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<p>Crossbreeding is a cornerstone of modern livestock improvement, combining desirable traits to enhance productivity and environmental resilience. This study conducts the first comprehensive genomic analysis of Red Angus × Chinese Red Steppe (RACS) crossbred cattle, evaluating their genetic architecture, diversity, and selection signatures relative to founder breeds (Red Angus and Chinese Red Steppe) and global populations. A total of 119 cattle, comprising 104 RACS crossbreds and 15 Chinese Red Steppes cattle, were genotyped using the GGP Bovine 100k SNP array. Additionally, the public available genotypic data generated using the BovineSNP50 chip from 550 animals across eight beef breeds (Angus, Hereford, Limousin, Charolais, Mongolian, Shorthorn, Red Angus, and Simmental) and one dairy breed (Holstein) were incorporated into the analysis. We aimed to (1) define the population structure of RACS cattle, (2) quantify their genomic diversity and inbreeding levels, and (3) pinpoint regions under selection linked to adaptive and economic traits. We employed runs of homozygosity (ROH) and population differentiation (Fst) analyses to detect selection signals. The results revealed that the crossbred (RACS), Angus, and Red Angus breeds exhibited similar clustering patterns in principal component analysis (PCA), but the crossbred population showed the highest nucleotide diversity and lowest inbreeding coefficients compared to other breeds. Notably, candidate regions associated with immune response, cold adaptation, and carcass traits were identified within the RACS population. These findings enhance our understanding of the genetic makeup of crossbred beef cattle and highlight their potential for genetic improvement, informing future selection and breeding strategies aimed at optimizing beef production in challenging environments.</p>

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Uncovering genomic diversity and signatures of selection in red Angus × Chinese red steppe crossbred cattle population

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月15日

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  • Elaheh Rostamzadeh Mahdabi,
  • Rugang Tian,
  • Jing Tian,
  • Hojjat Asadollahpour Nanaie,
  • Xiao Wang,
  • Meng Zhao,
  • Hui Li,
  • Baolige Dalai,
  • Yin Sai,
  • Wenhua Guo,
  • Yuan Li,
  • Hao Zhang,
  • Ali Esmailizadeh

摘要

Crossbreeding is a cornerstone of modern livestock improvement, combining desirable traits to enhance productivity and environmental resilience. This study conducts the first comprehensive genomic analysis of Red Angus × Chinese Red Steppe (RACS) crossbred cattle, evaluating their genetic architecture, diversity, and selection signatures relative to founder breeds (Red Angus and Chinese Red Steppe) and global populations. A total of 119 cattle, comprising 104 RACS crossbreds and 15 Chinese Red Steppes cattle, were genotyped using the GGP Bovine 100k SNP array. Additionally, the public available genotypic data generated using the BovineSNP50 chip from 550 animals across eight beef breeds (Angus, Hereford, Limousin, Charolais, Mongolian, Shorthorn, Red Angus, and Simmental) and one dairy breed (Holstein) were incorporated into the analysis. We aimed to (1) define the population structure of RACS cattle, (2) quantify their genomic diversity and inbreeding levels, and (3) pinpoint regions under selection linked to adaptive and economic traits. We employed runs of homozygosity (ROH) and population differentiation (Fst) analyses to detect selection signals. The results revealed that the crossbred (RACS), Angus, and Red Angus breeds exhibited similar clustering patterns in principal component analysis (PCA), but the crossbred population showed the highest nucleotide diversity and lowest inbreeding coefficients compared to other breeds. Notably, candidate regions associated with immune response, cold adaptation, and carcass traits were identified within the RACS population. These findings enhance our understanding of the genetic makeup of crossbred beef cattle and highlight their potential for genetic improvement, informing future selection and breeding strategies aimed at optimizing beef production in challenging environments.