Variance components for direct and maternal effects on body weights of Katahdin lambs

Abstract

The aim of this study was to estimate genetic parameters for BW in Katahdin lambs. Six animal models were used to study direct and maternal effects on birth weight (BWT), weaning weight (WWT), and postweaning weight (PWWT) using 41,066 BWT, 33,980 WWT, and 22,793 PWWT records collected over 17 yr in 100 flocks. Models included fixed effects of management group, dam age, type of birth (for BWT) or birth and rearing (for WWT and PWWT), and lamb age at weighing (fitted as a covariate for WWT and PWWT; all < 0.05). Variance components for random effects were estimated in sequentially more complex models and tested for significance with likelihood-ratio tests. A model that fitted only an additive animal effect overestimated additive variance for all BW, resulting in larger estimates of direct heritability than models that included maternal effects. Maternal effects explained variation ( < 0.05) in all BW. Heritability estimates for optimal models were 0.15 ± 0.01 for BWT, 0.18 ± 0.02 for WWT, and 0.20 ± 0.02 for PWWT. Estimates of maternal heritabilities were 0.14 for BWT, 0.10 for WWT, and 0.06 for PWWT, with SE = 0.01. Permanent environmental maternal effects explained 4 to 6% (±1%) of total phenotypic variances for these BW. Litter effects included temporary environmental effects common to littermates and a proportion of the dominance genetic variance and accounted for 16 to 19% (±1%) of phenotypic variance. Correlations between additive direct and maternal genetic effects were -0.14 for BWT, -0.23 for WWT, and -0.04 for PWWT but differed from 0 ( < 0.05) only for WWT. The total heritability predicted the total response in direct and maternal genetic effects from mass selection and was 0.23 for BWT, 0.20 for WWT, and 0.23 for PWWT. Direct and maternal additive, maternal permanent environmental, residual, and phenotypic correlations between BWT and WWT were 0.53 ± 0.05, 0.58 ± 0.06, 0.51 ± 0.06, 0.39 ± 0.01, and 0.44 ± 0.01, respectively; those between BWT and PWWT were 0.45 ± 0.06, 0.58 ± 0.08, 0.36 ± 0.08, 0.33 ± 0.01, and 0.37 ± 0.01 respectively; and those between WWT and PWWT were 0.85, 0.99, 0.92, 0.77, and 0.81, respectively, with SE ≤ 0.02. Therefore, both direct and maternal effects had an important impact on BW in Katahdin lambs. Models that included both additive and permanent environmental maternal effects as well as a temporary environmental litter effect should result in more accurate estimates of breeding values and better selection decisions.

Estimation of genetic parameters and comparison of breeding values for body weight with different models in a South African Merino stud

“…Neither the univariate direct variance and heritability or the univariate maternal variance and heritability in this study show a specific trend, which is not in line with most reported results. In most flocks, direct variance and heritability increase with age from weaning onwards, while maternal variance and heritability decrease with an increase in age (Snyman et al, 1995;Vaez Torshizi et al, 1996;Ngere et al, 2017). However, the univariate phenotypic variance in this study did show an increase with age of the animal.…”

“…Growth is regarded as one of the most important traits for the selection of replacement animals in both wool and mutton sheep enterprises (Snyman & Cloete, 2008), as the growth rate and body weight of lambs at different ages determine the profitability of the enterprise (Mohammadi et al, 2013;Ngere et al, 2017). Body weight is positively correlated with many other traits of economic importance, such as reproduction (Safari et al, 2007b;Olivier & Cloete, 2011;Olivier, 2014), adaptability (Singh et al, 2016), wool production (Baneh et al, 2013;Olivier, 2014) and survival at birth (Baneh et al, 2013).…”

“…It is important to apply the most appropriate model for the estimation of (co)variance components and genetic parameters for body weights. Restricted maximum likelihood models have been used extensively to estimate (co)variance components for body weight in sheep (Singh et al, 2016;Boujenane & Diallo, 2017;Kumar et al, 2017;Ngere et al, 2017). These models partition the phenotypic variance of a quantitative trait into additive genetic variance, environmental variance and other effects such as maternal, common environmental, or permanent environmental effects (Meyer, 1989).…”

Genetic parameters and direct and maternal effects for important traits in late‐feathering Qingyuan partridge hens in China

“…For body weight, we demonstrated that maternal genetic and permanent environmental effects should be simultaneously considered in the model, assuming the direct‐maternal genetic covariance zero. Similarly, it was found that maternal effects play an important role in body weight of pig (Alves et al., 2018), cattle (Aziz et al., 2005; Swalve, 1993), sheep (Kumar et al., 2017; Mohammadi et al., 2013; Ngere et al., 2017; Ulutas et al., 2013), turkey bird (Aslam et al., 2011), Japanese quail (Lotfi et al., 2012) and chicken (Hartmann et al., 2003; Koerhuis & Thompson, 1997; Tongsiri et al., 2019; Tosh et al., 2010). For shank length, shank girth and feather maturity, the best model (model 3) considered maternal genetic effect with the direct‐maternal genetic covariance zero.…”

Resistance of sheep from different genetic groups to gastrointestinal nematodes in the state of São Paulo, Brazil

“…In small ruminant production systems, adaptation to environmental conditions, such as heat tolerance and the ability to survive numerous endemic disease challenges is considered a critical factor (Bishop, 2012). With increasing demand for sheep in Brazil's Southeast region, https://doi.org/10.1016/j.smallrumres.2018.07.003 Received 10 February 2018; Received in revised form 10 July 2018; Accepted 11 July 2018 T methods to control of GINs have been studied, and the use of more resistant breeds is considered a viable alternative (Mugambi et al, 1997;Rocha et al, 2005;Amarante et al, 2009;McManus et al, 2009;Ngere et al, 2017). Analysis of faecal egg counts (FEC) has been used in many experiments to characterize the resistance to GINs of small ruminants, finding heritability levels ranging from 0.01 to 0.65 (Zvinorova et al, 2016).…”