BMC Genomics (Mar 2024)

Identification and characterization of structural variants related to meat quality in pigs using chromosome-level genome assemblies

  • Daehong Kwon,
  • Nayoung Park,
  • Suyeon Wy,
  • Daehwan Lee,
  • Woncheoul Park,
  • Han-Ha Chai,
  • In-Cheol Cho,
  • Jongin Lee,
  • Kisang Kwon,
  • Heesun Kim,
  • Youngbeen Moon,
  • Juyeon Kim,
  • Jaebum Kim

DOI
https://doi.org/10.1186/s12864-024-10225-1
Journal volume & issue
Vol. 25, no. 1
pp. 1 – 10

Abstract

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Abstract Background Many studies have been performed to identify various genomic loci and genes associated with the meat quality in pigs. However, the full genetic architecture of the trait still remains unclear in part because of the lack of accurate identification of related structural variations (SVs) which resulted from the shortage of target breeds, the limitations of sequencing data, and the incompleteness of genome assemblies. The recent generation of a new pig breed with superior meat quality, called Nanchukmacdon, and its chromosome-level genome assembly (the NCMD assembly) has provided new opportunities. Results By applying assembly-based SV calling approaches to various genome assemblies of pigs including Nanchukmacdon, the impact of SVs on meat quality was investigated. Especially, by checking the commonality of SVs with other pig breeds, a total of 13,819 Nanchukmacdon-specific SVs (NSVs) were identified, which have a potential effect on the unique meat quality of Nanchukmacdon. The regulatory potentials of NSVs for the expression of nearby genes were further examined using transcriptome- and epigenome-based analyses in different tissues. Conclusions Whole-genome comparisons based on chromosome-level genome assemblies have led to the discovery of SVs affecting meat quality in pigs, and their regulatory potentials were analyzed. The identified NSVs will provide new insights regarding genetic architectures underlying the meat quality in pigs. Finally, this study confirms the utility of chromosome-level genome assemblies and multi-omics analysis to enhance the understanding of unique phenotypes.

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