Genetic analysis of fat-to-protein ratio, milk yield and somatic cell score of Holstein cows in Japan in the first three lactations by using a random regression model

2015 ◽  
Vol 86 (12) ◽  
pp. 961-969 ◽  
Author(s):  
Akiko Nishiura ◽  
Osamu Sasaki ◽  
Mitsuo Aihara ◽  
Hisato Takeda ◽  
Masahiro Satoh
Keyword(s):  
Genetic Analysis ◽  
Regression Model ◽  
Somatic Cell ◽  
Milk Yield ◽  
Somatic Cell Score ◽  
Random Regression ◽  
Holstein Cows ◽  
Protein Ratio ◽  
Random Regression Model

Importance of genotype by environment interaction on genetic analysis of milk yield in Iranian Holstein cows using a random regression model

2019 ◽  
Vol 59 (8) ◽  
pp. 1438
Author(s):  
Y. Fazel ◽  
A. Esmailizadeh ◽  
M. Momen ◽  
M. Asadi Fozi
Keyword(s):  
Genetic Analysis ◽  
Regression Model ◽  
Milk Yield ◽  
Genetic Parameter ◽  
Genotype By Environment Interaction ◽  
Random Regression ◽  
Holstein Cows ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Random Regression Model

Changes in the relative performance of genotypes (sires) across different environments, which are referred to as genotype–environment interactions, play an important role in dairy production systems, especially in countries that rely on imported genetic material. Importance of genotype by environment interaction on genetic analysis of milk yield was investigated in Holstein cows by using random regression model. In total, 68945 milk test-day records of first, second and third lactations of 8515 animals that originated from 100 sires and 7743 dams in 34 herds, collected by the Iranian animal breeding centre during 2007–2009, were used. The different sires were considered as different genotypes, while factors such as herd size, herd milk average (HMA), herd protein average and herd fat average were used as criteria to define the different environments. The inclusion of the environmental descriptor improved not only the log-likelihood of the model, but also the Bayesian information criterion. The results showed that defining the environment on the basis of HMA affected genetic parameter estimations more than did the other environmental descriptors. The heritability of milk yield during lactating days reduced when sire × HMA was fitted to the model as an additional random effect, while the genetic and phenotypic correlations between lactating months increased. Therefore, ignoring this interaction term can lead to the biased genetic-parameter estimates, reduced selection accuracy and, thus, different ranking of the bulls in different environments.

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 Holstein cows using a random regression model

2005 ◽  
Vol 28 (1) ◽  
pp. 75-83 ◽  
Author(s):  
Jaime Araujo Cobuci ◽  
Ricardo Frederico Euclydes ◽  
Paulo Sávio Lopes ◽  
Claudio Napolis Costa ◽  
Robledo de Almeida Torres ◽  
...  
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Genetic Parameters ◽  
Random Regression ◽  
Holstein Cows ◽  
Random Regression Model ◽  
Estimation Of Genetic Parameters

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.

scholarly journals The use of a Bivariate Random Regression Model for Genetic Analysis of Milk Yield in Iranian Native Buffalo

2018 ◽  
Vol 9 (19) ◽  
pp. 102-112 ◽  
Author(s):  
Yousef Naderi ◽  
Mohsen Gholizadeh ◽  
Mostafa Madad ◽  
◽  
◽  
...  
Keyword(s):  
Genetic Analysis ◽  
Regression Model ◽  
Milk Yield ◽  
Random Regression ◽  
Random Regression Model
Sign in / Sign up

Export Citation Format

Share Document