摘要
本研究基于全基因组重测序数据,以全球76个马品种共406匹马为研究对象,采用主成分分析、系统发育树和祖先成分分析三种方法,在遗传学层面综合评估了现代马群体的遗传结构。此外,以核苷酸多样性为指标对现代马群体的遗传多样性进行了评估,并运用Fst和XP-EHH受选择方法检测不同马群体之间的基因组选择信号,筛选出了5个与藏马高原适应性显著相关的候选基因(HBB、EPAS1、TMEM247、RHOQ和ATP6V1E2),3个与中国西南马体型性状显著相关的候选基因(HMGA2、TBX3和TBX5),以及与欧美高度选育马的马术表演,行为和运动显著相关的基因(IGFBP3、IGFBP1、HSD7A和ADCY1)。该研究为进一步保护地方马种的种质资源提供科学依据,并揭示人工培育马种的选择方向。
This study,based on whole-genome sequencing data,used 406 horses from 76 horse breeds worldwide as the research subjects.Three sophisticated methodologies-principal component analysis,phylogenetic tree,and ancestral component analysis,were used to comprehensively assess the genetic structure of modern horses at the genetic level.Additionally,genetic diversity of modern horse populations was evaluated using nucleotide diversity as an indicator,and the Fst and XP-EHH selection methods were applied to detect genomic selection signals between different horse populations.Five candidate genes(HBB,EPAS1,TMEM247,RHOQ,and ATP6V1E2)significantly related to the high-altitude adaptation of Tibetan horses,three candidate genes(HMGA2,TBX3,and TBX5)significantly related to body size traits of Chinese Southwest horses,and genes(IGFBP3,IGFBP1,THSD7A,ADCY1)significantly related to the equestrian performances,behavior,and movements of highly selected horses in Europe and America were selected.This study provides a scientific basis for further protecting the germplasm resources of local horse breeds and reveals the selection direction of artificially cultivated horse breeds.
作者
徐媛
徐继平
刘小英
姜雨
XU Yuan;XU Jiping;LIU Xiaoying;JIANG Yu(College of Animal Science and Technology,Northwest A&F University,Yangling,Shaanzi 712100,China;Bulanghe Regional Animal Husbandry and Veterinary Workstation,Yulin,Shaanzi 719000,China)
出处
《畜牧兽医杂志》
2024年第5期1-7,23,共8页
Journal of Animal Science and Veterinary Medicine
关键词
全基因组重测序
群体结构
选择信号分析
候选基因
whole-genome sequencing
population structure
selection signal analysis
candidate genes
作者简介
第一作者:徐媛(1999-),女,博士研究生,主要从事家养动物基因组学研究工作。E-mail:xuyuanchn@163.com;通信作者:姜雨,E-mail:yu.jiang@nwafu.edu.cn。
