Description of lamb growth using random regression on field data

Fischer, T. M.; Werf, J. H. J. van der; Banks, Robert; Ball, Alexander

doi:10.1016/j.livprodsci.2004.02.004

Cited by 66 publications

(83 citation statements)

References 13 publications

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“…Growth slowed over the rest of the trajectory. A similar pattern appeared in the data used by Fischer et al (2004), and mean values corresponded well with other Poll Dorset data at similar ages. Figure 2 also shows how measures of FD increased throughout the 300-day trajectory, with a sharper increase relative to BW and EMD around 200 days of age, which coincided with the slowing of growth.…”

Section: Resultssupporting

confidence: 87%

“…Application of RR models to growth data is reasonably well documented in all major livestock industries (e.g. Fischer et al (2004), Huisman et al (2002), Meyer (2002) and Veerkamp and Thompson (1999)), however, they have not been applied to carcass composition data with the exception of Hassen et al (2003). In addition, multiple trait random regression (RR) models have been fitted between weight and other traits simultaneously (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Genetic analysis of weight, fat and muscle depth in growing lambs using random regression models

et al. 2006

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Genetic parameters were estimated using uni-and bi-variate random regression models for weight, eye-muscle depth and fat depth measures between 60 and 360 days of age. Each trait was measured up to five times in 50-day intervals following weaning on approximately 4000 Australian Poll Dorset Sheep. The model accounted for rearing type, dam age, management group and age of recording. The model used for analysing weight included quadratic, orthogonal polynomials for direct genetic and environmental effects, a linear polynomial for maternal genetic effects and heterogeneous error variance across ages. The fat and muscle analysis used linear orthogonal polynomials for direct genetic and environmental effects and heterogeneous error variance.Throughout the 300-day trajectory heritability for weight traits ranged from 0·20 to 0·31, while heritability for fat depth ranged from 0·24 to 0·34 and heritability for eyemuscle depth ranged from 0·24 to 0·40. Genetic correlations between repeated measures of the same trait at different ages were positive and declined as the age interval increased, to minimum values of 0·60, 0·31 and 0·50 for weight, fat and muscle respectively between 60 and 360 days of age. Genetic correlations between weight and fat and weight and eye muscle were moderate to high (0·6 to 0·8) and positive but decreased slightly with age. The genetic correlations between fat and muscle were moderate to high (0·5 to 0·7) throughout the 300-day trajectory. In all cases, the estimates produced in this study were reasonably consistent with the limited number of studies that exist in the reported literature. This study demonstrated the relationships that exist between repeated measures of weight, fat and muscle measures over time, which is of interest to prime lamb producers looking to select for specific breeding objectives or market end points requiring precise weight, fat and muscle combinations at certain ages. Disciplines Physical Sciences and Mathematics Publication DetailsFischer, T. M., van der Werf, J. H. J., Banks, R., Ball, A. J. & Gilmour, A. R. (2006). Genetic analysis of weight, fat and muscle depth in growing lambs using random regression models. Animal Science, 82 (1), 13-22. AbstractGenetic parameters were estimated using uni-and bi-variate random regression models for weight, eye-muscle depth and fat depth measures between 60 and 360 days of age. Each trait was measured up to five times in 50-day intervals following weaning on approximately 4000 Australian Poll Dorset Sheep. The model accounted for rearing type, dam age, management group and age of recording. The model used for analysing weight included quadratic, orthogonal polynomials for direct genetic and environmental effects, a linear polynomial for maternal genetic effects and heterogeneous error variance across ages. The fat and muscle analysis used linear orthogonal polynomials for direct genetic and environmental effects and heterogeneous error variance.Throughout the 300-day trajectory heritability for weight traits ranged from...

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Section: Resultssupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Genetic analysis of weight, fat and muscle depth in growing lambs using random regression models

et al. 2006

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“…Traits in younger ages also differ from each other, especially at the lowest ages (for instance, the correlation between growth traits at 2 and 4 weeks is 0.70). In other studies, it has been shown that the genetic control of weight at initial ages is different from that observed at later ages (Fischer et al, 2004b;Wilson et al, 2005;Szwaczkowski et al, 2007;Costa et al, 2008;El Faro et al, 2008;Oliveira et al, 2010). The genetic correlations of BW measured between 14 and 24 weeks estimated with the multivariate method were 0.89 and 0.97 in US and UK turkey populations, respectively (Kranis et al, 2006).…”

Section: Correlationsmentioning

confidence: 85%

“…Huisman et al (2002) obtained better results with RR in comparison with the traditional multivariate model. Fischer et al (2004b) proposed that RR could be a good alternative to MT genetic analysis. Kranis et al (2007) compared genetic parameter estimates using RR and MT models for egg production in turkeys and concluded that both models were equally effective to describe the dynamics of genetic variance over time.…”

Section: Correlationsmentioning

confidence: 99%

Estimates of the genetic parameters of turkey body weight using random regression analysis

Rafat

Namavar

Shodja

et al. 2011

Animal

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Random regression (RR) analysis has been recommended to estimate the genetic parameters of longitudinal data. The objective of this study was to evaluate the growth of turkeys using RR models. Data were collected from 957 turkeys and included 15 478 individual body weight recorded during the first week of life and between weeks 2 and 32 by 2-week intervals. To take into account the repeated measurements of weight for each animal, a specific overall growth curve was modelled using a cubic smoothing spline. Animal deviation to this curve was also modelled using an RR function. All data were analysed with the ASReml package. The results showed an increase in heritability estimates over the trajectory and peaked at 0.60 around 20 to 32 weeks of age. Genetic correlations showed that turkeys could be selected at earlier time points, at 12 weeks of age, in order to increase the growth rate. In general, genetic correlation estimates were higher among adjacent ages, decreasing markedly with the increase of distance between ages. Negative genetic correlations were observed between ages.

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“…Test day models (TDM) have been receiving considerable attention from research studies on the genetic improvement of dairy cattle (Gengler et al, 1999;Kettunen et al, 2000;Schaeffer et al, 2000;Swalve, 2000;Misztal et al, 2000;Jensen, 2002;Lopez-Romero and Carabañ o, 2003;Mrode and Swanson, 2004;Fischer et al, 2004;Schaeffer, 2004;Albuquerque and Meyer, 2005). The use of TDM allows a more accurate definition of contemporary groups (CG) and associated environmental effects, thus offering a more specific definition of the effects of the lactation stage and reproduction of dairy cows.…”

Section: Introductionmentioning

confidence: 99%

Genetic parameters for test day milk yields of first lactation Holstein cows by random regression models

Melo

Packer²,

Costa³

et al. 2007

Animal

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Covariance components for test day milk yield using 263 390 first lactation records of 32 448 Holstein cows were estimated using random regression animal models by restricted maximum likelihood. Three functions were used to adjust the lactation curve: the five-parameter logarithmic Ali and Schaeffer function (AS), the three-parameter exponential Wilmink function in its standard form (W) and in a modified form (W*), by reducing the range of covariate, and the combination of Legendre polynomial and W (LEG þ W). Heterogeneous residual variance (RV) for different classes (4 and 29) of days in milk was considered in adjusting the functions. Estimates of RV were quite similar, rating from 4.15 to 5.29 kg 2 . Heritability estimates for AS (0.29 to 0.42), LEG þ W (0.28 to 0.42) and W* (0.33 to 0.40) were similar, but heritability estimates used W (0.25 to 0.65) were highest than those estimated by the other functions, particularly at the end of lactation. Genetic correlations between milk yield on consecutive test days were close to unity, but decreased as the interval between test days increased. The AS function with homogeneous RV model had the best fit among those evaluated.

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Description of lamb growth using random regression on field data

Cited by 66 publications

References 13 publications

Genetic analysis of weight, fat and muscle depth in growing lambs using random regression models

Genetic analysis of weight, fat and muscle depth in growing lambs using random regression models

Estimates of the genetic parameters of turkey body weight using random regression analysis

Genetic parameters for test day milk yields of first lactation Holstein cows by random regression models

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