Estimation of genetic parameters for growth traits in Brangus cattle

 

 

F.W.C. Neser^{I, #}; J.B. van Wyk^I; M.D. Fair^I; P. Lubout^II; B.J. Crook^III

^IDepartment of Animal, Wildlife and Grassland Sciences, University of the Free State, PO Box 339, Bloemfontein 9300, South Africa
^IIBrangus Cattle Breeders' Society, P.O. Box 12465, Brandhof, Bloemfontein, 9324, South Africa
^IIIAgricultural Business Research Institute, UNE Armidale NSW 2351, Australia

 

 

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ABSTRACT

A combination of multiple trait and repeatability models were used to estimate genetic parameters for birth weight (BW), weaning weight (WW), yearling weight (YW), eighteen month weight (FW) and three measurements of mature weight (MW) using 23 768 records obtained from the South African Brangus Cattle Breed Society. The data covered a period of 7 generations from 1985 to 2010. Direct heritability estimates obtained were 0.21 ± 0.024, 0.23 ± 0.021, 0.22 ± 0.025, 0.29 ± 0.029 and 0.24 ± 0.019 for BW, WW, YW, FW and MW, respectively. Maternal heritability estimates for birth weight and weaning weight were 0.05 ± 0.01 and 0.11 ± 0.001, respectively. The direct genetic correlations between the different traits were all positive, ranging from moderate (0.43 ± 0.081) between YW and MW to high (0.99 ± 0.043) between WW and FW.

Keywords: (Co)variance components and ratios, repeatability models, South African Brangus cattle

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Introduction

The prediction of breeding values requires knowledge of the magnitude of the (co) variances of random effects in a model. Incorrect (co)variance components could lead to biased breeding values, especially in multiple trait analysis of growth traits, where erosion of records takes place over time because of selection and culling of animals. It could also lead to an incorrect measurement of the effectiveness of genetic selection. As there is limited information available on the genetic parameters for growth traits in Brangus cattle, the aim of the study was to estimate (co)variance components specific for the South African Brangus population which will be used in the national genetic evaluation of the breed.

 

Material en Methods

Data from 73 676 animals for birth weight (BW; n = 41 572), weaning weight (WW; n = 23 104), yearling weight (YW; n = 9 114) eighteen month weight (FW; n = 6 450) and mature weight (MW; n = 9 258) were originally available to estimate (co)variance components and subsequent genetic parameters in the breed. All incomplete records, as well as records outside four standard deviations from the mean, were disregarded. Herds with less than three years of recording, as well as contemporary groups with less than five records were also removed from the final data set used for analyses. The final dataset consisted of 53 841 weight records obtained from 23 768 animals in 78 herds, collected over a period of 26 years (1985 to 2010). A total of 1 623 sires and 415 sires of sires, as well as 18 491 dams with 1 028 sires of dams and 5 729 dams of dams were present in the data. The pedigree file consists of 58 973 animals born over a period of 8 generations.

 

 

The Brangus is a composite breed and in South Africa, an open register system is used. As different breed combinations are involved, it also means that heterosis can play a role in the estimation of genetic parameters. It is therefore important to include breed composition in the analysis. There were 1 398 different breed combinations that were consolidated into seven broad combinations according to likeness in breed composition and weaning weight performance.

 

 

Other fixed effects fitted were a concatenation of herd-year-season, sex and damage. Two distinct calving seasons were identified: from September to March was classified as season one, while April to August was classified as season two. Age of dam was expressed in years, starting with dams of two years and younger. All dams older than six years were grouped together. The following traits were corrected for weighing age to simplify the analysis: weaning-, yearling- and eighteen month weight.

Estimates of (co)variance components were obtained using the ASREML program (Gilmour et al., 2009) fitting single trait animal models. Mature cow weight, defined as the weight of the cow when the calf is weaned, was fitted as a repeated trait, with up to three weights possible per cow.

The following final models were fitted:

BW & WW: y = Xb + Z₁a+Z₂m+Z₃hysxs+e with cov(a,m)=0

YW & AW: y = Xb + Z₁a+e

MW: y_1,2,3 = Xb + Z₁a+e

Where: y = a vector of observations (BW, WW, YW, AW)

y_1,2,3 = a vector of repeated observations for mature weight

b = a vector of fixed effects

a = a vector of direct additive genetic effects

m = a vector of maternal additive effects

hysxs = a vector of herd-year-season x sire interaction

e = a vector of residuals

X, Z₁, Z₂ and Z₃ = incidence matrices relating observations to their respective fixed and random effects

It was assumed that:

V(a) = Aσ²a; V(m) = Aσ²m; V(hysxs) = Iσ²_hysxs; V(e) = Iσ²e

Where I is an identy matrix, σ²_a σ²_m> σ²_hysxs, and σ²_e is the direct additive-, maternal additive-, HYSxS -and environmental variance respectively.

The significance of random effects was tested using the log likelihood ratio tests after inclusion of one random effect in the model. A random effect was considered significant when its inclusion in the model caused a significant improvement in the log likelihood ratio. A chi-square distribution of α = 0.05 at one degree of freedom was used as a test statistic (3.841). When -2 times the difference between the log likelihoods was greater than this critical value, the inclusion of the particular random effect was considered to significantly improve the fit of the model (Swalve, 1993). The estimates obtained in the single trait analyses also act as starting values for the multiple trait analysis.

 

Results and Discussion

All non-genetic factors tested were significant (P <0.01) for all the traits under consideration and were thus included in the subsequent (co)variance analyses. A summary of the (co)variance components and ratios obtained in the analysis is presented in Table 3.

Direct and maternal heritability estimates for all the traits are within the parameter range described in the literature, albeit in the lower sector. Literature values for direct and maternal heritability estimates for BW vary from 0.07 (direct) and 0.04 (maternal) (Diop & Van Vleck, 1998) to 0.62 (direct) (Schoeman & Jordaan, 1999) and 0.24 (maternal) (Mohiuddin, 1993). The same values for WW vary from 0.07 (direct) (Plasse et al., 2002) and 0.06 (maternal) (Haile-Mariam & Kessa-Mersa, 1995) to 0.57 (direct) (Schoeman & Jordaan, 1999) and 0.21 (maternal) (Diop & Van Vleck, 1998). Direct heritability estimates for the other weight traits from the literature are: YW = 0.16 (Meyer, 1992) to 0.41 (Mohiuddin, 1993), FW = 0.13 (Plasse et al., 2002) to 0.22 (Meyer, 1992; Mostert et al., 1998) and MW = 0.33 (Crook et al., 2010) to 0.50 (Koots et al., 1994a).

The reason for the inclusion of HYSxS is well documented (Meyer, 1987; Neser et al., 1996; Van Niekerk et al., 2004). The high value of the HYSxS ratio for weaning weight (0.10) was, however, surprising, indicating that some re-ranking of sires might occur over different contemporary groups. This is higher than the value obtained by Neser et al. (1996) (0.08) in Bonsmara cattle as well as Pico et al. (2004) (0.06) in Brahman cattle.

As shown in Table 4, the direct genetic correlations between the different traits were all positive, ranging from moderate (0.44 between YW and MW) to high (0.99 between WW and FW). These results correspond to results obtained by Koots et al. (1994b), Van Niekerk et al. (2004) in Nguni cattle, Pico et al. (2004) in Brahman cattle and Van Niekerk et al. (2006) in Limousin cattle.

 

 

Conclusion

Direct heritability estimates were moderate (above 0.21) for all live weight traits while direct genetic correlations between the different traits were all positive, ranging from moderate (0.43) to high. This means that in animal breeding no decision can be taken in isolation. Selection on one trait will thus have consequences on all other traits as well. The estimation of (co)variance components should be seen as the first step in developing a proper breeding objective for the breed.

 

References

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# Corresponding author: neserfw@ufs.ac.za