Training in Line W/ Your Genetic Potential Can Boost Your Performance Gains More Than 600%, DNAFit™ Studies Say
A recent study that was conducted by a consortium of European researchers is now the first to impressively demonstrate that "matching the individual’s genotype with the appropriate training modality leads to more effective resistance training" (Jones. 2016) What the scientists some of whom work for a company that offers corresponding DNA tests won't tell you, though is that their test will eventually just help you to select the right sport, not to excel in the one sport you have already chosen.
Learn more about workouts, supplements, diet and more at the SuppVersityEventually, none of this should surprise you, though. Scientists and practitioners alike have suspected for centuries and known for decades that elite athletes are born, not formed in the gym. Association studies have identified dozens of genetic variants linked to training responses and sport-related traits (Table 1 provides a glimpse at the peak of a hitherto largely unknown iceberg of genetic variants that will influence your adaptation to specific training types).
|Table 1: List of known genetic variants that influence your adaptation to specific (resistance) training stimuli that were analyzed with the patented DNAFit Peak Performance Algorithm™ in the study at hand (Jones. 2016).|
In the previously cited study, Jones et al. proposed to do just that by the means of an algorithm that would allow athletes to achieve "greater results in response to high- or low-intensity resistance training programs by predicting athlete's potential for the development of power and endurance qualities" (Jones. 2016).The DNAFit algorithm which is designed to predict the response to high- or low-intensity resistance training programs invokes the 15 performance-associated gene polymorphisms from Table 1.
|Figure 1: Both studies used the same randomized, double-blinded crossover design (based on Jones. 2016).|
- study 1: athletes from different sports (n=28) / 55 Caucasian male University athletes, all aged 18-20 years, volunteered for the study, and 28 of them (height 180.7 ± 1.5 cm, weight 77.0 ± 2.1 kg) successfully completed it (27 athletes had not completed all aspects of the study due to either injury or illness); each participant was a member of first or second team, actively competing in British Universities and Colleges Sports (BUCS) leagues. The athletes competed in squash (n = 1), swimming (n = 7), running (n = 1), ski/snowboard (n = 4), soccer (n = 1), lacrosse (n = 2), badminton (n = 1), motorsport (n = 1), cycling (n = 4), cricket (n = 2), volleyball (n = 1), fencing (n = 1) and rugby union (n = 2). and
- study 2: soccer players (n=39) / 68 male soccer players, all aged 16-19 years, volunteered to participate in the study, and 39 of them (height 176.1 ± 1.0 cm, weight 68.9 ± 1.5 kg) successfully completed it (29 participants were withdrawn from the study due to non-adherence of set training volumes over the 8 weeks, or injury); each subject was a member of college soccer academy who actively competed in British Universities & Colleges Sport (BUCS) league.
No, the muscle or strength gains were not assessed: I am not sure why the scientists decided against measuring the lean / fat mass gains / losses. After all, their gene set included the thyrotropin-releasing hormone (TRH) receptor gene where polymorphisms at rs16892496 A/C that influences the secretion of thyroid-stimulating hormone (TSH) and prolactin (PRL) and has been found to modulate the amount of lean mass by Liu et al. in 2009. My best bets are that the reasons are financial ones (DXA is expensive, everything else inaccurate)strategic ones, with 8-weeks of training being unlikely to produce sufficiently inter-group differences in already trained athletes, given the small sample size(s) and range of sports that were included (esp. in study 1), or a mere consequence of the choice of protocols, which did not include a hypertrophy protocol (thus no measurement of muscle gains) and/or would obviously produce greater strength gains with the high intensity protocol (measuring those would thus be useless, too).As the authors point out, "[t]he study was double blinded, in that all were unaware of their ‘genetic potential status’, as determined by the DNAFit Peak Performance Algorithm™" (Jones. 2016). Since this also included the lead investigator who coached the participants during the 8 weeks of resistance training, the notion that 'this is the optimal training type for me / my trainee' should not have influenced the study outcomes.
- high-intensity trained with power genotype or
- low-intensity trained with endurance genotype,
|Figure 3: Inter-group comparisons of Aero3 increases (%) in response to high- or low-intensity training; left axes = power and endurance genes groups, right axes = all subjects (data from both cohorts | Jones. 2016).|
- the advantage of training 'according to your genotype' ranges from ~40% to ~80% even if you compare it to the average training response (Figure 1, "all");
- comparing training according to training in discordance with your genotype(s) yields differences that range from 55% up to 610% (the latter in the soccer players on the low intensity regimen for CMJ; Figure 1, study 2 / low intensity)
|Remember? Your Post-Workout Testosterone Levels Can Predict Your Gains - Study Takes Novel Approach to the T ↔ Muscle Link | Learn more|
Another thing we shouldn't forget is that getting big and buffed, the goal of a majority of male gymgoers, wasn't even investigated in the study at hand... I bet, though, that future studies with different training regimen and study populations (e.g. untrained individuals) will assess and probably find similar results for muscle and strength gains - And you know where you will be able to read about their results, right? | Comment on Facebook!
- Jones, N., et al. "A genetic-based algorithm for personalized resistance training." Biol Sport 33.2 (2016): 117-126.
- Liu, Xiao-Gang, et al. "Genome-wide association and replication studies identified TRHR as an important gene for lean body mass." The American Journal of Human Genetics 84.3 (2009): 418-423.