Genotype by environment interaction and model comparison for growth traits of Santa Ines sheep

Abstract

The objectives of the present study were to compare alternative models for the genetic evaluation and assess the importance of genotype by environment interaction (G×E) in the estimation of genetic parameters and genetic evaluation of birth weight (BW), weight at 60 days of age (W60) and weight at 180 days of age (W180) of Santa Ines sheep. Data comprise 7622 BW, 4673 W60 and 2830 W180 records from animals born in 44 Brazilian herds. Four models were used for the analyses: animal model (AM) with homogeneous residual variance (1), or heterogeneous residual variance (2), hierarchical reaction norms model (HRNM) with homogeneous (1) or heterogeneous residual variance (2). The models that best fit the BW, W60 and W180 data were AM2, HRNM1 and HRNM2 respectively. Thus, models for genetic evaluation that consider heterogeneity of variances are recommended to evaluate growth traits of sheep. The correlation between intercept and slope of the HRNM was higher than 0.70 for all traits studied, indicating that animals with higher average breeding values responded better to improvement in environmental conditions, a fact characterizing the scale effect of G×E. Therefore, G×E is an important factor to be considered in the estimation of genetic parameters and genetic evaluation of growth traits of sheep.

The effects of different farm environments on the performance of Texel sheep

“…Similar results were also observed by Pollot and Greeff (2004), Cardoso and Tempelman (2012) and Santana et al (2013b). Strandberg (2006) comments that this is not unexpected, particularly when using sire models.…”

“…It should be noted though that a number of sires had sensitivities close to, or equal to, zero, indicating that there was very little variation in their performance across environments and they were therefore not influenced as much by changes in feeding regimes. Similar examples of scaling and re-ranking were observed by both Pollot and Greeff (2004) and Santana et al (2013b), when using random regression models to investigate a number of sheep performance traits including faecal egg counts, wool, body and growth characteristics. Overall, as suggested by Haskell et al (2007), the presence of environmental sensitivity provides an opportunity for farmers to choose sires based on the FE of their farm.…”

“…Studies using sheep, such as Pollot and Greeff (2004), defined Merino production environments using the average value of each trait analysed, for each contemporary group identified. Similar methods have also been used by Santana et al (2013b) when studying Santa Ines sheep in Brazil.…”

“…In terms of animal breeding, early studies for lactation in dairy cattle used random regression test-day models to predict the lactation curves of individual dairy cows (Schaeffer and Dekkers, 1994). However, more recently, interest has grown with regards to their use to describe the variation of performance across environmental gradients such as those in dairy cattle (Calus et al, 2002;Kolmodin et al, 2002;Strandberg et al, 2009), beef cattle (Mattar et al, 2011;Santana et al, 2013a), pigs (Knap and Su, 2008) and sheep (Pollot and Greeff, 2004;Santana et al, 2013b).…”

“…This is particularly true for sheep systems, which can differ in a number of aspects such as climatic conditions, flock size, health status and level of inputs. There are a number of different methods to determine FE, including the use of specific environmental descriptors such as rainfall and temperature (Ravagnolo and Misztal, 2000;Fikse et al, 2003), the use of phenotypic means or deviations (Calus et al, 2002;Kolmodin et al, 2002) as well as the identification of contemporary groups (Pollott and Greeff, 2004;Mattar et al, 2011;Santana et al, 2013b).…”