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Genomic and transcriptomic predictors of triglyceride response to regular exercise
  1. Mark A Sarzynski1,2,
  2. Peter K Davidsen3,4,
  3. Yun Ju Sung5,
  4. Matthijs K C Hesselink6,
  5. Patrick Schrauwen6,
  6. Treva K Rice5,
  7. D C Rao5,
  8. Francesco Falciani3,
  9. Claude Bouchard1
  1. 1Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
  2. 2Department of Exercise Science, University of South Carolina, Columbia, SC, USA
  3. 3Centre for Computational Biology and Modelling, Institute for Integrative Biology, University of Liverpool, Liverpool, UK
  4. 4School of Immunity and Infection, University of Birmingham, Birmingham, UK
  5. 5Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA
  6. 6NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre, Maastricht, The Netherlands
  1. Correspondence to Dr Mark A Sarzynski, Department of Exercise Science, University of South Carolina, 921 Assembly St, Public Health Research Center Rm 305, Columbia, SC 29208, USA; sarz{at}


Aim We performed genome-wide and transcriptome-wide profiling to identify genes and single nucleotide polymorphisms (SNPs) associated with the response of triglycerides (TG) to exercise training.

Methods Plasma TG levels were measured before and after a 20-week endurance training programme in 478 white participants from the HERITAGE Family Study. Illumina HumanCNV370-Quad v3.0 BeadChips were genotyped using the Illumina BeadStation 500GX platform. Affymetrix HG-U133+2 arrays were used to quantitate gene expression levels from baseline muscle biopsies of a subset of participants (N=52). Genome-wide association study (GWAS) analysis was performed using MERLIN, while transcriptomic predictor models were developed using the R-package GALGO.

Results The GWAS results showed that eight SNPs were associated with TG training-response (ΔTG) at p<9.9×10−6, while another 31 SNPs showed p values <1×10−4. In multivariate regression models, the top 10 SNPs explained 32.0% of the variance in ΔTG, while conditional heritability analysis showed that four SNPs statistically accounted for all of the heritability of ΔTG. A molecular signature based on the baseline expression of 11 genes predicted 27% of ΔTG in HERITAGE, which was validated in an independent study. A composite SNP score based on the top four SNPs, each from the genomic and transcriptomic analyses, was the strongest predictor of ΔTG (R2=0.14, p=3.0×10−68).

Conclusions Our results indicate that skeletal muscle transcript abundance at 11 genes and SNPs at a number of loci contribute to TG response to exercise training. Combining data from genomics and transcriptomics analyses identified a SNP-based gene signature that should be further tested in independent samples.

  • Lipids
  • Intervention
  • Exercise

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