Physiol Genomics 48: 175–182, 2016.
First published January 12, 2016; doi:10.1152/physiolgenomics.00109.2015.
CALL FOR PAPERS
Review
Systems Biology and Polygenic Traits
Sports genetics moving forward: lessons learned from medical research
X C. Mikael Mattsson,1,2 Matthew T. Wheeler,1,3 Daryl Waggott,1,3 Colleen Caleshu,1,3,4
and Euan A. Ashley1,3,5
1
Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California;
Åstrand Laboratory of Work Physiology, The Swedish School of Sport and Health Sciences, Stockholm, Sweden;
3
Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Stanford University, Stanford,
California; 4Division of Medical Genetics, Department of Pediatrics, Stanford University, Stanford, California; and
5
Department of Genetics, Stanford University, Stanford, California
2
sports genetics; individual differences; genetic testing; sports medicine; precision
medicine
THE FIRST EVER SEQUENCING of the human genome was completed
in year 2000. It took many years and several research teams
from around the world, and the cost was approximately $3
billion US. Since then, we have seen dramatic advances in
technology and reduction in cost. By the end of 2015, an
estimated 400,000 human genomes will be sequenced at a
reagent cost of $1,000 per genome. Human genetic studies
have led to improved understanding of the genetic variation
present in all human genomes and definition of the genetic
underpinnings of many rare single-gene disorders. The genetics of most common diseases and traits have been harder to
fully explain than was initially expected. Genome-wide asso-
Address for reprint requests and other correspondence: C. M. Mattsson,
Stanford Univ., Falk CVRB, 870 Quarry Rd Ext., Stanford, CA 94305 (e-mail:
[email protected]).
ciation studies (GWAS) have now identified thousands of
typically common genetic variants associated with a diverse
array of intermediate and disease phenotypes, while resequencing studies (now generally exome or genome sequencing) have
expanded the range of genes and variants associated with
disease phenotypes. Despite substantial advancements, the heritability explained by current genetic knowledge is in general a
fraction of the total heritability for most traits as estimated by
twin studies (8).
Sports genetics would benefit from leveraging lessons
learned from human disease genetics, both to avoid past
mistakes and also to increase and improve the breadth and
depth of knowledge in the field. The scope of this review
article is to address some of the problems facing sports genetics
and to discuss them in light of experiences from medical
research. We will use cardiovascular medicine and endurance
sports as specific instructive examples, but the principles dis-
1094-8341/16 Copyright © 2016 the American Physiological Society
175
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.6 on June 14, 2017
Mattsson CM, Wheeler MT, Waggott D, Caleshu C, Ashley EA. Sports genetics
moving forward: lessons learned from medical research. Physiol Genomics 48: 175–182,
2016. First published January 12, 2016; doi:10.1152/physiolgenomics.00109.2015.—Sports
genetics can take advantage of lessons learned from human disease genetics. By righting
past mistakes and increasing scientific rigor, we can magnify the breadth and depth of
knowledge in the field. We present an outline of challenges facing sports genetics in the light
of experiences from medical research. Sports performance is complex, resulting
from a combination of a wide variety of different traits and attributes. Improving sports genetics will foremost require analyses based on detailed phenotyping. To find widely valid, reproducible common variants associated with
athletic phenotypes, study sample sizes must be dramatically increased. One
paradox is that in order to confirm relevance, replications in specific populations must be undertaken. Family studies of athletes may facilitate the discovery
of rare variants with large effects on athletic phenotypes. The complexity of the
human genome, combined with the complexity of athletic phenotypes, will
require additional metadata and biological validation to identify a comprehensive set of genes involved. Analysis of personal genetic and multiomic profiles
contribute to our conceptualization of precision medicine; the same will be the
case in precision sports science. In the refinement of sports genetics it is
essential to evaluate similarities and differences between sexes and among
ethnicities. Sports genetics to date have been hampered by small sample sizes
and biased methodology, which can lead to erroneous associations and overestimation of effect sizes. Consequently, currently available genetic tests based
on these inherently limited data cannot predict athletic performance with any
accuracy.
Review
176
SPORTS GENETICS MOVING FORWARD
cussed are applicable to most domains of disease and sports
genetics.
can be contrasted by the median amount of six variants being
analyzed in direct-to-consumer tests marketed for sport performance (68).
Physiological Traits Instead of Athletic Performance
Effect Sizes
In all individuals, common variants will affect disease risk
and athletic potential; delineation of the impact and effect of
these variants is and will be the subject of many ongoing and
future studies. However, common variants typically have small
effects (8). An athletic genome interpretation could delineate
rare variants with negative or positive effects, such as a
truncated erythropoietin receptor (20) or a genetic variant
leading to the lack of myostatin and subsequent muscular
hypertrophy (57), suggesting inborn advantage in endurance or
strength sports, or on the other hand mutations associated with
underlying cardiomyopathy, which, if present, could lead to
restriction from athletic activity (48). Although there are many
more rare variants of small to no effect, the impact of these
variants is difficult or impossible to determine with standard
methods and even very large sample sizes. So far, most studies
of sports-related performance have looked at common variants.
To get a more complete profile of a person’s intrinsic fitness
level, impactful rare and common alleles associated with response to activity and physiological potential must be integrated. In medicine a commonly used approach is to design a
custom genotypic array specific for the area of interest. One
example is the Metabochip for genetic studies of metabolic,
cardiovascular, and anthropometric traits, analyzing 200,000
single nucleotide polymorphisms (SNPs) (66), a number that
Intrinsic, Response, and Potential
Many studies have addressed the response to training, e.g.,
strength, power, or endurance (e.g., 11, 39). However, while
genetic determinants of response to training, as shown in
sedentary populations, are likely one component of the genetics of athletic ability, there are additional aspects affecting
ability that are known to have high heritability, including the
intrinsic baseline level and the maximal potential of ability. It
is known that there are different genetic variants involved in
intrinsic fitness level and response to endurance training (measured as improved V̇O2 max) (12, 13). There are not yet any
studies looking at all three levels combined in sports genetics.
A corresponding example from medicine is Duchenne (DMD)
and Becker (BMD) muscular dystrophy. Even though they
both are caused by mutations in the same gene (the dystrophin
protein coding gene) the outcome differs. DMD is usually
established between ages 2 and 5 yr, whereas only half of the
patients with BMD demonstrate muscle weakness at the age of
10. Patients with DMD typically lose independent ambulation
before 12 yr of age, but BMD patients typically remain ambulatory into their third or fourth decade (69). In sports genetics,
one might hypothesize that an athlete with the highest maximal
genetic athletic potential might well begin with a low intrinsic
level and have a slow response to training. A person with such
a genetic profile will likely have to train for a longer period of
time (years) before reaching athletic success. It is empirically
well known that some athletes have a quick response to
training but rather soon reach their limit, while other athletes
have a slow response but continue to improve their ability over
the course of many years and can reach very high performance
levels. For example, we hypothesize that there will be differences in the impactful genetic variants of athletes reaching
extremely high V̇O2 max (maximal potential) at an early age
(i.e., as teenagers) compared with athletes reaching the same
value at an older age (i.e., after 30 yr old).
Continuum from Health to Disease
An overall hypothesis is that human physiology exists as a
spectrum from disease to health. While this may not be valid
for many diseases where onset may be in late middle age or
older, elite athletes are often held up as the epitome of health.
This thought is supported by findings that elite athletes live
longer than the general population and also that endurance
athletes live longer than strength/power athletes (18, 55). Even
though physical exercise is beneficial for overall health, it is
not necessarily so that the training of elite athletes is the most
beneficial (42).
A possible way forward is interrogating the tails of distribution of continuous phenotypes, such as V̇O2 max, to identify
individuals with extreme “positive” phenotypes who may inform prevention, cause, and treatment of pathogenic disease
states. Studying these extremes will help identify genes and
pathways that contribute to both disease and extreme health
states (13, 15, 22, 28). The most probable relevance will be by
rare variants with large effects (e.g., 20), since the effect of
common variants with small effects might be overridden by
Physiol Genomics • doi:10.1152/physiolgenomics.00109.2015 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.6 on June 14, 2017
It has been known for more than 30 yr that several physiological traits important for sports performance have a substantial genetic component [e.g., cardiorespiratory fitness (10, 17,
46)]. In the early days there were great hopes to find THE
sports gene (indeed this remains an attractive concept to the
popular press); however, due to the wide range of traits and
factors contributing to athletic performance (e.g., physiological, anthropological, psychological, nutritional, environmental,
as well as serendipity), such a quest is inevitably bound to fail.
Even the pursuit to find THE gene(s) for performance in a more
defined sport setting, such as sprint running or endurance
cycling, has slim chances of real success, likely because even
these more clearly defined phenotypes are the combination of
multiple biological phenotypes, and probably because there are
several paths to the same level of athletic performance. Each of
these phenotypes and pathways may have a polygenic inheritance underlying, with major environmental interaction determining phenotype. An alternative way to address the situation
is to investigate each more narrowly defined piece of the
physiological traits involved in sports performance. For endurance sports, pertinent, measurable traits could include maximal
oxygen uptake (V̇O2 max), blood volume, capillary density, or
mitochondrial efficiency in addition to anthropomorphic traits.
As an analogy, “performance” is to sport as “heart disease” is
to medicine. It should be effective for sports geneticists to
investigate more detailed traits that contribute to performance
in the same way medicine has studied atherosclerotic heart
disease by, for example, investigating the various heritable
traits that contribute to coronary artery disease risk (hypertension, hyperlipidemia, inflammatory response).
Review
SPORTS GENETICS MOVING FORWARD
Analytical and Statistical Challenges for Sports Genetics
Small sample size. To date, the genetic study of sportsrelated traits has been limited to sample sizes of tens to several
hundreds (e.g., 9, 22, 39, 53). The evolution of GWAS in
medical research has clearly demonstrated that for common
variants [minor allele frequency (MAF) ⬎ 5%] with modest
effects [odds ratio (OR) ⬍ 1.2], truly massive sample sizes are
required on the orders of tens of thousands (58). In fact,
meta-analyses of ⬃100,000 people are still identifying novel
common genetic variants with impact on disease and anthropometric phenotypes (2, 41, 50, 56). While reduced power for
discovery in small sample sizes is generally intuitive, there is
also an issue of failure to replicate. These observations are
explored at length in seminal papers by Ionnaidis (36, 38),
including “Why most published research findings are false”
and “How to make more published research true.” The failure
to replicate is due to an inflation of false positives and an
overestimation of effect sizes. False positives can result from
technical issues related to statistical assumptions such as inflated variance when analyzing sparse data. For example, a
SNP with an MAF of 1% within a cohort of 200 is predicted by
Hardy-Weinberg equilibrium to have four heterozygotes vs.
196 common homozygotes. Even a 5% SNP within a cohort of
500 will have on average only one subject with a rare homozygote. In a genome-wide association study, sparse genotype
classes will co-occur (by chance) with trait outliers or confounders such as population subpopulations, which will bias or
even invalidate results. Similarly, exaggerated effect size estimates result in an underestimation of the required replication
sample size and subsequently a failure to replicate (37). False
positives and exaggerated effect sizes are particularly prevalent
among top ranked results when a significance threshold is
applied. This quantitative conundrum has been explored
through the application of the winner’s curse phenomenon
(71). That is, what is top, i.e., “wins,” is more likely to be an
outlier aspect of the sample and not reflect the general population. In fact, correction methods for results from modest
sample sizes (n ⫽ 500-1,500) have shown a dramatic (50 –
90%) reduction in effect size (61).
There is obviously a contradiction in the quest for increasing
samples size and the fact that the number of athletes at the
highest level is very limited. A lesson learned from medical
research regarding rare cancers, where the numbers are small
so that any lost patient is a loss to science, is that this requires
a focused development on infrastructure within hospitals and
across borders. Even though the numbers are limited the first
step is to formalize the infrastructure around athlete testing and
genetics so that it is standardized, integrated, and global. That
way, as many athletes as possible participate, and we capture
multiple phenotypes and covariates. Compared with other
genetic studies the numbers will still be low, but in only
including the outlier athletes our general hypothesis is that we
will be able to find rare variations of large effect. An effort of
this type has recently been launched (54). Another method is to
focus on tails of general populations, such as national registries, e.g., UK Biobank with 500,000 participants genotyped.
For outliers discovered in large cross-sectional registries, we
hypothesize that common variation of small or modest effect is
sufficiently powered for discovery. Combined, both methods
are hypothesized to identify similar genes or pathways.
Generalizability. To both confirm relevance and assess effect size accurately, it is imperative that replication in an
independent cohort and subsequent populations is undertaken.
For pragmatic reasons this has been limited in the genetics of
athletic physiology. Currently, there is an entire industry focused on generalizing genetic findings from effects measured
in small underpowered, unvalidated, athlete discovery sets.
That this is scientifically unsound and ethically dubious has
recently been addressed in a consensus statement (68). These
data should be treated as hypothesis-generating starting points,
and not for consumer interpretation or actionability. Applying
the stringency and oversight of device and medical regulatory
bodies would improve legitimacy in this area.
It’s important to emphasize that genetic association, regardless of how robust, does not infer causality. Causality inference
requires specific experimental and analytical methods (21, 24).
Therefore, utility for predicting sport proficiency or injury
likelihood has little merit. Examples of spurious association
include indirect associations through linkage disequilibrium
and confounder intermediates. Confounders of athletic performance could include variants associated with obesity, depression, or ethnicity.
Penetrance. While not specifically penetrance, one analytical challenge with physiological traits related to athletic performance is the latent presence of relevant variation in the
general population. A subset of the population that does not
undergo training for a host of socioeconomic, behavioral, and
biological reasons will harbor the same genetic variation as
trained athletes and thus dilute observed effects with direct
comparison with athletes. Athletic performance is a prominent
example of gene ⫻ environment interaction. In medicine, this
interaction is beginning to be studied, for example between
SNPs associated with Type 2 diabetes and environmental
factors (such as pollutants and nutrients) (52). The environmental factors are detected with a model analogous to GWAS
Physiol Genomics • doi:10.1152/physiolgenomics.00109.2015 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.6 on June 14, 2017
environmental effect. In a study comparing common diseasecausing variants elite athletes did not differ from controls (31).
There are a few examples where genetic variation in the
(super)healthy has been utilized in the prevention of disease.
An incredible example of the success of this hypothesis is work
identifying the cholesterol therapeutic target PCSK9. The
method of identification involved combining family studies of
hypercholesterolemia (high LDL) with population studies at
the extreme tails of LDL distribution (low LDL) to identify
contributing genes (1, 19, 33). A similar earlier example
resulted from the work done on the role of 5-alpha reductase in
enlarged prostates with a group of Caribbean androgen-insensitive hermaphrodites with small prostates (35). Since the
salutogenic approach (defined as focusing on factors that support health and well-being, rather than on factors that cause
disease) is inherent in sports science and sports genetics but
just recently has evoked interest in medical research, collaborations might be fruitful for both entities. In medicine there is
a national initiative in the US to advance precision medicine,
including through use of genetics at the bedside, to tailor
therapies to subcategories of diseases (4). Exercise could also
be one of the precision therapies that, after extensive additional
sports genetic research, can be tailored to every individual
based on for example genetic profile, training response, and
optimal dose (14).
177
Review
178
SPORTS GENETICS MOVING FORWARD
Different Adaptations and Profiles
Sex differences. The physiological path to athletic performance is in several aspects different among men and women,
and these differences cannot suitably be accounted for by
adjusting for body size and composition. Traits with published
sexual dichotomy include metabolism (fat metabolism and
lactate threshold), heat management, work efficiency, and
V̇O2 max adaptation (3, 32, 59, 60). Interesting work by Spina et
al. (60) studied exercise intervention in sedentary older men
and women. Both sexes increased V̇O2 max by ⬃20%; however,
each achieved it through a different mechanism. Men improved
primarily through increasing maximal cardiac output due to
enlarged stroke volume. In contrast, women increased oxygen
consumption by means of improving oxygen extraction of
working muscles due to greater capillarization and numbers of
mitochondria. To date, the fact that studies of the physiology of
sports performance have almost exclusively been conducted on
men underlines the importance of studies of both men and
women.
Ethnic differences. Up to the present time, the majority of
sports genetic studies, especially training studies, have investigated male Caucasian athletes with a few additional studies
on East African, Jamaican/African American, and East Asian
(Japanese and Chinese) cohorts (53). The anatomically modern
human evolved in Africa ⬃200,000 yr ago and migrated
throughout the world. One branch reached Europe some 40,000
yr ago, another branch reached East Asia about the same time,
and South America was inhabited ⬃10,000 yr ago (40). Over
the years, population bottlenecks associated with migration
have led to the evolution of different genetic profiles. An area
with some relevance for endurance performance is the adaptation to altitude. It has been shown that Andean, Tibetan, and
Ethiopian highlanders each have different physiological patterns of adaptation to cope with high altitude. Andean natives
living at high elevation have increased hemoglobin concentration, but Tibetans on the other hand can tolerate lower oxygen
saturation. In those aspects, high altitude-adapted Ethiopians
have similar levels as lowlanders but instead have superior
work efficiency (6). A relatively large volume of research has
addressed the genetic profiles of these adaptations and found
that genetic variation in different pathways are responsible in
each population (7, 60, 64, 65). If differences between ethnicities also holds true for physiological adaptations, such as
increase in V̇O2 max, optimal exercise training would most
likely also differ, suggesting a more distinct athletic individualization. In sports genetics, it is for example already known
that the frequency of the X-allele of the ACTN3 R577X
polymorphism is higher in the Caucasian population, and it has
been suggested that this mutation appeared when humans
migrated to Eurasia and that it has prevailed because it facilitated adaptation to the new environment (23).
Multiomic profiles. The field of sports genomics has thus far
focused predominantly on genetic variants within the inborn
genome. High-throughput techniques now allow interrogation
of the human multiome, including the epigenome, transcriptome, proteome, metabolome, and inflammasome, both within
and across tissues and cell types. Integration of tissue-specific
multiomic profiling with genomic and physiological data will
allow a more comprehensive understanding of the biological
effects of physical activity and the biological underpinnings of
athletic ability.
From a genetics perspective, multiomic profiling of individuals or families with unique athletic attributes may facilitate
the discovery or biological validation of genes and/or variants
implicated in the genetics of athletic ability. For instance,
increase in an aerobic exercise facilitating a gene’s expression
together with change in its binding or regulatory partners
identified through proteomic profiling may confirm that gene’s
role as a key actor in the athletic pathway, while hypothesisdirected or agnostic (e.g., network modelling) approaches to
multiomic data may identify genes or pathways to consider for
variant analysis and interpretation (5).
An advantage or limitation, depending on context, of much
of the multiome is the tissue and temporal specificity of a large
portion of the multiomic profile. While it is possible to sample
blood (and to a more limited extent muscle biopsy) multiomic
Physiol Genomics • doi:10.1152/physiolgenomics.00109.2015 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.6 on June 14, 2017
called EWAS (environment-wide association study) (51). A
similar approach in sports genetics, for example investigating
the association between gene variants or genetic profiles and
response to different training regimes, would have great potential.
However, from the statistical point of view, in almost all
situations, modelling interactions requires larger sample sizes
than main effects (49, 70). This further emphasizes the need for
substantially larger samples sizes, or effects, in sports genetics
than the current norm.
Heterogeneity. Performance is a qualitative phenotype, not a
physiological measurement. As discussed earlier it is advantageous to measure specific physiological traits, such as endophenotypes and subclinical traits, which are more likely to
have direct genetic underpinnings. In medical genetics, it is
very difficult to uncover broad disease spectrum phenotypes,
such as cardiovascular disease, because of the large number of
converging physiological influences. Beyond trait heterogeneity, any single trait is likely to have genetic (different genes)
and allelic (different SNPs in the same gene) variability.
Analytically modelling the joint effects of multiple genes or
allele features without exhaustive multiple testing is an active
area of research (27, 62). Moreover, interpretation of variation
in regulatory regions including the potential for epistasis and
epigenetics will be an ongoing effort for the community.
Leveraging network approaches to define key hubs and communities will be terrifically valuable in making sense of emerging disjoint results in this field (26, 30, 44). The key point is
that for a single observable trait there are multiple correlated
physiological and genetic underpinnings within an individual
and among a group of individuals.
Genetic architecture. Analytically, combining variants
across a gene is challenging if the effect of individual
variants is in the opposite direction. Generally, in disease
cohorts the expectation is a preponderance of deleterious
rare variant burden within genes. If the same genes are
involved in athletic physiology, then that leads to the hypothesis that we are looking for either a paucity of burden or
independent variants that confer an opposite effect. The
complexity of the genetic architecture is further likely to be
different among different genes making a single analytical
approach unlikely. Current evolution in burden testing tools
that allow dynamic weighting of variants within genes will
be crucial in solving these issues (43).
Review
SPORTS GENETICS MOVING FORWARD
Translation and Interpretation Challenges
One of the ultimate goals of sports genetics research is
translation of its findings to improve the training of athletes. In
fact, such application is already occurring, with some groups
using commercially available genetic tests to guide the training, and even selection, of athletes (67). Yet such application is
dependent on the rigor and robustness of the existing research,
which currently faces the many challenges that we have outlined here. Translation of the data to training of athletes is also
dependent on how the genetic data are interpreted. The particulars of how to interpret and apply those data arise from the
fact that sports performance and its various components are
ultimately determined by a complex interplay of multiple
genetic variants and multiple nongenetic factors. This is similar
to the genetics of diseases like diabetes and coronary artery
disease. A decade of work and thousands of publications have
disclosed that revealing the genetic basis of such diseases and
traits is far more elusive than we had hoped. For example,
approximately 100 locations in the genome have been implicated in coronary artery disease, yet in combination they
explain only a quarter of the genetic portion of the etiology and
only a tenth of the etiology overall (63). This inability to
explain the majority of the genetic etiology has been seen for
the vast majority of diseases and traits. To date the genetics of
sports performance has been subject to far less extensive and
less rigorous investigation than diseases like coronary artery
disease. As such, we understand an even smaller proportion of
the genetic etiology, and we are not as confident in that
understanding. This makes the application and interpretation of
current sports genetic tests challenging.
The currently available genetic tests assess a handful of
common variants purportedly associated with athletic ability
(such as ACTN3, ACE, NOS3) or injury (such as COL5A1,
COL1A1, MMP3) (67). Given the available data, we can be
quite confident in some of these associations (e.g., ACE for
endurance and ACTN3 for power events) (47), while most
others are somewhat suspect since these associations have not
been replicated and, at a minimum, need further investigation
before any clear conclusion can be drawn. However, interpretation of an athlete’s genetic test results for even the robust
associations needs to be done cautiously, taking into account
the fact that any one genetic variant explains only a small
fraction of performance and that we do not test for the majority
of the genetic variants that contribute to performance since
they have not yet been discovered. Thus, while there are robust
data showing that ACTN3 genotype is associated with potential
in power events, such as sprinting, they predict only a small
portion of overall athletic performance, both in sprinting and in
general. Also, bear in mind that even for the absolutely highest
level of sprinting performance, i. e., 100 meter running, this
gene variant, perhaps the best studied in sports genetics, is
neither predictive nor necessary for participation elite-level
competition. Given how few genetic associations are known
and how small their effect size likely is, the ability of current
sport genetic tests to predict the performance of an athlete is so
vastly limited that their use is fairly dubious. In medical
genetics the incorporation of genetic counseling has proved to
be a necessary tool in patient care. Genetic counselors are
master’s-level trained healthcare providers that specialize in
evaluating the genetic components of disease and disease risk.
Genetic counselors provide both education and brief psychotherapeutic counseling with the goals of improved client understand, coping, adaptation, and informed decision making.
Without guidance, the layperson patient cannot easily discern
the difference between the types of mutations, for example
between a variant of unknown significance and a likely benign
mutation, much less understand the significance of either a
truncating or frameshift variant. Often patients may misinterpret the effect size or miscalculate the heritability of a mutation
or not consider the genetic implications for past and future
generations. Integration of genetic counseling practices into
routine care removes these burdens and sources of confusion
from the patient and instead repurposes the raw genetic data
into an meaningful learning opportunity for the patient (16,
25). Both medical genetic tests and sports-related genetics
involve careful interpretation and application. If sports genetic
testing reaches its promise then, much like medical genetic
testing, it could have potential significant effects on an individual’s life trajectory. As such, genetic counseling should be
provided alongside any sports-related genetic testing.
Future Directions
As the body of knowledge in sports genetics expands and is
refined, the ability to apply it to athletic training in a valid and
meaningful way will increase. Much work is being done to
develop valid methodology in the application of genetic knowledge to the prediction of multifactorial, polygenic diseases and
traits (29). To get a comprehensive picture large studies are
needed to find common variants, in combination with family
studies to discover rare variants. Once genetic associations and
their OR have been identified in a robust and valid manner, a
genetic score can be developed by building a predictive model
that includes each genotype, weighted by its effect size. The
model then needs to be validated in a prospective cohort that
includes both genotyping and detailed phenotyping information. The ability of the genetic score to predict the trait better
Physiol Genomics • doi:10.1152/physiolgenomics.00109.2015 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.6 on June 14, 2017
profiles at multiple time points, even from elite athletes, other
pertinent human tissues of import (e.g., heart, lung, brain) in
the processes of training and athletic fitness are not practically
available for research from athletes. Autopsy studies of athletes
who die from other causes may provide some opportunity, but
issues of sample preservation and validity for transcriptomics
in particular become limiting. On the other hand, repeated
measures of blood or muscle omics from the same individuals
can greatly reduce the search space within the genome and
allow for focused inquiry into one or a few putative causal
pathways that are altered by a given intervention. Recent work
has shown the power of this repeated-measures method to look
at the epigenome in association with endurance exercise training (45).
Going forward, blood-borne biomarkers of training effects
or potential that are the synthesis of a genomic profile may be
identified through multiomic profiling. In cardiovascular disease, a few key biomarkers are clinically relevant for a number
of distinct disease processes, for example serum troponin for
myocardial infarction, myocarditis, or risk of heart failure.
Multiomic profiling may help identify a set of biomarkers
associated with athletic potential, training effects, or expected
response to a particular training regime.
179
Review
180
SPORTS GENETICS MOVING FORWARD
Conclusion
The scope of this article is to highlight some of the challenges facing sports genetics as the field moves forward. As
described above, some of the most important aspects are:
1) Since sports performance is very complex and a result of
a combination of many different traits and aspects, it is necessary to base analyses and conclusions of genetic profiles on
more detailed phenotyping.
2) To find valid common variants sample sizes must increase
dramatically. To identify rare variants with large effects, ethnicity-specific and family studies are needed.
3) Personalized genetic and multiomic profiles are prominent goals of precision sports science. To confirm relevance
and effect sizes, it is imperative that replications in specific
populations are undertaken. Sex- and ethnicity-specific analyses will be required to refine sports genetics.
4) Sports genetics to date has been hampered by small
sample sizes and biased methodology, which can lead to
erroneous associations and overestimation of effect sizes. As a
consequence, commercial genetic tests currently available cannot predict performance with any accuracy. Collaboration and
collation of larger cohorts, together with improved study design, are required to overcome these limitations.
DISCLOSURES
3.
4.
5.
6.
7.
8.
9.
10.
E. A. Ashley is cofounder of Personalis Inc., which performs DNA
sequencing and interpretation of human exomes and genomes.
11.
AUTHOR CONTRIBUTIONS
12.
Author contributions: C.M.M., M.W., D.W., C.C., and E.A.A. conception and
design of research; C.M.M., M.W., and D.W. interpreted results of experiments;
C.M.M. drafted manuscript; C.M.M., M.W., D.W., C.C., and E.A.A. edited and
revised manuscript; C.M.M., M.W., D.W., C.C., and E.A.A. approved final
version of manuscript.
13.
REFERENCES
1. Acton S, Rigotti A, Landschulz KT, Xu S, Hobbs HH, Krieger M.
Identification of scavenger receptor SRWBI as a high density lipoprotein
receptor. Science 271: 518 –520, 1996.
2. Al Olama AA, Kote-Jarai Z, Berndt SI, Conti DV, Schumacher F,
Han Y, Benlloch S, Hazelett DJ, Wang Z, Saunders E, Leongamornlert D, Lindstrom S, Jugurnauth-Little S, Dadaev T, Tymrakiewicz
M, Stram DO, Rand K, Wan P, Stram A, Sheng X, Pooler LC, Park
K, Xia L, Tyrer J, Kolonel LN, Le Marchand L, Hoover RN, Machiela
MJ, Yeager M, Burdette L, Chung CC, Hutchinson A, Yu K, Goh C,
14.
15.
16.
Ahmed M, Govindasami K, Guy M, Tammela TL, Auvinen A, Wahlfors T, Schleutker J, Visakorpi T, Leinonen KA, Xu J, Aly M,
Donovan J, Travis RC, Key TJ, Siddiq A, Canzian F, Khaw KT,
Takahashi A, Kubo M, Pharoah P, Pashayan N, Weischer M, Nordestgaard BG, Nielsen SF, Klarskov P, Røder MA, Iversen P, Thibodeau SN, McDonnell SK, Schaid DJ, Stanford JL, Kolb S, Holt S,
Knudsen B, Coll AH, Gapstur SM, Diver WR, Stevens VL, Maier C,
Luedeke M, Herkommer K, Rinckleb AE, Strom SS, Pettaway C,
Yeboah ED, Tettey Y, Biritwum RB, Adjei AA, Tay E, Truelove A,
Niwa S, Chokkalingam AP, Cannon-Albright L, Cybulski C,
Wokołorczyk D, Kluźniak W, Park J, Sellers T, Lin HY, Isaacs WB,
Partin AW, Brenner H, Dieffenbach AK, Stegmaier C, Chen C,
Giovannucci EL, Ma J, Stampfer M, Penney KL, Mucci L, John EM,
Ingles SA, Kittles RA, Murphy AB, Pandha H, Michael A, Kierzek
AM, Blot W, Signorello LB, Zheng W, Albanes D, Virtamo J, Weinstein S, Nemesure B, Carpten J, Leske C, Wu SY, Hennis A, Kibel AS,
Rybicki BA, Neslund-Dudas C, Hsing AW, Chu L, Goodman PJ,
Klein EA, Zheng SL, Batra J, Clements J, Spurdle A, Teixeira MR,
Paulo P, Maia S, Slavov C, Kaneva R, Mitev V, Witte JS, Casey G,
Gillanders EM, Seminara D, Riboli E, Hamdy FC, Coetzee GA, Li Q,
Freedman ML, Hunter DJ, Muir K, Gronberg H, Neal DE, Southey
M, Giles GG, Severi G; Breast and Prostate Cancer Cohort Consortium (BPC3); PRACTICAL (Prostate Cancer Association Group to
Investigate Cancer-Associated Alterations in the Genome) Consortium; COGS (Collaborative Oncological Gene-environment Study)
Consortium; GAME-ON/ELLIPSE Consortium, Cook MB, Nakagawa H, Wiklund F, Kraft P, Chanock SJ, Henderson BE, Easton DF,
Eeles RA, Haiman CA. A meta-analysis of 87,040 individuals identifies
23 new susceptibility loci for prostate cancer. Nat Genet 46: 1103–1109,
2014.
Asanuma H, Kakishima S, Ito H, Kobayashi K, Hasegawa E, Asami T,
Matsuura K, Roff DA, Yoshimura J. Evolutionary optimality in sex
differences of longevity and athletic performances. Sci Rep 4: 5425, 2014.
Ashley EA. The precision medicine initiative: a new national effort. JAMA
313: 2119 –2120, 2015.
Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a networkbased approach to human disease. Nat Rev Genet 12: 56 –68, 2011.
Beall CM, Cavalleri GL, Deng L, Elston RC, Gao Y, Knight J, Li C,
Li JC, Liang Y, McCormack M, Montgomery HE, Pan H, Robbins
PA, Shianna KV, Tam SC, Tsering N, Veeramah KR, Wang W,
Wangdui P, Weale ME, Xu Y, Xu Z, Yang L, Zaman MJ, Zeng C,
Zhang L, Zhang X, Zhaxi P, Zheng YT. Natural selection on EPAS1
(HIF2alpha) associated with low hemoglobin concentration in Tibetan
highlanders. Proc Natl Acad Sci USA 107: 11459 –11464, 2010.
Beall CM. Andean, Tibetan, and Ethiopian patterns of adaptation to
high-altitude hypoxia. Integr Comp Biol 46: 18 –24, 2006.
Bouchard C. Exercise genomics–a paradigm shift is needed: a commentary. Br J Sports Med 49: 1492–1496, 2015.
Bouchard C, An P, Rice T, Skinner JS, Wilmore JH, Gagnon J,
Pérusse L, Leon AS, Rao DC. Familial aggregation of V̇O2 max response
to exercise training: results from the HERITAGE Family Study. J Appl
Physiol 87: 1003–1008, 1999.
Bouchard C, Malina RM. Genetics of physiological fitness and motor
performance. Exerc Sport Sci Rev 11: 306 –339, 1983.
Bouchard C, Rankinen T. Individual differences in response to regular
physical activity. Med Sci Sports Exerc 33: S446 –S453, 2001.
Bouchard C, Rankinen T, Chagnon YC, Rice T, Pérusse L, Gagnon J,
Borecki I, An P, Leon AS, Skinner JS, Wilmore JH, Province M, Rao
DC. Genomic scan for maximal oxygen uptake and its response to training
in the HERITAGE Family Study. J Appl Physiol (1985) 88: 551–559,
2000.
Bouchard C, Sarzynski MA, Rice TK, Kraus WE, Church TS, Sung
YJ, Rao DC, Rankinen T. Genomic predictors of the maximal O2 uptake
response to standardized exercise training programs. J Appl Physiol 110:
1160 –1170, 2011.
Buford TW, Roberts MD, Church TS. Toward exercise as personalized
medicine. Sports Med 43: 157–165, 2013.
Bye A, Høydal MA, Catalucci D, Langaas M, Kemi OJ, Beisvag V,
Koch LG, Britton SL, Ellingsen Ø, Wisløff U. Gene expression profiling
of skeletal muscle in exercise-trained and sedentary rats with inborn high
and low VO2max. Physiol Genomics 35: 213–221, 2008.
Catts ZA, Hampel H. Certified genetic counselors: a crucial clinical
resource in the management of patients with suspected hereditary cancer
syndromes. Surg Oncol Clin N Am 24: 653–666, 2015.
Physiol Genomics • doi:10.1152/physiolgenomics.00109.2015 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.6 on June 14, 2017
than existing nongenetic factors needs to be assessed. Such
work in coronary artery disease has found that, while a genetic
risk score is independently statistically significantly better at
predicting disease than a clinical risk score, the actual difference in predictive power is small and the suggestion is to use
a combination of the two (29). If valid genetic associations can
be found for sports performance similar work could be done,
but as stated earlier, the ultimate utility of a genetic test that
predicts sports performance will depend on whether enough
reliable and robust genetic associations can be found to explain
a sufficiently large portion of the overall variability, which at
the moment is very far from the case. As with the example of
combined risk scores, a more relevant and practicable way of
utilizing a sports genetic score might be in determining what
type of training that will give the best response for each
individual, as suggested in the section about gene ⫻ environment interaction.
Review
SPORTS GENETICS MOVING FORWARD
40. Kundu S, Ghosh SK. Trend of different molecular markers in the last
decades for studying human migrations. Gene 556: 81–90, 2015.
41. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R,
Bellenguez C, DeStafano AL, Bis JC, Beecham GW, Grenier-Boley B,
Russo G, Thorton-Wells TA, Jones N, Smith AV, Chouraki V,
Thomas C, Ikram MA, Zelenika D, Vardarajan BN, Kamatani Y, Lin
CF, Gerrish A, Schmidt H, Kunkle B, Dunstan ML, Ruiz A, Bihoreau
MT, Choi SH, Reitz C, Pasquier F, Cruchaga C, Craig D, Amin N,
Berr C, Lopez OL, De Jager PL, Deramecourt V, Johnston JA, Evans
D, Lovestone S, Letenneur L, Morón FJ, Rubinsztein DC, Eiriksdottir
G, Sleegers K, Goate AM, Fiévet N, Huentelman MW, Gill M, Brown
K, Kamboh MI, Keller L, Barberger-Gateau P, McGuiness B, Larson
EB, Green R, Myers AJ, Dufouil C, Todd S, Wallon D, Love S,
Rogaeva E, Gallacher J, St George-Hyslop P, Clarimon J, Lleo A,
Bayer A, Tsuang DW, Yu L, Tsolaki M, Bossù P, Spalletta G, Proitsi
P, Collinge J, Sorbi S, Sanchez-Garcia F, Fox NC, Hardy J, Deniz
Naranjo MC, Bosco P, Clarke R, Brayne C, Galimberti D, Mancuso
M, Matthews F; European Alzheimer’s Disease Initiative (EADI);
Genetic and Environmental Risk in Alzheimer’s Disease; Alzheimer’s
Disease Genetic Consortium; Cohorts for Heart and Aging Research
in Genomic Epidemiology, Moebus S, Mecocci P, Del Zompo M,
Maier W, Hampel H, Pilotto A, Bullido M, Panza F, Caffarra P,
Nacmias B, Gilbert JR, Mayhaus M, Lannefelt L, Hakonarson H,
Pichler S, Carrasquillo MM, Ingelsson M, Beekly D, Alvarez V, Zou
F, Valladares O, Younkin SG, Coto E, Hamilton-Nelson KL, Gu W,
Razquin C, Pastor P, Mateo I, Owen MJ, Faber KM, Jonsson PV,
Combarros O, O’Donovan MC, Cantwell LB, Soininen H, Blacker D,
Mead S, Mosley TH Jr, Bennett DA, Harris TB, Fratiglioni L, Holmes
C, de Bruijn RF, Passmore P, Montine TJ, Bettens K, Rotter JI, Brice
A, Morgan K, Foroud TM, Kukull WA, Hannequin D, Powell JF,
Nalls MA, Ritchie K, Lunetta KL, Kauwe JS, Boerwinkle E, Riemenschneider M, Boada M, Hiltuenen M, Martin ER, Schmidt R, Rujescu
D, Wang LS, Dartigues JF, Mayeux R, Tzourio C, Hofman A, Nöthen
MM, Graff C, Psaty BM, Jones L, Haines JL, Holmans PA, Lathrop
M, Pericak-Vance MA, Launer LJ, Farrer LA, van Duijn CM, Van
Broeckhoven C, Moskvina V, Seshadri S, Williams J, Schellenberg
GD, Amouyel P. Meta-analysis of 74,046 individuals identifies 11 new
susceptibility loci for Alzheimer’s disease. Nat Genet 45: 1452–1458,
2013.
42. Lavie CJ, Lee DC, Sui X, Arena R, O’Keefe JH, Church TS, Milani
RV, Blair SN. Effects of running on chronic diseases and cardiovascular
and all-cause mortality. Mayo Clin Proc 90: 1541–1552, 2015.
43. Lee S, Emond MJ, Bamshad MJ, Barnes KC, Rieder MJ, Nickerson
DA; NHLBIGO Exome Sequencing Project-ESP Lung Project Team,
Christiani DC, Wurfel MM, Lin X. Optimal unified approach for
rare-variant association testing with application to small-sample casecontrol whole-exome sequencing studies. Am J Hum Genet 91: 224 –237,
2012.
44. Leiserson MD, Vandin F, Wu HT, Dobson JR, Eldridge JV, Thomas
JL, Papoutsaki A, Kim Y, Niu B, McLellan M, Lawrence MS,
Gonzalez-Perez A, Tamborero D, Cheng Y, Ryslik GA, Lopez-Bigas
N, Getz G, Ding L, Raphael BJ. Pan-cancer network analysis identifies
combinations of rare somatic mutations across pathways and protein
complexes. Nat Genet 47: 106 –114, 2015.
45. Lindholm ME, Marabita F, Gomez-Cabrero D, Rundqvist H, Ekström TJ, Tegnér J, Sundberg CJ. An integrative analysis reveals
coordinated reprogramming of the epigenome and the transcriptome in
human skeletal muscle after training. Epigenetics 9: 1557–1569, 2014.
46. Lortie G, Simoneau JA, Hamel P, Boulay MR, Landry F, Bouchard C.
Responses of maximal aerobic power and capacity to aerobic training. Int
J Sports Med 5: 232–236, 1984.
47. Ma F, Yang Y, Li X, Zhou F, Gao C, Li M, Gao L. The association of
sport performance with ACE and ACTN3 genetic polymorphisms: a
systematic review and meta-analysis. PLoS One 8: e54685, 2013.
48. Maron BJ, Chaitman BR, Ackerman MJ, Bayés de Luna A, Corrado
D, Crosson JE, Deal BJ, Driscoll DJ, Estes NA 3rd, Araújo CG, Liang
DH, Mitten MJ, Myerburg RJ, Pelliccia A, Thompson PD, Towbin
JA, Van Camp SP; Working Groups of the American Heart Association Committee on Exercise, Cardiac Rehabilitation, and Prevention;
Councils on Clinical Cardiology and Cardiovascular Disease in the
Young. Recommendations for physical activity and recreational sports
participation for young patients with genetic cardiovascular diseases.
Circulation 109: 2807–2816, 2004.
Physiol Genomics • doi:10.1152/physiolgenomics.00109.2015 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.6 on June 14, 2017
17. Chagnon YC, Allard C, Bouchard C. Red blood cell genetic variation in
Olympic endurance athletes. J Sports Sci 2: 121–129, 1984.
18. Clarke PM, Walter SJ, Hayen A, Mallon WJ, Heijmans J, Studdert
DM. Survival of the fittest: retrospective cohort study of the longevity of
Olympic medallists in the modern era. Br J Sports Med 49: 898 –902,
2015.
19. Cohen JC, Boerwinkle E, Mosley TH, Hobbs HH. Sequence variations
in PCSK9, low LDL, and protection against coronary heart disease. N Engl
J Med 354: 1264 –1272, 2006.
20. de la Chapelle A, Traskelin AL, Juvonen E. Truncated erythropoietin
receptor causes dominantly inherited benign human erythrocytosis. Proc
Natl Acad Sci USA 90: 4495–4499, 1993.
21. DiPetrillo K, Wang X, Stylianou IM, Paigen B. Bioinformatics toolbox
for narrowing rodent quantitative trait loci. Trends Genet 21: 683–692,
2005.
22. Eynon N, Moran M, Birk R, Lucia A. The champions’ mitochondria: is
it genetically determined? A review on mitochondrial DNA and elite
athletic performance. Physiol Genomics 43: 789 –798, 2011.
23. Fiuza-Luces C, Ruiz JR, Rodríguez-Romo G, Santiago C, GómezGallego F, Yvert T, Cano-Nieto A, Garatachea N, Morán M, Lucia A.
Are ‘endurance’ alleles ‘survival’ alleles? Insights from the ACTN3
R577X polymorphism. PLoS One 6: e17558, 2011.
24. Flint J, Valdar W, Shifman S, Mott R. Strategies for mapping and
cloning quantitative trait genes in rodents. Nat Rev Genet 6: 271–286,
2005.
25. Fowler SJ, Napolitano C, Priori SG. The genetics of cardiomyopathy:
genotyping and genetic counseling. Curr Treat Options Cardiovasc Med
11: 433–446, 2009.
26. Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9: 432–441, 2008.
27. Gaujoux R, Seoighe C. A flexible R package for nonnegative matrix
factorization. BMC Bioinformatics 11: 367, 2010.
28. Ghosh S, Vivar JC, Sarzynski MA, Sung YJ, Timmons JA, Bouchard
C, Rankinen T. Integrative pathway analysis of a genome-wide association study of VO2max response to exercise training. J Appl Physiol 115:
1343–1359, 2013.
29. Goldstein BA, Knowles JW, Salfati E, Ioannidis JP, Assimes TL.
Simple, standardized incorporation of genetic risk into non-genetic risk
prediction tools for complex traits: coronary heart disease as an example.
Front Genet 5: 254, 2014.
30. Gomez-Cabrero D, Abugessaisa I, Maier D, Teschendorff A, Merkenschlager M, Gisel A, Ballestar E, Bongcam-Rudloff E, Conesa A,
Tegnér J. Data integration in the era of omics: current and future
challenges. BMC Syst Biol 8, Suppl 2: I1, 2014.
31. Gómez-Gallego F, Ruiz JR, Buxens A, Altmäe S, Artieda M, Santiago
C, González-Freire M, Verde Z, Arteta D, Martínez A, Tejedor D, Lao
JI, Arenas J, Lucia A. Are elite endurance athletes genetically predisposed to lower disease risk? Physiol Genomics 41: 82–90, 2010.
32. Hill DW, Vingren JL. Effects of exercise mode and participant sex on
measures of anaerobic capacity. J Sports Med Phys Fitness 54: 255–263,
2014.
33. Horton JD, Cohen JC, Hobbs HH. PCSK9: a convertase that coordinates
LDL catabolism. J Lipid Res 50: S172–S177, 2009.
34. Huerta-Sanchez Jin XE, Asan Bianba Z, Peter BM, Vinckenbosch N,
Liang Y, Yi X, He M, Somel M, Ni P, Wang B, Ou X, Huasang
Luosang J, Cuo ZX, Li K, Gao G, Yin Y, Wang W, Zhang X, Xu X,
Yang H, Li Y, Wang J, Wang J, Nielsen R. Altitude adaptation in
Tibetans caused by introgression of Denisovan-like DNA. Nature 512:
194 –197, 2014.
35. Imperato-McGinley J, Guerrero L, Gautier T, German JL, Peterson
RE. Steroid 5alpha-reductase deficiency in man. An inherited form of
male pseudohermaphroditism. Birth Defects Orig Artic Ser 11: 91–103,
1975.
36. Ioannidis JP. How to make more published research true. PLoS Med 11:
e1001747, 2014.
37. Ioannidis JP. Why most discovered true associations are inflated. Epidemiology 19: 640 –648, 2008.
38. Ioannidis JP. Why most published research findings are false. PLoS Med
2: e124, 2005.
39. Karavirta L, Häkkinen K, Kauhanen A, Arija-Blázquez A, Sillanpää
E, Rinkinen N, Häkkinen A. Individual responses to combined endurance and strength training in older adults. Med Sci Sports Exerc 43:
484 –490, 2011.
181
Review
182
SPORTS GENETICS MOVING FORWARD
64.
65.
66.
67.
68.
69.
70.
71.
lenweider P, Voight BF, Vitart V, Uitterlinden AG, Uda M,
Tuomilehto J, Thompson JR, Tanaka T, Surakka I, Stringham HM,
Spector TD, Soranzo N, Smit JH, Sinisalo J, Silander K, Sijbrands EJ,
Scuteri A, Scott J, Schlessinger D, Sanna S, Salomaa V, Saharinen J,
Sabatti C, Ruokonen A, Rudan I, Rose LM, Roberts R, Rieder M,
Psaty BM, Pramstaller PP, Pichler I, Perola M, Penninx BW, Pedersen NL, Pattaro C, Parker AN, Pare G, Oostra BA, O’Donnell CJ,
Nieminen MS, Nickerson DA, Montgomery GW, Meitinger T,
McPherson R, McCarthy MI, McArdle W, Masson D, Martin NG,
Marroni F, Mangino M, Magnusson PK, Lucas G, Luben R, Loos RJ,
Lokki ML, Lettre G, Langenberg C, Launer LJ, Lakatta EG, Laaksonen R, Kyvik KO, Kronenberg F, König IR, Khaw KT, Kaprio J,
Kaplan LM, Johansson A, Jarvelin MR, Janssens AC, Ingelsson E, Igl
W, Kees Hovingh G, Hottenga JJ, Hofman A, Hicks AA, Hengstenberg C, Heid IM, Hayward C, Havulinna AS, Hastie ND, Harris TB,
Haritunians T, Hall AS, Gyllensten U, Guiducci C, Groop LC, Gonzalez E, Gieger C, Freimer NB, Ferrucci L, Erdmann J, Elliott P,
Ejebe KG, Döring A, Dominiczak AF, Demissie S, Deloukas P, de
Geus EJ, de Faire U, Crawford G, Collins FS, Chen YD, Caulfield MJ,
Campbell H, Burtt NP, Bonnycastle LL, Boomsma DI, Boekholdt SM,
Bergman RN, Barroso I, Bandinelli S, Ballantyne CM, Assimes TL,
Quertermous T, Altshuler D, Seielstad M, Wong TY, Tai ES, Feranil
AB, Kuzawa CW, Adair LS, Taylor HA Jr, Borecki IB, Gabriel SB,
Wilson JG, Holm H, Thorsteinsdottir U, Gudnason V, Krauss RM,
Mohlke KL, Ordovas JM, Munroe PB, Kooner JS, Tall AR, Hegele
RA, Kastelein JJ, Schadt EE, Rotter JI, Boerwinkle E, Strachan DP,
Mooser V, Stefansson K, Reilly MP, Samani NJ, Schunkert H,
Cupples LA, Sandhu MS, Ridker PM, Rader DJ, van Duijn CM,
Peltonen L, Abecasis GR, Boehnke M, Kathiresan S. Biological,
clinical and population relevance of 95 loci for blood lipids. Nature 466:
707–713, 2010.
Udpa N, Ronen R, Zhou D, Liang J, Stobdan T, Appenzeller O, Yin
Y, Du Y, Guo L, Cao R, Wang Y, Jin X, Huang C, Jia W, Cao D, Guo
G, Claydon VE, Hainsworth R, Gamboa JL, Zibenigus M, Zenebe G,
Xue J, Liu S, Frazer KA, Li Y, Bafna V, Haddad GG. Whole genome
sequencing of Ethiopian highlanders reveals conserved hypoxia tolerance
genes. Genome Biol 15: R36, 2014.
Valverde G, Zhou H, Lippold S, de Filippo C, Tang K, López Herráez
D, Li J, Stoneking M. A novel candidate region for genetic adaptation to
high altitude in Andean populations. PLoS One 10: e0125444, 2015.
Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, Chines PS, Burtt
NP, Fuchsberger C, Li Y, Erdmann J, Frayling TM, Heid IM, Jackson
AU, Johnson T, Kilpeläinen TO, Lindgren CM, Morris AP, Prokopenko I, Randall JC, Saxena R, Soranzo N, Speliotes EK, Teslovich TM,
Wheeler E, Maguire J, Parkin M, Potter S, Rayner NW, Robertson N,
Stirrups K, Winckler W, Sanna S, Mulas A, Nagaraja R, Cucca F,
Barroso I, Deloukas P, Loos RJ, Kathiresan S, Munroe PB, NewtonCheh C, Pfeufer A, Samani NJ, Schunkert H, Hirschhorn JN, Altshuler D, McCarthy MI, Abecasis GR, Boehnke M. The metabochip, a
custom genotyping array for genetic studies of metabolic, cardiovascular,
and anthropometric traits. PLoS Genet 8: e1002793, 2012.
Wagner JK. Playing with heart and soulѧ and genomes: sports implications and applications of personal genomics. PeerJ 1: e120, 2013.
Webborn N, Williams A, McNamee M, Bouchard C, Pitsiladis Y,
Ahmetov I, Ashley E, Byrne N, Camporesi S, Collins M, Dijkstra P,
Eynon N, Fuku N, Garton FC, Hoppe N, Holm S, Kaye J, Klissouras
V, Lucia A, Maase K, Moran C, North KN, Pigozzi F, Wang G.
Direct-to-consumer genetic testing for predicting sports performance and
talent identification: consensus statement. Br J Sports Med 49: 1486 –
1491, 2015.
Wein N, Alfano L, Flanigan KM. Genetics and emerging treatments for
Duchenne and Becker muscular dystrophy. Pediatr Clin North Am 62:
723–742, 2015.
Zhao G, Marceau R, Zhang D, Tzeng JY. Assessing gene-environment
interactions for common and rare variants with binary traits using genetrait similarity regression. Genetics 199: 695–710, 2015.
Zollner S, Pritchard JK. Overcoming the winner’s curse: estimating
penetrance parameters from case-control data. Am J Hum Genet 80:
605–615, 2007.
Physiol Genomics • doi:10.1152/physiolgenomics.00109.2015 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.6 on June 14, 2017
49. Murcray CE, Lewinger JP, Gauderman WJ. Gene-environment interaction in genome-wide association studies. Am J Epidemiol 169: 219 –226,
2009.
50. Nalls MA, Pankratz N, Lill CM, Do CB, Hernandez DG, Saad M,
DeStefano AL, Kara E, Bras J, Sharma M, Schulte C, Keller MF,
Arepalli S, Letson C, Edsall C, Stefansson H, Liu X, Pliner H, Lee JH,
Cheng R; International Parkinson’s Disease Genomics Consortium
(IPDGC); Parkinson’s Study Group (PSG) Parkinson’s Research:
The Organized GENetics Initiative (PROGENI); 23andMe; GenePD;
NeuroGenetics Research Consortium (NGRC); Hussman Institute of
Human Genomics (HIHG); Ashkenazi Jewish Dataset Investigator;
Cohorts for Health and Aging Research in Genetic Epidemiology
(CHARGE); North American Brain Expression Consortium (NABEC); United Kingdom Brain Expression Consortium (UKBEC);
Greek Parkinson’s Disease Consortium; Alzheimer Genetic Analysis
Group, Ikram MA, Ioannidis JP, Hadjigeorgiou GM, Bis JC, Martinez M, Perlmutter JS, Goate A, Marder K, Fiske B, Sutherland M,
Xiromerisiou G, Myers RH, Clark LN, Stefansson K, Hardy JA,
Heutink P, Chen H, Wood NW, Houlden H, Payami H, Brice A, Scott
WK, Gasser T, Bertram L, Eriksson N, Foroud T, Singleton AB.
Large-scale meta-analysis of genome-wide association data identifies six
new risk loci for Parkinson’s disease. Nat Genet 46: 989 –993, 2014.
51. Patel CJ, Bhattacharya J, Butte AJ. An environment-wide association
study (EWAS) on type 2 diabetes mellitus. PLoS One 5: e10746, 2010.
52. Patel CJ, Chen R, Kodama K, Ioannidis JP, Butte AJ. Systematic
identification of interaction effects between genome- and environmentwide associations in type 2 diabetes mellitus. Hum Genet 132: 495–508,
2013.
53. Pitsiladis Y, Wang G, Wolfarth B, Scott R, Fuku N, Mikami E, He Z,
Fiuza-Luces C, Eynon N, Lucia A. Genomics of elite sporting performance: what little we know and necessary advances. Br J Sports Med 47:
550 –555, 2013.
54. Pitsiladis YP, Tanaka M, Eynon N, Bouchard C, North KN, Williams
AG, Collins M, Moran CN, Britton SL, Fuku N, Ashley EA, Klissouras V, Lucia A, Ahmetov II, de Geus EJ, Alsayrafi M. The Athlome
Project Consortium: a concerted effort to discover genomic and other
“omic” markers of athletic performance. Physiol Genomics 10.1152/
physiolgenomics.00105.2015.
55. Sarna S, Sahi T, Koskenvuo M, Kaprio J. Increased life expectancy of
world class male athletes. Med Sci Sports Exerc 25: 237–244, 1993.
56. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci.
Nature 511: 421–427, 2014.
57. Schuelke M, Wagner KR, Stolz LE, Hübner C, Riebel T, Kömen W,
Braun T, Tobin JF, Lee SJ. Myostatin mutation associated with gross
muscle hypertrophy in a child. N Engl J Med 350: 2682–2688, 2004.
58. Sham PC, Purcell SM. Statistical power and significance testing in
large-scale genetic studies. Nat Rev Genet 15: 335–346, 2014.
59. Spina RJ. Cardiovascular adaptations to endurance exercise training in
older men and women. Exerc Sport Sci Rev 27: 317–332, 1999.
60. Spina RJ, Ogawa T, Kohrt WM, Martin WH 3rd, Holloszy JO,
Ehsani AA. Differences in cardiovascular adaptations to endurance exercise training between older men and women. J Appl Physiol 75: 849 –855,
1993.
61. Sun L, Dimitromanolakis A, Faye LL, Paterson AD, Waggott D;
DCCT Research Group/EDIC, Bull SB. BR-squared: a practical solution to the winner’s curse in genome-wide scans. Hum Genet 129:
545–552, 2011.
62. Szczurek E, Beerenwinkel N. Modeling mutual exclusivity of cancer
mutations. PLoS Comput Biol 10: e1003503, 2014.
63. Teslovich T, Musunuru K, Smith A, Edmondson AC, Stylianou IM,
Koseki M, Pirruccello JP, Ripatti S, Chasman DI, Willer CJ, Johansen CT, Fouchier SW, Isaacs A, Peloso GM, Barbalic M, Ricketts SL,
Bis JC, Aulchenko YS, Thorleifsson G, Feitosa MF, Chambers J,
Orho-Melander M, Melander O, Johnson T, Li X, Guo X, Li M, Shin
Cho Y, Jin Go M, Jin Kim Y, Lee JY, Park T, Kim K, Sim X,
Twee-Hee Ong R, Croteau-Chonka DC, Lange LA, Smith JD, Song K,
Hua Zhao J, Yuan X, Luan J, Lamina C, Ziegler A, Zhang W, Zee
RY, Wright AF, Witteman JC, Wilson JF, Willemsen G, Wichmann
HE, Whitfield JB, Waterworth DM, Wareham NJ, Waeber G, Vol-