Table Of ContentMolecular and Translational Medicine
Series Editors
William B. Coleman
Gregory J. Tsongalis
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Linda S. Pescatello Stephen M. Roth
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Editors
Exercise Genomics
Foreword by Claude Bouchard
Editors
Linda S. Pescatello Stephen M. Roth
Human Performance Laboratory Department of Kinesiology
Department of Kinesiology School of Public Health
Neag School of Education University of Maryland
University of Connecticut College Park, MD 20742-2611, USA
Storrs, CT 06269-1110, USA sroth1@umd.edu
Linda.Pescatello@uconn.edu
ISBN 978-1-60761-354-1 e-ISBN 978-1-60761-355-8
DOI 10.1007/978-1-60761-355-8
Springer New York Dordrecht Heidelberg London
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Linda’s dedication: I will conclude my
comments by acknowledging my colleagues
from the Department of Kinesiology at the
University of Connecticut and Department
of Cardiology at Hartford Hospital, and my
past and current students who continue to
inspire me with the enthusiasm for the work
they do. Most importantly, I acknowledge
my husband Dave, daughter Shannon, and
son Conor, my parents and other family
members, and my good friends who have
provided me with the love, support, and
balance that has enabled me to pursue a
career that continues to excite me.
Steve’s dedication: I am indebted to my
many colleagues in the field of exercise
genomics for their collaboration and
encouragement, and am particularly
grateful to my wife and three children
for their unconditional love and support.
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Foreword
We have seen in recent years a major increase in the number of scientific,
peer-reviewed papers dealing with the genetic and molecular basis of physical
activity level and indicators of health-related fitness and physical performance.
This information explosion has been complemented by a number of initiatives that
sought to integrate data and trends across technologies and areas of exercise science
and sports medicine. The first example of the latter was the 1997 book on Genetics
of Fitness and Physical Performance [1]. Subsequently, beginning in 2000, a series
of reviews focusing on the evolution of the fitness and performance gene map were
published in Medicine and Science in Sports and Exercise [2, 3]. This annual ency-
clopedic summary of the published research has now morphed into a new annual
review emphasizing the strongest publications with discussions on their implica-
tions [4]. A third major undertaking took the form of a volume published in the
Encyclopaedia of Sports Medicine series of the International Olympic Committee
dealing exclusively with the genetics and molecular basis of fitness and perfor-
mance [5]. Authors from 48 laboratories in 13 countries contributed the 33 chapters
of this large effort. The most recent addition to these initiatives is this volume
Exercise Genomics in a series on Molecular and Translational Medicine whose aim
is to provide integrated, horizontal views of where the field stands and how to
apprehend the future [6].
The editors, Drs. Pescatello and Roth, have asked me to write an introductory
comment on their volume with an emphasis on key findings from the HEealth, RIsk
factors, exercise TrAining and GEnetics, or HERITAGE Family Study. The primary
purpose of the HERITAGE, Family Study was to examine the health fitness-related
responses to 20 weeks of aerobic training in 742 sedentary, healthy subjects without
chronic disease from approximately 200 families [7].
It is well recognized by now that genetic variation plays a significant role in the
global human heterogeneity in exercise-related traits. This advancement has been
documented for many health-related fitness and performance endophenotypes and
phenotypes in several ethnic groups but perhaps more strikingly in the HERITAGE
Family Study. HERITAGE provided strong evidence that maximal oxygen uptake
(VO max) is characterized by a substantial genetic component among sedentary
2
adults, with an estimated heritability of at least 50% [8]. One of the underlying
vii
viii Foreword
assumptions of HERITAGE was that it would be easier to identify and dissect the
genetic component of the response to a standardized training program than to
undertake the same effort with traits measured in a cross-sectional cohort. In retro-
spect, this assumption appears to be correct.
In HERITAGE, after 20 weeks of exercise training, in 473 adults from 100 fami-
lies of Caucasians the mean increase in VO max was about 400 mL O• min−1 but
2 2
the standard deviation of the gain reached 200 mL O• min−1. There were individuals
2
who did not gain at all, and a large fraction who qualified as low responders. On
the other hand, a fraction registered a gain of at least 600 mL O• min−1, and some
2
improved by as much as 1,000 mL O• min−1. These individual differences in train-
2
ability were not randomly distributed as evidenced by the fact there was 2.5 times
more variance in the VO max gains between families compared to the variance in
2
response observed among family members. The heritability coefficients of the VO
2
max gains adjusted for age, sex, baseline body mass, and baseline VO max attained
2
47% [9]. The same trends were observed for training induced changes in fasting
insulin, insulin sensitivity, high-density lipoprotein cholesterol (HDL-C), exercise
blood pressure and heart rate, exercise stroke volume and cardiac output, indicators
of adiposity, and other phenotypes [10–20]. There is evidence from other studies
that similar patterns of human variation and familial aggregation are found for the
trainability of muscular strength and power as well as short-term predominantly
anaerobic performance [21, 22, 23].
For much of the last two decades, the focus of exercise genomics has been on
testing a single or a small number of markers in candidate genes. Such studies were
typically conducted on small number of subjects, often less than 100, and were
based on one-time, cross-sectional observations. Not only were these reports
grossly underpowered, but they were also potentially contaminated by the effects
of uncontrolled confounders [4]. More recently we have seen a trend towards the
use of larger sample sizes but they still remain small compared to recommendations
for contemporary human genomics research [24, 25]. Progress in exercise genom-
ics will require more prospective study designs but especially experimental studies
with large sample sizes and well-defined interventions.
Over the last 15 years, we have seen a shift in the way candidate genes were
identified or prioritized. An early approach was based on genome-wide scans using
panels of highly polymorphic microsatellite markers examined in family members.
This method yielded positional candidates, but few of them were confirmed in studies
that subjected them to direct testing. It turned out that this technology is not very
powerful when it comes to genes with small effect sizes. We are now beginning to
see genome-wide association studies (GWAS) with large panels of single nucleotide
polymorphisms (SNPs) focused on exercise-related traits [26]. This development
should provide a flurry of new candidate genes for further in-depth investigation.
Another important recent development is the use of gene expression profiling as a
tool to identify key genes that can subsequently be subjected to genetic exploration
[27, 28]. All these methodological advances will be helpful in the effort to identify
SNPs and genes associated with exercise endophenotypes and traits.
Foreword ix
Over the last few years, we have realized that the effect sizes of the genes typi-
cally identified through GWAS were quite small [29]. GWAS small effect sizes
have been repeatedly found with disease endpoints such as type 2 diabetes [30, 31],
obesity [32, 33], hypertension [34], ischemic heart disease [35, 36], and for physical
traits such as body height [37]. However, there is some indication it may be easier
to find genes and variants associated with exercise-related traits as the effect size is
larger for some of them [28]. This is not a trivial issue. There are a few reasons why
this may be so. For instance, in the case of GWAS focused on disease gene discov-
ery, the ability to identify a significant SNP is strongly influenced by the fact that
an unknown fraction of the subjects in the control group is not affected yet by the
disease but has the genetic predisposition. In exercise genomics, this weakness can
be almost completely eliminated if a well-defined and accurately measured pheno-
type such as VO max or maximal isometric strength is used. Exercise-oriented
2
studies would offer even cleaner phenotypes in situations where the changes in
muscular strength or cardiorespiratory fitness were investigated after exposure to an
appropriately standardized and fully controlled training program. In such an experi-
mental setting, the variance in response to training is unlikely to be influenced in a
major way by confounders, thus enhancing the ability to identify markers and genes
with relatively small effects in comparison to cross-sectional studies.
To expand on the previous paragraph, a whole body of twin and family research
indicates individuals with the same genotype respond more similarly to training
than those with different genotypes [38, 39]. In this regard, the variance in training
response together with its strong genetic determinant represents one of the most
striking examples of a genotype-environmental effect, in this case a genotype-
training interaction effect. The search for genetic markers of trainability is an area
of research that is likely to pay enormous dividend as the training gain constitutes
a powerful trait measured very reliably.
In a recent report, we demonstrated it was possible to identify DNA markers of
the VO max response to standardized exercise training programs [28]. We first
2
used skeletal muscle RNA expression profiling to produce a panel of 29 genes
whose baseline expression levels (i.e., in the sedentary state) predicted the VO max
2
training response. We combined these 29 targets with other candidates identified in
the HERITAGE Family Study and hypothesized DNA variants in 35 genes would
explain the heterogeneous responses to exercise training in humans. We genotyped
SNPs in these genes in the 473 white subjects of HERITAGE. In the end, we were
able to show that a panel of 11 SNPs could explain 23% of the variance in gains in
VO max, which corresponds to about ~50% of the estimated genetic variance for
2
VO max response in HERITAGE. Bioinformatic in silico studies suggested several
2
of the genes associated with these 11 SNPs were involved in developmental biology
pathways including angiogenesis.
Another example can be highlighted from the metabolic changes observed in
response to exercise training. Global gene expression profiling was used in the
HERITAGE Family Study to identify genes associated with insulin sensitivity
training response based on the minimal model computer-based method (MINMOD)
