Genomic and transcriptomic predictors of triglyceride response to regular exercise.
OBJECTIVE
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 (R(2)=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.
Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA Department of Exercise Science, University of South Carolina, Columbia, SC, USA.
Mark A
MA
Sarzynski
Centre for Computational Biology and Modelling, Institute for Integrative Biology, University of Liverpool, Liverpool, UK School of Immunity and Infection, University of Birmingham, Birmingham, UK.
Peter K
PK
Davidsen
Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.
Yun Ju
YJ
Sung
NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre, Maastricht, The Netherlands.
Matthijs K C
MK
Hesselink
NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre, Maastricht, The Netherlands.
Patrick
P
Schrauwen
Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.
Treva K
TK
Rice
Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.
D C
DC
Rao
Centre for Computational Biology and Modelling, Institute for Integrative Biology, University of Liverpool, Liverpool, UK.
Francesco
F
Falciani
Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA.
Claude
C
Bouchard
D000293:Adolescent; D000328:Adult; D000368:Aged; D015444:Exercise / Q000502:physiology*; D055106:Genome-Wide Association Study; D023281:Genomics; D005838:Genotype; D006801:Humans; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000502:physiology; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012313:RNA / Q000235:genetics; D059467:Transcriptome; D014280:Triglycerides / Q000378:metabolism*; D055815:Young Adult
D014280:Triglycerides; D012313:RNA
Exercise; Intervention; Lipids