Genome wide association study of response to interval and continuous exercise training: the Predict-HIIT study

Abstract

Background: Low cardiorespiratory fitness (V̇O_2peak) is highly associated with chronic disease and mortality from all causes. Whilst exercise training is recommended in health guidelines to improve V̇O_2peak, there is considerable inter-individual variability in the V̇O_2peak response to the same dose of exercise. Understanding how genetic factors contribute to V̇O_2peak training response may improve personalisation of exercise programs. The aim of this study was to identify genetic variants that are associated with the magnitude of V̇O₂peak response following exercise training.

Methods: Participant change in objectively measured V̇O₂peak from 18 different interventions was obtained from a multi-centre study (Predict-HIIT). A genome-wide association study was completed (n = 507), and a polygenic predictor score (PPS) was developed using alleles from single nucleotide polymorphisms (SNPs) significantly associated (P < 1 × 10^-5) with the magnitude of V̇O₂peak response. Findings were tested in an independent validation study (n = 39) and compared to previous research.

Results: No variants at the genome-wide significance level were found after adjusting for key covariates (baseline V̇O₂peak_, individual study, principal components which were significantly associated with the trait). A Quantile-Quantile plot indicates there was minor inflation in the study. Twelve novel loci showed a trend of association with V̇O₂peak response that reached suggestive significance (P < 1 × 10^-5). The strongest association was found near the membrane associated guanylate kinase, WW and PDZ domain containing 2 (MAGI2) gene (rs6959961, P = 2.61 × 10^-7). A PPS created from the 12 lead SNPs was unable to predict V̇O₂peak response in a tenfold cross validation, or in an independent (n = 39) validation study (P > 0.1). Significant correlations were found for beta coefficients of variants in the Predict-HIIT (P < 1 × 10^-4) and the validation study (P < × 10^-6), indicating that general effects of the loci exist, and that with a higher statistical power, more significant genetic associations may become apparent.

Conclusions: Ongoing research and validation of current and previous findings is needed to determine if genetics does play a large role in V̇O₂peak response variance, and whether genomic predictors for V̇O₂peak response trainability can inform evidence-based clinical practice. Trial registration Australian New Zealand Clinical Trials Registry (ANZCTR), Trial Id: ACTRN12618000501246, Date Registered: 06/04/2018, http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374601&isReview=true .

Keywords: GWAS; Genetics; Individual variability; Polygenic predictor score; V̇O2peak training response.

Fig. 1

Variability in V̇O₂peak response between participants included in the GWAS

Fig. 2

Quantile–Quantile (QQ) plot and genomic inflation factor λ. λ is the observed median of test statistic distribution divided by the expected median of the test statistic distribution. A genomic inflation factor greater than 1.1 indicates there may be some inflation of the GWAS P-values; resulting from factors such as population stratification or DNA sample quality. λ = lambda, base = baseline VO₂peak, study = individual study participant completed, PC6 = 6th principal component

Fig. 3

Manhattan Plot of whole Training Cohort. The X-axis represents genomic coordinates, with the negative logarithm of the association p-value for each variant displayed on the Y-axis. Different chromosomes are shown with different colours. The blue line indicates the suggestive significance threshold 1 × 10^–5

Fig. 4

Tenfold cross validation—no correlation between Polygenic Predictor Score (PPS) and V̇O₂peak response (R² = 0.027, P = 0.76). Red, green and blue dots represent likely non-responders, likely responders and uncertain responders, respectively