scholarly journals Genetic parameters for somatic cell score in the first three lactations of Czech Holstein and Fleckvieh breeds using a random regression model

2011 ◽  
Vol 56 (No. 6) ◽  
pp. 251-260 ◽  
Author(s):  
L. Zavadilová ◽  
J. Wolf ◽  
M. Štípková ◽  
E. Němcová ◽  
J. Jamrozik
Keyword(s):  
Regression Model ◽  
Somatic Cell ◽  
Genetic Correlations ◽  
Middle Part ◽  
Somatic Cell Score ◽  
Random Regression ◽  
Model Parameters ◽  
Environmental Correlations ◽  
Random Regression Model ◽  
Residual Variances

A multiple-lactation random regression model was applied to test-day somatic cell score (SCS) records from the first three lactations of Czech Holstein and Fleckvieh cows. For Holstein, the data included 26 314 cows, with 244 953, 76 188 and 26 153 test-day records in the first, second and third lactation, respectively. For Fleckvieh, the data included 24 061 cows, with 223 421, 93 358 and 31 305 test-day records in the first, second and third lactation, respectively. The linear model for SCS included the following factors (for the given parity): fixed herd-test date effect, fixed regressions on days in milk within the age-season class, random regressions for the animal genetic and random regressions for the permanent environmental effect of the cow. Third-degree Legendre polynomials were used for all regressions. Gibbs sampling was used to generate samples from the marginal posterior distributions of the model parameters. The resulting daily heritability ranged from 0.08 to 0.11 in the middle part of lactation and it increased only slightly with parity. Extremely high values (0.25, 0.21) observed especially at the beginning and end of the third lactation for Holstein might be caused by the "end-of-range" problem. The average daily heritabilities computed for the part of lactation between 45 and 255 days in milk (DIM) were in the range from 0.10 to 0.14. Daily permanent environmental variances were higher than the genetic variances and daily residual variances decreased with DIM. The residual variances in early lactation increased with lactation number. For both breeds, the highest genetic correlations computed for the part of lactation between DIM 45 and DIM 255 were obtained between the second and third lactation (0.95). The lowest daily genetic correlations of SCS in the same DIM between different lactations occurred at the beginning of lactation, especially between the first and third lactation. The permanent environmental correlations for selected DIM were lower than the respective genetic correlations.

Genetic trends and genetic correlations between 305-day milk yield, persistency and somatic cell score of Holstein cows in Brazil using random regression model

2019 ◽  
Vol 59 (2) ◽  
pp. 207 ◽  
Author(s):  
A. Haiduck Padilha ◽  
E. P. M. Alfonzo ◽  
D. S. Daltro ◽  
H. A. L. Torres ◽  
J. Braccini Neto ◽  
...  
Keyword(s):  
Regression Model ◽  
Somatic Cell ◽  
Genetic Correlation ◽  
Milk Yield ◽  
Genetic Correlations ◽  
Somatic Cell Score ◽  
Random Regression ◽  
Random Regression Model ◽  
The Difference ◽  
Estimated Breeding Values

The objective was to estimate genetic correlations for persistency, milk yield and somatic cell score (SCS) in Holstein cattle in Brazil. A dataset with 190389 records of test-day milk and of test-day SCS from 21824 cows was used. Two-trait random regression model with a fourth order Legendre polynomial was used. Persistency (PS) was defined as the difference between estimated breeding values (EBV) along different days in milk using two formulae: and PS2=(EBV290–EBV90). Larger values for PS2 or lower ones for PS1 indicate higher persistency. Heritability was 0.24 for 305-day milk yield, 0.14 for SCS up to 305 days, 0.15 for PS1 and 0.14 for PS2. Genetic correlation between 305-day milk yield and SCS up to 305 days was –0.47. Genetic correlation of 305-day milk yield with PS1 and PS2 was –0.32 and 0.30, respectively. Genetic correlation of SCS up to 305 days was 0.25 with PS1 and –0.20 with PS2. The additive genetic correlations between milk yield, SCS and persistency showed that selection for higher persistency or for low somatic cell score will increase 305-day milk yield.

scholarly journals Estimation of genetic parameters for test-day milk yield in Girolando cows using a random regression model

2021 ◽  
Vol 73 (1) ◽  
pp. 18-24
Author(s):  
E.P.B. Santos ◽  
G.L. Feltes ◽  
R. Negri ◽  
J.A. Cobuci ◽  
M.V.G.B. Silva
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Legendre Polynomials ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Random Regression ◽  
Third Order ◽  
Good Tool ◽  
Components Of Variance ◽  
Random Regression Model

ABSTRACT The objective of this study was to estimate the components of variance and genetic parameters of test-day milk yield in first lactation Girolando cows, using a random regression model. A total of 126,892 test-day milk yield (TDMY) records of 15,351 first-parity Holstein, Gyr, and Girolando breed cows were used, obtained from the Associação Brasileira dos Criadores de Girolando. To estimate the components of (co) variance, the additive genetic functions and permanent environmental covariance were estimated by random regression in three functions: Wilmink, Legendre Polynomials (third order) and Linear spline Polynomials (three knots). The Legendre polynomial function showed better fit quality. The genetic and permanent environment variances for TDMY ranged from 2.67 to 5.14 and from 9.31 to 12.04, respectively. Heritability estimates gradually increased from the beginning (0.13) to mid-lactation (0.19). The genetic correlations between the days of the control ranged from 0.37 to 1.00. The correlations of permanent environment followed the same trend as genetic correlations. The use of Legendre polynomials via random regression model can be considered as a good tool for estimating genetic parameters for test-day milk yield records.

Genetic evaluation of Holstein Friesian sires for daughter condition-score changes using a random regression model

1999 ◽  
Vol 68 (3) ◽  
pp. 467-475 ◽  
Author(s):  
H. E. Jonest ◽  
I. M. S. White ◽  
S. Brotherstone
Keyword(s):  
Genetic Analysis ◽  
Regression Model ◽  
Genetic Correlations ◽  
Random Regression ◽  
Stage Of Lactation ◽  
Random Regression Model ◽  
Holstein Friesian ◽  
Condition Score ◽  
Estimated Breeding Values ◽  
Stage 1

AbstractIn dairy cattle type classification schemes, heifers are condition scored (CS) only once during their first lactation. Although genetic analysis of condition-score changes is not possible using an animal model, the data can be analysed as repeated observations on the sire.CS records for 100 078 Holstein Friesian heifers, the progeny of 797 sires, were available. Sires differed in the shape of the regression of mean daughter CS on stage of lactation at both the phenotypic and genetic level. Genetic analysis was carried out using a random regression model (RRM) which can account for differences between sires in the shape of the CS curves. CS curves for individual sires were modelled using a cubic polynomial.Heritability estimates for CS at each stage of lactation generally increased through the lactation from 0·20 in stage 2 (days in milk 31 to 60) to 0·28 in later lactation stages. Genetic correlations between CS at different stages were generally high (0·80), with the exception of correlations with stage 1 (days in milk 1 to 30) which decreased to 0·63 with stages 6 and 7, suggesting that CS at stage 1 is under different biological control from CS at other stages of the lactation. Using RRM, sire estimated breeding values (EBVs) for CS at each stage of the lactation were estimated. Sire rankings on EBV at each stage were seen to change through early, mid and later lactation stages.

scholarly journals Genetic parameters for test-day model with random regressions for production traits of Czech Holstein cattle

2011 ◽  
Vol 50 (No. 4) ◽  
pp. 142-154 ◽  
Author(s):  
L. Zavadilová ◽  
J. Jamrozik ◽  
Schaeffer LR
Keyword(s):  
Milk Fat ◽  
Sampling Method ◽  
Random Regression ◽  
Model Parameters ◽  
Residual Variance ◽  
Posterior Distributions ◽  
Production Traits ◽  
Multiple Trait ◽  
Holstein Breed ◽  
Random Regression Model

Multiple-lactation random regression model was applied to test-day records of milk, fat and protein yields in the first three lactations of the Czech Holstein breed. Data included 9 583 cows, 89 584, 44 207 and 11 266 test-day records in the first, second and third lactation, respectively. Milk, fat and protein in the first three lactations were analysed separately and in a multiple-trait analysis. Linear model included herd-test date, fixed regressions within age-season class and two random effects: animal genetic and permanent environment modelled by regressions. Gibbs sampling method was used to generate samples from marginal posterior distributions of the model parameters. The single- and multiple-trait models provided similar results. Genetic and permanent environmental variances and heritability for particular days in milk were high at the beginning and at the end of lactation. The residual variance decreased throughout the lactation. The resulting heritability ranged from 0.13 to 0.52 and increased with parity.  

scholarly journals Genotypic parameters for egg production in pure breed hens by using random regression model

2017 ◽  
Vol 47 (5) ◽  
Author(s):  
Priscila Becker Ferreira ◽  
Paulo Roberto Nogara Rorato ◽  
Fernanda Cristina Breda ◽  
Vanessa Tomazetti Michelotti ◽  
Alexandre Pires Rosa ◽  
...  
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Egg Production ◽  
Random Regression ◽  
Broad Sense Heritability ◽  
Residual Matrix ◽  
Random Regression Model ◽  
Residual Covariance ◽  
Genotypic Correlations ◽  
Selection Of

ABSTRACT: This study aimed to test different genotypic and residual covariance matrix structures in random regression models to model the egg production of Barred Plymouth Rock and White Plymouth Rock hens aged between 5 and 12 months. In addition, we estimated broad-sense heritability, and environmental and genotypic correlations. Six random regression models were evaluated, and for each model, 12 genotypic and residual matrix structures were tested. The random regression model with linear intercept and unstructured covariance (UN) for a matrix of random effects and unstructured correlation (UNR) for residual matrix adequately model the egg production curve of hens of the two study breeds. Genotypic correlations ranged from 0.15 (between age of 5 and 12 months) to 0.99 (between age of 10 and 11 months) and increased based on the time elapsed. Egg production heritability between 5- and 12-month-old hens increased with age, varying from 0.15 to 0.51. From the age of 9 months onward, heritability was moderate with estimates of genotypic correlations higher than 90% at the age of 10, 11, and 12 months. Results suggested that selection of hens to improve egg production should commence at the ninth month of age.

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