基于简化基因组测序评估小骨山羊群体遗传多样性和群体结构

doi:10.11843/j.issn.0366-6964.2025.03.018

Abstract

Abstract:

The aim of this study was to analyze the genetic diversity and population genetic structure of the Small-boned goat population, and to provide a theoretical basis for clarifying whether it is a new germplasm resource. In this study, the Small-boned goats, Hexi cashmere goats and Inner Mongolia cashmere goats (Albas) were used as subjects, 20 adult healthy ewes were selected from each group. Genome-wide nucleotide polymorphisms (SNPs) were detected using the SLAF-seq technique. Genetic diversity was calculated by using perl programming for minor allele frequency (MAF), observed heterozygosity (Ho), expected heterozygosity (He), Nei diversity index (Nei), polymorphic information content (PIC), and Shannon Wiener index (SHI); PopLDdecay was used for linkage disequilibrium analysis (LD); and VCFtools was used for population differentiation index (Fst). Population genetic structure was illustrated by principal component analysis (PCA) using EIGENSOFT, construction of NJ trees by MEGA X and population structure analysis by ADMIXTURE. Kinship was illustrated by calculating IBS distances using PLINK, calculating kinship using GCTA and constructing matrices. A total of 5 253 776 SNPs were detected, most of which were located in the intergenic region. Small-boned goats had the highest genetic diversity indexes except Ho (0.197). The mean MAF, He, Nei, PIC, and SHI of Small-boned goats were 0.216, 0.302, 0.312, 0.247, and 0.466, respectively. LD analysis showed that Small-boned goats had the highest r² and the slowest rate of decay. Fst showed that the Small-boned goat had a low level of differentiation from the Hexi cashmere goat (0.043) and a medium level of differentiation from the Inner Mongolian cashmere goat (Albas, 0.052). PCA analysis showed that Small-boned goats were more aggregated with Hexi cashmere goats and Inner Mongolian cashmere goats (Albas), and some of the Small-boned goats could be distinguished from the other groups from PC1>0. The results of the NJ-tree showed that the majority of the Small-boned goats converged into independent, and the rest had mixed groups. The population structure analysis showed that K=1 was the optimal number of groups. Small-boned goats had a unique genetic structure. The IBS distance matrix and the kinship G matrix showed the same results, with the 3 groups being more distantly related to each other and more closely related within the Small-boned goat groups. The results showed that the Small-boned goat was lowly differentiated from the Hexi cashmere goat and moderately differentiated from the Inner Mongolian cashmere goat (Albas). The genetic diversity of Small-boned goats was higher than that of the other two populations, but there were problems of selection pressure and small population size. Some of the individuals in the Small-boned goat population were closely related to each other, and inbreeding existed. This study provides a theoretical basis for the rational development and utilization of the Small-boned goat.

Key words: Small-boned goat, germplasm resources, genetic diversity, population genetic structure, specific-locus amplified fragment sequencing

CLC Number:

S827.2

WANG Haoyu, MA Keyan, LI Taotao, LI Dengpan, ZHAO Qing, MA Youji. Population Genetic Diversity and Population Structure Analysis of Small-boned Goat Based on Specific-Locus Amplified Fragment Sequencing[J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(3): 1170-1179.

Figures/Tables 8

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群体Population	地点Location	样本量Sample size	样本类型Sample type
小骨山羊XG	甘肃省兰州市	20	血液
河西绒山羊HXC	甘肃省张掖市	20	血液
内蒙古绒山羊(阿尔巴斯型)IMC	内蒙古自治区鄂尔多斯市	20	血液

群体 Population	次要等位基因频率 MAF	期望杂合度 He	观察杂合度 Ho	基因多样性指数 Nei	多态信息含量 PIC	香浓维纳指数 SHI
河西绒山羊HXC	0.210	0.297	0.206	0.306	0.243	0.460
内蒙古绒山羊(阿尔巴斯型)IMC	0.213	0.299	0.209	0.308	0.244	0.462
小骨山羊XG	0.216	0.302	0.197	0.312	0.247	0.466

群体Population	河西绒山羊HXC	内蒙古绒山羊(阿尔巴斯型)IMC	小骨山羊XG
河西绒山羊HXC		0.034	0.043
内蒙古绒山羊(阿尔巴斯型)IMC			0.052
小骨山羊XG

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Population Genetic Diversity and Population Structure Analysis of Small-boned Goat Based on Specific-Locus Amplified Fragment Sequencing

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