A genetic-based algorithm for personalized resistance-training

Abstract

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.

Full text

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
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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
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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
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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.
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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.
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