Comparing alternative definitions of the contemporary group effect in Avileña Negra Ibérica beef cattle using classical and Bayesian criteria1

Abstract

Data on weaning weight from 12,740 animals were used to compare different definitions of contemporary groups (CG) for the genetic evaluation of the Avilena Negra Iberica beef cattle breed. Six alternative definitions for the CG effect were considered: herd-year-season of calving (HYS), with seasons defined according to the four natural seasons; herd-year-month of calving (HYM); herd clusters of 30 d (HC30-30) or 90 d (HC90-90); and adaptive herd clusters with two time limits, 30 and 90 d (HC30-90), and 30 and 180 d (HC30-180). A minimum of five observations in each CG class was required. This rendered substantial differences in loss of information, ranging from 0.7% of the total number of records for HC30-180 to 14% for HYM. Several classical statistics and Bayesian criteria for statistical model comparison were used. The use of classical criteria, such as the between- and within-CG variation and the accuracy of prediction, can be controversial because of their dependency on the unknown variance components. Residual variance decreased with the decrease in time span associated with the definition of CG. This was expected in this population because environmental conditions are highly variable throughout the year. However, estimates of the additive genetic variance for direct effects, which should not be affected by the definition of CG, were substantially larger for definitions involving larger time periods (HYS, HC90-90). When parameters used in the current evaluation procedure were used with all data sets, CG involving 30 d (HYM and HC30-30) were optimal in terms of providing the lowest/largest within-/between-CG variation. On the other hand, CG involving 90 d (HYS and HC90-90) yielded the poorest within-/between CG variation, with only a slight improvement of accuracy of prediction of direct genetic values over the other definitions. Bayes factors and cross-validation predictive densities allowed for improved discrimination among models. Models including CG spanning 30 d were more plausible and showed better predicting ability than models spanning 90 d. Adaptive CG showed intermediate results. Overall, it seems that average time span rendered by the different definitions had a major effect on the ranking of models. However, from the breeder's point of view, the loss of information associated with definitions involving shorter periods of time, such as HYM or HC30-30, might be unacceptable.

Estimation of genetic parameters and prediction of breeding values for multivariate threshold and continuous data in a simulated horse population using Gibbs sampling and residual maximum likelihood

“…Gibbs sampling (GS) and residual maximum likelihood (REML), with corresponding probability values (significance level of p < 0. 2002; Heringstad et al 2003;Carabañ o et al 2004;Andersen-Ranberg et al 2005). However, untransformed REML estimates for heritabilities of binary traits have been smaller and transformed REML estimates for heritabilities of binary traits larger than corresponding threshold GS estimates (Hoeschele & Gianola 1989;Boettcher et al 1998;Magnabosco et al 2000;Heringstad et al 2003;Hansen et al 2004a,b;Andersen-Ranberg et al 2005 Only simulated data allow studying the performance of linear versus threshold model analyses with specific data and pedigree constellations.…”

“…For different population and data structures it has been shown, that for binary traits BLUP and GS sire rankings are very similar (Boettcher et al 1998; Schenkel et al Table 2 Least square means (LSM) and 95% confidence intervals of the relative bias of additive genetic correlations (r g ) of the continuous trait T1 and the binary traits T2 to T5, estimated using phenotypic trait information on 10 000 animals (data set A), 5000 animals (data set B1) or 1000 animals (data set C1) or phenotypic and genotypic trait information on 5000 animals (data set B2) or 1000 animals (data set C2) for the investigated modes of (co)variance component estimation, i.e. Gibbs sampling (GS) Multivariate gentic analyses with GS & REML K. F. Stock et al 2002;Heringstad et al 2003;Carabañ o et al 2004;Andersen-Ranberg et al 2005). However, untransformed REML estimates for heritabilities of binary traits have been smaller and transformed REML estimates for heritabilities of binary traits larger than corresponding threshold GS estimates (Hoeschele & Gianola 1989;Boettcher et al 1998;Magnabosco et al 2000;Heringstad et al 2003;Hansen et al 2004a,b;Andersen-Ranberg et al 2005).…”

Estimation of variance components of milk, fat, and protein yields of Tunisian Holstein dairy cattle using Bayesian and REML methods

“…Furthermore, Rekaya et al (2003) argued that small herd sizes may cause problems when creating contemporary groups for genetic evaluation. Ugarte et al (1992) and Carabaño et al (2004) reported that contemporary groups were required to have at least five records to be included in the analysis. Variances were found to be heterogeneous across herds, and numerous methods to ac-count for that variation were developed (Meuwissen et al, 1996;Rekaya et al, 1999;Gengler et al, 2005).…”

Relative contribution of effects included in contemporary groups for adjusted and actual 120-day and 210-day weights in Nelore cattle in Brazil

“…Contemporary groups (CGs) are used in genetic evaluations to eliminate bias caused by systematic environmental effects such as differences in management, feeding and seasons (Van Vleck, 1987; Van Bebber et al , 1997; Carabaño et al , 2004; Cantet et al , 2005). However, a large number of CG can result in a small number of records per subclass, resulting in an increase in the variance of prediction errors and a reduction in the accuracy of genetic evaluations (Van Vleck, 1987; Van Bebber et al , 1997).…”

“…A balance between maximum accuracy and reduced bias must be achieved to optimize the definition of CG. The problem with the usual definition of CG is its arbitrary definition of periods of time that do not correspond to criteria for maximum accuracy and minimum bias (Schmitz et al ., 1991; Carabaño et al , 2004). In an attempt to resolve this problem several criteria to compare different definitions of CG that consider the estimated intra-CG variance, residual variance and accuracy of genetic evaluations have been proposed (Schmitz et al , 1991; Sivarajasingam, 1993; Van Bebber et al , 1997; Carabaño et al , 2004).…”