生长性状是肉牛的重要经济性状，对其功能基因定位一直是肉牛分子育种的研究热点。应用GWAS方法发掘数量性状候选基因是当前畜禽经济性状研究的主要分析策略，但是如何利用GWAS数据提高发现功能基因/位点的效能，是GWAS面对的挑战。后GWAS时代的主要任务之一就是要对复杂经济性状易感区域内的关键遗传变异进行精细定位，并阐明其生物学功能。.本研究基于团队构建的中国西门塔尔牛资源群体表型数据统计分析和高密度SNP芯片分型结果，开展肉牛10个生长性状指标的GWAS分析，选择与生长性状显著相关的候选基因组区域，应用目标区域测序(Target-Seq)技术，挖掘目标区域内全面遗传变异信息。通过对变异所在位点的精细定位，开展NGS-GWAS联合分析，并结合生物信息学方法，从基因组水平鉴定出目标区域内与肉牛生长性状最显著关联的关键SNP位点和候选基因，为进一步鉴别影响肉牛生长性状的主效基因和遗传变异奠定理论。

Growth production is significant economic trait, which makes the study of its functional genes become current research focuses in molecular breeding of beef cattle. Application of Genome-wide association study (GWAS) is becoming a research trend for the identification of a number of genetic variants and candidate genes in complex quantitative traits loci related to economically important traits of the main livestock and poultry. However, the challenge facing the next step of GWAS is how to perform further analysis by utilizing the genome-wide genotype data to identify more susceptibility genes/loci of complex traits. Fine mapping appears to be an effective strategy to identify the causative variants and genes in the susceptibility regions associated with economically important traits for the post-GWAS era.. Based on the statistical analysis of phenotypic data from our resource population of Chinese Simmental and the results of high-density SNP genotyping chip, GWAS for a total of 10 growth traits will be performed. Then, we will select the candidate genomic regions significantly related to growth traits and obtain comprehensive genome information in the trait-associated regions using Targeted Sequencing (Target-Seq) technology. Fine mapping will be carried out by the joint analysis of next-generation sequencing and genome-wide association study (NGS-GWAS). Combined with bioinformatics method, the causative variants and candidate genes in the susceptibility regions associated with growth traits of beef cattle will be identified, which will facilitate us to detect major genes and genetic variations involved in growth traits and provide insights into theoretic basis of functional validation.