Whole-genome resequencing to investigate the genetic diversity and mechanisms of plateau adaptation in Tibetan sheep

作者：Xue Li, Buying Han, Dehui Liu, Song Wang, Lei Wang, Quanbang Pei, Zian Zhang, Jincai Zhao, Bin Huang, Fuqiang Zhang, Kai Zhao, Dehong Tian · 发表于：Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology · 年份：2024 · DOI：10.1186/s40104-024-01125-1 · 被引用次数：23 · 研究领域：Genetic and phenotypic traits in livestock、Genetic Mapping and Diversity in Plants and Animals、Genetic diversity and population structure

INTRODUCTION: Tibetan sheep, economically important animals on the Qinghai-Tibet Plateau, have diversified into numerous local breeds with unique characteristics through prolonged environmental adaptation and selective breeding. However, most current research focuses on one or two breeds, and lacks a comprehensive representation of the genetic diversity across multiple Tibetan sheep breeds. This study aims to fill this gap by investigating the genetic structure, diversity and high-altitude adaptation of 6 Tibetan sheep breeds using whole-genome resequencing data. RESULTS: Six Tibetan sheep breeds were investigated in this study, and whole-genome resequencing data were used to investigate their genetic structure and population diversity. The results showed that the 6 Tibetan sheep breeds exhibited distinct separation in the phylogenetic tree; however, the levels of differentiation among the breeds were minimal, with extensive gene flow observed. Population structure analysis broadly categorized the 6 breeds into 3 distinct ecological types: plateau-type, valley-type and Euler-type. Analysis of unique single-nucleotide polymorphisms (SNPs) and selective sweeps between Argali and Tibetan sheep revealed that Tibetan sheep domestication was associated primarily with sensory and signal transduction, nutrient absorption and metabolism, and growth and reproductive characteristics. Finally, comprehensive analysis of selective sweep and transcriptome data suggested that Tibetan sheep b...