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Genomic prediction and genome-wide association studies for additive and dominance effects for body composition traits using 50 K and imputed high-density SNP genotypes in Yunong-black pigs.
Wu, Ziyi; Dou, Tengfei; Bai, Liyao; Han, Jinyi; Yang, Feng; Wang, Kejun; Han, Xuelei; Qiao, Ruimin; Li, Xiu-Ling; Li, Xin-Jian.
Afiliação
  • Wu Z; College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China.
  • Dou T; College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China.
  • Bai L; College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China.
  • Han J; College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China.
  • Yang F; College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China.
  • Wang K; College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China.
  • Han X; College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China.
  • Qiao R; College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China.
  • Li XL; College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China.
  • Li XJ; College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China.
J Anim Breed Genet ; 141(2): 124-137, 2024 Mar.
Article em En | MEDLINE | ID: mdl-37822282
ABSTRACT
Body composition traits are complex traits controlled by minor genes and, in hybrid populations, are impacted by additive and nonadditive effects. We aimed to identify candidate genes and increase the accuracy of genomic prediction of body composition traits in crossbred pigs by including dominance genetic effects. Genomic selection (GS) and genome-wide association studies were performed on seven body composition traits in 807 Yunong-black pigs using additive genomic models (AM) and additive-dominance genomic models (ADM) with an imputed high-density single nucleotide polymorphism (SNP) array and the Illumina Porcine SNP50 BeadChip. The results revealed that the additive heritabilities estimated for AM and ADM using the 50 K SNP data ranged from 0.20 to 0.34 and 0.11 to 0.30, respectively. However, the ranges of additive heritability for AM and ADM in the imputed data ranged from 0.20 to 0.36 and 0.12 to 0.30, respectively. The dominance variance accounted for 23% and 27% of the total variance for the 50 K and imputed data, respectively. The accuracy of genomic prediction improved by 5% on average for 50 K and imputed data when dominance effect were considered. Without the dominance effect, the accuracies for 50 K and imputed data were 0.35 and 0.38, respectively, and 0.41 and 0.43, respectively, upon considering it. A total of 12 significant SNP and 16 genomic regions were identified in the AM, and 14 significant SNP and 21 genomic regions were identified in the ADM for both the 50 K and imputed data. There were five overlapping SNP in the 50 K and imputed data. In the AM, a significant SNP (CNC10041568) was found in both body length and backfat thickness traits, which was in the PLAG1 gene strongly and significantly associated with body length and backfat thickness in pigs. Moreover, a significant SNP (CNC10031356) with a heterozygous dominant genotype was present in the ADM. Furthermore, several functionally related genes were associated with body composition traits, including MOS, RPS20, LYN, TGS1, TMEM68, XKR4, SEMA4D and ARNT2. These findings provide insights into molecular markers and GS breeding for the Yunong-black pigs.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Genoma / Estudo de Associação Genômica Ampla Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Genoma / Estudo de Associação Genômica Ampla Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals Idioma: En Ano de publicação: 2024 Tipo de documento: Article