Genetic parameters for behavioral and growth traits of Nellore cattle

J Anim Sci. 2023 Jan 3:101:skad280. doi: 10.1093/jas/skad280.

Abstract

The growing concern of consumers with the welfare of production animals searches welfare in a production system extremely important; thus, the study of animal temperament is necessary to select less excitable temperament animals resulting in healthy development and fewer accidents. The objective of this study was to estimate genetic parameters for traits related to animal temperament and growth traits of Nellore cattle. In addition to exploring the genetic pattern of these traits through cluster and principal component analysis (PCA), to reveal possible groups of individuals that express less excitable temperament and greater growth. A total of 2,332 measurements from 1,245 male and female Nellore cattle born between 2008 and 2016 were utilized in the study. The (co)variance components were estimated by Bayesian inference using a two-trait animal model. The heritability for temperament score (TS), flight speed (FS), body condition score (BCS), live weight (LW), and hip height (HH) were 0.08, 0.12, 0.06, 0.13, and 0.48, respectively. The genetic correlation between the temperament indicator traits was strong and positive (0.78 ± 0.24). The TS and FS showed a favorable or null genetic correlation with LW, BCS, and HH. The third cluster included animals with low EBV for TS and FS and with high EBV for BCS, LW, and HH. In the PCA, the PC1 was what best evidenced the aim of this study; thus, our findings suggest that we could explore select animals based on cluster 3 and PC1 in breeding programs to select Nellore cattle with less excitable temperament and greater growth.

Keywords: cluster analysis; genetic correlation; heritability; temperament; zebu.

Plain language summary

In our manuscript, we estimated the genetic parameters for indicator traits for animal temperament and growth traits in Nellore cattle, and we use the estimated breeding value of the evaluated animals in cluster analyses and principal component analyses to assess whether there are groups within the population that can be used as candidates for selection.

MeSH terms

  • Animals
  • Bayes Theorem
  • Cattle / genetics
  • Cobalt*
  • Female
  • Health Status*
  • Male
  • Parturition
  • Phenotype
  • Pregnancy

Substances

  • Cobalt