Co)variance Components for Birth and Weaning Weights of Shorthorn Beef Cattle in Australia and the United States

Q3 Multidisciplinary Walailak Journal of Science and Technology Pub Date : 2011-12-10 DOI:10.2004/WJST.V1I2.182
K. Kuha, H. Graser, S. Tumwasorn, D. Johnston
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引用次数: 1

Abstract

(Co)variance components and genetic parameters for birth (BW) and weaning weights (WW) of Shorthorn beef cattle in Australia (AU) and the United States (US) were estimated using Restricted Maximum Likelihood. Five different uni- and bivariate models were used to fit both traits within each country. In Model 1, only a direct genetic effect (a) was fitted. In Models 2 and 3, a maternal genetic effect (m) was added. A genetic covariance between direct-maternal effects [cov(a, m)] was ignored (model 2) or included (model 3). Models 4 and 5 both m and maternal permanent environment effects (pe), were allowed from model 1, and assumed cov(a,m) in the same manner as model 2 and 3, respectively. When ignoring m effect, the direct heritability estimates were inflated and differed markedly from other models. The likelihood ratio test showed that model 5 was the best fit for both traits in the US while models 2 and 4 were the fittest for BW and WW in AU, respectively. The estimates of direct, maternal, total heritabilities, and maternal permanent environment variance of the full model in AU and in the US (in parentheses) were 0.46 (0.48), 0.09 (0.05), 0.42 (0.42) and 0.00 (0.06) for BW, and 0.23 (0.32), 0.16 (0.09), 0.24 (0.26) and 0.13 (0.10) for WW, respectively. After m and pe were fitted, the estimate of total heritability decreased slightly for BW in both countries and for WW in AU, but decreased re-markedly for WW in the US. Estimate of direct-maternal genetic correlation was moderately negative and tended to be more negative after pe was fitted for both traits in the US. The parameters estimates using bivariate analysis were not different to the results from univariate analysis. This analysis yielded additive and maternal genetics correlations between BW and WW. These estimates were positive and medium to high correlation, which were higher in AU than in the US. Correlation of estimated breeding values for direct additive and maternal genetics between the full model and others were high and close to unity. The differences of some parameters between both countries indicate that joint genetic evaluation might require genotype by environment interaction to be considered.
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澳大利亚和美国短刺肉牛出生和断奶体重的方差成分
采用限制性最大似然法对澳大利亚和美国短刺肉牛出生体重(BW)和断奶体重(WW)的变异成分和遗传参数进行了估计。五个不同的单变量和双变量模型被用来拟合每个国家的这两个特征。在模型1中,只拟合了直接遗传效应(a)。在模型2和模型3中,加入了母体遗传效应(m)。直接母系效应[cov(A,m)]之间的遗传协方差被忽略(模型2)或包含(模型3)。模型4和模型5 (m和母系永久环境效应(pe))从模型1中被允许,并分别以与模型2和模型3相同的方式假设cov(A,m)。当忽略m效应时,直接遗传力估计值被夸大,与其他模型明显不同。似然比检验表明,模型5在美国最适合这两个性状,而模型2和模型4分别在澳大利亚最适合体重和体重。在AU和US(括号内),全模型的直接遗传力、母系遗传力、总遗传力和母系永久环境方差分别为0.46(0.48)、0.09(0.05)、0.42(0.42)和0.00 (0.06),WW分别为0.23(0.32)、0.16(0.09)、0.24(0.26)和0.13(0.10)。拟合m和pe后,两国的BW和非盟的WW的总遗传力估计值略有下降,但美国的WW估计值再次显著下降。在美国,对两种性状进行pe拟合后,直接母系遗传相关性的估计为中等负相关,并且趋于更负相关。使用双变量分析的参数估计与单变量分析的结果没有差异。该分析得出了体重和体重之间的加性和母系遗传相关性。这些估计值是正的和中到高的相关性,非盟高于美国。全模型与其他模型的直接加性和母系遗传育种估计值相关性高且接近一致。两国间某些参数的差异表明,联合遗传评价可能需要考虑环境相互作用的基因型。
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来源期刊
Walailak Journal of Science and Technology
Walailak Journal of Science and Technology Multidisciplinary-Multidisciplinary
CiteScore
0.80
自引率
0.00%
发文量
0
审稿时长
24 weeks
期刊介绍: The Walailak Journal of Science and Technology (Walailak J. Sci. & Tech. or WJST), is a peer-reviewed journal covering all areas of science and technology, launched in 2004. It is published 12 Issues (Monthly) by the Institute of Research and Innovation of Walailak University. The scope of the journal includes the following areas of research : - Natural Sciences: Biochemistry, Chemical Engineering, Chemistry, Materials Science, Mathematics, Molecular Biology, Physics and Astronomy. -Life Sciences: Allied Health Sciences, Biomedical Sciences, Dentistry, Genetics, Immunology and Microbiology, Medicine, Neuroscience, Nursing, Pharmaceutics, Psychology, Public Health, Tropical Medicine, Veterinary. -Applied Sciences: Agricultural, Aquaculture, Biotechnology, Computer Science, Cybernetics, Earth and Planetary, Energy, Engineering, Environmental, Food Science, Information Technology, Meat Science, Nanotechnology, Plant Sciences, Systemics
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