长期的人工选择使猪的生产性能得到显著提高, 与选择相关的基因组区域也随之发生特定遗传变异表征(选择信号)。不同类型品种所受到选择强度不一, 选择信号亦不相同, 选择性清扫分析已逐渐成为选择信号的主要检测手段。文章基于商用型大白猪(n=45)和地方猪品种通城猪(n=45)的猪60K SNP芯片分型数据, 借助遗传分化系数Fst法进行选择信号检测分析。利用gPLINK软件设定质控标准, 共计34 304个SNPs被筛选出用于统计分析。使用Genepop软件包计算两个猪品种之间的遗传分化参数Fst, 所得Fst平均值为0.3209。选取Fst > 0.7036(即占总Fst值数目的1%), 共计344个SNPs被选择出来。SNP位置注释显示这些位点涉及到79个候选基因(Sus scrofa Build 9)。利用在线软件Ingenuity Pathway Analysis对候选基因的生物学通路进行网络分析, 发现它们多与生长繁殖及免疫应答有关, 如NCOA6、ERBB4、RUNX2和APOB等基因。研究结果为进行猪产肉、抗病等性状候选基因和致因突变深入挖掘提供了有益参考。
The production performance of pigs has been significantly improved due to long-term artificial selection, and the specific variation characterizations (selection signatures) emerged from the selected genome regions. Different types of breeds are subjected to different selection intensities and had different selection signatures. Selective sweep analysis is one of major methods to detect the selection signatures. In this study, based on the 60K BeadChip genotyping data of both commercial Large White (n=45) and local Tongcheng pigs (n=45), genetic differentiation coefficient Fst was applied to detect the selection signatures. Using gPLINK software to set quality control standards, a total of 34 304 SNPs were selected for statistical analysis. Fst values between two breeds were estimated with Genepop package and the average Fst value was 0.3209. Setting Fst>0.7036 (1% of total number of Fst values) as selection threshold, 344 SNPs were obtained and SNP location annotation indicated that there were 79 candidate genes (Sus scrofa Build 9). Furthermore, network analysis was performed using Ingenuity Pathway Analysis and the preliminary results suggested that most genes were in-volved in growth, reproduction, and immune response, such as NCOA6, ERBB4, RUNX2, and APOB genes. The findings from this study will contribute to further identification of candidate genes and causal mutations implying for meat production and disease resistance in pig.
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