Genome-wide comparative analyses reveal selection signatures underlying adaptation and production in Tibetan and Poll Dorset sheep

Yuehui Ma,Xianglan Xue,Qianjun Zhao,Adam Abied,Liqing Ma,Wenqiang Wang,Yingyue Zhang,Weijun Guan,Yabin Pu,Yue Liu,Yangyang Ding,Jianlin Han,Jijun Guo,Shengguo Zhao,Joram M Mwacharo

doi:10.1038/s41598-021-81932-y

Abstract

The identification of genome-wide selection signatures can provide insights on the mechanisms of natural and/or artificial selection and uncover genes related to biological functions and/or phenotypes. Tibetan sheep are an important livestock in Tibet, providing meat and wool for Tibetans who are renown for breeding livestock that adapt well to high altitudes. Using whole-genome sequences with an effective sequencing depth of 5×, we investigated the genomic diversity and structure and, identified selection signatures of White Tibetan, Oula and Poll Dorset sheep. We obtained 30,163,679 Single Nucleotide Polymorphisms (SNPs) and 5,388,372 indels benchmarked against the ovine Oar_v4.0 genome assembly. Next, using FST, ZHp and XP-EHH approaches, we identified selection signatures spanning a set of candidate genes, including HIF1A, CAPN3, PRKAA1, RXFP2, TRHR and HOXA10 that are associated with pathways and GO categories putatively related to hypoxia responses, meat traits and disease resistance. Candidate genes and GO terms associated with coat color were also identified. Finally, quantification of blood physiological parameters, revealed higher levels of mean corpuscular hemoglobin measurement and mean corpuscular hemoglobin concentration in Tibetan sheep compared with Poll Dorset, suggesting a greater oxygen-carrying capacity in the Tibetan sheep and thus better adaptation to high-altitude hypoxia. In conclusion, this study provides a greater understanding of genome diversity and variations associated with adaptive and production traits in sheep.

Highlights

Sheep (Ovis aries) is one of the first domesticated livestock species whose ancestors were primarily distributed in the Fertile Crescent approximately 10,000 years a go[1]
The levels of MCH and Mean corpuscular hemoglobin concentration (MCHC) for the two local Tibetan sheep breeds were higher compared to those of Poll Dorset (P < 0.01; Supplementary Fig. S1). It may indicate a higher oxygen-carrying capacity in the blood of Tibetan sheep due to the fact that Tibetan sheep are long-term inhabitants of high altitude environments. Though it remains to be ascertained, it has been suggested in other literature that blood physiological parameters such as hemoglobin levels often play a major role in mediating adaptive response to plateau hypoxia
HOXA10 occurred in a candidate region defined by a strong selection signature in the Poll Dorset and we suggest that this gene could be an important factor underlying reproductive performance

Summary

Introduction

Sheep (Ovis aries) is one of the first domesticated livestock species whose ancestors were primarily distributed in the Fertile Crescent approximately 10,000 years a go[1]. We focus the genome-wide analysis on the Tibetan sheep, an economically important livestock breed in the high altitude Qinghai-Tibetan plateau[2], which provides meat, milk, wool and skins for nomadic and semi-nomadic people[3]. Since the dawn of agriculture, artificial selection has continuously added to, and/or deducted from, existing variations the same way natural selection has impacted biodiversity in nature Following their domestication and dispersal, the Tibetan sheep gradually adapted to the plateau environment and human requirements. Using FST analysis, Zhang et al conducted a genome-wide selection signal detection in five sheep breeds and identified genes related to important traits. Genome-wide selective signature analysis of resequencing data from 77 Chinese domestic and three wild sheep revealed several novel candidate genes related to extreme environmental adaptation. Dramatic hematological differences have been observed between species living at high-altitudes and the ones at low a ltitudes[50,51,52]

Methods

Results

Conclusion