摘要
目的:通过全基因组关联分析(genome-wide association studies,GWAS)筛选卧推1RM抗阻训练效果相关遗传标记,联合表型指标构建卧推1RM抗阻训练效果预测模型,并采用生物信息学方法分析遗传标记的可能作用机制,以期为制定精准化的运动健身指导方案提供依据。方法:193名非规律运动成年人完成12周抗阻力量训练,训练干预前后测试卧推1RM、身体成分、肌肉厚度等表型指标。采集受试者DNA,利用Illumina Infinium CGA-24v1-0芯片进行全基因组基因型解析。应用PLINK1.9软件进行GWAS分析,并筛选卧推1RM训练效果相关单核苷酸多态性位点(single-nucleotide polymorphisms,SNPs)。采用平均值法计算权重后的基因组多基因得分(genomics polygenic scores,GPGS),采用K-mean方法对卧推1RM变化百分比进行聚类分析,采用前进法Logistic回归分析建立以基因组-表型指标为预测因子的卧推1RM训练效果综合预测模型,采用HaploReg v4.1、GTEx、KEGG对纳入模型的SNPs进行生物功能注释。结果:1)12周抗阻训练后,卧推1RM平均提高36.25%(P<0.01),个体差异变化范围为-31.25%~176.92%;2)GWAS显示35个SNPs与卧推1RM抗阻训练效果显著关联(P<1×10-6),7个SNPs被纳入基因组学回归模型,可解释训练效果个体差异的39.6%,其中rs79726572、rs112183859、rs77187527的解释度分别为13.7%、9%、8%;权重后的GPGS平均得分为3.12,变化范围为-3.93~27.21;3)联合GPGS与表型指标构建的综合模型,卧推初始值、BMI、右上肢肌肉质量、左躯干肌肉质量、GPGS得分被纳入综合模型(AUC为0.952,约登指数0.767,cut off值0.251);4)生物信息学分析表明,卧推1RM训练效果基因组预测模型中的7个SNPs所在基因或受调控基因功能与肌生成等相关;受调控的基因富集于Generic Transcription Pathway、Developmental Biology、Myogenesis等37条信号通路(P<0.01,FDR<0.01)。结论:首次基于GWAS筛选出rs79726572、rs112183859、rs77187527等7个与卧推1
Objective:The genome-wide association analysis(GWAS)was used to screen genetic markers associated with resistance training effects of bench press 1RM,then a predictive model of bench press 1RM resistance training effect was constructed by combining with phenotye indicators;in addition,the bioinformatics was used to analyze the possible mechanisms of genetic markers in order to provide references for formulate precise exercise and fitness programs.Methods:193 non-regular exercise adults completed a 12-week resistance training program,the phenotypic indicators such as bench press 1RM,body composition,and muscle thickness were tested before and after the training intervention.The subjects’DNA was collected,and whole-genome genotyping analysis was performed using the Illumina Infinium CGA-24v1-0 chip.GWAS analysis was conducted using PLINK1.9 software,and single-nucleotide polymorphisms(SNPs)affecting bench press 1RM training effect were screened.The mean method was used to calculate the genomics polygenic scores(GPGS)after weighting.The K-mean method was used to cluster the percentage change in bench press 1RM and the forward logistic regression analysis was used to build a comprehensive model for predicting the training effect of genomic-phenotye indicators.HaploReg v4.1,GTEx,and KEGG were used to annotate the SNPs included in the model for biofunctionality.Results:1)After 12 weeks of resistance training,the mean improvement in bench press 1RM was 36.25%(P<0.01),but the individual variation was significant which was ranging from-31.25%to 176.92%.2)GWAS showed 35 SNPs were significantly associated with the effect of bench press 1RM resistance training(P<1×10-6),and 7 SNPs were included in the genomics regression model to explain 39.6%of the individual differences in training effect,with rs79726572,rs112183859 and rs77187527 explaining 13.7%,9%and 8%,respectively;the mean GPGS score after weighting was 3.12(range of variation:-3.93 to 27.21).3)The initial values of prone push,BMI,right upper limb muscle mass,left
作者
梅涛
李燕春
李晓霞
杨晓琳
李亮
晏冰
何子红
MEI Tao;LI Yanchun;LI Xiaoxia;YANG Xiaolin;LI Liang;YAN Bing;HE Zihong(Beijing Sport University,Beijing 100084,China;Shandong Sport University,Jinan 250102,China;Sultan Idris Education University,Tanjung Malin 35900,Malasia;China Institute of Sport Science,Beijing 100061,China)
出处
《体育科学》
北大核心
2023年第4期61-72,共12页
China Sport Science
基金
国家重点研发计划“主动健康和老龄化科技应对”重点专项(2018YFC2000602)。
关键词
训练效果
全基因组关联分析
表型
预测模型
精准化健身指导方案
training effect
genome-wide association analysis
phenotype
predictive models
precision fitness instruction program
