Date of Award

2025

Keywords

recovery, genetics, performance

Document Type

Thesis - ECU Access Only

Publisher

Edith Cowan University

Degree Name

Doctor of Philosophy

School

School of Medical and Health Sciences

Abstract

Fatigue is a complex and multifaceted phenomenon that is an integral part of the training process. However, effective fatigue management is crucial for achieving training adaptations and avoiding overtraining or injuries. Among other factors fitness seems to be an important moderator, as it appears that stronger individuals are able to sustain greater workloads and recover at a faster pace compared to their less trained counterparts. Currently it is believed that apart from training and environmental factors, genetic makeup significantly influences an individual’s fitness status, with increasing evidence supporting this claim. Specifically, multiple gene variants have been associated with physical traits, such as height, strength, aerobic capacity and others. However, recently researchers have shifted towards a total genotype score (TGS) approach, acknowledging the complexity of most human traits. Therefore, with the use of TGS, researchers examine the cumulative effect of gene variants on physical traits of their interest. However, a notable gap exists in the current literature regarding the cumulative impact of genetic variants on physical capacities and recovery dynamics. This thesis developed a multi-stage, data-driven genomic analysis methodology for constructing TGS, providing a framework to elucidate the role of genetic factors in physical performance and recovery. The primary objectives of this thesis were to investigate the impact of genetic factors on fitness and recovery dynamics, assess how these factors influence responses to a concurrent training programme, and contribute to a broader understanding of the interplay between genetics and training adaptations, with the ultimate goal of enhancing evidence-based practices in training optimisation and personalised athletic performance strategies.

Chapter 3 explored the effect of total fitness score (TFS), defined as a composite score of handgrip strength (HGS) and maximum aerobic power (MAP), on the time course of recovery following a single training bout. The findings indicated that the high TFS, although there were no statistically significant differences in jump height between the groups, the group showed a significant increase in eccentric displacement—indicative of greater muscle lengthening under load—compared to baseline (p = 0.033) 24 hours after the resistance training session. This was observed despite the higher workload performed during the resistance training session [hedge’s g =1.93 (0.56-3.02)]. This study highlighted that individuals with higher fitness levels were able to recover at the same pace as their less trained counterparts, despite performing higher workloads during training sessions.

In Chapter 4, a Genome-Wide Association Study (GWAS) was conducted using data from the United Kingdom (UK) Biobank study to explore the association between gene variants and the physical traits of interest. Additionally, the creation of a TGS using GWAS results was employed to assess the utility of these data in explaining individual differences in physical capacities. Only one single nucleotide polymorphism (SNP) was associated with MAP at the most stringent p-value threshold (p < 5 × 10⁻⁶) for both males (rs1231637) and females (rs13289480), but these findings were not replicated in the validation cohort. Notably, in the validation cohort, SNPs associated with these performance indicators were not validated at either of the two most stringent p-value thresholds (p < 5 × 10⁻⁶ and p < 5 × 10⁻⁵), further emphasizing the need for replication studies. Regarding the utility of TGS, it was found that individuals in the high hgsTGS₅ₑ₋₆ group exhibited significantly greater HGS performance than those in the low group (p = 0.0084), as well as higher TFS performance (p = 0.0127) in comparison to the low group. This study suggests that TGS may influence physical performance; however, further validation is necessary, as many of the SNPs incorporated into the scores were not replicated in the validation cohort.

Chapter 5 examined the applicability of the findings from Chapter 4 in an independent cohort of individuals. Specifically, the study investigated the effect of TGS on physical capacities and the time course of recovery following a training session. The results indicated that TGS has minimal sensitivity in detecting differences in recovery dynamics and physical capacities. It was found that hgsTGS₅ₑ₋₄ had a significant main effect on HGS performance (p = 0.001), MAP (p = 0.019), and TFS (p = 0.025); however, post hoc analysis did not reveal any further significant differences between groups. The findings regarding the effect of TGS on the time course of recovery were more variable. Notably, the medium mapTGS₅ₑ₋₂ group exhibited a significantly greater decrease in reactive strength index modified (RSImod) immediately after resistance training compared to the high mapTGS₅ₑ₋₂ group (p < 0.05). The results of this study further highlight the complexity of using TGS to assess performance, even when only validated SNPs are included in its calculation. At present, TGS appears to have limited practical utility.

In Chapter 6, the utility of these scores in detecting differences in training responses following an 8-week concurrent training programme was examined. All participants had similar TGS values, and no clear relationship was observed between genetic makeup and training adaptations. Nevertheless, the training programme appeared to be effective in improving lower-body strength as all participants increased their 10 repetition maximum (10RM) in leg press by 15% to 40%. However, for three out of four participants, a reduction in the rate of force development (RFD) was observed following the 8-week training programme. This decline may be attributed to the training programme per se, as it primarily focused on hypertrophy (8–12 repetitions). In contrast, upper-body strength responses were trivial for three out of four participants, with only one individual exhibiting a 50% improvement. Notably, lean mass decreased in all participants, an outcome that could be attributed to low dietary protein intake ( < 1.2 g/kg/day) throughout the training programme. Overall, this study contributed to our understanding of concurrent training adaptations; however, the effect of genetic variation on training adaptations warrants further investigation as in this study there was no clear association between genetic makeup and training response.

Collectively, these studies provide a comprehensive examination of the impact of fitness on the time course of recovery, as well as the complexities of using genetic makeup to explain variations in physical capacities and training responses. Additionally, a framework for investigating the role of genetic variation in physical performance and training adaptations was developed, offering a valuable tool for future research in this area.

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Access to this thesis is embargoed until 16th June 2031 

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