Sarzynski Mark A, Davidsen Peter K, Sung Yun Ju, Hesselink Matthijs K C, Schrauwen Patrick, Rice Treva K, Rao D C, Falciani Francesco, Bouchard Claude
Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA Department of Exercise Science, University of South Carolina, Columbia, SC, USA.
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.
Br J Sports Med. 2015 Dec;49(23):1524-31. doi: 10.1136/bjsports-2015-095179. Epub 2015 Oct 21.
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.
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.
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)).
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.
我们进行了全基因组和转录组分析,以确定与甘油三酯(TG)对运动训练反应相关的基因和单核苷酸多态性(SNP)。
在HERITAGE家庭研究的478名白人参与者中,测量了为期20周的耐力训练计划前后的血浆TG水平。使用Illumina BeadStation 500GX平台对Illumina HumanCNV370-Quad v3.0 BeadChips进行基因分型。Affymetrix HG-U133+2阵列用于定量一部分参与者(N = 52)基线肌肉活检的基因表达水平。使用MERLIN进行全基因组关联研究(GWAS)分析,同时使用R包GALGO开发转录组预测模型。
GWAS结果显示,8个SNP与TG训练反应(ΔTG)相关,p<9.9×10(-6),另外31个SNP的p值<1×10(-4)。在多变量回归模型中,前10个SNP解释了ΔTG中32.0%的变异,而条件遗传力分析表明,4个SNP在统计学上解释了ΔTG的所有遗传力。基于11个基因基线表达的分子特征预测了HERITAGE中27%的ΔTG,这在一项独立研究中得到了验证。基于基因组和转录组分析中各自排名前四的SNP的综合SNP评分是ΔTG最强的预测指标(R(2)=0.14,p=3.0×10(-68))。
我们的结果表明,11个基因的骨骼肌转录丰度和多个位点的SNP有助于TG对运动训练的反应。结合基因组学和转录组学分析的数据,确定了一个基于SNP的基因特征,应在独立样本中进一步测试。