Model comparison and estimation of genetic parameters for body weight in commercial broilers

N Maniatis, N Demiris, A Kranis, G Banos, A Kominakis

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

The availability of powerful computing and advances in algorithmic efficiency allow for the consideration of increasingly complex models. Consequently, the development and application of appropriate statistical procedures for model evaluation is becoming increasingly important. This paper is concerned with the application of an alternative model determination criterion (conditional Akaike Information Criterion, cAIC) in a large dataset comprising 203 323 body weights of broilers, pertaining to 7 (BW7) and 35 (BW35) days of age. Seven univariate and seven bivariate models were applied. Direct genetic, maternal genetic and maternal environmental (c2) effects were estimated via REML. The model evaluation criteria included conditional Akaike Information Criterion (cAIC), Bayesian Information Criterion (BIC) and the standard Akaike Information Criterion (henceforth marginal; mAIC). According to cAIC the best-fitting model included direct genetic, maternal genetic and c2 effects. Maternal heritabilities were low (0.10 and 0.03) compared to the direct heritabilities (0.17 and 0.21), while c2 was 0.05 and 0.04 for BW7 and BW35, respectively. BIC and mAIC favoured a model that additionally included a directmaternal genetic covariance, resulting in highly negative direct-maternal genetic correlations ( 0.47 and 0.64 for BW7 and BW35, respectively) and higher direct heritabilities (0.25 and 0.28 for BW7 and BW35, respectively). Results suggest that cAIC can select different animal models than mAIC and BIC with different biological properties.
Original languageEnglish
Pages (from-to)67 - 77
Number of pages11
JournalCanadian Journal of Animal Science
Volume93
Issue number1
DOIs
Publication statusPrint publication - Mar 2013
Externally publishedYes

Bibliographical note

1023517

Keywords

  • Bivariate analysis
  • Maternal effects
  • Model evaluation criteria
  • direct-maternal genetic correlation

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