Genetic Evaluation of Dairy Cattle Using Test Day Yields and Random Regression Model

Jamrozik, J.; Schaeffer, L.R.; Dekkers, Jack C. M.

doi:10.3168/jds.s0022-0302(97)76050-8

Cited by 202 publications

(164 citation statements)

References 8 publications

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“…Similar trends for variance estimates of genetic and permanent effects throughout the lactation were observed among the ASME1 (Figure 1a (Figure 3b) increased from the beginning to the end of lactation, when they reached markedly elevated values in comparison to the estimates obtained from the other models. These results differ from those reported by Jamrozik et al (1997b), who obtained similar results using both the AS and the W curve and from those from Jamrozik et al (1997a), who described a reduction in genetic variance from the beginning to the 25th day of lactation and then stability in values through to the end of lactation when using the W model.…”

Section: Discussioncontrasting

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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Section: Discussioncontrasting

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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“…One of the main justifications for performing genetic evaluation on early lactation yield was to minimise possible bias that may occur due to culling based on early lactation (Wilmink, 1988). In recent years with the popularisation of RR models that consider milk yield on each day of lactation, this may not be a major problem (Jamrozik et al, 1997). Nevertheless, in the current study, the simple correlation between sires EBVs for the first 300 days ð P 300 4 EBVÞ and 4 to 540 days ð P 540 n¼4 EBVÞ was high (0.94) for milk yield, suggesting the advantage from considering longer lactations is small.…”

Section: --mentioning

confidence: 99%

“…Therefore, particularly herds that aim to milk cows after the standard 305 days could select sires for persistency of milk yield in addition to milk yield provided official genetic evaluation for persistency are released. EBVs for persistency could be a byproduct of an RR test-day genetic evaluation (Jamrozik et al, 1997). The simple correlation between the sum of EBVs from days 4 to 540 with persistency of yield calculated according to formula (1) is 0.21 for sire with 20 or more progeny, indicating that the additional benefit from considering persistency is only marginal.…”

Section: --mentioning

confidence: 99%

Genetic and phenotypic parameters of lactations longer than 305 days (extended lactations)

Haile‐Mariam¹,

Goddard

2008

Animal

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Test-day milk yield and somatic cell count data over extended lactation (lactation to 540-600 days) were analysed considering part lactations as different traits and fitting random regression (RR) models. Data on Australian Jersey and Holstein Friesian (HF) were used to demonstrate the shape of the lactation curve and data on HF were used for genetic study. Test-day data from about 100 000 cows that calved between 1998 and 2005 were used for this study. In all analyses, a sire model was used.When part lactations were considered as different traits, protein yield early in the lactation (e.g. first 2 months) had a genetic correlation of about 0.8 with protein yield produced after 300 days of lactation. Genetic correlations between lactation stages that are adjacent to each other were high (0.9 or more) within parity. Across parities, genetic correlations were high for both protein and milk yield if they are within the same stage of lactation. Phenotypic correlations were lower than genetic correlations. Heritability of milk-yield traits estimated from the RR model varied from 0.15 at the beginning of the lactation to as high as 0.37 by the 4th month of lactation. All genetic correlations between different days in milk were positive, with the highest correlations between adjacent days in milk and decreasing correlations with increasing time-span. The pattern of genetic correlations between milk yield in the second 300 days (301 to 600 days of lactation) do not markedly differ from the pattern in the first 300 days of lactation. The lowest estimated genetic correlation was 0.15 between milk yield on days 45 and 525 of lactation. The result from this study shows that progeny of bulls with high estimated breeding values for yield traits and those that produce at a relatively high level in the first few months are the most likely candidates for use in herds favouring extended lactations.

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“…Random regression (RR) has been widely used for genetic analysis of longitudinal data from many of the major animal breeding industries world wide and has also been implemented into routine large scale animal breeding applications [4]. Estimates of derived genetic parameters such as heritability at given points along the trajectory are commonly published from such studies and comment is often made about the accuracy and robustness of RR models.…”

Section: Introductionmentioning

confidence: 99%

Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information REML

Fischer

Gilmour²,

Werf

2004

Genet. Sel. Evol.

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-Approximate standard errors (ASE) of variance components for random regression coefficients are calculated from the average information matrix obtained in a residual maximum likelihood procedure. Linear combinations of those coefficients define variance components for the additive genetic variance at given points of the trajectory. Therefore, ASE of these components and heritabilities derived from them can be calculated. In our example, the ASE were larger near the ends of the trajectory. random regression / heritability / approximate standard error / genetic parameter / residual maximum likelihood

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Genetic Evaluation of Dairy Cattle Using Test Day Yields and Random Regression Model

Cited by 202 publications

References 8 publications

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

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

Genetic and phenotypic parameters of lactations longer than 305 days (extended lactations)

Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information REML

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