Random Regression Analysis of Calving Interval of Japanese Black Cows

We estimated genetic parameters for the calving interval of Japanese Black cows using a random regression model and a repeatability model. We analyzed 92,019 calving interval records of 36,178 cows. Pedigree data covered 390,263 individuals. Age of cow at previous calving for each record ranged from 18 to 120 months. We used up to the second-order Legendre polynomials based on age at previous calving as sub-models for random regression analysis, and assumed a constant error variance across ages. Estimated heritability was 0.12 to 0.20 with the random regression model and 0.17 with the repeatability model. With the random regression model, the estimated genetic correlation between ages was ≥0.87, and those between 24 and 36 months, 24 and 84 months, and 36 and 84 months were 0.99, 0.95, and 0.97, respectively. Spearman’s rank correlation between breeding values of 36,178 cows with their own records estimated by the random regression model with those estimated using the repeatability model was ≥0.97, and the rank correlation was ≥0.94 for 314 sires of these cows. These results support the validity of fitting a repeatability model to the records of the calving interval of Japanese Black cows for evaluation of breeding values.

Download Full-text

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

Pesquisa Agropecuária Brasileira ◽

10.1590/s1678-3921.pab2019.v54.00325 ◽

2019 ◽

Vol 54 ◽

Cited By ~ 1

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.

Download Full-text

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

Animal Science ◽

10.1017/s1357729800052358 ◽

2002 ◽

Vol 74 (2) ◽

pp. 189-197 ◽

Cited By ~ 1

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.

Download Full-text

Estimation of (co)variance components and breeding values for test-day milk production traits of Holstein dairy cattle via Bayesian approach

Biotechnology in Animal Husbandry ◽

10.2298/bah1401015m ◽

2014 ◽

Vol 30 (1) ◽

pp. 15-28 ◽

Cited By ~ 1

Author(s):

R. Mosharraf ◽

J. Shodja ◽

M. Bohlouli ◽

S. Alijani ◽

S.A. Rafat

Keyword(s):

Dairy Cattle ◽

Regression Model ◽

Milk Production ◽

Milk Fat ◽

Variance Components ◽

Random Regression ◽

Production Traits ◽

Milk Production Traits ◽

Breeding Values ◽

Random Regression Model

Genetic parameters of milk, fat, and protein yields were estimated in the first lactation of Holstein dairy cattle. The records were collected during the period 2006 to 2011 and analyzed fitting the random regression model. The data included 41178, 25397 and 18716 test-day records of milk, fat and protein yields, respectively that produced by 4746, 3437 and 2525 cows respectively. Fixed effects in model included herd-year-month of test day and age-season of calving. The fixed and random regressions were modeled with normalized Legendre polynomials and (co)variance components were estimated by Bayesian method and Gibbs sampling was used to obtain posterior distributions. Estimates of heritability for milk, fat and protein yields ranged from 0.18 to 0.26; 0.06 to 0.11 and 0.09 to 0.22, respectively. Heritabilities for 305-d milk, fat and protein yields were 0.36, 0.23 and 0.29, respectively. For milk and protein yields, heritabilities were lower at the early of lactation due to the trends of lower additive genetic variance, higher permanent environmental variance. Genetic correlations for milk, fat and protein yields ranged from 0.14 to 1.00; 0.39 to 1.00 and 0.27 to 1.00, respectively. Ranges of estimated breeding values for 305-d yield of milk, fat and protein yields were from -1194.48 to 1412.44; -210.57 to 271.22 and -194.08 to 203.25, respectively. According to the results of this study, random regression model seems to be a flexible and reliable procedure for the genetic evaluation of milk production traits and it can be useful in the breeding programs for Iranian dairy cattle.

Download Full-text

Genetic evaluation of growth traits in Iranian Kordi Sheep using random regression model with homogeneous and heterogeneous residual variances

Genetika ◽

10.2298/gensr1702469m ◽

2017 ◽

Vol 49 (2) ◽

pp. 469-482 ◽

Cited By ~ 1

Author(s):

Ali Mohammadi ◽

Mohammad Farhadian

Keyword(s):

Regression Model ◽

Fourth Order ◽

Environmental Effects ◽

Growth Traits ◽

Genetic Parameter ◽

Error Variance ◽

Genetic Evaluation ◽

Random Regression ◽

Random Regression Model ◽

Fifth Order

The purpose of this study was estimation of genetic parameter using random regression model (RRM) with various error variance in Iranian Kordi sheep. The data (consisting of 7875 weight records from birth to 360 days of age) were collected during the period 2000 to 2013 from the rearing and breeding station of Kordi sheep in Shirvan, Iran. The independent variables were Legendre polynomials (LP) of age at weighing and orders of fit from 2 to 5 were considered. Analyses were carried out fitting sets of random regression coefficients due to direct additive genetic, direct and maternal permanent environmental effects, with heterogeneous and homogeneous error variances. To compare the model were used different criteria such as LogL, AIC, BIC and LRT. The best fitting RRM among homogeneous error variance was the model with a LP of fourth order for fixed effect, fourth order for direct additive genetic and fifth order for direct and maternal permanent environmental effects (model 4455). Among the models with heterogeneous error variances different, model 7 (Heterogeneous error variances of 72 various classes), was selected as the best model. The variances increased along the trajectory from 3.75 to 12.81, 4.43 to 30.28 and 1.49 to 8.49; 0.25 to 27.94, 0.03 to 12.32 and 0.15 to 22.66 for direct additive genetic, direct and maternal permanent environmental effect by homogeneous and heterogeneous error variances, respectively. The direct heritability ranged from 0.15 to 0.41 and 0.11 to 0.56 by homogeneous and heterogeneous error variances, respectively. Genetic correlation between adjacent test days was more than between distant test days. This research has demonstrated the possibility of application of RRM with heterogeneous error variance for genetic evaluation of Iranian Kordi Sheep.

Download Full-text

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

Ciência Rural ◽

10.1590/0103-8478cr20141631 ◽

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.

Download Full-text