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R E S E A R C H A R T I C L E Open Access
No association between ACTN3 R577X and
ACE I/D polymorphisms and endurance
running times in 698 Caucasian athletes
Ioannis D. Papadimitriou
1†
, Sarah J. Lockey
2†
, Sarah Voisin
1
, Adam J. Herbert
2
, Fleur Garton
3
, Peter J. Houweling
4
,
Pawel Cieszczyk
5
, Agnieszka Maciejewska-Skrendo
5
, Marek Sawczuk
6
, Myosotis Massidda
7
, Carla Maria Calò
7
,
Irina V. Astratenkova
8
, Anastasia Kouvatsi
9
, Anastasiya M. Druzhevskaya
8
, Macsue Jacques
1
, Ildus I. Ahmetov
8,10
,
Georgina K. Stebbings
2
, Shane Heffernan
2
, Stephen H. Day
2
, Robert Erskine
11,14
, Charles Pedlar
12
, Courtney Kipps
14
,
Kathryn N. North
4,13
, Alun G. Williams
2,14†
and Nir Eynon
1,4*†
Abstract
Background: Studies investigating associations between ACTN3 R577X and ACE I/D genotypes and endurance
athletic status have been limited by small sample sizes from mixed sport disciplines and lack quantitative measures
of performance. Aim: To examine the association between ACTN3 R577X and ACE I/D genotypes and best personal
running times in a large homogeneous cohort of endurance runners.
Methods: We collected a total of 1064 personal best 1500, 3000, 5000 m and marathon running times of 698 male
and female Caucasian endurance athletes from six countries (Australia, Greece, Italy, Poland, Russia and UK). Athletes
were genotyped for ACTN3 R577X and ACE ID variants.
Results: There was no association between ACTN3 R577X or ACE I/D genotype and running performance at any
distance in men or women. Mean (SD) marathon times (in s) were for men: ACTN3 RR 9149 (593), RX 9221 (582),
XX 9129 (582) p= 0.94; ACE DD 9182 (665), ID 9214 (549), II 9155 (492) p= 0.85; for women: ACTN3 RR 10796 (818),
RX 10667 (695), XX 10675 (553) p= 0.36; ACE DD 10604 (561), ID 10766 (740), II 10771 (708) p= 0.21. Furthermore,
there were no associations between these variants and running time for any distance in a sub-analysis of athletes
with personal records within 20% of world records.
Conclusions: Thus, consistent with most case-control studies, this multi-cohort quantitative analysis demonstrates it
is unlikely that ACTN3 XX genotype provides an advantage in competitive endurance running performance. For ACE
II genotype, some prior studies show an association but others do not. Our data indicate it is also unlikely that ACE
II genotype provides an advantage in endurance running.
Keywords: ACTN3, ACE, Genomics, Athletic performance, Endurance, Champions
Background
Although the likelihood of becoming an elite athlete is
probably influenced by genetic variations across the hu-
man genome [1, 2], there is currently no evidence for a
common genetic profile specific to elite endurance
athletes, even when utilising a Genome-Wide Associ-
ation Study (GWAS) approach [3]. However, there is
considerable evidence suggesting that ACTN3 R577X
and ACE I/D gene variants do influence muscle per-
formance and metabolism in humans [4].
A common null polymorphism (rs1815739) was identi-
fied in the ACTN3 gene, which results in the replace-
ment of an arginine (R) residue with a premature stop
codon (X) at amino acid 577. Approximately 18% of the
world population (~1.5 billion individuals) harbour the
ACTN3 577XX genotype and consequently are
* Correspondence: nir.eynon@vu.edu.au
†
Equal contributors
1
Institute of Sport, Exercise and Active Living (ISEAL), Victoria University,
Victoria, Australia
4
Murdoch Children’s Research Institute, Melbourne, Australia
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Papadimitriou et al. BMC Genomics (2018) 19:13
DOI 10.1186/s12864-017-4412-0
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
completely deficient in α-actinin-3 protein. Importantly,
α-actinin-3 deficiency does not cause any obvious
muscle disease [5].
An association between the ACTN3 R577X genotype
and athletic performance was initially found in a cohort of
elite Australian athletes [6], with a very low proportion of
elite sprint/power athletes harbouring the 577XX geno-
type. This genotype distribution pattern was quite consist-
ent in other independent cohorts of elite athletes and has
since been replicated in Finnish [7], Greek [8], Russian [9],
Israeli [10], Polish [11] and Japanese [12] athletes.
A tendency for a higher proportion of elite athletes car-
rying the 577XX genotype was also found in Australian
athletes excelling in aerobic activities [6], showing some
evidence for association of this genotype with endurance
performance. While this association was replicated in
some cohorts of athletes [10, 13] other studies have shown
no association between the ACTN3 R577X genotypes and
endurance athletic status [7, 8]. Furthermore, a large study
with Russian endurance athletes found that the frequency
of the XX genotype was lower in endurance athletes than
in controls [14], demonstrating the conflicting results be-
tween the association of this gene variant and endurance
athletic performance. In line with this finding, an analysis
comparing 50 elite male endurance cyclists and 52
Olympic-level endurance runners with 123 sedentary male
controls [15] found no difference in genotype frequencies
between controls and either of the two athlete groups.
There was also no association between R577X genotypes
and a common measure of endurance performance - max-
imal oxygen uptake (VO
2max
) - in either of the athlete
groups. Cross-sectional studies showed no association
of ACTN3 XX genotype with endurance performance
[15, 16] as well, and debate is ongoing on whether the
ACTN3 gene influences endurance performance. In a
different human sporting context, namely the team sport
of rugby union, the R allele has recently been associated
with success in playing positions reliant on sprinting
speed, while the X allele was associated with playing de-
mands allowing relatively short recovery times [17].
Another candidate gene associated with elite perform-
ance is the ACE I/D polymorphism. The absence (dele-
tion allele, D) rather than the presence (insertion allele,
I) of a 287 base pair fragment is associated with higher
tissue [18] and serum [19] ACE activity. While not dir-
ectly functional [20], the ACE I/D polymorphism is re-
lated to ACE activity and accounts for up to 40% of the
variation in circulating ACE activity in Caucasians [19].
An association between the ACE I/D polymorphism and
athletic performance was initially found in a cohort of
British mountaineers [21], with a very a low proportion
of elite mountaineers harbouring the ACE DD genotype.
This genotype distribution pattern was replicated in co-
horts of elite endurance athletes [22, 23]. However,
conflicting data also exist with ACE I/D genotype and
endurance performance [24, 25] also found no associ-
ation between the ACE I/D genotype with VO
2max
or its
response to a 20-week endurance training programme in
the HERITAGE Family study. Nevertheless, a more re-
cent meta-analysis concluded that ACE II genotype was
associated with superior endurance performance, with
an odds ratio of 1.35 [4], however this was not replicated
in the GAMES GWAS cohort analysis [3].
One of the limitations of most of the abovementioned
studies investigating the association between the ACTN3
R577X and the ACE I/D genotypes and athletic status is
the grouping of endurance athletes from mixed sport
disciplines and events (e.g. middle distance runners, long
distance runners, cyclists, swimmers), or analysing team
sport athletes from a single sport yet with some varia-
tions in physiological demand according to playing pos-
ition [17]. These approaches, while understandable given
the very low number of World-class competitors in a
single sport or event, reduce the consistency of the
phenotype. Furthermore, those studies only used a sim-
ple case-control design based on athletic status without
looking at measurable (quantitative) traits within the
compared groups [26] and no studies have quantitatively
linked those genotypes with endurance performance
(e.g. running times) in elite athletes.
We sought to address these limitations by providing
deeper insight into the possible association between the
ACTN3 R577X and the ACE I/D variants and endurance
performance. In the present study, we used the same
quantitative approach previously introduced in elite
sprinters [27] that showed both ACTN3 R577X and ACE
I/D genotypes have a substantial association with sprint
performance (100-400 m run) at the elite level. The aim
of this study was to examine the association between the
ACTN3 R577X and ACE I/D variants and personal best
running times in 1500 m, 3000 m, 5000 m, 10,000 m
and marathon in a large cohort of male and female
Caucasian endurance runners.
Methods
The methodology been used in genotyping, data collec-
tion and statistical analysis has been previously described
[17, 27].
Participants
A total of 1064 personal best 1500, 3000, 5000, 10,000 m
and marathon running times of 698 Caucasian endur-
ance athletes (441 males and 257 females) from
Australia (n= 14), Greece (n= 16), Italy (n= 9), Poland
(n= 60), Russia (n= 17) and the UK (n= 582), were ana-
lysed (Table 1). The endurance runners’personal best
times in official competitions were found online
(www.iaaf.org and www.thepowerof10.info) or provided
Papadimitriou et al. BMC Genomics (2018) 19:13 Page 2 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
by coaches or the athletes themselves and independently
corroborated.
We grouped the participants’personal best times by
event (1500, 3000, 5000, 10,000 m or marathon) as has
been previously described [27]. First, we analysed the
whole cohort of males and females separately. Then, we
also performed a sub-analysis in males including only
the endurance runners with times that were within 20%
of the current world record of the examined events, fol-
lowing a similar approach to our recently published
work [27]. We did not analyse the females in this sub-
analysis because the sample size was too low (i.e. n<5
for the XX genotype and n< 6 for the II genotype). We
used the following world records as references:
Male endurance runners. 3:26.00 in the 1500 m
(Hicham El Guerrouj, Morocco), 12:37.35 in the
5000 m (Kenenisa Bekele, Ethiopa, 26:17.53 in the
10,000 m (Kenenisa Bekele, Ethiopia), 2:02:57 in the
Marathon Dennis Kipruto Kimetto, Kenya);
Female endurance runners. 3:50.07 in the 1500 m
(Genzebe Dibaba, Ethiopia), 14:11.15 in the 5000 m
(Tirunesh Dibaba, Ethiopia), 29:17.45 in the
10,000 m (Almaz Ayana, Ethiopia), 2:17:42 in the
Marathon (Paula Radcliffe, UK).
Genotyping
In the UK ~70% of the samples were collected as whole
blood, ~20% as buccal swabs, ~10% as saliva. As has
been previously described [17] blood was drawn from a
superficial forearm vein into an EDTA tube and stored
in sterile tubes at −20 °C until processing. Saliva samples
were collected into Oragene DNA OG-500 collection
tubes (DNA Genotek, Ottawa, Ontario, Canada) accord-
ing to the manufacturer’s protocol and stored at room
temperature until processing. Sterile buccal swabs
(Omni swab; Whatman, Springfield Mill, UK) were
rubbed against the buccal mucosa of the cheek for 30 s.
Tips were ejected into sterile tubes and stored at −20 °C
until processing. Genomic DNA was isolated from buc-
cal epithelium, or white blood cells. In the UK DNA iso-
lation was performed with the QIAamp DNA Blood
Mini kit and standard spin column protocol, following
the manufacturer’s instructions (Qiagen, West Sussex,
Table 1 Mean (SD) 1500 m, 3000 m, 5000 m, 10,000 m and marathon best running times in (a) males and (b) females in the three
ACTN3 R577X genotypes
(a)
ACTN3R577X males RR
N= 380
34.7%
RX
N= 492
44.9%
XX
N= 224
20.4%
Additive
(RR = 0, RX = 1, XX = 2)
Recessive
(RR = RX = 0, XX = 1)
Running time 1500 m (s) 232.6 (10.7)
43.5%
234.2 (13.2)
39.1%
234.0 (15.1)
17.4%
p= 0.54 p= 0.81
Running time 3000 m (s) 509.6 (27.5)
37.2%
517.0 (28.0)
44.2%
518.1 (26.9)
18.6%
p= 0.08 p= 0.37
Running time 5000 m (s) 902.1 (61.7)
33.6%
912.2 (61.8)
46.2%
913.2 (59.5)
20.2%
p= 0.25 p= 0.58
Running time 10,000 m (s) 1860.9 (109.2)
34.8%
1885.7 (125.8)
42.9%
1889.3 (112.1)
22.4%
p= 0.22 p= 0.51
Running time marathon (s) 9148.6 (593.0)
30.4%
9220.7 (582.1)
47.6%
9129.1 (581.6)
22.0%
p= 0.94 p= 0.41
(b)
ACTN3R577X females RR
N= 156
29.6%
RX
N= 301
57.1%
XX
N=70
13.3%
Additive
(RR = 0, RX = 1, XX = 2)
Recessive
(RR = RX = 0, XX = 1)
Running time 1500 m (s) 269.4 (15.4)
34.7%
268.3 (14.0)
55.6%
262.6 (15.4)
9.7%
p= 0.35 p= 0.30
Running time 3000 m (s) 600.9 (44.7)
30.4%
602.8 (37.3)
61.6%
600.2 (48.5)
8.0%
p= 0.93 p= 0.89
Running time 5000 m (s) 1028.2 (79.1)
29.2%
1048.9 (97.1)
59.4%
1048.2 (88.9)
11.5%
p= 0.39 p= 0.83
Running time 10,000 m (s) 2067.6 (153.6)
27.7%
2101.0 (159.9)
57.4%
2067.6 (153.6)
14.9%
p= 0.61 p= 0.91
Running time marathon (s) 10,796.4 (818.2)
28%
10,667.3 (695.3)
54%
10,675.3 (552.8)
18%
p= 0.36 p= 0.78
All running times are expressed in seconds because statistical analyses were performed on times converted to seconds. The last two columns of the table
correspond to the p-value of the linear regression, using an additive or a recessive genetic model. The percentage values represent the genotype proportions
Papadimitriou et al. BMC Genomics (2018) 19:13 Page 3 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
UK). In brief, 200 μL of whole blood/saliva, or one buc-
cal swab, was lysed and incubated, the DNA washed,
and the eluate containing isolated DNA stored at 4 °C.
The Australian, Greek and Italian endurance runners’
DNA samples were genotyped using the polymerase
chain reaction (PCR)-restriction fragment length poly-
morphism (RFLP) method as previously described [5].
The DNA samples of the UK, Polish and Russian endur-
ance runners were genotyped in duplicates using an al-
lelic discrimination assay on a Step One Real-Time PCR
instrument (Applied Biosystems, Carlsbad, California,
USA) with TaqMan® probes. To discriminate ACTN3
R577X (rs1815739) and the ACE I/D alleles, a TaqMan®
Pre-Designed SNP Genotyping Assay was used (assay
ID: C_590093_1_ for rs1815739 (ACTN3 R577X) and
C__29403047_10 for rs4341 (a tag SNP in perfect
linkage disequilibrium with the 287-bp ACE I/D in
Caucasians [28])), including appropriate primers and
fluorescently labelled (FAM and VIC) MGB™probes to
detect the alleles. For the genotyping of the UK samples
the StepOnePlus and Chromo4 (Bio-Rad, Hertfordshire,
UK) were used.
Statistical analysis
To compare the endurance athletes’running times be-
tween ACTN3 R577X or ACE I/D genotypes, we con-
verted the running times to seconds and initially used
the one-way analysis of variance (ANOVA). Then a sim-
ple linear regression with running time as the dependent
variable and genotypes as the independent variable was
also applied. We used two genetic models: the additive
model where RR = 0, RX = 1 and XX = 2, or DD = 0, ID
= 1 and II = 2, and the recessive genetic model where
RR = RX = 0 and XX = 1 or DD = ID = 0 and II = 1 as has
been previously described [27]. Males and females were
analysed separately. The level of significance was set at
0.05. All data analyses were conducted with the R statis-
tical software with the lme4 and lrtest packages.
Results
The mean (SD) personal best 1500 m, 3000 m, 5000 m,
10,000 m and marathon running times, according to the
ACTN3 and ACE genotype and distribution, are
presented in Tables 1 and 2, respectively.
Table 2 Mean (SD) 1500 m, 3000 m, 5000 m, 10,000 m and marathon best running times in (a) males and (b) females in the three
ACE I/D genotypes
(a)
ACE I/D males DD
N= 314
32.4%
ID
N= 452
46.6%
II
N= 204
21.0%
Additive
(DD = 0, ID = 1, II = 2)
Recessive
(DD = ID = 0, II = 1)
Running time 1500 m (s) 233.3 (16.4)
29.9%
235.1 (11.6)
45.8%
235.7 (13.1)
24.3%
p= 0.50 p= 0.67
Running time 3000 m (s) 519.5 (28.0)
34.7%
517.7 (29.4)
43.7%
518.1 (24.7)
21.6%
p= 0.77 p= 0.93
Running time 5000 m (s) 914.1 (62.9)
31.7%
918.9 (60.8)
48.0%
916.1 (53.4)
20.3%
p= 0.80 p= 0.93
Running time 10,000 m (s) 1882.6 (101.3)
32.4%
1894.1 (116.6)
47.5%
1908.0 (132.1)
20.1%
p= 0.36 p= 0.45
Running time marathon (s) 9181.8 (665.1)
32.4%
9213.7 (549.0)
47.0%
9155.3 (491.5)
20.6%
p= 0.85 p= 0.57
(b)
ACE I/D females DD
N= 127
28.4%
ID N= 229
51.2%
II
N=91
20.4%
Additive
(DD = 0, ID = 1, II = 2)
Recessive
(DD = ID = 0, II = 1)
Running time 1500 m (s) 268.7 (11.8)
37.5%
271.1 (18.8)
45.8%
263.9 (14.5)
16.7%
p= 0.65 p= 0.32
Running time 3000 m (s) 595.1 (31.2)
27.0%
612.9 (41.2)
53.9%
617.9 (40.2)
19.1%
p= 0.05 p= 0.30
Running time 5000 m (s) 1062.1 (65.0)
26.0%
1057.2 (103.0)
50.6%
1052.9 (95.9)
23.4%
p= 0.76 p= 0.81
Running time 10,000 m (s) 2140.8 (112.6)
30.6%
2090.4 (170.3)
52.8%
2095.1 (184.6)
16.7%
p= 0.48 p= 0.85
Running time marathon (s) 10,604.3 (560.9)
27.4%
10,765.9 (740.0)
51.3%
10,771.1 (707.8)
21.3%
p= 0.21 p= 0.61
All running times are expressed in seconds because statistical analyses were performed on times converted to seconds. The last two columns of the table
correspond to the p-value of the linear regression, using an additive or a recessive genetic model. The percentage values represent the genotype proportions
Papadimitriou et al. BMC Genomics (2018) 19:13 Page 4 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
ANOVA revealed no differences among the three geno-
types (p>0.05) of either ACTN3 or ACE and running per-
formance at any distance. Similarly, linear regression
analysis using an additive or recessive genetic model also
showed no association between running time and genotype
of either genetic variant at any running distance (p>0.05).
No association between ACTN3 R577X or ACE I/D
genotypes and personal best times in the whole cohort
In males and females alike and regardless of the chosen
statistical analysis or genetic model, we found no associ-
ation between either ACTN3 R577X or ACE I/D genotype
and 1500 m, 3000 m, 5000 m, 10,000 m or marathon
personal best times (Figs. 1 and 2, Tables 1 and 2).
No association between ACTN3 R577X or ACE I/D
genotypes and personal best time in males within 20% of
the world record
In males only, we conducted a sub-analysis of the ath-
letes displaying times within 20% of the World record
for the corresponding event, to see whether an associ-
ation with the ACTN3 R577X or ACE I/D variants could
be detected at the high end of the performance
spectrum. Regardless of the chosen statistical analysis or
genetic model, we found no association between ACTN3
R577X or ACE I/D genotypes and 1500 m, 3000 m,
5000 m, 10,000 m or marathon personal best time for
those athletes within 20% of the world record (Table 3).
Discussion
Here, we have utilised similar approach as we previously
did in elite sprinters [27] in a large cohort of elite
Caucasian endurance runners. This quantitative assess-
ment of genotype with qualifying time in 1064 personal
best times of 698 elite endurance runners suggests that the
potential association between ACTN3 R577X and ACE I/D
genotypes and elite endurance running time is unproven.
In the present study, we examined whether a genotype
association existed within athletes competing in particu-
lar endurance-running events (1500 m, 3000 m, 5000 m,
10,000 m and marathon) and in a subset of high-level
athletes with personal-best times within 20% of the
World record. Previous reports have grouped together
endurance athletes from mixed endurance sports disci-
plines and events without quantifying measures of their
actual endurance performance [6, 8–12]. Here, we have
embraced a more stringent approach and included only
endurance runners whose main sporting discipline was
the 1500, 3000, 5000, 10,000 m or marathon, including
their personal-best running performance. In this manner,
we were able to differentiate between events that are es-
timated to have a different energy reliance on the aer-
obic system ranging from 77 to 86% (1500 m), 86-94%
(3000 m) whereby it becomes increasingly dependent on
aerobic metabolism up to the Marathon [29]. Despite
addressing these subtle performance requirement differ-
ences, our results suggest that neither the ACTN3
R577X nor ACE I/D polymorphisms are likely to influ-
ence Caucasian endurance runners’personal best times
in 1500, 3000, 5000, 10,000 m and marathon, even at the
high end of the performance spectrum.
The Actn3 KO mouse model attempted to mimic the
ACTN3 R577X polymorphism in humans. Metabolically,
the KO mice show a higher activity of oxidative enzymes
and a lower activity of enzymes involved in the anaerobic
pathway [30]. In addition, KO mice show enhanced glyco-
gen accumulation due to lower glycogen phosphorylase
activity [30, 31]. Their fast skeletal muscle fibre properties
Fig. 1 Individual 1500 m, 3000 m, 5000 m, 10,000 m personal best times in (a) male and (b) female endurance athletes according to their ACTN3
R577X genotype. Data are shown as boxplots and time is expressed in seconds. The red dashed line on each plot corresponds to the
competition entry standard for the 2016 Olympic Games. 3000 m is not an Olympic event, so there is no red dashed line for this event
Papadimitriou et al. BMC Genomics (2018) 19:13 Page 5 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Fig. 2 Individual 1500 m, 3000 m, 5000 m, 10,000 m personal best times in (a) male and (b) female endurance athletes according to their ACE I/
D genotype. Data are shown as boxplots and time is expressed in seconds. The red dashed line on each plot corresponds to the competition
entry standard for the 2016 Olympic Games. 3000 m is not an Olympic event, so there is no red dashed line for this event
Table 3 Mean (SD) 1500 m, 5000 m, 10,000 m and marathon best running times in males within 20% of the world record in (a) the
three ACTN3 R577X genotypes and (b) the three ACE I/D genotypes
(a)
ACTN3R577X males RR
N= 296
37.0%
RX
N= 340
42.6%
XX
N= 163
20.4%
Additive
(RR = 0, RX = 1, XX = 2)
Recessive
(RR = RX = 0, XX = 1)
Running time 1500 m (s) 230.8 (7.5)
44.2%
230.0 (8.1)
38.0%
228.7 (11.8)
17.8%
p= 0.34 p= 0.42
Running time 3000 m (s) 497.0 (16.2)
38.3%
502.2 (16.6)
42.8%
503.1 (18.8)
18.9%
p= 0.08 p= 0.37
Running time 5000 m (s) 857.5 (29.9)
34.6%
861.2 (30.2)
45.0%
861.7 (31.2)
20.4%
p= 0.52 p= 0.76
Running time 10,000 m (s) 1789.4 (66.0)
36.8%
1776.8 (66.6)
42.4%
1788.7 (70.7)
20.8%
p= 0.83 p= 0.76
Running time marathon (s) 8502.7 (330.0)
33.3%
8471.5 (302.1)
43.1%
8462.0 (337.8)
23.6%
p= 0.64 p= 0.76
(b)
ACE I/Dmales DD
N = 229
33.7%
ID
N= 306
45.0%
II
N= 145
21.3%
Additive
(DD = 0, ID = 1, II = 2)
Recessive
(DD = ID = 0, II = 1)
Running time 1500 m (s) 228.1 (11.0)
29.2%
231.6 (8.0)
46.9%
232.1 (8.2)
24.0%
p= 0.11 p= 0.39
Running time 3000 m (s) 501.8 (16.9)
34.0%
502.1 (18.3)
43.3%
505.3 (17.0)
22.7%
p= 0.49 p= 0.42
Running time 5000 m (s) 866.9 (29.2)
34.4%
861.5 (33.7)
45.9%
858.6 (29.2)
19.7%
p= 0.32 p= 0.52
Running time 10,000 m (s) 1795.5 (66.8)
35.5%
1789.8 (73.2)
47.7%
1786.4 (64.8)
16.8%
p= 0.70 p= 0.79
Running time marathon (s) 8495.1 (287.9)
34.1%
8507.6 (343.5)
42.9%
8457.5 (327.6)
22.9%
p= 0.79 p= 0.64
All running times are expressed in seconds because statistics were performed on times converted to seconds. The last two columns of the table correspond to
the p-value of the linear regression, using an additive or a recessive genetic model. The percentage values represent the genotype proportions
Papadimitriou et al. BMC Genomics (2018) 19:13 Page 6 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
shift towards a slower metabolic profile, which has been
linked to an increase in calcineurin signalling activity [32]
and theoretically can favour endurance performance. Top-
level endurance running performance is considered to be
predominately based on the metabolic profile of slow-
twitch (type I) and some recruitment of intermediate (type
IIa) fibres due to the high reliance on the aerobic energy
system, which has been hypothesized to favour the 577XX
genotype. However, it may also depend on the endurance
runner’s ability to recruit a greater number of type IIa
myofibres during tactical surges (competitively critical
phases requiring increase in pace) or finishing stages of a
race (a sprint over a short distance), both of which require
an increase in anaerobic energy/muscle recruitment (and
may favour the 577RR genotype). While it is difficult to
determine the relative contribution of muscle fibres in hu-
man competitive performance, it is well understood that
murine muscle contains a significantly higher percentage
of myofibres with faster twitch properties than human
muscle and any association with the presence/absence of
α-actinin-3 protein would probably be enhanced in this
model. Therefore, any such association in humans might
be extremely limited, thus offering no tangible or detect-
able advantage to a competitive α-actinin-3 deficient
(ACTN3 577XX) endurance runner. In our study, we in-
cluded only Caucasian endurance athletes because thus
far we have recruited insufficient individuals of other eth-
nicities for effective analysis, so we cannot rule out the
possibility that an association exists between ACTN3
R577X or ACE I/D and endurance running performance
in elite runners with different geographic ancestry. How-
ever, most published associations on which our original
hypothesis was based involved athletes or other individ-
uals who were Caucasians.
Endurance performance is considered to be a complex
trait effected by both genetic and environment (training)
[33]. As recently shown, not only metabolism but also an-
thropometric and biomechanical factors are important in
determining elite performance success [34]. Here we have
not found evidence that supports the involvement of the
two gene variants we studied in endurance running per-
formance. Endurance running performance is dependent
on extensive training and there is little evidence of either
ACTN3 R577X or ACE I/D being associated with training
responses of aerobic parameters [35]. Interestingly, in
addition to this, it has been suggested that epigenetic
modifications related to the ACE gene may also have a
part to play in these discrepant findings [36]. Indeed, epi-
genetic factors such as CpG islands that modify the ex-
pression of genes without alteration to the DNA coding
sequence have been identified in the ACE gene promoter
[37]. Therefore, future studies on the ACE gene, in rela-
tion to human endurance, could benefit if these epigenetic
factors that regulate ACE expression [37] were considered
in addition to the I/D genotype [36]. These epigenetic fac-
tors reported to influence ACE activity cannot be con-
trolled by our quantitative approach and sophisticated
experimental designs are required to control more exter-
nal factors and possibly explain some of the discrepant
findings between ACTN3 R577X and ACE I/D genotype
and endurance phenotypes.
The multi-centre cohort we used is larger than any pre-
vious such study regarding endurance performance, but a
very small effect size could still go undetected using our
sample size. It is increasingly recognised that the reality of
complex human biology is that inter-individual differences
in endurance performance are expected to be influenced
by many common and perhaps rare genetic variations;
none of which have been discovered at a genome-wide
significance level or consistently replicated [3].
Our study of quantitative measures of endurance per-
formance in a large homogeneous group of elite endur-
ance runners suggests that the potential association
between ACTN3 R577X and ACE I/D genotypes and elite
endurance running performance should be regarded as
unproven. Large population studies are needed to detect
significant proportions of the underlying genetic profile
and biology contributing to endurance performance.
Conclusions
In conclusion, this study has presented evidence that the
ACTN3 and ACE polymorphisms are not associated with
running performance in elite athletes, contrary to our
hypotheses and again exposing the fallacy of products
offered by numerous direct-to-consumer (DTC) genetic
testing companies [38]. Our understanding of the gen-
etic influences on human physical performance is evolv-
ing rapidly in the postgenomic era. Much more work
remains to be done to answer a number of major ques-
tions in the field. One study utilising a genome-wide ap-
proach has been published recently [3] and future
studies investigating the genomic contribution to elite
endurance performance using genome-wide and targeted
sequencing approaches are still needed to discover more
genetic variants contributing to human physical per-
formance capability. Understanding both genetic and en-
vironmental contributions, and how they interact, will
be beneficial in understanding elite performance and
muscle function in sport, health and disease.
Abbreviations
DTC: Genetic testing companies: Direct-to-Consumer genetic testing com-
panies; GWAS: Genome-Wide Association Study
Acknowledgments
IDP wishes to dedicate this paper to all athletes from all countries who
participated in this multi-centre study.
Funding
No funding was received to assist in the preparation of this manuscript.
Papadimitriou et al. BMC Genomics (2018) 19:13 Page 7 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Availability of data and materials
The Authors declare that they have full control of the primary data and they
agree to allow the journal to review their data if requested. The datasets
used and/or analysed during the current study are available from the
corresponding author on reasonable request.
Authors’contributions
IDP: conceived the idea, done the initial analysis, wrote the paper and
contributed in data collection and interpretation of the results. SJL, SV, AJH,
FG, PJH, PC, AM, MS, MM, CMC, IVA, AK, AMD, MJ, IIA, GKS, SH, SHD, RE, CP,
CK, KNN, AGW, NE: provided feedback on the initial draft, involved in data
collection, data analysis, statistics, revised the manuscript and made
substantial contributions in interpretation of the results. All authors read and
approved the final manuscript.
Ethics approval and consent to participate
Ethical approval was obtained from the Human Research Ethics Committees
of the Children’s Hospital at Westmead (2003/086), the RCH Human Research
Ethics Committee (35172), the ethics committee of the Manchester
Metropolitan University, the Lithuanian National Committee of Biomedical
Ethics, the Ethics Committee of Kazan State Medical University, the Ethics
Committee of Gdansk University, the Ethics Committee of the University of
Cagliari, and the UHWI/UWI/FMS Ethics Committee. All studies were
conducted in accordance with the ethical standard laid down in the 1964
Declaration of Helsinki and its later amendments. From all athletes, informed
consent to participate in the study was obtained and all participants signed
a consent form.
Consent for publication
This manuscripts doesn’t include images/videos or any information relating
to an individual person or participant.
Competing interests
All authors declare that they have no competing interests to declare that are
directly relevant to the content of this manuscript. The corresponding author
recently became a member of the editorial board of this journal.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Institute of Sport, Exercise and Active Living (ISEAL), Victoria University,
Victoria, Australia.
2
Sports Genomics Laboratory, Manchester Metropolitan
University, Crewe, UK.
3
Institute for Molecular Bioscience, University of
Queensland, Queensland, Australia.
4
Murdoch Children’s Research Institute,
Melbourne, Australia.
5
Faculty of Physical Education, Gdansk University of
Physical Education and Sport, Gdansk, Poland.
6
Faculty of Tourism and
Recreation, Gdansk University of Physical Education and Sport, Gdańsk,
Poland.
7
Department of Life and Environmental Sciences, University of
Cagliari, Cagliari, Italy.
8
Sports Genetics Laboratory, St Petersburg Research
Institute of Physical Culture, St Petersburg, Russia.
9
Department of Genetics
Development and Molecular Biology, Aristotle University of Thessaloniki,
Thessaloniki, Greece.
10
Laboratory of Molecular Genetics, Kazan State Medical
University, Kazan, Russia.
11
Research Institute for Sport and Exercise Sciences,
Liverpool John Moores University, Liverpool, UK.
12
School of Sport, Health
and Applied Science, St Mary’s University College, Twickenham, UK.
13
Department of Paediatrics, University of Melbourne, Victoria, Australia.
14
Institute of Sport, Exercise and Health, University College London, London,
UK.
Received: 27 October 2017 Accepted: 22 December 2017
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