Genetic variants associated with exercise performance in both moderately trained and highly trained individuals

Methylome and proteome integration in human skeletal muscle uncover group and individual responses to high-intensity interval training

Exercise is a major beneficial contributor to muscle metabolism, and health benefits acquired by exercise are a result of molecular shifts occurring across multiple molecular layers (i.e., epigenome, transcriptome, and proteome). Identifying robust, across-molecular level targets associated with exercise response, at both group and individual levels, is paramount to develop health guidelines and targeted health interventions. Sixteen, apparently healthy, moderately trained (VO2 max = 51.0 ± 10.6 mL min-1  kg-1 ) males (age range = 18-45 years) from the Gene SMART (Skeletal Muscle Adaptive Responses to Training) study completed a longitudinal study composed of 12-week high-intensity interval training (HIIT) intervention. Vastus lateralis muscle biopsies were collected at baseline and after 4, 8, and 12 weeks of HIIT. DNA methylation (~850 CpG sites) and proteomic (~3000 proteins) analyses were conducted at all time points. Mixed models were applied to estimate group and individual changes, and methylome and proteome integration was conducted using a holistic multilevel approach with the mixOmics package. A total of 461 proteins significantly changed over time (at 4, 8, and 12 weeks), whilst methylome overall shifted with training only one differentially methylated position (DMP) was significant (adj.p-value < .05). K-means analysis revealed cumulative protein changes by clusters of proteins that presented similar changes over time. Individual responses to training were observed in 101 proteins. Seven proteins had large effect-sizes >0.5, among them are two novel exercise-related proteins, LYRM7 and EPN1. Integration analysis showed bidirectional relationships between the methylome and proteome. We showed a significant influence of HIIT on the epigenome and more so on the proteome in human muscle, and uncovered groups of proteins clustering according to similar patterns across the exercise intervention. Individual responses to exercise were observed in the proteome with novel mitochondrial and metabolic proteins consistently changed across individuals. Future work is required to elucidate the role of these proteins in response to exercise.

Genome-wide association study on coordination and agility in 461 Chinese Han males

There is growing evidence that genetic factors can influence human athletic performance. In many sports performances, excellent coordination and agility are the keys to mastery. However, few studies have been devoted to identifying genetic influences on athletic performance. Methods: We generated a derived measure of coordination and agility from the data of hexagonal jumps and T-runs and conducted genome-wide association and meta-analysis studies focused on coordination and agility. Results: The phenotypic correlation and genetic covariance analysis indicated that hexagonal jumps and T-runs were possibly influenced by the same set of genetic factors (R = 0.27, genetic covariance = 0.59). Meta-analysis identified rs117047321 genome-wide significant association (N = 143, P < 10E-5) with coordination and agility, and this association was replicated in the replication group (N = 318, P < 0.05). The CG genotype samples of this single nucleotide polymorphism (SNP) required a longer average movement time than the CC genotype samples, and the CG genotype only exists in Asia, which may belong to the East Asia-specific variation. This SNP is located on MYO5B, which is highly expressed in tissues such as the brain, heart, and muscle, suggesting that this locus might be a genetic factor related to human energy metabolism. Conclusion: Our study indicated that genetic factors can affect the athletic performance of coordination and agility. These findings may provide valuable insights for using genetic factors to evaluate sports characteristics.

The genetics of human performance

Human physiology is likely to have been selected for endurance physical activity. However, modern humans have become largely sedentary, with physical activity becoming a leisure-time pursuit for most. Whereas inactivity is a strong risk factor for disease, regular physical activity reduces the risk of chronic disease and mortality. Although substantial epidemiological evidence supports the beneficial effects of exercise, comparatively little is known about the molecular mechanisms through which these effects operate. Genetic and genomic analyses have identified genetic variation associated with human performance and, together with recent proteomic, metabolomic and multi-omic analyses, are beginning to elucidate the molecular genetic mechanisms underlying the beneficial effects of physical activity on human health.

Predictive Genetic Testing by the U.S. Military: Legal and Ethical Issues

INTRODUCTION

Precision medicine is a significant component of the military medical vanguard. One area of growing interest involves predictive genetic testing (PGT)-which can be used for both medical evaluation and operational planning. Predictive genetic testing is likely to play an increasingly important role in the military, in terms of both medically related testing to predict the risk of disease or injury and testing for non-medical traits that may be relevant to military performance.

MATERIALS AND METHODS

This article describes predictive tests that currently are in use by the military or that might be of interest to the military. The article also explores the risks and benefits associated with PGTs, describes the ambiguities in the current laws and directives governing the military use of PGT, and proposes a set of guidelines for the use of PGTs by the military.

RESULTS

There is no publicly available law or DoD policy that prevents the military from conducting PGT before or after accession. Currently, the only genetic testing routinely employed by the U.S. military is for medical purposes. In addition to non-routine genetic testing to diagnose genetic diseases and conditions, the military also uses targeted testing for predictive purposes. As additional predictive genetic tests are developed and become widely used, the military can be expected to employ those that are of relevance. Predictive military genetic testing of active duty service members could reduce their risk of illness and injury, improve their physical and mental fitness, enhance the health and well-being of the unit, make mission accomplishment more certain and efficient, and reduce medical and other costs for the military and veterans. Moreover, individuals with genetic variants that might enhance the likelihood of successfully completing a military mission could be preferred for certain positions or assignments, such as special operations. At the same time, there are risks that genetic information may be used for improper purposes or may stigmatize service members.

CONCLUSIONS

Predictive genetic testing is likely to play an increasingly important role in the military, in terms of both medically related testing to predict the risk of disease or injury and testing for non-medical traits that may be relevant to military performance. In instances where PGT meets standard scientific measures of validity and utility, test results can be used to promote the health and welfare of individual service members, units, and military missions. In cases where PGT does not rise to the level of meeting standard scientific criteria, officials should proceed cautiously in incorporating the information into clinical care and military decision-making. There needs to be an appropriate method of collectively calculating risks and benefits. Moreover, although military directives prohibit "unlawful discrimination," this term has received no elaboration in any publicly available military pronouncements. This lacuna should be rectified to provide proper guidance to service members, medical personnel, and the public. Although the promise of PGT may compel military officials to consider ways to maximize the use of test results, the risk of undermining military goals with unverified uses also should be considered appropriately.

No Change - No Gain; The Effect of Age, Sex, Selected Genes and Training on Physiological and Performance Adaptations in Cross-Country Skiing

The aim was to investigate the effect of training, sex, age and selected genes on physiological and performance variables and adaptations before, and during 6 months of training in well-trained cross-country skiers. National-level cross-country skiers were recruited for a 6 months observational study (pre - post 1 - post 2 test). All participants were tested in an outside double poling time trial (TT_DP), maximal oxygen uptake in running (RUN-VO_2max), peak oxygen uptake in double poling (DP-VO_2peak), lactate threshold (LT) and oxygen cost of double poling (C_DP), jump height and maximal strength (1RM) in half squat and pull-down. Blood samples were drawn to genetically screen the participants for the ACTN3 R577X, ACE I/D, PPARGC1A rs8192678, PPARG rs1801282, PPARA rs4253778, ACSL1 rs6552828, and IL6 rs1474347 polymorphisms. The skiers were instructed to train according to their own training programs and report all training in training diaries based on heart rate measures from May to October. 29 skiers completed all testing and registered their training sufficiently throughout the study period. At pre-test, significant sex and age differences were observed in TT_DP (p < 0.01), DP-VO_2peak (p < 0.01), C_DP (p < 0.05), MAS (p < 0.01), LT_v (p < 0.01), 1RM half squat (p < 0.01), and 1RM pull-down (p < 0.01). For sex, there was also a significant difference in RUN-VO_2max (p < 0.01). No major differences were detected in physiological or performance variables based on genotypes. Total training volume ranged from 357.5 to 1056.8 min per week between participants, with a training intensity distribution of 90-5-5% in low-, moderate- and high-intensity training, respectively. Total training volume and ski-specific training increased significantly (p < 0.05) throughout the study period for the whole group, while the training intensity distribution was maintained. No physiological or performance variables improved during the 6 months of training for the whole group. No differences were observed in training progression or training adaptation between sexes or age-groups. In conclusion, sex and age affected physiological and performance variables, with only a minor impact from selected genes, at baseline. However, minor to no effect of sex, age, selected genes or the participants training were shown on training adaptations. Increased total training volume did not affect physiological and performance variables.

Interindividual Variation in Cardiorespiratory Fitness: A Candidate Gene Study in Han Chinese People

Cardiorespiratory fitness, as assessed through peak oxygen uptake (VO2peak), is a powerful health indicator. We aimed to evaluate the influence of several candidate causal genetic variants on VO2peak level in untrained Han Chinese people. A total of 1009 participants (566 women; age [mean ± SD] 40 ± 14 years, VO2peak 29.9 ± 7.1 mL/kg/min) performed a maximal incremental cycling test for VO2peak determination. Genomic DNA was extracted from peripheral whole blood, and genotyping analysis was performed on 125 gene variants. Using age, sex, and body mass as covariates, and setting a stringent threshold p-value of 0.0004, only one single nucleotide polymorphism (SNP), located in the gene encoding angiotensin-converting enzyme (rs4295), was associated with VO2peak (β = 0.87; p < 2.9 × 10-4). Stepwise multiple regression analysis identified a panel of three SNPs (rs4295 = 1.1%, angiotensin II receptor type 1 rs275652 = 0.6%, and myostatin rs7570532 = 0.5%) that together accounted for 2.2% (p = 0.0007) of the interindividual variance in VO2peak. Participants carrying six 'favorable' alleles had a higher VO2peak (32.3 ± 8.1 mL/kg/min) than those carrying only one favorable allele (24.6 ± 5.2 mL/kg/min, p < 0.0001). In summary, VO2peak at the pre-trained state is partly influenced by several polymorphic variations in candidate genes, but they represent a minor portion of the variance.