基于全基因组数据对新疆5个地方绵羊品种特异性SNP位点的筛选及鉴定

doi:10.16431/j.cnki.1671-7236.2025.10.021

摘要/Abstract

摘要： 【目的】多浪羊、罗布羊、阿勒泰羊、巴什拜羊和吐鲁番黑羊是新疆具有代表性的地方绵羊品种，本研究聚焦新疆这5个主要地方绵羊品种，旨在明确其遗传特征，建立快速识别策略，为地方品种的精准鉴定与种质资源保护提供科学依据。【方法】围绕新疆地方绵羊品种的群体结构分析与品种鉴定，选取具有代表性的27个绵羊群体共206份样本，涵盖新疆5个主要地方品种(罗布羊、阿勒泰羊、巴什拜羊、多浪羊和吐鲁番黑羊)，以及多个培育品种和引进品种，兼顾地理分布、育种背景与经济类型，全面反映当前绵羊遗传资源结构。利用全基因组单核苷酸多态性(SNP)变异检测与群体遗传分析评估新疆地方绵羊品种的遗传独立性，通过设置case-control的方法计算群体分化指数(Fst)和等位基因频率差异(ΔAF)初步筛选品种特征变异位点，结合随机森林算法(RF)挖掘新疆5个地方绵羊品种的最优特异性SNP标记，并采用主成分分析(PCA)和邻接法(NJ)构建系统进化树鉴定结果的准确性。【结果】本研究针对新疆5个地方绵羊品种进行筛选，通过结合Fst、ΔAF和RF进行筛选与优化，并进行PCA和系统进化树验证，最终成功筛选到多浪羊30个SNPs、巴什拜羊47个SNPs、罗布羊46个SNPs、阿勒泰羊50个SNPs以及吐鲁番黑羊46个SNPs。这些SNP位点均经过准确率达98%左右的筛选标准鉴定，能够高效区分各品种。【结论】本研究基于全基因组数据建立了一套基于SNP标记的新疆5个地方绵羊品种特异性遗传标签体系，能够在分子层面高效、精准地进行品种鉴定，为后续研发专门品种的分子鉴定试剂盒提供了理论基础，实现了对新疆5个地方绵羊品种的快速、准确识别，助力品种资源管理与精准育种。

关键词: 绵羊; 全基因组重测序; 单核苷酸多态性(SNP); 品种鉴定

Abstract: 【Objective】 Dolang sheep,Luobu sheep,Altay sheep,Bashibai sheep,and Turpan Black sheep are representative local sheep breeds in Xinjiang.This study focused on five major local sheep breeds in Xinjiang,aiming to characterize their genetic features and develop a rapid identification strategy,thereby providing a scientific basis for precise breed identification and the conservation of genetic resources.【Method】 This study focused on the analysis of population structure and breed identification of local sheep breeds in Xinjiang.A total of 206 samples from 27 representative sheep populations were selected,encompassing five major local breeds (Luobu sheep,Altay sheep,Bashibai sheep,Dolang sheep,and Turpan Black sheep),as well as several cultivated and introduced breeds.The sample design considered geographic distribution,breeding background,and economic type,providing a comprehensive reflection of the current genetic structure of sheep resources.Whole-genome single nucleotide polymorphism (SNP) variation detection and population genetic analysis were performed to assess the genetic independence of Xinjiang local sheep breeds.Using a case-control approach,the fixation index (Fst) and allele frequency difference (ΔAF) were calculated to preliminarily screen for breed-specific variants.A random forest (RF) algorithm was subsequently employed to identify the optimal set of breed-specific SNP markers for the five local sheep breeds.Principal component analysis (PCA) and Neighbor-Joining (NJ) phylogenetic tree construction were used to validate the accuracy of the identification results.【Result】 This study conducted a targeted selection of specific markers for five indigenous sheep breeds in Xinjiang.By integrating Fst,ΔAF,and RF algorithms for comprehensive screening and optimization,and validating the selected markers through PCA and NJ phylogenetic tree construction,a set of highly discriminative SNP was successfully identified.Specifically,30 SNPs were selected for Dolang sheep,47 SNPs for Bashibai sheep,46 SNPs for Luobu sheep,50 SNPs for Altay sheep,and 46 SNPs for Turpan Black sheep.All selected SNPs met stringent selection criteria,achieving an identification accuracy of approximately 98%,and demonstrated strong capability for efficient and precise breed differentiation.【Conclusion】 Leveraging whole-genome data,this study developed a SNP-based breed-specific genetic labeling system for five indigenous sheep breeds in Xinjiang.This system enabled rapid and accurate molecular-level identification,providing a robust theoretical basis for the future development of breed-specific molecular diagnostic kits.The approach offered an effective tool for the precise identification of local sheep breeds in Xinjiang,thereby supporting genetic resource management and advancing precision breeding initiatives.

Key words: sheep; whole-genome resequencing; single nucleotide polymorphism (SNP); breed identification

中图分类号:

S813.3

缑帅帅, 刘玲玲, 曹行, 陈秋明, 吉飞龙, 刘武军. 基于全基因组数据对新疆5个地方绵羊品种特异性SNP位点的筛选及鉴定[J]. 中国畜牧兽医, 2025, 52(10): 4754-4764.

GOU Shuaishuai, LIU Lingling, CAO Hang, CHEN Qiuming, JI Feilong, LIU Wujun. Screening and Identification of Specific SNP for 5 Local Sheep Breeds in Xinjiang Based on Whole-genome Data[J]. China Animal Husbandry & Veterinary Medicine, 2025, 52(10): 4754-4764.

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