Variance component estimation with longitudinal data: a simulation study with alternative methods

A pedigree structure distributed in three different places was generated. For each offspring, phenotypicinformation was generated for five different ages (12, 30, 48, 66 and 84 months). The data file was simulated allowing someinformation to be lost (10, 20, 30 and 40%) by a random process and by selecting the ones with lower phenotypic values,representing the selection effect. Three alternative analysis were used, the repeatability model, random regression model andmultiple-trait model. Random regression showed to be more adequate to continually describe the covariance structure ofgrowth over time than single-trait and repeatability models, when the assumption of a correlation between successivemeasurements in the same individual was different from one another. Without selection, random regression and multiple-traitmodels were very similar

Buffalos milk yield analysis using random regression models

Data comprising 1,719 milk yield records from 357 females (predominantly Murrah breed), daughters of 110 sires, with births from 1974 to 2004, obtained from the Programa de Melhoramento Genetic de Bubalinos (PROMEBUL) and from records of EMBRAPA Amazonia Oriental - EAO herd, located in Belem, Para, Brazil, were used to compare random regression models for estimating variance components and predicting breeding values of the sires. The data were analyzed by different models using the Legendre's polynomial functions from second to fourth orders. The random regression models included the effects of herd-year, month of parity date of the control; regression coefficients for age of females (in order to describe the fixed part of the lactation curve) and random regression coefficients related to the direct genetic and permanent environment effects. The comparisons among the models were based on the Akaike Infromation Criterion. The random effects regression model using third order Legendre's polynomials with four classes of the environmental effect were the one that best described the additive genetic variation in milk yield. The heritability estimates varied from 0.08 to 0.40. The genetic correlation between milk yields in younger ages was close to the unit, but in older ages it was low

The impact of heterogeneity of variances on the genetic evaluation of performance traits in Nellore cattle

The objective was to evaluate the existence of heterogeneity of variances and its impact on the genetic evaluation of ponderal performance in sires of the Nellore breed. We used records of adjusted body weights at 210 (W210), 365 (W365), and 450 (W450) days of age. Both W365 and W450 were combined by principal component analyses using the first component (PC). Average daily gain (ADG) was obtained by difference between W450 and W210. The classes of standard deviations (SD) for W210, PC, and ADG were obtained by the standardization of means of herd-year means subclasses, with positive values composing the high SD and values equal and less than zero composing the SD. The model included the fixed effects of contemporary group and age at calving as a covariate, random genetic additive, and maternal genetic (except for PC) effects, and the permanent maternal environment. Variance components were obtained by Gibbs sampling. Posterior means of heritability in analyses without considering heterogeneity of variances ranged from 0.15±0.01 to 0.31±0.01. Posterior means of genetic correlations between the two classes of SD for W210, PC, and ADG were equal to 0.85±0.04, 0.83±0.03, and 0.71±0.08, respectively. Spearman correlation to breeding values of sires for ADG as the selection intensity increased in them, and the correlations between breeding values in general analyses were more correlated with those predicted in the high DP. Therefore, when there is a higher selection intensity on the sires only for the ADG criterion, there is a significant presence of the heterogeneity of variances and impact on the genetic evaluation of the sires. Thus, for ADG, the predictions of breeding values obtained by the genetic evaluation model in which the heterogeneity of variances are not considered are more weighted by the class of greater heterogeneit