Xu, Song-Song, Akhatayeva, Zhanerke, Liu, Jiaxin, Feng, Xueyan, Yu, Yi, Badaoui, Bouabid, Esmailizadeh, Ali, Kantanen, Juha, Amills, Marcelo, Lenstra, Johannes A, Johansson, Anna, Coltman, David W, Liu, George E, Curik, Ino, Orozco Ter Wengel, Pablo ORCID: https://orcid.org/0000-0002-7951-4148, Paiva, Samuel R, Zinovieva, Natalia A, Zhang, Linwei, Yang, Ji, Liu, Zhihong, Wang, Yachun, Yu, Ying and Li, Meng-Hua
2024.
Genetic advancements and future directions in ruminant livestock breeding: From reference genomes to multiomics innovations.
Science China Life Sciences
10.1007/s11427-024-2744-4
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Abstract
Ruminant livestock provide a rich source of products, such as meat, milk, and wool, and play a critical role in global food security and nutrition. Over the past few decades, genomic studies of ruminant livestock have provided valuable insights into their domestication and the genetic basis of economically important traits, facilitating the breeding of elite varieties. In this review, we summarize the main advancements for domestic ruminants in reference genome assemblies, population genomics, and the identification of functional genes or variants for phenotypic traits. These traits include meat and carcass quality, reproduction, milk production, feed efficiency, wool and cashmere yield, horn development, tail type, coat color, environmental adaptation, and disease resistance. Functional genomic research is entering a new era with the advancements of graphical pangenomics and telomere-to-telomere (T2T) gap-free genome assembly. These advancements promise to improve our understanding of domestication and the molecular mechanisms underlying economically important traits in ruminant livestock. Finally, we provide new perspectives and future directions for genomic research on ruminant genomes. We suggest how ever-increasing multiomics datasets will facilitate future studies and molecular breeding in livestock, including the potential to uncover novel genetic mechanisms underlying phenotypic traits, to enable more accurate genomic prediction models, and to accelerate genetic improvement programs.
Item Type: | Article |
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Date Type: | Published Online |
Status: | Published |
Schools: | Biosciences |
Publisher: | Springer Verlag (Germany) / SpringerOpen |
ISSN: | 1674-7305 |
Date of First Compliant Deposit: | 1 November 2024 |
Date of Acceptance: | 24 September 2024 |
Last Modified: | 07 Nov 2024 04:30 |
URI: | https://orca.cardiff.ac.uk/id/eprint/172948 |
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