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Genetic evaluation for latent variables derived from factor analysis in broilers.

José Teodoro PaivaHinayah Rojas de OliveiraMoyses NascimentoAna Carolina Campana NascimentoH T SilvaR F HenriquesPaulo Sávio LopesFabyano Fonseca E SilvaRenata VeronezeJosé Bento Sterman FerrazJoanir Pereira ElerElisangela Chicaroni de MattosLeila Gênova Gaya
Published in: British poultry science (2019)
1. The aim of this study was to investigate the associations between several carcass, performance and meat quality traits in broilers through factor analysis and use the latent variables (i.e. factors) as pseudo-phenotypes in genetic evaluations.2. Factors were extracted using the principal components method and varimax rotation algorithm. Genetic parameters were estimated via Bayesian inference under a multiple-trait animal model.3. All factors taken together explained 71% of the original variance of the data. The first factor, denominated as 'weight', was associated with carcass and body weight traits; and the second factor, defined as 'tenderness', represented traits related to water-holding capacity and shear force. The third factor, 'colour', was associated with traits related to meat colour, whereas the fourth, referenced as 'viscera', was related to heart, liver and abdominal fat.4. The four biological factors presented moderate to high heritability (ranging from 0.35 to 0.75), which may confer genetic gains in this population.5. In conclusion, it seems possible to reduce the number of traits in the genetic evaluation of broilers using latent variables derived from factor analysis.
Keyphrases
  • genome wide
  • dna methylation
  • body weight
  • copy number
  • heat stress
  • machine learning
  • body mass index
  • physical activity
  • deep learning
  • big data
  • quality improvement
  • artificial intelligence