基于全基因组选择信号的中国荷斯坦牛产奶性状基因定位

Genomics techniques provide two ways of phenotype-genome and genome-phenotype to better understand genetic background of important traits in farm animals. However, there are quite few successful reports in the field of phenotype-genome, in which linkage analysis, association study and candidate gene study are widely used to identify genes underlying quantitative traits. Alternatively, identification of genes by detecting genomic selection signatures becomes promising even without phenotypic information. A common feature of farm animal populations is that they are subject to a selection pressure which clearly exceeds the level of selective forces in natural selection. It is of interest to identify regions in the genome that show a measurable reaction to selection pressure. Interestingly, such regions can be detected by analyzing patterns in the genomic data alone and do not require phenotypic information. The project aims to identify genes underlying milk production traits by detecting genomic selection signatures in Chinese Holstein. In the first, reliable detection of selection signatures are obtained with implementing different approaches within/between population analysis. Afterwards, the relations of significant selection signature with milk production traits is cross validated using genome-wide association study, genomic selection analysis, bioinformatics analysis, to finally map genes controlling with milk production traits in Chinese Holstein. In addition, novel approach regarding detecting selection signatures will be proposed and strategy towards genome-phenotype is explored as well.

基因组技术的发展为从基因组水平上更好了解畜禽重要经济性状遗传基础提供了从表型到基因组和从基因组到表型两种思路。但在从表型到基因组的研究中，通过连锁分析、关联分析、候选基因分析研究基因与重要经济性状间的联系，目前仅有少量成功报道。而从基因组到表型研究则仅通过基因组信息分析进行基因定位。近代大多数畜禽品种都经历过强度较高的人工选择，这种选择痕迹可以通过基因组选择信号检测出来，因此，即使在没有表型观测值的情况下，仅根据选择信号也可鉴定出具有重要功能的基因。基于此，本项目利用全基因组选择信号对中国荷斯坦牛产奶性状基因定位，首先采用不同的统计方法，从品种间和品种内两个层次检测和筛选可靠的选择信号，然后利用中国荷斯坦牛产奶性状的全基因组关联分析、基因组选择分析和生物信息学分析等三个途径验证选择信号与产奶性状的联系，定位产奶性状基因。同时研究新的选择信号检测方法，探索基于基因组信息的基因定位新策略。

本项目结合当前生物技术和统计方法发展，力图构建基于基因组选择信号检测，实现从“高密度芯片和测序数据到表型”的基因定位策略，与已完成的“从表型到基因型”的基因定位策略形成互补。本项目建立了以荷斯坦牛为主、包括西门塔尔及中国地方黄牛等18个群体的50K芯片、800K芯片和测序数据库，提出了选择信号的复合策略检测新方法，展现出了更高的选择信号检测效力；为了进一步提高50K芯片包含的基因组信息，本研究同时探讨了50K芯片缺失基因型填充策略，提出了将常见的50K芯片填充至800K芯片和测序数据的策略，为相关研究奠定了基础；本研究基于芯片和测序数据进行了选择信号检测，通过与全基因组关联分析和基因组选择分析结果验证，验证了如DGAT1等多个基因受到了选择影响，本项目也为研究牛驯化提供了思路。