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Population Structure and Selection Signal Analysis of Nanyang Cattle Based on Whole-Genome Sequencing Data.

Authors :
Zhang, Yan
Wei, Zhitong
Zhang, Man
Wang, Shiwei
Gao, Tengyun
Huang, Hetian
Zhang, Tianliu
Cai, Hanfang
Liu, Xian
Fu, Tong
Liang, Dong
Source :
Genes; Mar2024, Vol. 15 Issue 3, p351, 11p
Publication Year :
2024

Abstract

With a rich breeding history, Nanyang cattle (NY cattle) have undergone extensive natural and artificial selection, resulting in distinctive traits such as high fertility, excellent meat quality, and disease resistance. This makes them an ideal model for studying the mechanisms of environmental adaptability. To assess the population structure and genetic diversity of NY cattle, we performed whole-genome resequencing on 30 individuals. These data were then compared with published whole-genome resequencing data from 432 cattle globally. The results indicate that the genetic structure of NY cattle is significantly different from European commercial breeds and is more similar to North–Central Chinese breeds. Furthermore, among all breeds, NY cattle exhibit the highest genetic diversity and the lowest population inbreeding levels. A genome-wide selection signal analysis of NY cattle and European commercial breeds using Fst, θπ-ratio, and θπ methods revealed significant selection signals in genes associated with reproductive performance and immunity. Our functional annotation analysis suggests that these genes may be responsible for reproduction (MAP2K2, PGR, and GSE1), immune response (NCOA2, HSF1, and PAX5), and olfaction (TAS1R3). We provide a comprehensive overview of sequence variations in the NY cattle genome, revealing insights into the population structure and genetic diversity of NY cattle. Additionally, we identify candidate genes associated with important economic traits, offering valuable references for future conservation and breeding efforts of NY cattle. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20734425
Volume :
15
Issue :
3
Database :
Complementary Index
Journal :
Genes
Publication Type :
Academic Journal
Accession number :
176563589
Full Text :
https://doi.org/10.3390/genes15030351