Scientific Reports (Sep 2022)

SNP discovery and association study for growth, fatness and meat quality traits in Iberian crossbred pigs

  • C. Óvilo,
  • N. Trakooljul,
  • Y. Núñez,
  • F. Hadlich,
  • E. Murani,
  • M. Ayuso,
  • C. García-Contreras,
  • M. Vázquez-Gómez,
  • A. I. Rey,
  • F. Garcia,
  • J. M. García-Casco,
  • C. López-Bote,
  • B. Isabel,
  • A. González-Bulnes,
  • K. Wimmers,
  • M. Muñoz

DOI
https://doi.org/10.1038/s41598-022-20817-0
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 16

Abstract

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Abstract Iberian pigs and its crosses are produced to obtain high-quality meat products. The objective of this work was to evaluate a wide panel of DNA markers, selected by biological and functional criteria, for association with traits related to muscle growth, fatness, meat quality and metabolism. We used 18 crossbred Iberian pigs with divergent postnatal growth patterns for whole genome sequencing and SNP discovery, with over 13 million variants being detected. We selected 1023 missense SNPs located on annotated genes and showing different allele frequencies between pigs with makerdly different growth patterns. We complemented this panel with 192 candidate SNPs obtained from literature mining and from muscle RNAseq data. The selected markers were genotyped in 480 Iberian × Duroc pigs from a commercial population, in which phenotypes were obtained, and an association study was performed for the 1005 successfully genotyped SNPs showing segregation. The results confirmed the effects of several known SNPs in candidate genes (such as LEPR, ACACA, FTO, LIPE or SCD on fatness, growth and fatty acid composition) and also disclosed interesting effects of new SNPs in less known genes such as LRIG3, DENND1B, SOWAHB, EPHX1 or NFE2L2 affecting body weight, average daily gain and adiposity at different ages, or KRT10, NLE1, KCNH2 or AHNAK affecting fatness and FA composition. The results provide a valuable basis for future implementation of marker-assisted selection strategies in swine and contribute to a better understanding of the genetic architecture of relevant traits.