Key words
genome-wide association study - strength training effects - isokinetic strength - personalized exercise - predictive model
Introduction
In sedentary adults, skeletal muscle mass typically decreases by
3%–8% every decade [1],
accompanied by varying degrees of muscle strength decline [2]. Low muscle mass and strength are known risk factors for all-cause
mortality, either independently or combined [3]
[4]. Strength training is the preferred approach for
improving muscle mass and strength [5]. However,
individual differences in training effects exist, at least partially due to
differences in subject backgrounds, which results in different responses of the body
to similar stimuli [6]. These individual differences
in training effects are the basis for the development of personalized fitness
guidance programs, which have gained increasing attention in sports science in
recent years. Isokinetic muscle strength testing is widely recognized as the
“gold standard” for evaluating muscle strength [7]. Isokinetic muscle strength can also be used to
evaluate ankle joint status in chronic ankle instability (CAI) [8] and anterior cruciate ligament (ACL) reconstruction
status [9] in sports injuries. The ratio of
isokinetic quadriceps to hamstring muscle strength can predict the occurrence of
hamstring muscle injury [10]
[11]. If the eccentric strength of hamstring muscles is weaker, especially
when the angular velocity is lower than 60°/s, hamstring muscle
injury is likely to occur [12]. This study focuses on
changes in isokinetic leg press muscle strength, which can be improved by long-term
resistance training, and is associated with relatively few reports on individual
differences in isokinetic muscle strength.
Individual differences in muscle strength training effects are determined by a
combination of innate genetic factors and environmental factors. Previous studies
have demonstrated that genetic factors alone, or its interaction with training,
significantly influence individual differences in response to exercise training
[13]
[14].
Candidate gene studies have revealed that the ACTN3 gene can explain
2.1% of strength training effects [15], while
the single nucleotide polymorphism (SNP) rs3136617 and rs2296135 of the
IL15RA gene accounts for 3.5% and 7.1% of the variation
in lean body weight training effects from regression models, respectively [16]. Additionally, the response of different
ACE genotypes in muscle strength after training varies [17]. Meta-analysis reveals that the six candidate
genes (ACE, ACTN3, AKT1, COX4I1, mTOR, and VEGF-A) collectively account for
72% of the variation in the muscular strength phenotype as assessed based on
1RM evaluation [18]. However, training effects are
complex traits determined by multiple genetic markers [19], and the candidate gene approach is unlikely to be sufficient for
developing personalized exercise fitness programs.
Genome-wide association study (GWAS) is a method for identifying genetic markers and
polymorphisms in the entire genome of multiple individuals. By obtaining genotypes
and conducting statistical analysis at the population level, the most likely genetic
markers that determine a trait can be identified [20]
[21]. The polygenic score (PGS) is a
weighted sum of the effects of effective alleles associated with a specific trait,
including behaviors, characteristics, or diseases [22]. It can be used to estimate an individual’s risk of
developing a certain phenotype or trait [23]. Since
training effects are influenced by both genetic and environmental factors, a
training effect prediction model that combines genetic markers and phenotypic
indicators can provide the most comprehensive explanation for individual
differences.
In this study, we utilized GWAS methods to screen for genetic markers, mainly SNPs,
related to the strength training effects of isokinetic leg
press strength in a Chinese cohort. We constructed a genomic prediction model,
based on the identified genetic markers and then combined phenotype indicators, to
establish a comprehensive genomic-phenotypic model for predicting resistance
training effects. Additionally, we conducted bioinformatics analysis on the lead
SNPs to gain insights into the possible mechanisms by which they affect training
effects. Our findings provide operational approaches for developing precision
fitness guidance programs and they serve as a basis for further research.
Materials and Methods
Participants
The study was conducted on a Chinese strength training cohort. The inclusion
criteria for research subjects were as follows: (1) Participants had no prior
experience with resistance training and were classified as non-regular
exercisers [24]
[25] using the Global Physical Activity Questionnaire (GPAQ) [26]; (2) Participants with no risk of resistance
training-related injuries as determined by the Physical Activity Readiness
Questionnaire (PAR-Q); (3) Participants had no adverse dietary habits and
maintained a regular diet during the intervention period, as determined by the
Chinese Resident Nutrition and Health Survey Food Frequency Questionnaire.
Exclusion criteria for participants: (1) Participants who were unable to
complete the intervention due to injuries, illnesses, or other reasons. (2)
Participants with incomplete data collection. (3) Participants who were not of
Chinese Han ethnicity. All subjects voluntarily participated and completed
informed consent forms. A total of 193 participants of Chinese Han ethnicity
were included in the study, comprising 95 males (with an average age of
20±1 years, height of 177.8±5.8 cm, and weight of
71.3±12.4 kg) and 98 females (with an average age of 20±3 years,
height of 164.7±5.9 cm, and weight of 56.5±9.2 kg). The study
was approved by the Ethics Committee of Sports Science Experiments at Beijing
Sport University (Ethics Approval Number: 2019191H).
Procedures
Subjects who met the inclusion criteria were enrolled in a 12-week resistance
training program. Prior to the intervention, detailed instructions were provided
to familiarize the subjects with the standardized movement patterns and ensure
their comprehension of the exercises. Throughout the intervention, close
monitoring of training loads was implemented to ensure adherence to the
prescribed training volume and adherence to the standardized movements.
Phenotypic measurements and DNA samples were collected both before and after the
12-week intervention to facilitate subsequent analysis in the predictive models,
with a 72-hour gap between the testing and training sessions.
Resistance training program
The resistance training program involved the use of a Smith machine for
intervention, in which back squats and bench presses were performed with a load
equivalent to 70% of the one-repetition maximum (1RM). Subjects
completed 5 sets of 10 repetitions with a 2-minute rest between sets, twice a
week, for a period of 12 weeks. To accommodate changes in strength growth, a 1RM
test was conducted every 4 weeks to determine a new training load [27]. During the intervention process,
participants’ training loads were monitored, and they were required to
complete the entire training volume. If a participant was unable to complete the
training independently, minimal assistance was provided to ensure that the
training stimulus on the body remained consistent after completing the same
training content.
One repetition maximum
Participants begin with a warm-up activity, which involves performing back
squats/bench presses at 40% of their subjective 1RM perception.
After warming up, the weight is increased by 15–20 kg on top of the
warm-up load, and they complete 3–5 back squats/bench presses. A
rest period of 2–4 minutes follows, after which the weight is increased
again by 15–20 kg (for back squats) or 5–10 kg (for bench
presses), and they complete 2–3 back squats/bench presses. A
further rest period of 2–4 minutes is taken, and the previous step is
repeated. If the participant successfully completes the lift, continue to
increase the load; if they fail, reduce the load by 5–10 kg (for back
squats) or 2.5–5 kg (for bench presses) until they can complete 1RM with
proper technique. The determination should be made within five attempts for both
back squats and bench presses 1RM.
Muscle mass
The GE Lunar iDXA dual-energy X-ray bone densitometer (GE Healthcare, USA) was
utilized to measure muscle mass. Before testing, it was ensured that the subject
had not undergone a barium meal examination, radioactive isotope injection, or
injection or oral contrast agent for CT and MRI examination within the last 7
days. The subject was required to fast for a minimum of two hours, remove any
clothing that could affect the test results, and lie flat on the instrument
table. The subject’s basic information was entered into enCORE (2011),
and the scanning frame was set to scan layer by layer from head to foot to
obtain muscle mass measurements.
Muscle thickness
GE portable color ultrasound diagnostic system LOGIQ e (GE Healthcare, USA) was
used to measure the thickness of the rectus femoris, rectus femoris-vastus
intermedius, and pectoralis major muscles according GE LOGIQ user manual [28]. To measure the thickness of the rectus
femoris muscle, the marker was placed at the midpoint of the line connecting the
anterior superior iliac spine to the upper edge of the patella. The subject lay
supine with both legs naturally relaxed and at shoulder width. For the thickness
of pectoralis major muscle, the marker was placed at the midpoint of the line
connecting the anterior axillary line to the nipple in males and at one-third
the distance from the anterior axillary line in females. The instrument was
calibrated before the test and subject information was entered. Muscle thickness
was measured on both sides using a 12 MHz linear array ultrasound probe
perpendicular to the direction of the muscle fibers. Three measurements for each
side were recorded and averaged.
Isokinetic leg press maximum peak force
The isokinetic leg press maximum strength of the lower limbs was measured using
the ISOMED isokinetic dynamometer (ISOMED 2000, Germany) [29]. The testing procedure was as follows: (1)
Prior to commencing the test, the equipment was calibrated by the testing
personnel. (2) Participants engaged in a warm-up session on-site, which included
a 200-meter slow run, 2 sets of 10 repetitions of bodyweight squats, and 2 sets
of 10 repetitions of lunge squats. (3) Participants were secured to the ISOMED,
with the pelvis and back snug against the seatback, and a strap was used to
fasten the waist. The soles of the participants' feet were positioned
firmly on the footplate component. During leg press, the reference range of
motion for the knee and hip joints was set at 90° to 130°, and
the speed of the leg press was set at 10 cm/s. (4) Once the participants
are securely fastened to the ISOMED, they execute 3–5 movements
mirroring the speed and range of motion of the formal test. This acclimatizes
them to the movement velocity and range. Afterward, they embark on a submaximal
warm-up involving 3–5 incremental loads, spanning from 20% to
80% of their perceived maximum intensity during exercise (e. g.
25%, 50%, 75%). Subsequent to this warm-up phase,
participants are required to perform at least one maximal intensity exercise.
(5) Throughout the testing procedure, participants were instructed to exert
maximum force with both legs. Each participant completed three leg press tests.
The peak force (PF) during the concentric phase of the leg press was used for
subsequent analysis.
Chip-based whole genome genotyping and quality control
Venous blood (5 mL) was collected from each participant for DNA extraction, using
a magnetic bead-based genomic DNA extraction kit (Tiangen, China). The DNA
concentration and purity were determined using a Nanodrop 2000 (Thermo Fisher
Scientific, USA), while DNA integrity was assessed through agarose gel
electrophoresis. For optimal quality, the DNA concentration should exceed 100
ng/μl, the OD260/280 ratio of the sample should be
higher than 1.8, and agarose gel electrophoresis should show a clear main band
without signs of degradation. DNA samples that passed the quality control stage
were genotyped using an Infinium chip (CGA-24v1–0) (Illumina, USA). The
genotyping results were analyzed using GenomeStudio 2.0 (Illumina, USA), and the
data were formatted. Pre-imputation genotypes were quality-controlled, and the
quality control and imputation methods were consistent with previous studies
[30]
[31]. The
genotype data were imputed using Eagle/Minimac4 with default parameters
(chunk size of 10 Mb and step size of 3 Mb) against the 1000 Genomes Project
Phase 3 v5 reference haplotypes. The imputed chip data were quality-controlled
using plink1.9 software based on quality control standards [32], which included the following exclusion
criteria: 1) minimum allele frequency less than 5% (MAF<0.05);
2) not in Hardy-Weinberg equilibrium
(p<1×10–5); 3) SNPs with more than
10% missing genotypes (mind 0.01); and 4) individuals with more than
10% missing genotypes (geno 0.01). After genotype imputation, 4,110,727
SNPs were retained. Subsequent GWAS analysis was conducted using the
quality-controlled SNPs.
Statistical analysis
Data entry, processing, and statistical analyses were performed using Excel 2016
and SPSS 19.0. Descriptive statistics are presented as mean±standard
deviation (Mean±SD). The normal distribution of the data was assessed
using the K-S test. The training effect was represented as the percentage
(ΔPF) change in isokinetic leg press maximum strength before and after
the intervention. The quartile method was employed to classify subjects’
responses to the training effect. Negative-responders were defined as
those with a ΔPF≤0%, low responders as those with
0<ΔPF≤25%, medium responders as those with
25%<ΔPF≤50%, and high responders as
those with ΔPF>50%.The overall training effect was
assessed using a paired-sample t-test with a significance level of
P<0.05. Principal component analysis (PCA) was used for population
stratification quality control.
Plink 1.9 software was used for genome-wide association analysis, with the
initial value of isokinetic leg press maximum strength, sex, age, and the first
10 principal components of PCA analysis as covariates. The significance level
was defined as p<1×10–5. The genome-wide
significance was p<5×10–8.
The genomic inflation factor (λ) was calculated for GWAS association
results to evaluate bias and the influence of population stratification [33]. GWAS Manhattan plots were drawn using the R
package CMplot. Lead SNPs were selected using FUMA [34].
The weighted PGSs were calculated using the PRSice average method [35]:
(n represents the number of effective alleles, i represents the
number of selected lead SNPs, and beta represents the beta value obtained
from GWAS.)
Linear regression was used to establish the relationship between the training
effect on isokinetic leg press maximum strength and PGS. The GWAS-selected lead
SNPs were used as independent variables (x) to establish the genomic prediction
model. The training effect prediction model was established using stepwise
regression with the GWAS-selected lead SNPs and phenotype indicators (sex, age,
initial isokinetic leg press maximum strength, muscle mass, and muscle
thickness) as independent variables (x).
The selected genetic markers were subjected to bioinformatics analysis using
SNPnexus [36] and REACTOME [37] databases.
Results
Individual variability in leg press response to resistance training
Following the 12-week strength training intervention, isokinetic leg press
maximum strength of the subjects significantly increased
(ΔPF=15.39%, p=1.55E-4) ([Fig. 1a]). The range of individual differences in
strength improvement varied from −55.83% to 151.20%
([Fig. 1b]). The histogram reveals a
positively skewed distribution, characterized by a longer tail on the right
side. Among the subjects, 35.4% had ΔPF values
of≤0%, 33.3% had ΔPF between 0% and
25%, 19.3% had ΔPF between 25% and 50%,
and 12.0% had ΔPF values>50% ([Fig. 1c]).
Fig. 1 Individual variations in the training effect on isokinetic
leg press maximum strength. (A: Change in isokinetic leg press maximum
strength pre- and post-intervention; B: Individual variations in the
training effect on maximal isokinetic leg press strength; C:
Distribution of individual variations in the training effect on
isokinetic leg press maximum strength).**indicated
p<0.01 for the paired samples t-test.
Genome-wide association analysis
Eighty-five SNPs exhibited significant associations with the change in isokinetic
leg press maximum strength after 12 weeks of resistance training
(p<1×10–5) ([Fig. 2]
,
[Table 1]). The
inflation coefficient λ was calculated to be 1.014 ([Fig. 3]), indicating that the p-value was not
influenced by population stratification and there was no false positive. Among
these SNPs, 14 were identified as lead SNPs
(p<1×10–5), with nine SNPs reaching
genome-wide significance (p<5×10–8) ([Table 1]).
Fig. 2 Manhattan plot of GWAS analysis of the training effect on
isokinetic leg press maximum strength. The x-axis represents
chromosomes, which are distinguished by different colors. The y-axis
represents -log10 (P), and the color of the legend represents the number
of SNPs on each chromosome. The dashed line indicates a significance
level of p<1×10–5, and the solid line
indicates a significance level of
p<5×10–8.
Fig. 3 Quantile-Quantile (QQ) plot and genomic inflation factor
λ.λ≈1, the curve lifts up at the back end,
indicating that the p-values are not due to population stratification,
and there is no false positive.
Table 1 GWAS analysis of the training effect on isokinetic
leg press maximum strength.
|
rsID*
|
CHR
|
Position
|
REF Allele
|
ALT Allele (IUPAC)
|
Minor Allele
|
MAF
|
Beta
|
GWAS_P
|
Function
|
Overlapped Gene
|
Nearest Upstream Gene
|
Nearest Downstream Gene
|
|
rs2619732
|
5
|
153877722
|
T
|
G
|
G
|
0.08
|
37.79
|
4.15E-09
|
None
|
None
|
CTB-158E9.2
|
CIR1P1
|
|
rs283442
|
5
|
153881665
|
T
|
C
|
C
|
0.07
|
37.79
|
4.15E-09
|
None
|
None
|
CTB-158E9.2
|
CIR1P1
|
|
rs283443
|
5
|
153881676
|
A
|
G
|
G
|
0.08
|
37.79
|
4.15E-09
|
None
|
None
|
CTB-158E9.2
|
CIR1P1
|
|
rs283445
|
5
|
153882962
|
T
|
A
|
A
|
0.06
|
37.79
|
4.15E-09
|
None
|
None
|
CTB-158E9.2
|
CIR1P1
|
|
rs1419957
|
2
|
230029563
|
G
|
A
|
A
|
0.26
|
20.46
|
2.45E-08
|
non-coding intronic,intronic
|
PID1
|
None
|
None
|
|
rs3101863
|
5
|
153862679
|
A
|
C
|
C
|
0.06
|
34.52
|
2.56E-08
|
None
|
None
|
HAND1
|
CTB-158E9.1
|
|
rs9812977
|
3
|
48720303
|
G
|
A
|
A
|
0.09
|
37.38
|
3.67E-08
|
5upstream,intronic,5utr
|
NCKIPSD
|
None
|
None
|
|
rs176104
|
5
|
153865158
|
A
|
G
|
G
|
0.07
|
34.14
|
4.27E-08
|
non-coding intronic
|
CTB-158E9.1
|
None
|
None
|
|
rs442533
|
5
|
153867280
|
G
|
A
|
A
|
0.08
|
34.14
|
4.27E-08
|
None
|
None
|
CTB-158E9.1
|
CTB-158E9.2
|
|
rs17116169
|
5
|
153837482
|
G
|
A
|
A
|
0.05
|
34.12
|
6.82E-08
|
3utr,3downstream
|
SAP30L
|
None
|
None
|
|
rs111287651
|
5
|
153843293
|
T
|
M
|
A
|
0.05
|
33.16
|
7.93E-08
|
None
|
None
|
SAP30L
|
HAND1
|
|
rs72795453
|
5
|
153844742
|
C
|
T
|
T
|
0.05
|
33.16
|
7.93E-08
|
None
|
None
|
SAP30L
|
HAND1
|
|
rs72795454
|
5
|
153850486
|
A
|
T
|
T
|
0.06
|
33.16
|
7.93E-08
|
None
|
None
|
SAP30L
|
HAND1
|
|
rs1419956
|
2
|
230029360
|
G
|
A
|
A
|
0.29
|
19.28
|
1.12E-07
|
non-coding intronic,intronic
|
PID1
|
None
|
None
|
|
rs17676310
|
2
|
230030367
|
T
|
M
|
C
|
0.12
|
19.38
|
1.23E-07
|
non-coding intronic,intronic
|
PID1
|
None
|
None
|
|
rs9973574
|
2
|
230031411
|
T
|
C
|
C
|
0.26
|
19.38
|
1.23E-07
|
non-coding intronic,intronic
|
PID1
|
None
|
None
|
|
rs2869782
|
3
|
117084425
|
T
|
M
|
T
|
0.26
|
27.11
|
1.36E-07
|
intronic
|
LSAMP
|
None
|
None
|
|
rs11900642
|
2
|
230028108
|
A
|
G
|
G
|
0.26
|
18.94
|
1.92E-07
|
non-coding intronic,intronic
|
PID1
|
None
|
None
|
|
rs10171031
|
2
|
230031029
|
C
|
W
|
A
|
0.25
|
18.94
|
2.10E-07
|
non-coding intronic,intronic
|
PID1
|
None
|
None
|
|
rs62190426
|
2
|
230033010
|
A
|
G
|
G
|
0.12
|
18.59
|
6.33E-07
|
non-coding intronic,intronic
|
PID1
|
None
|
None
|
|
rs61973994
|
13
|
101762201
|
C
|
T
|
T
|
0.18
|
17.15
|
7.26E-07
|
intronic
|
NALCN
|
None
|
None
|
|
rs145706639
|
2
|
230031680
|
GGAAGG
|
-
|
-
|
0.25
|
18.44
|
8.38E-07
|
non-coding intronic,intronic
|
PID1
|
None
|
None
|
|
rs73044028
|
19
|
35657612
|
G
|
A
|
A
|
0.12
|
16.7
|
8.62E-07
|
3utr,non-coding intronic,intronic
|
FXYD5
|
None
|
None
|
|
rs11900497
|
2
|
230027887
|
A
|
G
|
G
|
0.26
|
18.11
|
1.03E-06
|
non-coding intronic,intronic
|
PID1
|
None
|
None
|
|
rs73044024
|
19
|
35657334
|
A
|
G
|
G
|
0.12
|
16.56
|
1.13E-06
|
non-coding intronic,3utr,intronic
|
FXYD5
|
None
|
None
|
|
rs12326802
|
18
|
73657606
|
A
|
G
|
A
|
0.33
|
19.69
|
1.31E-06
|
None
|
None
|
RP11–173L6.1
|
RP11–357H3.1
|
|
rs2073948
|
19
|
35651806
|
A
|
T
|
T
|
0.09
|
16.77
|
1.49E-06
|
non-coding intronic,intronic,3downstream
|
FXYD5
|
None
|
None
|
|
rs150686717
|
19
|
35655985
|
T
|
-
|
-
|
0.12
|
16.34
|
1.54E-06
|
non-coding intronic,intronic
|
FXYD5
|
None
|
None
|
|
rs56058408
|
19
|
35655990
|
G
|
H
|
T
|
0.12
|
16.34
|
1.54E-06
|
non-coding intronic,intronic
|
FXYD5
|
None
|
None
|
|
rs73044014
|
19
|
35653275
|
C
|
T
|
T
|
0.12
|
17.13
|
1.79E-06
|
non-coding intronic,5upstream,intronic
|
FXYD5
|
None
|
None
|
|
rs12470314
|
2
|
230035585
|
C
|
T
|
T
|
0.12
|
18.35
|
1.85E-06
|
non-coding intronic,intronic
|
PID1
|
None
|
None
|
|
rs8010482
|
14
|
81815011
|
C
|
W
|
C
|
0.30
|
14.54
|
1.98E-06
|
intronic
|
STON2
|
None
|
None
|
|
rs73044015
|
19
|
35653348
|
T
|
A
|
A
|
0.09
|
16.09
|
2.16E-06
|
non-coding intronic,5upstream,intronic
|
FXYD5
|
None
|
None
|
|
rs9945171
|
18
|
73690476
|
T
|
C
|
C
|
0.09
|
22.92
|
2.44E-06
|
None
|
None
|
RP11–173L6.1
|
RP11–357H3.1
|
|
rs691587
|
21
|
43776179
|
G
|
W
|
G
|
0.20
|
−14.15
|
2.45E-06
|
None
|
None
|
TFF2
|
TFF1
|
|
rs3774611
|
3
|
53840665
|
G
|
A
|
A
|
0.39
|
−12.75
|
3.04E-06
|
intronic
|
CACNA1D
|
None
|
None
|
|
rs172825
|
21
|
43773253
|
C
|
R
|
C
|
0.15
|
−13.96
|
3.58E-06
|
None
|
None
|
TFF2
|
TFF1
|
|
rs12442146
|
15
|
70446518
|
G
|
H
|
T
|
0.25
|
17.9
|
3.79E-06
|
None
|
None
|
TLE3
|
RNU6–745P
|
|
rs225338
|
21
|
43772432
|
A
|
S
|
A
|
0.15
|
−13.74
|
4.35E-06
|
None
|
None
|
TFF2
|
TFF1
|
|
rs225339
|
21
|
43772438
|
G
|
H
|
G
|
0.15
|
−13.74
|
4.35E-06
|
None
|
None
|
TFF2
|
TFF1
|
|
rs12480160
|
20
|
49990155
|
A
|
G
|
G
|
0.12
|
17.7
|
4.64E-06
|
None
|
None
|
AL035457.1
|
AL079339.1
|
|
rs2359132
|
3
|
53836937
|
A
|
G
|
G
|
0.21
|
−12.48
|
4.80E-06
|
intronic
|
CACNA1D
|
None
|
None
|
|
rs78781545
|
13
|
101762277
|
T
|
C
|
C
|
0.05
|
16.52
|
5.05E-06
|
intronic
|
NALCN
|
None
|
None
|
|
rs75318253
|
4
|
112680279
|
A
|
G
|
G
|
0.06
|
−24.05
|
5.47E-06
|
None
|
None
|
RP11–255I10.2
|
RP11–269F21.1
|
|
rs79268297
|
4
|
112680281
|
G
|
T
|
T
|
0.06
|
−24.05
|
5.47E-06
|
None
|
None
|
RP11–255I10.2
|
RP11–269F21.1
|
|
rs283444
|
5
|
153882472
|
C
|
T
|
T
|
0.11
|
25.4
|
5.61E-06
|
None
|
None
|
CTB-158E9.2
|
CIR1P1
|
|
rs112928349
|
3
|
48714335
|
G
|
C
|
C
|
0.14
|
28.73
|
5.71E-06
|
intronic,non-coding intronic
|
NCKIPSD
|
None
|
None
|
|
rs12107252
|
3
|
48691316
|
T
|
C
|
C
|
0.21
|
28.73
|
5.71E-06
|
coding nonsyn
|
CELSR3
|
None
|
None
|
|
rs12107418
|
3
|
48689787
|
A
|
G
|
G
|
0.20
|
28.73
|
5.71E-06
|
intronic
|
CELSR3
|
None
|
None
|
|
rs12715429
|
3
|
48709246
|
G
|
W
|
T
|
0.14
|
28.73
|
5.71E-06
|
3downstream,intronic
|
NCKIPSD
|
None
|
None
|
|
rs13070798
|
3
|
48705934
|
T
|
C
|
C
|
0.14
|
28.73
|
5.71E-06
|
intronic
|
NCKIPSD
|
None
|
None
|
|
rs13324119
|
3
|
48710606
|
G
|
C
|
C
|
0.13
|
28.73
|
5.71E-06
|
3downstream,intronic
|
NCKIPSD
|
None
|
None
|
|
rs144754472
|
3
|
48710301
|
-
|
AAC
|
AAC
|
0.14
|
28.73
|
5.71E-06
|
3downstream,intronic
|
NCKIPSD
|
None
|
None
|
|
rs2286651
|
3
|
48694147
|
G
|
A
|
A
|
0.10
|
28.73
|
5.71E-06
|
coding syn
|
CELSR3
|
None
|
None
|
|
rs2286652
|
3
|
48689192
|
G
|
A
|
A
|
0.10
|
28.73
|
5.71E-06
|
intronic
|
CELSR3
|
None
|
None
|
|
rs2302295
|
3
|
48690110
|
T
|
C
|
C
|
0.14
|
28.73
|
5.71E-06
|
intronic
|
CELSR3
|
None
|
None
|
|
rs28452701
|
3
|
48708575
|
C
|
T
|
T
|
0.13
|
28.73
|
5.71E-06
|
intronic
|
NCKIPSD
|
None
|
None
|
|
rs34051806
|
3
|
48713669
|
AT
|
-
|
-
|
0.13
|
28.73
|
5.71E-06
|
non-coding intronic,intronic
|
NCKIPSD
|
None
|
None
|
|
rs3733086
|
3
|
48699519
|
C
|
T
|
T
|
0.10
|
28.73
|
5.71E-06
|
coding syn
|
CELSR3
|
None
|
None
|
|
rs3821875
|
3
|
48697654
|
C
|
R
|
G
|
0.10
|
28.73
|
5.71E-06
|
coding nonsyn|nonsyn
|
CELSR3
|
None
|
None
|
|
rs6768448
|
3
|
48714165
|
A
|
G
|
G
|
0.20
|
28.73
|
5.71E-06
|
non-coding intronic,intronic
|
NCKIPSD
|
None
|
None
|
|
rs71074265
|
3
|
48702890
|
CT
|
-
|
-
|
0.10
|
28.73
|
5.71E-06
|
intronic
|
NCKIPSD
|
None
|
None
|
|
rs9809222
|
3
|
48714411
|
C
|
T
|
T
|
0.20
|
28.73
|
5.71E-06
|
3downstream,non-coding intronic,intronic
|
NCKIPSD
|
None
|
None
|
|
rs9811318
|
3
|
48702606
|
A
|
G
|
G
|
0.20
|
28.73
|
5.71E-06
|
intronic
|
NCKIPSD
|
None
|
None
|
|
rs9858236
|
3
|
48709529
|
G
|
A
|
A
|
0.13
|
28.73
|
5.71E-06
|
3downstream,intronic
|
NCKIPSD
|
None
|
None
|
|
rs9873726
|
3
|
48712367
|
C
|
T
|
T
|
0.20
|
28.73
|
5.71E-06
|
non-coding intronic,intronic
|
NCKIPSD
|
None
|
None
|
|
rs9877501
|
3
|
48718390
|
C
|
K
|
G
|
0.20
|
28.73
|
5.71E-06
|
5upstream,intronic,3downstream
|
NCKIPSD
|
None
|
None
|
|
rs9877794
|
3
|
48718388
|
G
|
C
|
C
|
0.19
|
28.73
|
5.71E-06
|
3downstream,5upstream,intronic
|
NCKIPSD
|
None
|
None
|
|
rs730741
|
4
|
168610140
|
T
|
C
|
C
|
0.49
|
15.8
|
5.74E-06
|
None
|
None
|
RN7SKP188
|
RP11–521F1.1
|
|
rs742992
|
20
|
49988935
|
G
|
A
|
A
|
0.10
|
17.62
|
5.92E-06
|
None
|
None
|
AL035457.1
|
AL079339.1
|
|
rs9812200
|
3
|
48695667
|
G
|
A
|
A
|
0.21
|
28.56
|
6.12E-06
|
intronic
|
CELSR3
|
None
|
None
|
|
rs9836462
|
3
|
48712791
|
A
|
G
|
G
|
0.21
|
28.56
|
6.12E-06
|
non-coding intronic,intronic
|
NCKIPSD
|
None
|
None
|
|
rs9841602
|
3
|
48713570
|
A
|
G
|
G
|
0.21
|
28.56
|
6.12E-06
|
non-coding intronic,intronic
|
NCKIPSD
|
None
|
None
|
|
rs1033597
|
20
|
49980774
|
A
|
T
|
T
|
0.08
|
18.98
|
7.40E-06
|
None
|
None
|
AL035457.1
|
AL079339.1
|
|
rs13353481
|
3
|
48709463
|
C
|
R
|
G
|
0.13
|
28.53
|
7.40E-06
|
3downstream,intronic
|
NCKIPSD
|
None
|
None
|
|
rs79076253
|
13
|
101762151
|
C
|
T
|
T
|
0.05
|
16.6
|
7.50E-06
|
intronic
|
NALCN
|
None
|
None
|
|
rs893365
|
3
|
53841146
|
C
|
T
|
T
|
0.40
|
−12.08
|
8.31E-06
|
intronic
|
CACNA1D
|
None
|
None
|
|
rs113945797
|
18
|
73688365
|
A
|
T
|
T
|
0.04
|
21.8
|
8.41E-06
|
None
|
None
|
RP11–173L6.1
|
RP11–357H3.1
|
|
rs12480218
|
20
|
49990445
|
G
|
T
|
T
|
0.09
|
17.45
|
8.73E-06
|
None
|
None
|
AL035457.1
|
AL079339.1
|
|
rs1039718
|
18
|
73681247
|
G
|
A
|
A
|
0.22
|
23.34
|
9.30E-06
|
None
|
None
|
RP11–173L6.1
|
RP11–357H3.1
|
|
rs28578250
|
18
|
73680467
|
C
|
T
|
T
|
0.22
|
23.34
|
9.30E-06
|
None
|
None
|
RP11–173L6.1
|
RP11–357H3.1
|
|
rs28612749
|
18
|
73680476
|
C
|
K
|
T
|
0.22
|
23.34
|
9.30E-06
|
None
|
None
|
RP11–173L6.1
|
RP11–357H3.1
|
|
rs111397679
|
20
|
49981156
|
G
|
T
|
T
|
0.10
|
17.86
|
9.65E-06
|
None
|
None
|
AL035457.1
|
AL079339.1
|
|
rs56050577
|
19
|
35660138
|
G
|
T
|
T
|
0.09
|
15.35
|
9.89E-06
|
non-coding intronic,intronic
|
FXYD5
|
None
|
None
|
|
rs140487242
|
18
|
73668588
|
ATACAC
|
-
|
-
|
0.03
|
22.51
|
9.98E-06
|
None
|
None
|
RP11–173L6.1
|
RP11–357H3.1
|
*in the rsID column, lead SNPs are in bold.
Correlation analysis between PGS and isokinetic leg press maximum
strength
There was a significant positive correlation between ΔPF and PGS
(r=0.68, p<0.01). The regression equation is
Y=0.09*X+1.02, with an X-axis intercept of
−11.89 and a Y-axis intercept of 1.02. The PGS score for
negative-responders is less than 1.02; for low-responders it is
1.02<PGS≤3.17; for middle-responders it is
3.17<PGS≤5.31, and for high-responders it is PGS>5.31
([Fig. 4]).
Fig. 4 Relationship between the training effect on isokinetic leg
press maximum strength (ΔPF) and PGS. The slope of the equation
is 0.09, the X-axis intercept is −11.89, and the Y-axis
intercept is 1.02.
Models for predicting the effectiveness of isokinetic leg press maximum
strength training
Seven SNPs (rs1419957, rs2619732, rs8010482, rs73044028, rs12480160, rs12326802,
rs61973994) out of the 14 lead SNPs were incorporated in the genomic prediction
model (model R2=0.404) ([Table
2]). By incorporating PGS and phenotypic indicators, the comprehensive
model included the PGS, initial values of isokinetic peak force, and sex, which
accounted for 75.4% of the variation in training effect (i. e.
the model R2, which is the sum of the coefficients’
R2) ([Table 3]). The PGS could
explain 49.9% of the variance in leg press response
(R2=0.499), and the second highest predictor was the initial
values of isokinetic peak force, which could explain 22.9% of the
variance in leg press response (R2=0.229).
Table 2 Models for predicting the training effect on
isokinetic leg press maximum strength based on SNPs.
|
coefficient
|
Unstandardized Coefficients
|
Standardized Coefficients
|
Sig.
|
R2
|
Adjusted R2
|
|
B
|
Std. Error
|
BETA
|
|
(constant)
|
−9.239
|
3.149
|
|
0.004
|
|
|
|
rs1419957
|
14.308
|
3.741
|
0.261
|
0
|
0.14
|
0.134
|
|
rs2619732
|
26.172
|
7.288
|
0.244
|
0
|
0.085
|
0.08
|
|
rs8010482
|
8.641
|
3.264
|
0.179
|
0.009
|
0.057
|
0.053
|
|
rs73044028
|
8.249
|
3.479
|
0.168
|
0.019
|
0.041
|
0.037
|
|
rs12480160
|
10.751
|
4.126
|
0.175
|
0.01
|
0.03
|
0.026
|
|
rs12326802
|
10.452
|
4.162
|
0.168
|
0.013
|
0.027
|
0.023
|
|
rs61973994
|
8.123
|
3.463
|
0.155
|
0.02
|
0.024
|
0.02
|
Table 3 Comprehensive models for predicting the training
effect on isokinetic leg press maximum strength based on PGS and
phenotypic indicators.
|
coefficient
|
Unstandardized Coefficients
|
Standardized Coefficients
|
Sig.
|
R2
|
Adjusted R2
|
|
B
|
Std. Error
|
BETA
|
|
(constant)
|
40.608
|
3.128
|
|
0
|
|
|
|
PGS
|
5.206
|
0.283
|
0.699
|
0
|
0.499
|
0.497
|
|
isokinetic maximum flexor strength
|
−0.014
|
0.001
|
−0.58
|
0
|
0.229
|
0.228
|
|
SEX
|
11.996
|
2.674
|
0.192
|
0
|
0.026
|
0.025
|
Biological functional analysis
The results of gene ontology analysis (GO) showed that SNPs associated with
maximal isokinetic leg press training response were mainly enriched in 20
biological processes, including inorganic ion transmembrane transport,
epithelial structure maintenance, and organ or tissue-specific immune response.
Additionally, they were enriched in seven molecular functions, including sodium
channel regulator activity, inorganic molecular entity transmembrane transporter
activity, and carbohydrate binding, as well as seven cellular components,
including transmembrane transporter complex, intercalated disc, and RNA
polymerase II transcription regulator complex ([Fig.
5]).
Fig. 5 GO analysis enriched terms. This figure illustrates the
results of the Gene Ontology (GO) analysis, including biological
processes, cellular components, and molecular functions. The size of the
bubbles represents the number of genes enriched in the corresponding GO
terms, while the color of the bubbles indicates the magnitude of the
enrichment p-values, transitioning from blue to red as the p-values
decrease.
The lead SNPs were analyzed by SNPnexus for REACTOME pathway analysis. Five SNPs
(rs3774611, rs9812977, rs2869782, rs8010482, rs61973994) may play a role in
REACTOME pathways related to muscle contraction, metabolism, and growth and
development ([Table 4]).
Table 4 Biological pathway analysis of SNPs related to the
training effect of maximal isokinetic leg press
strength.
|
Pathway ID
|
Description
|
Parent(s)
|
p-Value
|
Genes Involved
|
Variation IDs
|
|
R-HSA-5576893
|
Phase 2 – plateau phase
|
Muscle contraction
|
0.0117
|
CACNA1D
|
rs3774611
|
|
R-HSA-400042
|
Adrenaline,noradrenaline inhibits insulin secretion
|
Metabolism
|
0.0131
|
CACNA1D
|
rs3774611
|
|
R-HSA-5663213
|
RHO GTPases Activate WASPs and WAVEs
|
Signal Transduction
|
0.0164
|
NCKIPSD
|
rs9812977
|
|
R-HSA-419037
|
NCAM1 interactions
|
Developmental Biology
|
0.0196
|
CACNA1D
|
rs3774611
|
|
R-HSA-5576892
|
Phase 0 – rapid depolarisation
|
Muscle contraction
|
0.0196
|
CACNA1D
|
rs3774611
|
|
R-HSA-375165
|
NCAM signaling for neurite out-growth
|
Developmental Biology
|
0.0293
|
CACNA1D
|
rs3774611
|
|
R-HSA-422356
|
Regulation of insulin secretion
|
Metabolism
|
0.0358
|
CACNA1D
|
rs3774611
|
|
R-HSA-163125
|
Post-translational modification: synthesis of GPI-anchored
proteins
|
Metabolism of proteins
|
0.0417
|
LSAMP
|
rs2869782
|
|
R-HSA-8856825
|
Cargo recognition for clathrin-mediated endocytosis
|
Vesicle-mediated transport
|
0.0485
|
STON2
|
rs8010482
|
|
R-HSA-163685
|
Integration of energy metabolism
|
Metabolism
|
0.0490
|
CACNA1D
|
rs3774611
|
|
R-HSA-2672351
|
Stimuli-sensing channels
|
Transport of small molecules
|
0.0490
|
NALCN
|
rs61973994
|
Discussion
This is the first study at the whole-genome level to require genetic markers with
resistance training response of leg press. Fourteen lead SNPs were identified, with
a PGS greater than 5.31 were classified as high responders. Furtherly, combing
genetic and phenotypic variables, a comprehensive predictive model was established,
which explain 75.4% of the variance. Bioinformatics analysis revealed that
the lead SNPs might play a role in REACTOME pathways related to muscle contraction,
metabolism, and growth and development.
Evaluating isokinetic muscle strength is important in assessing the effectiveness of
strength training and sports injury rehabilitation for athletes, and improving
isokinetic muscle strength is beneficial for maintaining muscle strength and
improving posture balance [38]. A 12-week progressive
resistance training program was found to improve the isokinetic peak contraction
force of knee joint muscles in postmenopausal women [39], and a 12-week dynamic resistance training program had a significant
effect on improving the peak isokinetic hip/leg extension muscle force in
elderly men with osteoporosis [40]. In our study, 12
weeks of resistance training significantly improved the isokinetic leg press maximum
strength (with an average increase of 15.39%), while there exist individual
differences in sensitivity to the training program, resulting in varying training
effects among subjects. Although strength-related indicators such as biceps curls,
leg extension, and shoulder press 1RM increased significantly (P=0.001)
after 12 weeks of progressive resistance training intervention from a previous
study, there were differences in response rates, with 11.7%, 5.9%,
and 29.4% of subjects showing ineffective responses, respectively [41]. After 12 weeks of progressive resistance training
targeting the elbow flexor muscles, the subjects’ range of isometric
strength in their arms showed a significant difference, ranging from a decrease of
32% to an increase of 149% (−15.9 to 52.6 kg) [42]. The intervention approach used in this study
adhered to the commonly practiced load and frequency of strength training, which has
been proven highly effective in enhancing 1RM muscle strength [43]. However, concerning isokinetic strength changes,
resistance training exhibits significant variability in improving isokinetic
strength ([Fig. 1b] shows that around one-third of
participants experienced a decrease in isokinetic strength post-training). Similar
findings were observed in other studies, where roughly one-third of participants
either showed a decline or no change in isokinetic (60°/sec) and
isometric leg extensor strength after a 12-week resistance training program.
Conversely, another third of participants displayed a slight increase in both
isokinetic and isometric strength, while the remaining third experienced a
noteworthy improvement in both types of strength [44]. These results imply that a reduction in isokinetic strength after
resistance training is not uncommon in long-term studies and may be linked to
individual differences, physiological responses, or other factors. It is noteworthy
that despite meticulous control over the intervention and testing processes, this
study observed highly overlapping error bars ([Fig.
1a]). Similar observations were made in other studies, exemplified by a
16-week resistance training intervention involving 113 participants, showing a
14±12% increase in 1RM after 8 weeks and a 31±23%
increase after 16 weeks. This overlapping trend is considered a reasonable outcome
in studies with larger participant sizes, indicating genuine individual variations
in the effects of training.
Individual differences in training effects are influenced by both innate genetic
factors and environmental factors. It is widely accepted that the individual factors
affecting training effects under the same program mainly come from innate genetic
factors (genomics) and postnatal physical characteristic factors (phenotypes) [45]. Among the phenotype factors, multiple indicators
such as age, sex, BMI, and muscle mass can affect the training effects of resistance
training. Age and sex can influence the response of specific muscle fiber types to
resistance training. Studies have shown a significant increase in the percentage of
type I fibers (40~51%; p<0.05) after resistance training in
young women, but not in young men or elderly people [46]. Additionally, after 12 weeks of progressive resistance training, the
1RM of the bench press increased significantly in male and female subjects, but the
degree of improvement was different, with 14% and 23%, respectively,
indicating sex differences [47]. Muscle mass is the
foundation of muscle strength and therefore may also be a factor affecting training
effects. As for genetic factors, the heritability of muscle strength and power is
about 52% [48]. Candidate gene studies have
identified four genes with seven SNPs (ACE
rs4646994/rs1799752/rs4340/rs13447447, ACTN3
rs1815739, IL15RA rs2296135, PPARA rs4253778) that are related to
resistance training effects [49]. There may be more
genetic markers determining training effects, but currently, there is a lack of GWAS
studies at the genomic level.
In this study, we utilized GWAS analysis to identify 85 SNPs that exhibited a
significant association (p<1×10–5) with the
percentage change in isokinetic leg press maximum strength after 12 weeks of
resistance training. Among these SNPs, 9 (rs2619732, rs283442, rs283443, rs283445,
rs1419957, rs3101863, rs9812977, rs176104, and rs442533) reached genome-wide
significance (p<5×10–8). Fourteen SNPs
(rs2619732, rs1419957, rs9812977, rs2869782, rs61973994, rs73044028, rs12326802,
rs8010482, rs691587, rs3774611, rs12442146, rs12480160, rs79268297, and rs730741)
were identified as lead SNPs, which are the most strongly associated SNPs with the
phenotype of interest during genetic association analysis [34].
Of these SNPs, 10 (rs2619732, rs283442, rs283443, rs283445, rs176104, rs442533,
rs12326802, rs12480160, rs79268297, and rs730741) were found to be located near
pseudogenes, lincRNAs, and miRNAs. Pseudogenes, lincRNAs, and miRNAs are three types
of non-coding RNA in the genome [50]
[51]. Pseudogenes are thought to be products of gene
duplication and rearrangement, possessing DNA sequences and structures similar to
normal genes but lacking the regulatory elements and coding regions required by
normal genes. They may play a role in regulating gene expression or become toxic
genes that affect normal gene expression [52].
LincRNAs are non-coding RNAs longer than 200 nt and are usually located between two
genes in the genome. They do not participate in protein synthesis but regulate gene
expression, chromatin modification, histone modification, and RNA processing [53]. MiRNAs are non-coding RNAs approximately
20–24 nucleotides long that can bind to the 3’ untranslated region
(3'UTR) of target genes, inducing their degradation or inhibiting their
translation. LincRNAs and miRNAs have been shown to play important roles in
regulating gene expression, cell proliferation, apoptosis, differentiation, immune
response, and many diseases such as tumors, cardiovascular diseases, and
neuromuscular diseases [54]
[55]. However, their mechanisms in skeletal muscle growth, development,
and function require further exploration.
In this study, the other SNPs discovered were primarily located in non-coding regions
of genes. These regions, which include promoter regions, enhancer regions, and
transcription factor binding sites, can regulate gene transcription and translation
[56]. SNPs located in these regions may affect
transcription factor binding, promoter methylation status, or recruitment of RNA
polymerase, which can influence gene expression and function [57]. Moreover, these SNPs play a critical role in
disease susceptibility, drug response, and training sensitivity, as evidenced in
this study.
One of the identified SNPs, rs1419957, is located in the intron of the
Phosphotyrosine Interaction Domain Containing 1 (PID1) gene, which is involved in
energy metabolism in skeletal muscle and adipose tissue. PID1, showing significant
upregulation in abdominal adipose tissue of obese individuals, is associated with
glucose uptake pathways and insulin resistance in both adipose and muscle tissues
[58].
Another SNP discovered, rs3101863, is an intergenic variation located between the
Heart And Neural Crest Derivatives Expressed 1 (HAND1) and CTB-158E9.1 genes. The
protein encoded by the HAND1 gene comprises a basic helix-loop-helix (bHLH) domain,
facilitating its interaction with target genes and regulation of cardiac
developmental processes [59]. CTB-158E9.1 is a long
intergenic non-coding RNA (lincRNA), and at present, there is no research linking
either of these genes to muscle strength. Rs9812977 is located at the 5'UTR
region of the NCK interacting protein with SH3 domain (NCKIPSD) gene on
chromosome 3. The NCKIPSD gene is involved in signal transduction and contributes to
the assembly of myofibrils into sarcomeres, as well as the formation of stress
fibers. Furthermore, this protein plays a crucial role in the development and
maintenance of dendritic spines, while also exerting regulatory control over
synaptic activity in neurons [60]
[61]. REACTOME pathway analysis shows that rs9812977
affects the RHO GTPases Activate WASPs and WAVEs signaling pathway. Rs2869782 is
situated within the intron of the LSAMP gene. Additionally, the protein encoded by
this gene has been identified as a potential tumor suppressor. Nevertheless, there
is currently no available research elucidating its direct impact on skeletal muscle.
LSAMP is one of the target genes of miR-206, a skeletal muscle-specific
miRNA that promotes the transformation of type II fast glycolytic fibers into type I
slow oxidative fibers [62]. REACTOME pathway analysis
suggests that rs2869782 affects the metabolism of the proteins pathway
(i. e. post-translational modification: synthesis of GPI-anchored proteins).
Rs61973994 is situated within the intron of the NALCN gene, which encodes for the
NALCN ion channel, also known as the sodium leak channel. In recent years, many
studies have reported that NALCN plays an important role in many other basic
physiological processes, such as motor function, pain sensitivity, and circadian
rhythms [63]. Given the important role of NALCN in
congenital motor neuron development, it is speculated that rs61973994 may be related
to this gene’s effect on the isokinetic leg press maximum strength during
steady-state cycling. REACTOME pathway analysis suggests that rs61973994 affects the
Transport of small molecules (Stimuli-sensing channels) pathway. Rs73044028 is
located at the 3’ UTR region of the FXYD5 gene on chromosome 19.
The FXYD family is a recently discovered group of small-molecule single-pass
transmembrane proteins that have ion channel or ion channel regulatory functions,
and are closely associated with the structure and function of Na,K-ATPase. The FXYD
family comprises a group of small-molecule single-pass transmembrane proteins,
including FXYD1–7 in mammals. These proteins are known for their involvement
in ion channel regulation and their close association with the structure and
function of Na,K-ATPase. Among them, FXYD1, also referred to as phospholemman, and
FXYD5, known as dysadherin, are the predominant FXYD proteins found in skeletal
muscle [64]. Studies have demonstrated that
participation in high-intensity interval training (HIIT) results in a reduction in
FXYD5 levels and an elevation in the relative distribution of glycosylated
NKAβ1 within type IIa muscle fibers. Additionally, a negative correlation
has been observed between the abundance of FXYD5 in type IIa muscle fibers and
maximal oxygen consumption [65]. Rs8010482 is located
at an intron of the STON2 gene on chromosome 12. The STON2 gene encodes a membrane
protein that regulates endocytic complexes. This protein interacts with
synaptotagmin 1, which is required for neurotransmitter release, and is involved in
synaptic vesicle recycling. Multi-tissue eQTL analysis shows that rs8010482 has the
strongest normalized effect size (NES=0.152,
P=9.1×10–7) on STON2 expression in skeletal
muscle ([Fig. 5]). REACTOME pathway analysis
suggests that rs8010482 affects the vesicle-mediated transport signaling pathway.
Rs691587 is located between the TFF2 and TFF1 genes, which belong to
the trefoil factor (TFF) family. TFF1 was first identified in the MCF-7 breast
cancer cell line, and its full gene sequence was subsequently cloned [66]. Since then, numerous studies have found abnormal
expression of TFF family proteins (mainly TFF1 and TFF3) in various solid tumors
[67]
[68]
[69]. Research has shown that TFF2 is associated
with energy metabolism and can regulate skeletal muscle mass. TFF2 KO mice
had reduced gastrocnemius muscle mass but an increased percentage of gastrocnemius
muscle mass (because of simultaneous weight loss). Tff2 KO mice exhibited increased
mitochondrial energy production and improved energy utilization in skeletal muscle,
resulting in higher energy expenditure.[70].
Rs3774611 is located at the intron of the alpha1 D subunit of the calcium
voltage-gated channel (CACNA1D) gene. The CACNA1D gene mediates the entry of
calcium ions into excitable cells and is involved in various calcium-dependent
processes, including muscle contraction, hormone or neurotransmitter release, and
gene expression. REACTOME pathway analysis showed that this SNP may affect muscle
contraction (Phase 0 – rapid depolarization, Phase 2 – plateau
phase), metabolic pathways (adrenaline, noradrenaline inhibits insulin secretion,
regulation of insulin secretion, integration of energy metabolism), and growth and
development (NCAM1 interactions, NCAM signaling for neurite outgrowth). Rs12442146
is located between the RNU6–745P and TLE3 genes. The upstream
gene is a pseudogene, which is studied to a lesser extent. The downstream gene
encodes a protein that is involved in the regulation of skeletal muscle growth and
development. ChIP assays have revealed that TLE3 disrupts the binding of MyoD to the
promoter region of myogenin, indicating that TLE3 functions in the maintenance of
skeletal muscle homeostasis by suppressing the differentiation of satellite cells
through inhibiting the transcriptional activity of MyoD [71].
In the model predicting training effects on muscle strength from isokinetic leg press
using the lead SNPs as independent variables, seven SNPs (rs1419957, rs2619732,
rs8010482, rs73044028, rs12480160, rs12326802, rs61973994) were included, and the
model’s explanatory power for the training effect was 40.4%
(coefficient of determination R2=0.404). When phenotype
indicators including sex, age, the initial value of isokinetic leg press strength,
muscle mass, and muscle thickness, were added as independent variables along with
the PGS, the coefficient of determination for the training effect prediction model
increased to 75.4%. PGS emerged as a predictive factor with high explanatory
power for training effects. A genetic risk score known as PGS is calculated based on
the genetic information of multiple SNPs to predict an individual’s risk of
developing a particular disease or exhibiting a specific trait. PGS can assess an
individual’s genetic risk more accurately and provide personalized exercise
training recommendations than single SNPs. Currently, PGS is mainly used in
precision medicine to predict complex diseases [72]
[73], and it is rarely used to evaluate
sports performance or training effects. In this current study, the PGS constructed
by lead SNPs was positively correlated with isokinetic leg press strength
(r=0.68, p<0.01), suggesting that PGS can distinguish different
responders to strength training. Those who are identified as negative-responders
need to analyze possible reasons and adjust their training programs. In the
predictive model established in this study, the combined explanatory power of
multiple genetic markers (PGS) for the effectiveness of maximum muscle strength
training in isokinetic leg press is 49.9%, indicating that genetic factors
and phenotype factors (initial value, sex, combined 25.5%) are equally
important in determining maximum muscle strength in isokinetic leg press. In the
comprehensive model for predicting the resistance training effect, the initial value
of isokinetic leg press strength was included as phenotype indicator, with
coefficients of determination of 22.9%. These results suggest that the
initial value has a greater impact on the training effect, and individuals with
lower initial values are more likely to achieve higher improvements. Kassiano et al.
compared changes in lower limb muscle strength after 12 weeks of resistance training
in subjects with different levels of muscle strength, and they found that the
maximal muscle strength of leg extension changed more in subjects with lower
baseline values than those with higher baseline values [ESdiff=−0.45
(95%CI: −0.86, −0.04), P=0.030] [74]. Subjects with different initial values achieved
varying training effects on leg muscles, single-leg press, and maximal isometric
torque in knee extension after strength training, with improvements of
3.3±3.3%, 42±17%, and 8±10%,
respectively, indicating individual differences [75].
After combined training (resistance training+aerobic training), well-trained
individuals (with high initial values) showed negative effects on the maximum weight
of leg press and squat (effect size of −0.35, p<0.01) when compared
to resistance training alone, but there was no negative effect in individuals with
moderate training levels (−0.20, p=0.08) or those who were untrained
(with low initial values) (effect size of 0.03, p=0.87). These findings
suggest that the training effect of different protocols is also related to the
initial value [76]. Sex is considered one of the
predictive factors influencing the training effects of isokinetic leg press muscle
strength. Multiple studies have consistently demonstrated that the capacity for
strength adaptation is not influenced by one’s sex. Research conducted
revealed that following an 8-week resistance training program, both female and male
participants exhibited comparable improvements in isokinetic and isometric peak
torque [77]. Another study revealed that although
males demonstrated higher peak torque values for elbow flexors compared to females
before and after the intervention, no significant gender difference was observed in
terms of changes in strength [78]. Indeed, in this
study, despite including sex as a variable in the predictive model, it was found
that sex accounted for only 2.6% of the variation in the training effects of
isokinetic leg press muscle strength. This further supports the notion that the
benefits of resistance training in enhancing muscle strength are not heavily reliant
on an individual’s sex.
Contributor’s Statement
None
