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

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.

scholarly journals Test-day or 305-day milk yield for genetic evaluation of Gir cattle

2019 ◽  
Vol 54 ◽  
Author(s):  
Rodrigo Junqueira Pereira ◽  
Denise Rocha Ayres ◽  
Mário Luiz Santana Junior ◽  
Lenira El Faro ◽  
Aníbal Eugênio Vercesi Filho ◽  
...  
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Breeding Values ◽  
Random Regression Model ◽  
The Mean ◽  
Yield Record ◽  
Genetic Evaluations ◽  
Dairy Breed

Abstract: The objective of this work was to compare genetic evaluations of milk yield in the Gir breed, in terms of breeding values and their accuracy, using a random regression model applied to test-day records or the traditional model (TM) applied to estimates of 305-day milk yield, as well as to predict genetic trends for parameters of interest. A total of 10,576 first lactations, corresponding to 81,135 test-day (TD) records, were used. Rank correlations between the breeding values (EBVs) predicted with the two models were 0.96. The percentage of animals selected in common was 67 or 82%, respectively, when 1 or 5% of bulls were chosen, according to EBVs from random regression model (RRM) or TM genetic evaluations. Average gains in accuracy of 2.7, 3.0, and 2.6% were observed for all animals, cows with yield record, and bulls (sires of cows with yield record), respectively, when the RRM was used. The mean annual genetic gain for 305-day milk yield was 56 kg after 1993. However, lower increases in the average EBVs were observed for the second regression coefficient, related to persistency. The RRM applied to TD records is efficient for the genetic evaluation of milk yield in the Gir dairy breed.

scholarly journals Random Regression Model for Genetic Analysis Milk Traits Holstein Friesian Cow

2019 ◽  
Vol 3 (10) ◽  
pp. 205-216
Author(s):  
Snežana Trivunović ◽  
Miroslav Plavšić ◽  
Denis Kučević
Keyword(s):  
Genetic Analysis ◽  
Regression Model ◽  
Random Regression ◽  
Milk Traits ◽  
Random Regression Model ◽  
Holstein Friesian

Estimation of genetic variation for macro- and micro-environmental sensitivities of milk yield and composition in Holstein cows using double hierarchical generalized linear models

2019 ◽  
Vol 86 (2) ◽  
pp. 145-153 ◽  
Author(s):  
Jamshid Ehsaninia ◽  
Navid Ghavi Hossein-Zadeh ◽  
Abdol Ahad Shadparvar
Keyword(s):  
Genetic Variation ◽  
Regression Model ◽  
Milk Yield ◽  
Protein Yield ◽  
Random Regression ◽  
Residual Variance ◽  
Variance Model ◽  
Random Regression Model ◽  
The Mean ◽  
Fat Yield

AbstractThe aim of this study was to estimate genetic parameters for environmental sensitivities in milk yield and composition of Iranian Holstein cows using the double hierarchical generalized linear model (DHGLM) method. Data set included test-day productive records of cows which were provided by the Animal Breeding Center and Promotion of Animal Products of Iran during 1983 to 2014. In the DHGLM method, a random regression model was fitted which included two parts of mean and residual variance. A random regression model (mean model) and a residual variance model were used to study the genetic variation of micro-environmental sensitivities. In order to consider macro-environmental sensitivities, DHGLM was extended using a reaction norm model, and a sire model was applied. Based on the mean model, additive genetic variances for the mean were 38.25 for milk yield, 0.23 for fat yield and 0.03 for protein yield in the first lactation, respectively. Based on the residual variance model, additive genetic variances for residual variance were 0.039 for milk yield, 0.030 for fat yield and 0.020 for protein yield in the first lactation, respectively. Estimates of genetic correlation between milk yield and macro- and micro-environmental sensitivities were 0.660 and 0.597 in the first lactation, respectively. The results of this study indicated that macro- and micro-environmental sensitivities were present for milk production traits of Iranian Holsteins. High genetic coefficient of variation for micro-environmental sensitivities indicated the possibility of reducing environmental variation and increase in uniformity via selection.

Efficiency of part lactation test day records for genetic evaluations using fixed and random regression models

2002 ◽  
Vol 74 (2) ◽  
pp. 189-197 ◽  
Author(s):  
R. A. Mrode ◽  
G. J. T. Swanson ◽  
C. M. Lindberg
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Regression Models ◽  
Rank Correlation ◽  
Random Regression ◽  
Moderate Correlation ◽  
Data Set ◽  
Random Regression Model ◽  
Holstein Friesian ◽  
Genetic Evaluations

AbstractThe efficiency of part lactation test day (TD) records in first parity for the genetic evaluation of bulls and cows using a random regression model (RRM) and a fixed regression model (FRM) was studied, modelling the random and fixed lactation curves by Legendre polynomials. The data set consisted of 9 242 783 TD records for first lactation milk yield of 1 134 042 Holstein Friesian heifers. The efficiency of both models with part lactation TD records was examined by comparing predicted transmitting abilities (PTAs) for 305-day milk yield for 114 bulls and their 4697 daughters, from analyses where the maximum number of TD records of these daughters was restricted to the initial 2, 4 or 6 TDs with those estimated from 10 TDs. The correlations of PTAs estimated from 2, 4 or 6 TDs with those from 10 TDs computed for cows and bulls within each model were very similar. A rank correlation of 0·91 (0·92 FRM) was obtained for cows between PTAs based on 2 TDs and those from 10 TDs. The correlation increased to 0·96 with 4 TDs and 0·98 with 6 TDs. For bulls, correlations between PTAs estimated from 4 or 6 TDs with those estimated from 10 TDs were high at 0·98 and 0·99 respectively. With 2 TDs, the correlation was 0·95. The average under-prediction of PTAs with 2, 4 or 6 TDs relative to 10 TDs was generally higher and more variable with a FRM compared with a RRM for highly persistent cows and bulls. A similar trend was observed for mean over-prediction of PTAs, except for the initial predictions based on 2 TDs when the RRM gave a higher mean over-prediction for bulls and their daughters with low persistency but high initial TD records. The range of over and under-predictions were large (up to 200 kg milk) for some bulls when only 2 TDs were included in both models. A moderate correlation of 0·64 was obtained between persistency evaluations estimated from 10 TDs with those estimated from 2 TDs. The correlation increased to 0·71 with 4 TDs included and 0·85 with 6 TDs. The moderately high correlation between 6 TDs and 10 TDs of 0·85 was unexpected given the high correlation of 0·99 between PTAs for yield estimated from 6TDs with those estimated from 10 TDs.

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