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Genetic and phenotypic correlations between backfat thickness and weight at 28 weeks of age, and reproductive performance in primiparous Landrace sows raised under tropical conditions.

Praew ThiengpimolSkorn KoonawootrittrironThanathip Suwanasopee
Published in: Tropical animal health and production (2022)
Backfat thickness could reflect the energy reserve of female pigs that is required for their reproductivity, especially gilts that might be selected as replacements. In this study, genetic and phenotypic correlations between backfat thickness (BF) and body weight (BW) at 28 weeks of age, and reproduction traits were estimated. They were considered for the possibility of using BF at the pre-selective stage as an early indicator of sow's reproduction potential. Pedigree information, BF and BW at 28 weeks of age, age at first farrowing (AFF), transformed proportion of piglet loss at birth (tPL), and transformed weaning to first service interval (tWSI) of 806 primiparous Landrace sows were used to estimate the variance components by a restricted maximum likelihood procedure with an average information algorithm for multivariate analysis. The genetic correlation between BF and BW was 0.70 ± 0.13. Both BF and BW had a negative genetic correlation with AFF but not with tWSI. Genetic correlation estimates between tPL and other traits were unclear due to high standard error. The genetic correlation between AFF and tWSI was 0.78 ± 0.36. There were 19.35% of sires, 26.34% of dams, and 25.81% of sows that had preferable estimated breeding values for BF, BW, AFF, and WSI. These values indicated the feasibility of using selection index to improve BF and BW at the pre-selective stage and reduce AFF and tWSI of replacement gilt simultaneously. The estimation of genetic correlation between PL and other traits warrants further study in larger populations.
Keyphrases
  • genome wide
  • copy number
  • body weight
  • dna methylation
  • gene expression
  • climate change
  • risk assessment
  • body mass index
  • weight loss
  • deep learning
  • intensive care unit
  • social media
  • minimally invasive
  • neural network