title: Analysis of beef cattle longitudinal data applying a nonlinear model.
abstract: The objective of this work was to evaluate the Nelore beef cattle, growth curve parameters using the Von Bertalanffy function in a nested Bayesian procedure that allowed estimation of the joint posterior distribution of growth curve parameters, their (co)variance components, and the environmental and additive genetic components affecting them. A hierarchical model was applied; each individual had a growth trajectory described by the nonlinear function, and each parameter of this function was considered to be affected by genetic and environmental effects that were described by an animal model. Random samples of the posterior distributions were drawn using Gibbs sampling and Metropolis-Hastings algorithms. The data set consisted of a total of 145,961 BW recorded from 15,386 animals. Even though the curve parameters were estimated for animals with few records, given that the information from related animals and the structure of systematic effects were considered in the curve fitting, all mature BW predicted were suitable. A large additive genetic variance for mature BW was observed. The parameter a of growth curves, which represents asymptotic adult BW, could be used as a selection criterion to control increases in adult BW when selecting for growth rate. The effect of maternal environment on growth was carried through to maturity and should be considered when evaluating adult BW. Other growth curve parameters showed small additive genetic and maternal effects. Mature BW and parameter k, related to the slope of the curve, presented a large, positive genetic correlation. The results indicated that selection for growth rate would increase adult BW without substantially changing the shape of the growth curve. Selection to change the slope of the growth curve without modifying adult BW would be inefficient because their genetic correlation is large. However, adult BW could be considered in a selection index with its corresponding economic weight to improve the overall efficiency of beef cattle production.
authors.affiliation: Faculdade de Ciências Agrárias e Veterinárias, UNESP, Jaboticabal, SP, 14884900, Brazil.
authors.forename: S
authors.initials: S
authors.lastname: Forni
authors.forename: M
authors.initials: M
authors.lastname: Piles
authors.forename: A
authors.initials: A
authors.lastname: Blasco
authors.forename: L
authors.initials: L
authors.lastname: Varona
authors.forename: H N
authors.initials: HN
authors.lastname: Oliveira
authors.forename: R B
authors.initials: RB
authors.lastname: Lôbo
authors.forename: L G
authors.initials: LG
authors.lastname: Albuquerque
mesh_terms: D000704:Analysis of Variance; D000818:Animals; D001499:Bayes Theorem; D001835:Body Weight / Q000235:genetics* / Q000502:physiology; D001947:Breeding / Q000379:methods*; D002417:Cattle / Q000235:genetics* / Q000254:growth & development*; D005260:Female; D008137:Longitudinal Studies; D008297:Male; D008954:Models, Biological; D008957:Models, Genetic; D012641:Selection, Genetic