Analyses of sequential weights of Nellore cattle using multiple trait and random regression models

• Ano de defesa: 2001
• Autor(a) principal: Nobre, Paulo Roberto Costa
• Orientador(a): Lopes, Paulo Sávio
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Viçosa
• Palavras-chave em Inglês: REML; Gibbs sampling; Expected progeny differences
• Área do conhecimento CNPq: Ciências Agrárias
• Link de acesso: http://www.locus.ufv.br/handle/123456789/11233

The objective of the first study was to obtain genetic parameters for sequential weights of beef cattle using RRM on data sets with missing and no missing traits, and to compare these estimates with those obtained by MTM. Growth curves of Nellore cattle were analyzed using body weights measured at ages ranging from 1 day (birth weight) to 733 days. Two data samples were created: one with 71,867 records from herds with missing traits and the other with 74,601 records from herds with no missing traits. Records preadjusted to a fixed age were analyzed by a multiple trait model (MTM), which included the effects of contemporary group, age of dam class, additive direct, additive maternal, and maternal permanent environment. Analyses were by restricted maximum likelihood (REML) with 5 traits at a time. The random regression model (RRM) included the effects of age of animal, contemporary group, age of dam class, additive direct, additive maternal, permanent environment, and maternal permanent environment. Legendre cubic polynomials were used to describe the random effects. Estimates of covariances by MTM were similar for both data sets, although those from the missing data set showed more variability from age to age. The estimates from RRM were similar to those from MTM only for the complete -trait case and showed large artifacts for the case of missing traits. Estimates of additive direct-maternal correlations under RRM for some ages approached -1.0, and most likely contained artifacts. If many traits are missing, the best approach to obtaining parameters for RRM would be conversion from smoothed MTM estimates. The purpose of the second study was estimation of parameters of models and data sets as in the first study by a Bayesian methodology – Gibbs sampling, and to make comparisons with their estimates by REML. Analyses were by a Bayesian method for all 9 traits. MTM estimated covariance components and genetic parameters for birth weight and sequential weights and RRM for all ages. Estimates of additive direct variance from herds with missing traits increased from birth weight through weight at 551 to 651 days with MTM. However, this component also increased for the sample with no missing traits after this age. Additive direct and residual estimated variance with RRM increased over all ages for both samples. For MTM, additive direct and maternal heritabilities were greater from the sample with herds with missing traits than those values from herds with no missing traits. The estimates from RRM were slightly lower than those from MTM for the sample with no missing traits; however, additive maternal heritabilities from MTM were greater than those using RRM. The estimated additive direct genetic correlations for each pair of traits were slightly higher for the first age (birth weight) using MTM than RRM. The range of additive maternal genetic correlations was lower than that for additive direct genetic correlations with MTM and RRM. Due to the fact that covariance components based on RRM were inflated for herds with missing traits, MTM should be used and converted to covariance functions. As well, for analyses with standard models where inferences on shapes of parameters are not important, analyses by REML may be more robust. The first goal of the third study was to implement the genetic evaluation of weights for a large population of beef cattle using the random regression model. The second goal was to compare these evaluations with those obtained from a multitrait evaluation. Expected progeny differences (EPD) were computed by two methods: a finite method using sparse factorization (SF) and interating (IT) by preconditioned conjugate gradient (PCG). The correlations between EPDs from MTM and RRM by SF and IT were ≤ .43 until the random regressions were orthogonalized. After orthogonalization high computing requirements of RRM were reduced by removing regressions corresponding to very low eigenvalues and by replacing the random error effects with weights. Correlations between EPDs from MTM and RRM for the additive direct effect were .87, .89, .89, .87, and .86 for W1 (weight at 60 days), W2 (weight at 252 days), W3 (weight at 243 days), W5 (weight at 426 days), and W7 (weight at 601 days), respectively. The corresponding correlations for the additive maternal effect were .85, .86, .88, .85 and .84, respectively. These low correlations were mostly due to differences in variances between the models and, to a lesser degree, due to better accounting for environmental effects and more data by RRM. The RRM applied to beef weights may be poorly conditioned numerically.