Genetic parameters for carcass and meat quality traits and their relationships to liveweight and wool production in hogget Merino rams

Article first published online: 9 APR 2008Genetic parameters for carcass and meat quality traits of about 18-month-old Merino rams (n = 5870), the progeny of 543 sires from three research resource flocks, were estimated. The estimates of heritability for hot carcass weight (HCW) and the various fat and muscle dimension measurements were moderate and ranged from 0.20 to 0.37. The brightness of meat (colour L*, 0.18 ± 0.03 standard error) and meat pH (0.22 ± 0.03) also had moderate estimates of heritability, although meat relative redness (colour a*, 0.10 ± 0.03) and relative yellowness (colour b*, 0.10 ± 0.03) were lower. Heritability estimates for live weights were moderate and ranged from 0.29 to 0.41 with significant permanent maternal environmental effects (0.13 to 0.10). The heritability estimates for the hogget wool traits were moderate to high and ranged from 0.27 to 0.60. The ultrasound measurements of fat depth (FATUS) and eye muscle depth (EMDUS) on live animals were highly genetically correlated with the corresponding carcass measurements (0.69 ± 0.09 FATC and 0.77 ± 0.07 EMD). Carcass tissue depth (FATGR) had moderate to low genetic correlations with carcass muscle measurements [0.18 ± 0.10 EMD and 0.05 ± 0.10 eye muscle area (EMA)], while those with FATC were negative. The genetic correlation between EMD and eye muscle width (EMW) was 0.41 ± 0.08, while EMA was highly correlated with EMD (0.89 ± 0.0) and EMW (0.78 ± 0.04). The genetic correlations for muscle colour with muscle measurements were moderately negative, while those with fat measurements were close to zero. Meat pH was positively correlated with muscle measurements (0.14 to 0.17) and negatively correlated with fat measurements (−0.06 to −0.18). EMDUS also showed a similar pattern of correlations to EMD with meat quality indicator traits, although FATUS had positive correlations with these traits which were generally smaller than their standard error. The genetic correlations among the meat colour traits were high and positive while those with meat pH were high and negative, which were all in the favourable direction. Generally, phenotypic correlations were similar or slightly lower than the corresponding genetic correlations. There were generally small to moderate negative genetic correlations between clean fleece weight (CFW) and carcass fat traits while those with muscle traits were close to zero. As the Merino is already a relatively lean breed, this implies that particular attention should be given to this relationship in Merino breeding programmes to prevent the reduction of fat reserves as a correlated response to selection for increased fleece weight. The ultrasound scan traits generally showed a similar pattern to the corresponding carcass fat and muscle traits. There was a small unfavourable genetic correlation between CFW and meat pH (0.19 ± 0.07).J.C. Greeff, E. Safari, N.M. Fogarty, D.L. Hopkins, F.D. Brien, K.D. Atkins, S.I. Mortimer and J.H.J. Van Der Wer

Ajustamento para heterogeneidade de variâncias para produção de leite e gordura entre rebanhos da raça Pardo-Suíça no Brasil Adjustment for heterogeneity of variance for milk and fat yield among herds of Brown Swiss in Brazil

Para verificar o efeito da inclusão das interações reprodutor x rebanho e reprodutor x rebanho-ano como fator de ajustamento da heterogeneidade de variância, registros de produção de leite e gordura foram estratificados, com base no desvio-padrão fenotípico da produção de leite ajustada, em duas classes: baixo (<1.280kg) e alto (>1.280kg) desvio-padrão. Três modelos foram utilizados, sem e com interação reprodutor x rebanho e reprodutor x rebanho-ano, em análises de característica única, geral e em cada classe de desvio-padrão. Médias e componentes de variâncias foram maiores na classe de alto desvio-padrão. Na classe de baixo desvio-padrão, a herdabilidade não se alterou com a inclusão dos efeitos de interação no modelo, sendo de 0,34 para produção de leite e de 0,32 para produção de gordura. Na classe de alto desvio-padrão, as herdabilidades foram: 0,37, 0,35 e 0,36, e de 0,35, 0,32 e 0,35, para produção de leite e gordura, nos modelos sem, com interação reprodutor x rebanho e com interação reprodutor x rebanho-ano, respectivamente. A inclusão do efeito de interação reprodutor x rebanho nos modelos foi significativa (P<0,01) para produção de gordura, em análise geral e na classe de alto desvio-padrão, pelo teste da razão de verossimilhança.<br>In order to verify the effect of including the interactions of sire x herd and the sire x herd-year, as a adjust factor of the variance heterogeneity, registers of milk and fat yields were classified into two classes of standard deviation: low (<1.280kg) and high (>1.280kg), based on phenotypic standard deviation of the milk production adjusted. Three models, without and with interaction of sire x herd and sire x herd-year, were used in the general univariate analyses and in each standard deviation class. Averages and variance components were higher in the high standard deviation. In the class of low standard deviation, heritability didn't alter with the inclusion of the interaction effects in the model, being of 0.34 for milk yield and 0.32 for fat yield. In the class of high standard deviation, heritabilities were: 0.37, 0.35 and 0.36, and of 0.35, 0.32 and 0.35, for milk and fat yield, in the models without and with interaction of sire x herd and with interaction of sire x herd-year, respectively. The inclusion of the interaction of sire x herd was significant (P<0.01) for fat yield, in general analyses and in the high standard deviation class, in the likelihood ratio test

Efeitos da transformação de uma variável com distribuição normal em sua inversa sobre os parâmetros de sua distribuição usando técnicas de Monte Carlo Effects of transforming a normally distributed variable into its inverse on parameters of the distribution using Monte Carlo techniques

Foram realizados quatro estudos de simulação para verificar a distribuição de inversas de variáveis com distribuição normal, em função de diferentes variâncias, médias, pontos de truncamentos e tamanhos amostrais. As variáveis simuladas foram GMD, com distribuição normal, representando o ganho médio diário e DIAS, obtido a partir da inversa de GMD, representando dias para se obter determinado peso. Em todos os estudos, foi utilizado o sistema SAS® (1990) para simulação dos dados e para posterior análise dos resultados. As médias amostrais de DIAS foram dependentes dos desvios-padrão utilizados na simulação. As análises de regressão mostraram redução da média e do desvio-padrão de DIAS em função do aumento na média de GMD. A inclusão de um ponto de truncamento entre 10 e 25% do valor da média de GMD reduziu a média de GMD e aumentou a de DIAS, quando o coeficiente de variação de GMD foi superior a 25%. O efeito do tamanho dos grupos nas médias de GMD e DIAS não foi significativo, mas o desvio-padrão e CV amostrais médios de GMD aumentaram com o tamanho do grupo. Em virtude da dependência entre a média e o desvio-padrão e da variação observada nos desvios-padrão de DIAS em função do tamanho do grupo, a utilização de DIAS como critério de seleção pode diminuir a acurácia da variação. Portanto, para a substituição de GMD por DIAS, é necessária a utilização de um método de análise robusto o suficiente para a eliminação da heterogeneidade de variância.<br>Four simulation studies were conducted to verify the distribution of the inverse of variables with normal distribution, relatively to variances, averages, truncation points and sample sizes. The variables simulated were GMD, with normal distribution and representing average daily gain, and DIAS defined as a multiple of the inverse of GMD and representing days to reach a fixed body weight. The SAS® (1990) system was used, for simulation of the data, and for subsequent analysis of the results in all studies. The standard deviations simulated for GMD significantly affected DIAS sampling averages. The regression analyses showed a reduction on the mean and in the standard deviation of DIAS as a function of the increase in the average of GMD. Including a truncation point at about 10 to 25% of the mean value reduced the mean of GMD and increased the mean of DIAS when the coefficient of variation of GMD was above 25%. Size of the groups did not significantly affect averages of GMD or DIAS. Standard deviation and CV of GMD increased with the increase on group size. Due to the dependence between the average and the standard deviation and the variation observed in the standard deviations of DIAS as a function of group size, the use of DIAS as selection criteria may reduce the accuracy of the genetic evaluation. Therefore, in order to substitute GMD by DIAS, it is necessary the use of a method of analysis robust enough to eliminate the heterogeneity of variance