ESTIMATION OF DIRECT AND MATERNAL EFFECTS ON BODY WEIGHT IN MERINO SHEEP USING RANDOM REGRESSION MODELS

 

K.R. Nemutandani1#, W.J. Oliver1, M.A. Snyman1 & C. Visser2

1 Grootfontein Agricultural Development Institute, Private Bag X529, Middelburg (EC), 5900, South Africa

2 Department of Animal and Wildlife Sciences, University of Pretoria, Pretoria, 0084

#Corresponding author: Khetho Nemutandani

 

 

Background: In many sheep production enterprises, body weight is regarded as the most important trait for selection of replacement animals. Body weight, especially early body weights, is influenced to a large extent by maternal effects. This could complicate the partitioning of variance and estimation of breeding values. Random regression models have been used recently to model growth rate in cattle and to a lesser extent in sheep.

 

Aim: The aim of this study was to apply random regression models to estimate direct and maternal effects on body weight in Merino sheep.

 

Methodologies: The dataset used in this study comprised body weight data recorded from birth until six years of age in the Grootfontein Merino stud from 1968 to 2012. The total number of ram and ewe lambs for which birth weight was recorded, were 7794 and 8317 respectively. These were the progeny of 3814 dams and 359 sires. The number of records available for adult ewes at six years of age was 703. Fixed effects for year-season of birth, sex, rearing status and age of the dam were included in the models. Random regression models fitted included direct genetic, maternal genetic and animal and maternal permanent environmental effects as random effects in various combinations. These models were fitted either with splines separating ages 1, 2, 4, 8, 12, 15, 20, 32, 44, 56 and 68 months, splines separating ages 1, 4, 15 and 68 months or no splines. The random effects were modelled using cubic spline functions. Polynomials up to the second degree were fitted for the direct genetic and maternal genetic random effects. Residual variances were modelled considering one (assuming homogeneity of variances across all ages) or two age classes divided as follows: 1 to 12 and 15 to 68 months of age. Output values were processed to obtain (co)variances and genetic parameters for the specific body weights at the different ages.

 

Results: The model with the highest LogL included both the direct and maternal genetic effects fitted as first degree polynomials, 11 spline classes and two age classes. The direct and maternal genetic variance components ranged from 4.0 to 3483.4 and 0.5 to 1005.2 respectively from birth until 68 months of age. Phenotypic variance ranged from 15.2 to 4529.6 and residual variance from 10.7 to 30.3. Direct and maternal heritabilities increased with age and ranged from 0.26 to 0.77 and 0.03 to 0.22 respectively.

 

Discussion: Direct and maternal genetic and phenotypic variances obtained were much higher than those usually obtained for body weight in sheep. Residual variance was comparable to those obtained with univariate models using the same data set. Furthermore, the large increase in maternal variance with age was also contradictory to most reported literature. The reason for inflated estimates cannot be explained, although similar erratic (co)variance estimates for older ages with fewer data records were reported for this type of modelling.

 

Conclusions: Further work on model specification need to be done to try and explain the inflated values obtained when fitting random regression models to data sets comprising body weights from birth until adult ages.

 

Published

Proc. 50th Congr. S. Afr. Soc. Anim. Sci. Port Elizabeth, September 2017