Italia) and Wattbike Pro (Wattbike, Not-
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120
Jones N et al.
tingham, UK), respectively. Participants performed a standardized
warm up before every testing session with the CMJ preceding the
Aero3. Subjects were requested to arrive for testing in a rested and
hydrated state and to refrain from caffeine intake for at least 12 hours
before testing. Testing took place on the same time and weekday on
each occasion, to ensure a consistent placement within the subject’s
usual schedule.
Genotyping
Upon enrollment into study each participant volunteered a saliva
sample, which was collected through sterile and self-administered
buccal swabs. Samples were sent to IDna Genetics laboratory (Nor-
wich, UK) within thirty-six hours, where analysis of the genes detailed
in Table 1 was undertaken. DNA was extracted and puried using
the Isohelix Buccalyse DNA extraction kit BEK-50 (Kent, UK). DNA
samples were amplied by real-time PCR on an ABI7900 real-time
thermocycler (Applied Biosystem, Waltham, USA).
Calculation of power/endurance ratio
Following the analysis, the DNAFit Peak Performance Algorithm™
was used to determine percentage power/endurance score (P/E) ra-
tio, similar to the research conducted by Egorova et al. [46]. Ini-
tially, each allele was given a point (0, 1, 2, 3 or 4) depending on
the effect of the polymorphism on performance (power/muscle hy-
pertrophy or endurance with respect to response to training). The
strength of the rating was based on the evidence from cumulative
literature results averaged over time. The total points for the P/E were
expressed as a percentage of P/E and then combined to give the
balance percentage. A percentage-ranking list was then complied
using this score. Every other participant on the list then undertook
high- or low-intensity resistance training. To clarify, someone who is
75% power but does low-intensity resistance training would be doing
mismatched genotype training, while a participant rated as 75%
endurance that completed low-intensity resistance training would be
doing matched genotype training. A threshold for 50% was used as
the splitting value in this process.
Statistical analysis
Statistical analysis was conducted in SPSS, Version 20 (Chicago, IL).
The required sample size for this study was validated using the Mann-
Whitney test. The chi-square test was used to test genotype distribu-
tions for deviation from Hardy-Weinberg equilibrium. The non-para-
metric 2-sample paired test was performed matching “before” and
“after” measurements from each individual tested. A 2-sided Mann-
Whitney test for 2 independent samples was used to compare gains
in CMJ and Aero3 between groups. Differences in phenotypes between
different genotype groups were analysed using ANOVA or unpaired t
test. Spearman’s (non-parametric) correlations were used to assess
the relationships between the genotype score and performance tests.
The squared correlation coefcient R2 was used as a measure of
explained variance. Bonferroni’s correction for multiple testing was
performed by multiplying the P value with the number of tests where
appropriate. All data are presented as mean (standard deviation; SD).
Statistical signicance was set at a P value < 0.05.
RESULTS
Eciency of dierent training modalities. All performance param-
eters increased signicantly (<0.001) in response to low- and high-
intensity resistance training when the results of two studies were
combined. No signicant differences in explosive power (CMJ: 5.4
(5.0) vs. 4.6 (6.1)%, P = 0.547) and aerobic tness (Aero3: 4.3
(3.8) vs. 4.3 (3.7)%, P = 0.711) gains were observed between
low- and high-intensity resistance training groups, indicating that i)
both training modalities can be used to improve these performance
parameters and ii) results of responses to both training types can be
combined for the analysis where appropriate.
Association analysis between genotypes and phenotypes
With some exceptions for the GABPB1 and VDR gene polymorphisms
in Study 2 (due to the low sample sizes in terms of population genet-
ics), genotype distributions of 15 gene polymorphisms amongst all
athletes of both studies were in Hardy-Weinberg equilibrium (Table2).
To assess the association between each polymorphism and per-
formance parameters we used the combined data of two studies.
After Bonferroni’s correction for multiple testing the results were
considered signicant with P < 0.0033 (i.e. 0.05/15). In accordance
with the literature data (Table 1), we found that athletes with the
ACE DD (P > 0.1 for CMJ, P > 0.1 for Aero3), ACTN3 Arg/Arg (P
= 0.065 for CMJ, P = 0.0038 for Aero3), CRP rs1205 GG (P >
0.1 for CMJ, P = 0.0833 for Aero3), PPARGC1A Ser/Ser (P =
0.065 for CMJ, P = 0.0499 for Aero3) and VDR AA (P > 0.1 for
CMJ, P > 0.1 for Aero3) genotypes demonstrated a tendency to
have greater gains in one or two performance tests compared with
the opposite genotype carriers after high-intensity resistance training,
while the latter (except for the PPARGC1A polymorphism) better
responded to the low-intensity training (ACE II: P > 0.1 for CMJ,
P = 0.0355 for Aero3; ACTN3 Ter/Ter: P > 0.1 for CMJ, P > 0.1
for Aero3; CRP rs1205 AA: P = 0.0224 for CMJ, P > 0.1 for Aero3;
VDR GG (P > 0.1 for CMJ, P = 0.0311 for Aero3). No signicant
differences in CMJ and Aero3 gains were observed between different
genotype groups with respect to the other polymorphisms (data not
shown). However, given that the latter 10 polymorphisms have
recently been reported to be associated with endurance, power and
muscle-specic traits, and the fact that each contributing gene can
explain only a small portion of the observed interindividual differ-
ences in training-induced effects, we felt justied in retaining all 15
genetic markers for further analysis.
Eect of dierent training modalities and genetic proles on per-
formance parameters
Based on power/endurance genotype score (see Methods), in two
studies we identied 39 athletes (58.2%) with endurance genotype
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Biology of Sport, Vol. 33 No2, 2016
121
Genes and personalized training
and 28 athletes (41.8%) with power genotype proles. Changes in
CMJ and Aero3 tests of athletes with predominantly endurance or
power genotype proles from both studies after 8 weeks of low- and
high-resistance training are presented in Tables 3 and 4. In both
studies it was shown that athletes with endurance genotype prole
had greater benets from the low-intensity resistance training, while
athletes with power genotype prole better responded to the high-
intensity resistance training. As expected, the outcomes were more
prominent in the Study 2 with homogeneous cohort (i.e. soccer
players). Furthermore, we found that power genotype score (%) of
athletes from both studies was positively correlated with CMJ (r =
0.56; P = 0.0005) and Aero3 (r = 0.39; P = 0.0199) increases
(%) in response to high-intensity training, while endurance genotype
score (%) was positively correlated with CMJ (r = 0.37; P = 0.0399)
and Aero3 (r = 0.51; P = 0.0032) increases (%) in response to
low-intensity training, indicating that power genotype score explained
14-32% of the variation in physiological parameters of athletes.
In accordance with power/endurance genotype score and training
modality, 34 athletes performed matched training (high-intensity
training with power genotype (n=15) or low-intensity training with
endurance genotype (n=19)), while other 33 athletes completed
mismatched training (high-intensity training with endurance genotype
(n=20) or low-intensity training with power genotype (n=13)). In
study 1, the athletes from the matched group have signicantly in-
creased their results in CMJ (P=0.0005) and Aero3 (P=0.0004).
On the other hand, athletes from the mismatched group have shown
non-signicant improvements in CMJ (P=0.175) and less prominent
results in Aero3 (P=0.0134) (Table 5). In study 2, soccer players
from the matched group have also demonstrated signicantly great-
er (P<0.0001) performance changes in both tests compared to
mismatched group (Table 5).
Determinants of variability in response to resistance training
With respect to the changes in CMJ gains (%), the athletes from both
studies (n = 67) were divided into tertiles: high responders (increase
in CMJ from 7.4 to 19.4%; n = 23), moderate responders (increase
Gene and variation Study Genotypes MAF, % PHW
AA AB BB
ACE rs4646994 I/D S1 DD 10 ID 11 II 7 I 44.6 0.2776
S2 14 16 9 43.6 0.3005
ACTN3 rs1815739 C/T S1 CC 8CT 10 TT 10 T 53.6 0.1356
S2 12 21 6 42.3 0.5199
ADRB2 rs1042713 G/A S1 GG 16 GA 10 AA 2 A 25.0 0.8011
S2 21 13 5 29.5 0.2153
ADRB2 rs1042714 C/G S1 CC 5CG 15 GG 8 G 55.4 0.6572
S2 14 16 9 43.6 0.3005
AGT rs699 T/C S1 TT 9 TC 15 CC 4C41.1 0.5723
S2 17 17 5 34.6 0.8171
BDKRB2 rs1799722 C/T S1 CC 9CT 14 TT 5 T 42.9 0.9122
S2 15 17 7 39.7 0.5745
COL5A1 rs12722 C/T S1 TT 8 TC 17 CC 3 C 41.1 0.1784
S2 13 17 9 44.9 0.4576
CRP rs1205 A/G S1 GG 12 GA 12 AA 4 A 35.7 0.7243
S2 21 12 6 30.8 0.0828
GABPB1 rs7181866 A/G S1 AA 27 AG 1 GG 0 G 1.8 0.9233
S2 36 2 1 5.1 0.0031*
IL6 rs1800795 C/G S1 GG 10 GC 13 CC 5C41.1 0.8289
S2 17 16 6 35.9 0.4977
PPARA rs4253778 G/C S1 GG 21 GC 5CC 2C16.1 0.0736
S2 26 11 2 19.2 0.5653
PPARGC1A rs8192678 G/A S1 GG 7 GA 18 AA 3A 42.9 0.0982
S2 15 17 7 39.7 0.5745
TRHR rs16892496 A/C S1 AA 14 AC 9CC 5C 33.9 0.1342
S2 15 17 7 39.7 0.5745
VDR rs1544410 A/G S1 GG 11 GA 16 AA 1 A 32.1 0.1009
S2 16 11 12 44.9 0.0073*
VEGFA rs2010963 G/C S1 GG 13 GC 11 CC 4C 33.9 0.5126
S2 18 18 3 30.8 0.6028
TABLE 2.
Genotype distributions and minor allele frequencies of candidate genes in athletes of two studies.
Note: MAF - minor allele frequency; S1 - Study 1; S2 - Study 2. *PHW < 0.05 - not consistent with Hardy-Weinberg equilibrium.
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122
Jones N et al.
in CMJ from 2.7 to 7.2%; n = 22) and non- or low responders
(increase in CMJ from -8.4 to 2.5%; n=22). There was a signicant
linear trend for the proportion of matched-trained athletes among
the high responders (82.6%), moderate responders (50.0%) and
non- or low responders (18.2%) (χ2=18.7, P < 0.0001). Similarly,
when considering increases of Aero3 (%), we found a signicant
linear trend for the proportion of matched-trained athletes among
the high (increase in Aero3 from 6.0 to 13.2%; n = 22) responders
(86.4%), moderate (increase in Aero3 from 2.0 to 5.9%; n = 23)
responders (47.8%) and non- or low (increase in Aero3 from -6.1
to 1.9%; n = 22) responders (18.2%) (χ2=20.5, P < 0.0001). In
other words, among non- or low responders to any type of resistance
Group Increase in CMJ, % P1
Low-intensity RT P2 (paired test) High-intensity RT P2 (paired test)
Study 1
All athletes (n = 28) 6.4 (5.8) 0.0009* 4.1 (8.1) 0.131 0.369
Athletes with P genotype (n = 11) 3.8 (5.0) 0.156 7.0 (6.7) 0.125 0.429
Athletes with E genotype (n = 17) 8.2 (5.9) 0.0078* 2.2 (8.8) 0.813 0.067
P3 = 0.272 P3 = 0.353
Study 2
All athletes (n = 39) 4.6 (4.3) 0.0056* 5.0 (4.7) <0.0001* 0.932
Athletes with P genotype (n = 17) 1.0 (4.6) 0.578 7.1 (5.9) 0.0059* 0.0046*
Athletes with E genotype (n = 22) 7.1 (1.0) 0.002* 3.2 (2.5) 0.0005* 0.0008*
P3 = 0.0002* P3 = 0.0056*
Studies 1 and 2
All athletes (n = 67) 5.4 (5.0) <0.0001* 4.6 (6.1) 0.0002* 0.547
Athletes with P genotype (n = 28) 2.3 (4.8) 0.1465 7.1 (5.9) 0.0006* 0.0052*
Athletes with E genotype (n = 39) 7.6 (4.0) <0.0001* 2.8 (5.7) 0.051 0.0012*
P3 = 0.0022* P3 = 0.0098*
Note: *P < 0.05 - statistically different values between groups; P - power; E - endurance, RT - resistance training. P1 - comparison between athletes
with different training types (i.e. low-intensity vs. high-intensity); P2 - signicant increases in CMJ (paired test); P3 - comparison between athletes with
different genotype proles (i.e. power genotype vs. endurance genotype) of the same training modality
TABLE 3.
Intergroup comparisons of CMJ increases (%) in response to high- or low-intensity training
Group Increase in Aero3, % P1
Low-intensity RT P2 (paired test) High-intensity RT P2 (paired test)
Study 1
All athletes (n = 28) 2.6 (3.1) 0.0103* 4.4 (4.4) 0.0017* 0.618
Athletes with P genotype (n = 11) 2.0 (4.3) 0.3125 6.0 (3.9) 0.0625 0.178
Athletes with E genotype (n = 17) 3.0 (2.2) 0.0078* 3.4 (4.6) 0.0391* 0.541
P3 = 0.776 P3 = 0.284
Study 2
All athletes (n = 39) 5.8 (3.7) <0.0001* 4.2 (3.3) <0.0001* 0.218
Athletes with P genotype (n = 17) 1.7 (0.5) 0.0156* 6.8 (2.5) 0.002* 0.002*
Athletes with E genotype (n = 22) 8.7 (1.6) 0.002* 2.1 (2.3) 0.0161* <0.0001*
P3 = 0.0001* P3 = 0.002*
Studies 1 and 2
All athletes (n = 67) 4.3 (3.8) <0.0001* 4.3 (3.7) <0.0001* 0.711
Athletes with P genotype (n = 28) 1.8 (2.8) 0.0171* 6.5 (2.9) <0.0001* 0.0004*
Athletes with E genotype (n = 39) 6.0 (3.5) <0.0001* 2.6 (3.3) 0.0004* 0.0013*
P3 = 0.0004* P3 = 0.0026*
Note: *P < 0.05 - statistically different values between groups; P - power; E - endurance, RT - resistance training. P1 - comparison between athletes
with different training types (i.e. low-intensity vs. high-intensity); P2 - signicant increases in Aero3 (paired test); P3 - comparison between athletes with
different genotype proles (i.e. power genotype vs. endurance genotype) of the same training modality
TABLE 4.
Intergroup comparisons of Aero3 increases (%) in response to high- or low-intensity training
- - - - -
Biology of Sport, Vol. 33 No2, 2016
123
Genes and personalized training
training, 82% of athletes (both for CMJ and Aero3) were from the
mismatched group, while high responders were predominantly
matched athletes (83% and 86% for CMJ and Aero3, respectively;
P < 0.0001 for the comparison between non- or low responders and
high responders). Accordingly, after 8 weeks of resistance training
the odds of achieving more favorable outcomes in CMJ and Aero3
were 21 and 28.5 times, respectively, greater (P < 0.0001) for
matched than mismatched genotype training (when rst and third
tertiles were compared).
DISCUSSION
To the best of our knowledge, this is the rst study to examine the
efcacy of using genetic proling methods to target training of both
power and endurance qualities of athletes. The results of our study
demonstrated that all performance parameters increased signi-
cantly in response to 8-weeks of either low- or high-intensity resistance
training without differences between the two training modalities,
however, the magnitude of training effects was strongly related to the
association between genetic prole and training modality. Our main
nding is that matching individual genotype with the appropriate
mode of training led to more substantial resistance training benets,
for both power and endurance matched participants. More speci-
cally, in the rst athletes from the matched group demonstrated
signicantly enhanced results in explosive power and aerobic tness,
while the gains realized by the mismatched athletes were of lesser
magnitude. Importantly, these results were replicated in the second
study, using a homogenous cohort of athletes.
There was also a positive correlation between power genotype
score of athletes and performance changes in response to high-in-
tensity training, as well as a positive correlation between endurance
genotype score and increases in performance tests in response to
low-intensity training: ndings suggesting that the commonly observed
heterogeneity in resistance training-induced explosive power and
aerobic tness responses may be partly explained by genetic factors
and selected training modalities. Another important nding was that
among non- or low responders to resistance training, most athletes
were from the mismatched group, while high responders were pre-
dominantly matched athletes. These results suggest personalized
training prescription based on genetic proling may help some indi-
viduals overcome unresponsiveness to resistance training.
Exercise training response is inuenced by a multitude of deter-
minants including genetics, environmental factors, measurement
errors and others. Studies suggest that muscle strength and explosive
power are under moderate to high genetic control with heritabilities
ranging between 30 and 84% [17, 47]. Numerous studies reported
the association between individual differences in strength/anaerobic
power phenotypes in response to resistance/anaerobic power training
and gene variations [16, 17]. Accordingly, several gene polymor-
phisms in our study were found to be individually linked with training
responses. For instance, the II genotype of the ACE and XX (Ter/Ter)
genotype of the ACTN3 genes (known as endurance markers) were
associated (or tended to correlate) with increases in aerobic tness
in response to low-intensity resistance training, while the ACE DD
and ACTN3 RR (Arg/Arg) genotypes (known as power/strength mark-
ers) carriers demonstrated greater improvement of performance pa-
rameters in response to high-intensity resistance training, which is
consistent with previous ndings [48-51].
The likely mechanism through which the polygenic prole (i.e.
prole composed of 15 polymorphisms) of athletes was associated
with training responses could be the link between genetic variations
and skeletal muscle characteristics, such as muscle bre composition.
Of note, 5 of 15 gene polymorphisms (ACE I/D, ACTN3 rs1815739
C/T, PPARA rs4253778 G/C, PPARGC1A rs8192678 G/A and VEG-
FA rs2010963 G/C) included in our panel, have recently been re-
ported to be associated with muscle bre type [18]. It is well known
that slow-twitch muscle bres better respond to low-intensity resis-
TABLE 5.
Comparisons of CMJ and Aero3 increases (%) in response to resistance training between matched and mismatched groups.
Study Group P3
Matched athletes Mismatched athletes
Study 1 n =14 P1 (paired test) n = 14 P2 (paired test)
Change in CMJ, % 7.8 (5.9) 0.0005* 2.9 (7.2) 0.175 0.0596
Change in Aero3, % 4.0 (3.1) 0.0004* 2.8 (4.3) 0.0134* 0.2456
Study 2 n =20 n = 19
Change in CMJ, % 7.1 (4.1) <0.0001* 2.4 (3.5) 0.0053* <0.0001*
Change in Aero3, % 7.7 (2.2) <0.0001* 1.9 (1.8) 0.0004* <0.0001*
Studies 1 and 2 n =34 n =33
Change in CMJ, % 7.4 (4.9) <0.0001* 2.6 (5.3) 0.0152* <0.0001*
Change in Aero3, % 6.2 (3.2) <0.0001* 2.3 (3.1) <0.0001* <0.0001*
Note: *P1 and P2 < 0.05 - signicant increases in CMJ and Aero3 (paired test); *P3 < 0.05 - signicant difference between matched and mismatched
groups. Matched athletes - high-intensity trained with endurance genotype or low-intensity trained with power genotype; mismatched athletes - high-
intensity trained with power genotype or low-intensity trained with endurance genotype.
- - - - -
124
Jones N et al.
REFERENCES
tance or aerobic (endurance) training, while fast-twitch muscle bres
are better suited for high-intensity (power and strength) training [8,
13, 15]. Consequently, elite endurance athletes have a remarkably
high proportion of slow-twitch muscle bres, whereas muscles of top
sprinters and weightlifters predominantly consist of fast-twitch mus-
cle bres [15]. Interestingly, Sukhova et al. [52] have shown that
speed skaters whose muscle bre composition did not correspond to
their distance specialty (i.e. speed skaters with increased proportion
of slow-twitch muscle bres who performed sprint training and speed
skaters with predominantly fast-twitch muscle bres who performed
endurance training) had destructive alterations of their muscles (with
possible negative effect on physical performance), indicating that
individuals should train and select sports in accordance with their
genetic potential. One might speculate that non- or low-responders
to different training modalities in our study genetically were not
suited for selected resistance training types. On the other hand, there
are many more factors at the molecular, cellular, tissue and organ
system levels that may determine individual responses to resistance
training. For instance, Petrella et al. [53] have demonstrated that
extreme responders (in terms of hypertrophy of muscle bres) to a
16-week resistance training program showed a markedly higher ac-
tivation of their satellite cells and greater myonuclei addition compared
with moderate responders and non-responders.
Our study has some limitations, which have to be pointed out.
Firstly, this was a relatively small study: only 28 athletes from Study
1 and 39 athletes from Study 2 completed the resistance training
programs. However, the power calculation suggested that the sample
size was sufcient to adequately fulll the study’s main objective.
Secondly, the sample was taken from a wide range of sporting dis-
ciplines, all of which were commonly exposed to different forms and
levels of training and competition stresses: a factor which could
conceivably inuence training responses. Furthermore, the low num-
ber of weekly training sessions, which were by necessity completed
in tandem with sport-specic training, may well have confounded
the experimental manipulation. However, athletes from Study 2 were
all soccer players and thus represented the homogeneous group with
more signicant results. Further studies involving untrained (unt)
subjects and strength athletes with more carefully controlled total
training loads are warranted. Third, the subjects of our studies per-
formed a short-term, nonperiodized resistance training. It has been
shown that systematically varying volume and intensity (i.e. periodized
training) is most effective for long-term progression compared with
programs with the stable training variables [3]. Therefore, although
we have shown that genetically matched nonperiodized training was
effective during resistance training program, one might speculate that
even in this case the manipulation of training variables is necessary
for long-term resistance training progression. Fourth, the results of
our study may be applicable only for specic training goals, such as
improvement of explosive power and aerobic performance with one
of two different modalities. Although loads of < 45% of 1 RM (i.e.,
performed with very high repetitions) may increase strength in un-
trained individuals [54], whereas trained weightlifters appear respon-
sive only to heavier loading [55]. Further research analyzing genetic
determinants of improvement of absolute strength and skeletal mus-
cle hypertrophy is needed. Finally, in our study we have used a
validated panel of a limited number (n=15) of gene polymorphisms
associated with power/strength, endurance and other muscle-specif-
ic traits, which could explain only 14-32% of the variation in
phys-
iological parameters of
athletes in our study. Undoubtedly there are
likely to be many more genetic variants associated with responses
to different modalities of resistance training that remain to be identi-
ed. Therefore, it is logical to conclude that the picture we see in the
future may become clearer as more genetic markers are included in
the panel.
CONCLUSIONS
In conclusion, our results suggest that using genetic proling to bet-
ter match individual genotype with appropriate training modality may
be a powerful tool to aid more personalized, and precise, resistance
training prescription in the future.
Acknowledgements
The authors would like to acknowledge the University of Manchester’s
Sport Department and Athletic Union as well as the Portsmouth
College for the allowing their students/athletes the chance to volun-
teer as participants in the study. Also thanks must go to all the
coaches of DNA Sports Performance Ltd and Suraci Consultancy who
took part in data collection and training for the participants. DNAFit
supported this original research by providing all genetic testing. Fi-
nally the authors would also like to acknowledge the hard work and
effort of the participants in this study, who without their hours and
hours of testing and training these results would have remained hid-
den from the world.
Conict of interests: the authors declared no conict of interests
regarding the publication of this manuscript.
1. American College of Sports Medicine.
American College of Sports Medicine
position stand. Progression models in
resistance training for healthy adults. Med
Sci Sports Exerc. 2009;41(3):687-708.
2. Vikmoen O, Ellefsen S, Trøen Ø, Hollan I,
Hanestadhaugen M, Raastad T,
Rønnestad BR. Strength training improves
cycling performance, fractional utilization
of VO2 max and cycling economy in
female cyclists. Scand J Med Sci Sports.
2015 Apr 18. doi: 10.1111/sms.12468.
3. Kraemer WJ, Ratamess NA.
Fundamentals of resistance training:
progression and exercise prescription. Med
Sci Sports Exerc. 2004;36(4):674-688.
4. McGlory C, Phillips SM. Exercise and the
Regulation of Skeletal Muscle
- - - - -
Biology of Sport, Vol. 33 No2, 2016
125
Genes and personalized training
Hypertrophy. Prog Mol Biol Transl Sci.
2015;135:153-173.
5. Campos GE, Luecke TJ, Wendeln HK,
Toma K, Hagerman FC, Murray TF,
Ragg KE, Ratamess NA, Kraemer WJ,
Staron RS. Muscular adaptations in
response to three different resistance-
training regimens: specicity of repetition
maximum training zones. Eur J Appl
Physiol. 2002;88(1-2):50-60.
6. Wilson GJ, Newton RU, Murphy AJ,
Humphries BJ. The optimal training load
for the development of dynamic athletic
performance. Med Sci Sports Exerc.
1993;25(11):1279-1286.
7. McBride JM, Triplett-McBride T, Davie A,
Newton RU. The effect of heavy- vs.
light-load jump squats on the
development of strength, power, and
speed. J Strength Cond Res.
2002;16(1):75-82.
8. Netreba AI, Popov DV, Liubaeva EV,
Bravyĭ IaR, Prostova AB, Lemesheva IuS,
Vinogradova OL. Physiological effects of
using the low intensity strength training
without relaxation in single-joint and
multi-joint movements. Ross Fiziol Zh Im
I M Sechenova. 2007;93(1):27-38.
9. Mitchell CJ, Churchward-Venne TA,
West DW, Burd NA, Breen L, Baker SK,
Phillips SM. Resistance exercise load
does not determine training-mediated
hypertrophic gains in young men. J Appl
Physiol (1985). 2012;113(1):71-77.
10. Fry AC. The role of resistance exercise
intensity on muscle bre adaptations.
Sports Med. 2004;34(10):663-679.
11. Kosek DJ, Kim JS, Petrella JK, Cross JM,
Bamman MM. Efcacy of 3 days/wk
resistance training on myober
hypertrophy and myogenic mechanisms
in young vs. older adults. J Appl Physiol
(1985). 2006;101(2):531-544.
12. Hubal MJ, Gordish-Dressman H,
Thompson PD, Price TB, Hoffman EP,
Angelopoulos TJ, Gordon PM, Moyna
NM, Pescatello LS, Visich PS, Zoeller RF,
Seip RL, Clarkson PM. Variability in
muscle size and strength gain after
unilateral resistance training. Med Sci
Sports Exerc. 2005;37(6):964-972.
13. Pipes TV. Strength training and ber
types. Scholastic Coach. 1994;63:67-
70.
14. Simoneau J-A, Bouchard C. Genetic
determinism of ber type proportion in
human skeletal muscle. FASEB J.
1995;9:1091-1095.
15. Andersen JL, Schjerling P, Saltin B.
Muscle, genes, and athletic performance.
Sci Am. 2000;283(3):48-55.
16. Bray MS, Hagberg JM, Pérusse L,
Rankinen T, Roth SM, Wolfarth B,
Bouchard C. The human gene map for
performance and health-related tness
phenotypes: the 2006-2007 update.
Med Sci Sports Exerc. 2009;41(1):35-
73.
17. Hughes DC, Day SH, Ahmetov II,
Williams AG. Genetics of muscle strength
and power: polygenic prole similarity
limits skeletal muscle performance. J
Sports Sci. 2011;29(13):1425-34.
18. Ahmetov II, Vinogradova OL, Williams
AG. Gene polymorphisms and ber-type
composition of human skeletal muscle.
Int J Sport Nutr Exerc Metab.
2012;22(4):292-303.
19. Ahmetov II, Fedotovskaya ON. Current
Progress in Sports Genomics. Adv Clin
Chem. 2015;70:247-314.
20. Ma F, Yang Y, Li X, Zhou F, Gao C, Li M,
Gao L. The association of sport
performance with ACE and ACTN3
genetic polymorphisms: a systematic
review and meta-analysis. PLoS One.
2013;8(1):e54685.
21. Wang G, Mikami E, Chiu LL, DE Perini A,
Deason M, Fuku N, Miyachi M,
Kaneoka K, Murakami H, Tanaka M,
Hsieh LL, Hsieh SS, Caporossi D,
Pigozzi F, Hilley A, Lee R, Galloway SD,
Gulbin J, Rogozkin VA, Ahmetov II,
Yang N, North KN, Ploutarhos S,
Montgomery HE, Bailey ME, Pitsiladis YP.
Association analysis of ACE and ACTN3
in elite Caucasian and East Asian
swimmers. Med Sci Sports Exerc.
2013;45(5):892-900.
22. Yang N, Arthur DG, Gulbin JP, Hahn AG,
Beggs AH, Easteal S, North K. ACTN3
genotype is associated with human elite
athletic performance. Am J Hum Genet.
2003;73(3):627-631.
23. Wolfarth B, Rankinen T, Mühlbauer S,
Scherr J, Boulay MR, Pérusse L,
Rauramaa R, Bouchard C. Association
between a beta2-adrenergic receptor
polymorphism and elite endurance
performance. Metabolism.
2007;56(12):1649-1651.
24. Tsianos GI, Evangelou E, Boot A,
Zillikens MC, van Meurs JB,
Uitterlinden AG, Ioannidis JP.
Associations of polymorphisms of eight
muscle- or metabolism-related genes with
performance in Mount Olympus marathon
runners. J Appl Physiol (1985).
2010;108(3):567-574.
25. McCole SD, Shuldiner AR, Brown MD,
Moore GE, Ferrell RE, Wilund KR,
Huberty A, Douglass LW, Hagberg JM.
Beta2- and beta3-adrenergic receptor
polymorphisms and exercise
hemodynamics in postmenopausal
women. J Appl Physiol (1985).
2004;96(2):526-530.
26. Gomez-Gallego F, Santiago C, González-
Freire M, Yvert T, Muniesa CA,
Serratosa L, Altmäe S, Ruiz JR, Lucia A.
The C allele of the AGT Met235Thr
polymorphism is associated with power
sports performance. Appl Physiol Nutr
Metab. 2009;34(6):1108-1111.
27. Zarębska A, Sawczyn S, Kaczmarczyk M,
Ficek K, Maciejewska-Karłowska A,
Sawczuk M, Leońska-Duniec A, Eider J,
Grenda A, Cięszczyk P. Association of
rs699 (M235T) polymorphism in the
AGT gene with power but not endurance
athlete status. J Strength Cond Res.
2013;27(10):2898-2903.
28. Posthumus M, Schwellnus MP, Collins M.
The COL5A1 gene: a novel marker of
endurance running performance. Med Sci
Sports Exerc. 2011;43(4):584-589.
29. Brown JC, Miller CJ, Posthumus M,
Schwellnus MP, Collins M. The COL5A1
gene, ultra-marathon running
performance, and range of motion. Int J
Sports Physiol Perform. 2011;6(4):485-
496.
30. Obisesan TO, Leeuwenburgh C, Phillips T,
Ferrell RE, Phares DA, Prior SJ,
Hagberg JM. C-reactive protein genotypes
affect baseline, but not exercise
training-induced changes, in C-reactive
protein levels. Arterioscler Thromb Vasc
Biol. 2004;24(10):1874-1879.
31. Kuo HK, Yen CJ, Chen JH, Yu YH,
Bean JF. Association of cardiorespiratory
tness and levels of C-reactive protein:
data from the National Health and
Nutrition Examination Survey 1999-
2002. Int J Cardiol. 2007;114(1):28-
33.
32. He Z, Hu Y, Feng L, Lu Y, Liu G, Xi Y,
Wen L, McNaughton LR. NRF2 genotype
improves endurance capacity in response
to training. Int J Sports Med.
2007;28(9):717-721.
33. Eynon N, Sagiv M, Meckel Y, Duarte JA,
Alves AJ, Yamin C, Sagiv M,
Goldhammer E, Oliveira J. NRF2 intron 3
A/G polymorphism is associated with
endurance athletes’ status. J Appl Physiol
(1985). 2009;107(1):76-79.
34. Ruiz JR, Buxens A, Artieda M, Arteta D,
Santiago C, Rodríguez-Romo G, Lao JI,
Gómez-Gallego F, Lucia A. The -174 G/C
polymorphism of the IL6 gene is
associated with elite power performance.
J Sci Med Sport. 2010;13(5):549-553.
35. Eider J, Cieszczyk P, Leońska-Duniec A,
Maciejewska A, Sawczuk M, Ficek K,
Kotarska K. Association of the 174 G/C
polymorphism of the IL6 gene in Polish
power-orientated athletes. J Sports Med
Phys Fitness. 2013;53(1):88-92.
36. Ahmetov II, Gavrilov DN, Astratenkova IV,
Druzhevskaya AM, Malinin AV,
Romanova EE, Rogozkin VA. The
association of ACE, ACTN3 and PPARA
gene variants with strength phenotypes in
middle school-age children. J Physiol Sci.
2013;63(1):79-85.
37. Lopez-Leon S, Tuvblad C, Forero DA.
Sports genetics: the PPARA gene and
athletes’ high ability in endurance sports.
A systematic review and meta-analysis.
Biol Sport. 2016;33:3-6.
38. Lucia A, Gómez-Gallego F, Barroso I,
Rabadán M, Bandrés F, San Juan AF,
Chicharro JL, Ekelund U, Brage S,
Earnest CP, Wareham NJ, Franks PW.
PPARGC1A genotype (Gly482Ser)
predicts exceptional endurance capacity
- - - - -
126
Jones N et al.
in European men. J Appl Physiol (1985).
2005;99(1):344-348.
39. Maciejewska A, Sawczuk M, Cieszczyk P,
Mozhayskaya IA, Ahmetov II. The
PPARGC1A gene Gly482Ser in Polish and
Russian athletes. J Sports Sci.
2012;30(1):101-113.
40. Liu XG, Tan LJ, Lei SF, Liu YJ, Shen H,
Wang L, Yan H, Guo YF, Xiong DH,
Chen XD, Pan F, Yang TL, Zhang YP,
Guo Y, Tang NL, Zhu XZ, Deng HY,
Levy S, Recker RR,Papasian CJ, Deng
HW. Genome-wide association and
replication studies identied TRHR as an
important gene for lean body mass. Am J
Hum Genet. 2009;84(3):418-423.
41. Wang P, Ma LH, Wang HY, Zhang W, Tian
Q, Cao DN, Zheng GX, Sun YL.
Association between polymorphisms of
vitamin D receptor gene ApaI, BsmI and
TaqI and muscular strength in young
Chinese women. Int J Sports Med.
2006;27(3):182-186.
42. Windelinckx A, De Mars G, Beunen G,
Aerssens J, Delecluse C, Lefevre J,
Thomis MA. Polymorphisms in the
vitamin D receptor gene are associated
with muscle strength in men and women.
Osteoporos Int. 2007;18(9):1235-1242.
43. Prior SJ, Hagberg JM, Paton CM,
Douglass LW, Brown MD, McLenithan JC,
Roth SM. DNA sequence variation in the
promoter region of the VEGF gene
impacts VEGF gene expression and
maximal oxygen consumption. Am J
Physiol Heart Circ Physiol.
2006;290(5):1848-1855.
44. Ahmetov II, Khakimullina AM, Popov DV,
Missina SS, Vinogradova OL, Rogozkin
VA. Polymorphism of the vascular
endothelial growth factor gene (VEGF)
and aerobic performance in athletes.
Hum Physiol. 2008;34:477-481.
45. Batterham AM, Hopkins WG. A decision
tree for controlled trails. Sportsci.
2005;9:33-39.
46. Egorova ES, Borisova AV, Mustana LJ,
Arkhipova AA, Gabbasov RT,
Druzhevskaya AM, Astratenkova IV,
Ahmetov II. The polygenic prole of
Russian football players. J Sports Sci.
2014;32(13):1286-93.
47. Calvo M, Rodas G, Vallejo M, Estruch A,
Arcas A, Javierre C, Viscor G, Ventura JL.
Heritability of explosive power and
anaerobic capacity in humans. Eur J Appl
Physiol. 2002;86(3):218-225.
48. Montgomery HE, Marshall R,
Hemingway H, Myerson S, Clarkson P,
Dollery C, Hayward M, Holliman DE,
Jubb M, World M, Thomas EL, Brynes AE,
Saeed N, Barnard M, Bell JD, Prasad K,
Rayson M, Talmud PJ, Humphries SE.
Human
gene for physical performance.
Nature. 1998;393(6682):221-222.
49. Folland J, Leach B, Little T, Hawker K,
Myerson S, Montgomery H, Jones D.
Angiotensin-converting enzyme genotype
affects the response of human skeletal
muscle to functional overload. Exp
Physiol. 2000;85:575-579.
50. Pescatello LS, Kostek MA, Gordish-
Dressman H, Thompson PD, Seip RL,
Price TB, Angelopoulos TJ, Clarkson PM,
Gordon PM, Moyna NM, Visich PS,
Zoeller RF, Devaney JM, Hoffman EP. ACE
ID genotype and the muscle strength and
size response to unilateral resistance
training. Med Sci Sports Exerc.
2006;38(6):1074-1081.
51.
Pereira A, Costa AM, Izquierdo M, Silva AJ,
Bastos E, Marques MC. ACE I/D and
ACTN3 R/X polymorphisms as potential
factors in modulating exercise-related
phenotypes in older women in response
to a muscle power training stimuli. Age
(Dordr). 2013;35(5):1949-1959.
52. Sukhova ZI, Ivanitskaia VV, Makarova LF,
Poluéktova BP, Iazvikov VV. Features of
the ultrastructural organization of the
muscles of skaters in relation to their
sport specialization and muscle ber
composition. Arkh Anat Gistol Embriol.
1985;89(12):87-90.
53.
Petrella JK, Kim JS, Mayhew DL, Cross JM,
Bamman MM. Potent myober
hypertrophy during resistance training in
humans is associated with satellite
cell-mediated myonuclear addition: a
cluster analysis. J Appl Physiol (1985).
2008;104(6):1736-42.
54. Stone WJ, Coulter SP. Strength/endurance
effects from three resistance training
protocols with women. J Strength Cond
Res. 1994;8:231-234.
55. Häkkinen K, Komi PV, Alén M,
Kauhanen H. EMG, muscle bre and
force production characteristics during a
1 year training period in elite weight-
lifters. Eur J Appl Physiol Occup Physiol.
1987;56(4):419-27.
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... This highlights the impact of genetic markers in determining the outcome and benefit of aerobic exercise (Cagnin et al., 2019). Studies have also identified heritability to affect VO 2 max response to exercise training by 47% (Jones et al., 2016). More than a dozen genetic variants have been linked to exercise-related traits and outcomes, and this paved way for the development of genetics-based algorithms for personalized training programs. ...
... The frequency of nonresponders in this study was found to be 82% from the mismatched group. This reiterates the effectiveness of genetically-tailored exercise programs (Jones et al., 2016). Study literature till date reports on the presence of 36 genetic markers from mitochondrial DNA, Y chromosome, as well as autosomal genes to be linked to elite athlete status, whereas 39 genetic markers from 19 genes and mitochondrial DNA have been linked to interindividual variability in response to endurance/strength training (Ahmetov and Rogozkin, 2009). ...
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Any form of physical activity, including exercise, is linked with preventing several diseases including metabolic disorders, cancer, and mood disorders. Beyond benefits, its therapeutic activity is inconclusive in terms of types, intensity, and individual training status, and this could be a major research for prescribing exercise as a therapeutic strategy. Exercise and its myriad forms occupy the space on clinical recommendation, which implies that quantifiable benefits of the same have been proven. Further, the benefits of exercise and its impact have also been found to have a genetic underlying interaction, which has created a niche of personal genomics, wherein apart from diet, an exercise regimen also becomes tailorable for every individual. Many subjective well-being reports highlighted daily exercise to keep mental and general health in excellent conditions, and the uncertainties around it. Thus, adopting an exercise behavior and inculcating it as a routine has been recommended. Further, the kind of benefit that can be extracted out of exercise and training is to a great extent influenced by genetic markers around fat, obesity, hunger, as well as satiety. Genetic markers can also impact the body temperature during exercise, making the entire experience of training either comfortable or unpleasant. Thus, there is an overwhelming amount of scientific evidence that has gathered around the benefits of exercise, which has become a pressing need from the 21st century when the belief in the value of exercise started waning, and that spiked up the era of lifestyle and noncommunicable ailments.
... The meta-analyses of genetic associations with the power or endurance athletic status make these associations more accurate and account for the differential effects between subgroups of sex and race. The most studied polymorphisms ACE (rs1799752) and ACTN3 (rs1815739) have been associated with both: power and endurance athletic performance in multiple studies [4][5][6]. ...
... For the TGS model of the polygenic profile to be useful in practice in predicting a trait, it should correlate with the trait and it has to be accurate in its predictions [13]. In most cases, polygenic profiles are derived from the associations of genetic variants and traits published in scientific literature such as in Jones, 2016 [5] by companies, individuals, or sports geneticists. A certain degree of arbitrariness exists in assigning a score to a genotype in a polygenic model that expresses how favourable for the trait that genotype is. ...
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Total genotype score (TGS) reflects additive effect of genotypes on predicting a complex trait such as athletic performance. Scores assigned to genotypes in the TGS should represent an extent of the genotype’s predisposition to the trait. Then, combination of genotypes highly ranks those individuals, who have a trait expressed. Usually, the genotypes are scored by the evidence of a genotype–phenotype relationship published in scientific studies. The scores can be revised computationally using genotype data of athletes, if available. From the available genotype data of 180 Lithuanian elite athletes we created an endurance-mixed-power performance TGS profile based on known ACE rs1799752, ACTN3 rs1815739, and AMPD1 rs17602729, and an emerging MB rs7293 gene markers. We analysed an ability of this TGS profile to stratify athletes according to the sport category that they practice. Logistic regression classifiers were trained to compute the genotype scores that represented the endurance versus power traits in the group of analysed athletes more accurately. We observed differences in TGS distributions in female and male group of athletes. The genotypes with possibly different effects on the athletic performance traits in females and males were described. Our data-driven analysis and TGS modelling tools are freely available to practitioners.
... Although further studies are required to elucidate its possible role, the MCT1 polymorphism could be considered in the future to learn more about RU status. Future research should also concentrate on the investigation of genomics DNA profiling, anthropometric, and physical measurements combined to create tailored training programs to achieve optimal performance even in team sports, as suggested by some authors [33,34]. However, recent research must be taken in consideration to be aware about the difficult predictability of athletes' level discrimination starting from genetic information [35]. ...
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Athletic performance is influenced by many factors such as the environment, diet, training and endurance or speed in physical effort and by genetic predisposition. Just a few studies have analyzed the impact of genotypes on physical performance in rugby. The aim of this study was to verify the modulation of genetic influence on rugby-specific physical performance. Twenty-seven elite rugby union players were involved in the study during the in-season phase. Molecular genotyping was performed for: angiotensin-converting enzyme (ACE rs4646994), alfa-actinin-3 (ACTN3 rs1815739) and monocarboxylate transporter 1 (MCT1 rs1049434) and their variants. Lean mass index (from skinfolds), lower-limb explosive power (countermovement jump), agility (505), speed (20 m), maximal aerobic power (Yo-yo intermittent recovery test level 1) and repeated sprint ability (12 × 20 m) were evaluated. In our rugby union players ACE and ACTN3 variants did not show any influence on athletic performance. MCT1 analysis showed that TT-variant players had the highest peak vertical power (p = 0.037) while the ones with the AA genotype were the fastest in both agility and sprint tests (p = 0.006 and p = 0.012, respectively). Considering the T-dominant model, the AA genotype remains the fastest in both tests (agility: p = 0.013, speed: p = 0.017). Only the MCT1 rs1049434 A allele seems to be advantageous for elite rugby union players, particularly when power and speed are required.
... For categorical traits, such as endurance versus sprint athlete status or power athlete status versus non-athlete control status, chi-square, Fisher's exact or logistic regression tests are typically employed [13,31,32,57]. For continuous traits, such as VO2max, strength and/or specific anthropometric indicators of power output, associations with genetic markers are inferred using regression analysis or one-way analysis of variance (ANOVA) [7,[58][59][60][61]. In many cases, these simpler methods are preferred to the more complex polygenic scoring methods because of the limitations associated with analyzing relatively small samples of elite athletes. ...
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Human athletic performance is a complex phenotype influenced by environmental and genetic factors, with most exercise-related traits being polygenic in nature. The aim of this article is to outline some of the challenge faced by sports genetics as this relatively new field moves forward. This review summarizes recent advances in sports science and discusses the impact of the genome, epigenome and other omics (such as proteomics and metabolomics) on athletic performance. The article also highlights the current status of gene doping and examines the possibility of applying genetic knowledge to predict athletes' injury risk and to prevent the rare but alarming occurrence of sudden deaths during sporting events. Future research in large cohorts of athletes has the potential to detect new genetic variants and to confirm the previously identified DNA variants believed to explain the natural predisposition of some individuals to certain athletic abilities and health benefits. It is hoped that this article will be useful to sports scientists who seek a greater understanding of how genetics influences exercise science and how genomic and other multi-omics approaches might support performance analysis, coaching, personalizing nutrition, rehabilitation and sports medicine, as well as the potential to develop new rationale for future scientific investigation.
... Likewise, a genetic-based algorithm, differentiating either endurance or power athletes and individualizing training with either lowor high-intensity resistance training, indicated that matching the individual's genotype with the appropriate training modality leads to more effective resistance training (N. Jones et al., 2016). Nevertheless, a second group of evidence, including a small meta-analysis, was not able to corroborate this hypothesis, as no significant differences between low-and high-load training were found on the increase of the cross-sectional area in either ST or FT fibers or muscles (Grgic, 2020;Schoenfeld et al., 2020). ...
Thesis
The human skeletal muscle consists of two major cell types, slow-twitch fibers (also called type I fibers) and fast-twitch fibers (or type II fibers). These fibers have distinct characteristics, as fast-twitch fibers are able to generate a large amount of power at high shortening velocities, while slow-twitch fibers have a better energy efficiency, a higher resistance to fatigue and a more robust structural integrity. On average, most humans will dispose of a 50% slow-twitch and a 50% fast-twitch distribution. However a big heterogeneity exists, what results in people with predominantly slow or fast muscle fibers. The typology of a person is mostly genetically determined and is present across most muscles of the body. Taken together, the fact that muscle fibers have distinct characteristics and that muscle typologies range over the whole continuum from predominantly slow to fast in human, will have important implications for sports performance. Nevertheless, these typologies are currently not used in the daily coaching practice. This is probably due to the invasiveness of the current ‘gold’ standard to measure the muscle typology: a muscle biopsy, which is a labor intensive method and harbors a low generalizability. In 2011, our group introduced a non-invasive way to estimate the muscle fiber type composition through the measurement of carnosine – a metabolite which is abundantly available in fast-twitch fibers – using proton magnetic resonance spectroscopy (1H-MRS). The non-invasiveness of this technique enables the use in both the sports practice and science, and renews the interest of the muscle typology in sports. In the first study, the 1H-MRS method to determine the muscle typology was further optimized with the ultimate goal to make it applicable on various scanner systems of multiple vendors. 1H-MRS was found to be a reliable method to quantify carnosine in the muscle. Furthermore, best practices were proposed to prevent often encountered methodological problems and step by step guidelines were developed to allow broader utilization of this technique. Secondly, we investigated if pre-puberty carnosine measurements could give insights in the post-puberty carnosine concentrations, which would allow application of this technique in early specialization sports (study 2). Carnosine was shown to be a trackable metabolite through the disruptive puberty period (R2=0.249-0.670), which confirms the potential of the current technique to scan both future talents and elite athletes. Next to the methodological optimization, the relevance of the muscle typology for talent identification was examined. Before the start of the thesis, the construct validity of our method was already confirmed in athletics, in which clear differences were determined in the muscle typology of either sprint or endurance disciplines. Despite the fact that a comparable distribution of the muscle typologies could be expected in other cyclic sports such as cycling and swimming, this was not yet investigated in elite athletes. Therefore, study 3 established the muscle typologies of 80 world-class cyclists. Clear differences were found in the muscle typology between cycling events. Keirin, bicycle motocross racing (BMX), sprint and 500 m to 1 km time trial cyclists can be considered as fast typology athletes. Time trial, points race, scratch, and omnium consist of intermediate typology athletes, while most individual pursuit, single-stage, cyclo-cross, mountain bike, and multistage cyclists have a slow typology. Nevertheless, this distribution was not present in 73 elite swimmers (study 4), as no clear differences in the muscle typology were detected between short and long distance swimming events in the different strokes. However, there was some evidence to suggest that truly world-class sprint swimmers had a faster muscle fiber type composition when compared to elite swimmers competing at the international level. Moreover, breaststroke swimmers were identified to have a faster muscle typology in comparison to the either freestyle, backstroke or butterfly swimmers. Elite soccer players (n=118) were found to have an on average intermediate typology, which matches with the intermittent nature of this sport (study 6). In contrary to our hypothesis, no differences in the muscle typology were detected between different positions (keeper, defender, midfielder and striker). A big heterogeneity was established over all positions, indicating that the muscle typology is not of major importance for talent identification in soccer. To determine the influence of the muscle typology on individualized training and recovery cycles, we investigated if fatigue and recovery were different when both slow and fast typology subjects were exposed to the same high-intensity training (study 5). Fatigue during three Wingate tests, determined by the power drop, was 20% higher in fast typology athletes. Even though the same work was done during these Wingate tests, also the recovery from these Wingate tests was found to be 15 times slower in fast typology athletes (20 min in slow typology vs. longer than 5 h in fast typology). If a training plan would be composed with a minimum of recovery in between the training sessions, recovery might be insufficient for fast typology athletes, possibly rendering them with a higher risk for muscle strains. In study 6, we studied if the muscle typology is a risk factor for muscle strains in elite soccer players. We discovered that fast typology soccer players had a 5.3 times higher chance to get a hamstring injury, when compared to slow typology soccer players during a prospective longitudinal follow-up study over three seasons. Next to a higher accumulation of fatigue, a higher vulnerability in fast typology players could be expected due to the lower structural integrity in fast fibers. Bringing together, the muscle typology is an important characteristic, which could be non-invasively monitored using 1H-MRS. This technique could help athletes to make a scientific based decision on their ideal discipline during talent orientation. Moreover, it could help coaches tailoring training to enlarge the athletes’ muscle potential and to prevent fatigue accumulation. This endeavor might partly prevent fast typology athletes to be at a higher risk for strain injuries. Consequently, we believe that measuring the muscle fiber typology of athletes should be considered as a valuable procedure to help athletes to fully develop their potential based on the smart use of muscle profiling.
... The principle of individualization is an essential consideration when designing a resistance training program to optimize the adaptations to training (Borresen and Lambert, 2009;Kiely, 2012). A few studies have shown that training adaptations are enhanced when training is tailored to an individual (Jones et al., 2016). Despite a paucity of interventional studies on autoregulation, several studies have utilized autoregulation training via the progressive resistance exercise (PRE) model. ...
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Musculoskeletal disorders such as tendinopathy are having an increasing burden on society and health systems. Tendinopathy is responsible for up to 30% of musculoskeletal disorders, having a high incidence in athletes and the general population. Although resistance training has shown short-term effectiveness for treating lower limb tendinopathy, more comprehensive exercise protocols and progression methods are required due to poor long-term outcomes. The most common resistance training protocols are pre-determined and standardised, which presents significant limitations. Current standardized protocols do not adhere to scientific resistance training principles and do not consider individual factors or take the importance of individualised training into account. Resistance training programs in tendinopathy are currently not achieving required intensity and dosage, leading to high recurrence rates. Therefore, better methods for individualising and progressing resistance training are required to improve outcomes. One potential method is autoregulation, which allows individuals to progress training at their own rate, taking individual factors into account. Despite being found effective for increasing strength in healthy athletes, autoregulation methods have not been investigated in tendinopathy. The purpose of this narrative review was threefold: first to give an overview and critical analysis of individual factors involved in tendinopathy and current resistance training protocols and their limitations. Secondly, to give an overview of the history, methods and application of autoregulation strategies both in sports performance and physiotherapy. Finally, a theoretical adaptation of a current tendinopathy resistance training protocol with autoregulation methods is presented, providing an example of how the method could be implemented in clinical practice or future research.
... According to Bell and Wenger [23], slow isokinetic velocity is defined as 1.75 rad/sec (� 100˚/s) and fast as 3.51 to 5.24 rad/sec (201˚to 300˚/s). Additionally, different kinds of training (aerobic, force, plyometric, power, etc.), interacting with different genetic profiles (responsive to slow or fast training), define the kind of force predominantly developed in an athlete [29][30][31]. Bell and Wenger [23], in their review of physiological adaptations to isokinetic evaluation, observed that with an isokinetic evaluation velocity of 240˚/s it is possible to detect a specificity effect of fast training (high-velocity resistance training), that is, the torque improvement that occurs only with an evaluation velocity of 240˚/s (or at very similar velocities) but that does not occur at slow evaluation velocities (30˚/s, 60˚/s, 90˚/s, or 96˚/s). They observed that when individuals trained with lower contraction velocities, lower torque improvement was detected for 240˚/s. ...
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The aim of this study is to analyze how isokinetic knee and hip peak torques and round-house kick velocities are related to expertise level (elite vs. sub-elite) in taekwondo athletes. Seven elite and seven sub-elite athletes were tested for kick-specific variables (KSV, composed of kinematic variables and power of impact) and for concentric isokinetic peak torque (PT) at 60˚/s and 240˚/s. First, KSVs and PTs were compared between groups, then PTs were correlated with KSVs. Parametric variables with larger effect sizes (Cohen's d) were entered in a stepwise linear discriminant analysis (LDA), generating an equation to estimate competitive level. Between-group differences were found in hip flexors (p = 0.04, d = 0.92) and extensors (p = 0.04, d = 0.96) with PT at 240˚/s. Hip flexion PT at 60˚/s and 240˚/s correlated negatively with kick time (R =-0.46, p = 0.0499 and R =-0.62, p = 0.01 respectively). Hip flexion torque at 60˚/s correlated positively (R = 0.52, p = 0.03) with peak linear velocity of the foot (LVF) and power of impact (R = 0.51, p = 0.03). Peak torque of hip extension at 60˚/s and hip abduction at 240˚/s also correlated with LVF (R = 0.56, p = 0.02 and R = 0.46, p = 0.0499). Hip extension at 60˚/s correlated positively with peak linear velocity of the knee (R = 0.48, p = 0.04). The LDA showed an accuracy of 85.7% (p = 0.003) in predicting expertise level based on hip flexion and extension torques at 240˚/s and on knee extension velocity during the kick. The study demonstrates that hip muscle strength is probably the dominant muscular factor for determining kick performance. Knee angular velocity combined with hip torques is the best discriminator for competitive level in taekwondo athletes.
Article
Background Stress affects learning during training, and virtual reality (VR) based training systems that manipulate stress can improve retention and retrieval performance for firefighters. Brain imaging using functional Near Infrared Spectroscopy (fNIRS) can facilitate development of VR-based adaptive training systems that can continuously assess the trainee’s states of learning and cognition. Objective The aim of this study was to model the neural dynamics associated with learning and retrieval under stress in a VR-based emergency response training exercise. Methods Forty firefighters underwent an emergency shutdown training in VR and were randomly assigned to either a control or a stress group. The stress group experienced stressors including smoke, fire, and explosions during the familiarization and training phase. Both groups underwent a stress memory retrieval and no-stress memory retrieval condition. Participant’s performance scores, fNIRS-based neural activity, and functional connectivity between the prefrontal cortex (PFC) and motor regions were obtained for the training and retrieval phases. Results The performance scores indicate that the rate of learning was slower in the stress group compared to the control group, but both groups performed similarly during each retrieval condition. Compared to the control group, the stress group exhibited suppressed PFC activation. However, they showed stronger connectivity within the PFC regions during the training and between PFC and motor regions during the retrieval phases. Discussion While stress impaired performance during training, adoption of stress-adaptive neural strategies (i.e., stronger brain connectivity) were associated with comparable performance between the stress and the control groups during the retrieval phase.
Article
Background and purpose: Soccer is a complicated team sport in which performance, depends on physiological capabilities. Determining and addressing influential genetic factors can help an effective selecting process and guiding talented athletes and personalizing their exercises. This study aims to assess the potential importance of polymorphism of ACTN3, MCT1, PPARGC1A, ACSL1 and PPARA genes in professional soccer players in Iranian Pro League. Methodology: In this research, 30 professional players of a soccer team in Iranian Pro League were studied. The control group includes 100 non-athlete men whose genomic DNA were extracted from their saliva. Genotype detection using PCR-RFLP method was conducted to identifying polymorphism in ACTM3, PPARGC1A, genes. Frequency of these two polymorphisms among soccer players and control group was determined by statistical test Chi Squared (χ2). Results: Our statistical analysis show a significant difference in XX genotypic frequency in ACTN3 gene polymorphism between soccer players and control group (P = 0.022). Whereas, RR genotypic frequency show no significance difference between soccer players and control group (P = 0.058). Also, it was found that GG genotypic frequency in PPARGC1A gene polymorphism is statistically significant (P = 0.023). (In all genotypes P > 0.05). Conclusion: The results showed that the rs8192678 polymorphism of PPARGC1A gene, can probably be a genetic marker for detecting and discovering talented people in the Iranian populations. In addition, regarding to the literatures, polymorph of ACTN3, individually or in combination, can be considered as a marker gene in soccer.
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Skeletal muscle is a critical organ serving as the primary site for postprandial glucose disposal and the generation of contractile force. The size of human skeletal muscle mass is dependent upon the temporal relationship between changes in muscle protein synthesis (MPS) and muscle protein breakdown. The aim of this chapter is to review our current understanding of how resistance exercise influences protein turnover with a specific emphasis on the molecular factors regulating MPS. We also will discuss recent data relating to the prescription of resistance exercise to maximize skeletal muscle hypertrophy. Finally, we evaluate the impact of age and periods of disuse on the loss of muscle mass and the controversy surround the etiology of muscle disuse atrophy.
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The purpose of this study was to investigate the effect of adding heavy strength training to well-trained female cyclists' normal endurance training on cycling performance. Nineteen female cyclists were randomly assigned to 11 weeks of either normal endurance training combined with heavy strength training (E+S, n = 11) or to normal endurance training only (E, n = 8). E+S increased one repetition maximum in one-legged leg press and quadriceps muscle cross-sectional area (CSA) more than E (P < 0.05), and improved mean power output in a 40-min all-out trial, fractional utilization of VO2 max and cycling economy (P < 0.05). The proportion of type IIAX-IIX muscle fibers in m. vastus lateralis was reduced in E+S with a concomitant increase in type IIA fibers (P < 0.05). No changes occurred in E. The individual changes in performance during the 40-min all-out trial was correlated with both change in IIAX-IIX fiber proportion (r = -0.63) and change in muscle CSA (r = 0.73). In conclusion, adding heavy strength training improved cycling performance, increased fractional utilization of VO2 max , and improved cycling economy. The main mechanisms behind these improvements seemed to be increased quadriceps muscle CSA and fiber type shifts from type IIAX-IIX toward type IIA. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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Understanding the genetic architecture of athletic performance is an important step in the development of methods for talent identification in sport. Research concerned with molecular predictors has highlighted a number of potentially important DNA polymorphisms contributing to predisposition to success in certain types of sport. This review summarizes the evidence and mechanistic insights on the associations between DNA polymorphisms and athletic performance. A literature search (period: 1997-2014) revealed that at least 120 genetic markers are linked to elite athlete status (77 endurance-related genetic markers and 43 power/strength-related genetic markers). Notably, 11 (9%) of these genetic markers (endurance markers: ACE I, ACTN3 577X, PPARA rs4253778 G, PPARGC1A Gly482; power/strength markers: ACE D, ACTN3 Arg577, AMPD1 Gln12, HIF1A 582Ser, MTHFR rs1801131 C, NOS3 rs2070744 T, PPARG 12Ala) have shown positive associations with athlete status in three or more studies and six markers (CREM rs1531550 A, DMD rs939787 T, GALNT13 rs10196189 G, NFIA-AS1 rs1572312 C, RBFOX1 rs7191721 G, TSHR rs7144481 C) were identified after performing genome-wide association studies (GWAS) of African-American, Jamaican, Japanese and Russian athletes. On the other hand, the significance of 29 (24%) markers was not replicated in at least one study. Future research including multicenter GWAS, whole-genome sequencing, epigenetic, transcriptomic, proteomic and metabolomic profiling and performing meta-analyses in large cohorts of athletes is needed before these findings can be extended to practice in sport.
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Abstract Research concerned with predictors of talent in football has highlighted a number of potentially important and partially inherited measures such as body size, anaerobic power, aerobic capacity, agility, psychological profile, game intelligence and susceptibility to injuries. Genotyping for performance-associated DNA polymorphisms at an early age could be useful in predicting later success in football. The aim of the study was to investigate individually and in combination the association of common gene polymorphisms with football player's status. A total of 246 Russian football players and 872 controls were genotyped for 8 gene polymorphisms, which were previously reported to be associated with athlete status. Four alleles (ACE D, ACTN3 Arg577, PPARA rs4253778 C and UCP2 55Val) were first identified, showing discrete associations with football player's status. Next, we determined the total genotype score (TGS, from the accumulated combination of the 4 polymorphisms, with a maximum value of 100 for the theoretically optimal polygenic score) in athletes and controls. The mean TGS was significantly higher in football players (52.0 (17.6) vs. 41.3 (15.5); P < 0.0001) than in controls. These data suggest that the likelihood of becoming a football player depends on the carriage of a high number of "favourable" gene variants.
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Aim: Interleukin-6 (IL6), has been called by some authors "an exercise factor" due to its pleiotropic effects during physical training. Several studies indicated that change in the guanine bases to cytosine at position -174 affects the transcription of the IL6 gene, and finally IL6 production level. The aim of this study was to perform confirmation studies to analyze the possible importance of the IL6 -174 G/C (rs1800795) polymorphism gene in Polish power-orientated athletes. Methods: The study was carried out on two groups of men of the same Caucasian descent: 158 power-orientated athletes and 254 volunteers not involved in competitive sport. DNA was extracted from buccal cells donated by the subjects. Genotyping was carried out by polymerase chain reaction (PCR). Significance was assessed by Chi square (χ2) analysis. Results: The results revealed that the frequency of the IL6 -174 GG genotype (53.16% vs. 35.82%; P=0.002) and G allele (68.67% vs. 57.87%; P=0.03) were significantly higher in the Polish power-orientated athletes compared to controls. Conclusion: These data suggest that the G allele could be one of the factors influencing the power-orientated sport performance. However, these conclusions should be supported with more experimental studies on other IL6 polymorphisms and other genes.
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Genetic polymorphism is suggested to be associated with human physical performance. The angiotensin I-converting enzyme insertion/deletion (ACE I/D) polymorphism and the α-actinin-3 gene (ACTN3) R577X polymorphism have been most widely studied for such association analysis. However, the findings are frequently heterogeneous. We aim to summarize the associations of ACE I/D and ACTN3 R577X with sport performance by means of meta-analysis. We systematically reviewed and quantitatively summarized published studies, until October 31, 2012, on relationship between ACE/ACTN3 genetic polymorphisms and sports performance, respectively. A total of 366 articles on ACE and 88 articles on ACTN3 were achieved by literature search. A significant association was found for ACE II genotype compared to D allele carriage (DD+ID) with increased possibility of physical performance (OR, 1.23; 95% CI, 1.05-1.45). With respect to sport discipline, the II genotype was found to be associated with performance in endurance athletes (OR, 1.35; 95% CI, 1.17-1.55). On the other hand, no significant association was observed for ACTN3 RR genotype as compared to X allele carriage (XX+RX) (OR, 1.03; 95% CI, 0.92-1.15). However, when restricted the analyses to power events, a significant association was observed (OR, 1.21; 95% CI, 1.03-1.42). Our results provide more solid evidence for the associations between ACE II genotype and endurance events and between ACTN3 R allele and power events. The findings suggest that the genetic profiles might influence human physical performance.
Article
Unlabelled: A meta-analysis was performed with the aim of re-evaluating the role of the peroxisome proliferator activated receptor alpha (PPARA) gene intron 7 G/C polymorphism (rs4253778) in athletes' high ability in endurance sports. Design: A meta-analysis of case control studies assessing the association between the G/C polymorphisms of the PPARA gene and endurance sports was conducted. The Cochrane Review Manager software was used to compare the genotype and allele frequencies between endurance athletes and controls to determine whether a genetic variant is more common in athletes than in the general population. Five studies, encompassing 760 endurance athletes and 1792 controls, fulfilled our inclusion criteria. The pooled odds ratio (and confidence intervals, CIs) for the G allele compared to the C allele was 1.65 (95% CI 1.39-1.96). The pooled OR for the GG genotype compared to the GC genotype was 1.79 (95% CI 1.44-2.22), and for the GG genotype compared to the CC genotype 2.37 (95% CI 1.40-3.99). There was no evidence of heterogeneity (I(2) =0%) or of publication bias. Athletes with high ability in endurance sports had a higher frequency of the GG genotype and G allele.
Article
Fifty college women were randomly assigned to one of three resistance training protocols that employed progressive resistance with high resistance/low repetitions (HRLR), medium resistance/medium repetitions (MRMR), and low resistance/high repetitions (LRHR). The three groups trained on the same resistance exercises for 9 weeks at 3 sets of 6 to 8 RM, 2 sets of 15 to 20 RM, and 1 set of 30 to 40 RM, respectively. Training included free weights and multistation equipment. The 1-RM technique was used for strength testing, and muscular endurance tests consisted of maximum repetitions either at a designated resistance or at a percentage of 1-RM. There were significant pre/post strength increases in both upper and lower body tests, but no significant posttreatment difference in muscular strength among the three protocols. Absolute muscular endurance increased significantly on 4 of 6 pre/post comparisons, while relative endurance increased significantly on only 4 of 12 comparisons. HRLR training yielded greater strength gains. LRHR training generally produced greater muscular endurance gains, and the percentage increase in absolute endurance was approximately twice the increase in strength for all groups. Lower body gains in both strength and endurance were greater than upper body gains.
Article
Thus far, genetic studies of the renin-angiotensin system (RAS) with respect to athletic performance or athlete status have mainly focused on the angiotensin-converting enzyme (ACE) gene and its insertion/deletion (I/D) polymorphism. The aim of this study was to investigate the functional rs699 (M235T) polymorphism in AGT, the second most important gene of the RAS, for association with athletic status and level of performance. The study included 123 endurance athletes and 100 power-oriented athletes who were classified as elite or sub-elite according to competitive achievements at the international level as well as 354 unrelated, sedentary control subjects. The M235T genotype and allele distributions differed significantly between power and endurance athletes (P<0.0001 and P<0.0002, genotypes and alleles respectively) as well as between power athletes and control subjects (P<0.0001 and P<0.0002, genotypes and alleles respectively). The frequency of the CC genotype in the power athlete group was 2.2 times higher and 3.1 times higher than in the control and endurance groups, respectively. No difference was found in M235T allele distribution between elite and sub-elite athletes, either in power-oriented or endurance-oriented athletes. We conclude that the CC genotype of the M235T polymorphism is over-represented in Polish power athletes, suggesting that the AGT M235T variant is associated with power athletes' status.