scholarly journals Estimating Genetic Parameters using a Random Regression Model

2008 ◽  
pp. 53-55
Author(s):  
Szilárd Márkus ◽  
Eva Němcová ◽  
István Fazekas ◽  
István Komlósi
Keyword(s):  
Regression Model ◽  
Variance Components ◽  
Genetic Parameters ◽  
Random Regression ◽  
Environmental Variance ◽  
Data Set ◽  
Computational Costs ◽  
Random Regression Model ◽  
Holstein Friesian ◽  
Estimation Of Variance

One of the most important part of the genetic evaluation using a random regression model is the estimation of variance components. This is the topic of many papers because the large computational costs. We can use restricted maximum likelihood (REML), Gibbs sampling and ℜ method for the estimation of genetic parameters. The variance components are necessary to calculate the heritabilities and repeatabilities.The aim of our paper is to estimate the variance components using a random regression repeatability model from test day data set of Hungarian Holstein-Friesian dairy cows and to analyse the change of additive genetic and permanent environmental variance, heritability and repeatability over lactation.

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.

scholarly journals Genetic parameters for test day milk yield of first lactation Holstein cows estimated by random regression using Legendre polynomials

2008 ◽  
Vol 37 (4) ◽  
pp. 602-608 ◽  
Author(s):  
Claudio Napolis Costa ◽  
Claudio Manoel Rodrigues de Melo ◽  
Irineu Umberto Packer ◽  
Ary Ferreira de Freitas ◽  
Nilson Milagres Teixeira ◽  
...  
Keyword(s):  
Variance Components ◽  
Legendre Polynomials ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Random Regression ◽  
Holstein Cows ◽  
Residual Variance ◽  
Environmental Variance ◽  
Environmental Correlations ◽  
Lp Estimates

Data comprising 263,390 test-day (TD) records of 32,448 first parity cows calving in 467 herds between 1991 and 2001 from the Brazilian Holstein Association were used to estimate genetic and permanent environmental variance components in a random regression animal model using Legendre polynomials (LP) of order three to five by REML. Residual variance was assumed to be constant in all or in some classes of lactation periods for each LP. Estimates of genetic and permanent environmental variances did not show any trend due to the increase in the LP order. Residual variance decreased as the order of LP increased when it was assumed constant, and it was highest at the beginning of lactation and relatively constant in mid lactation when assumed to vary between classes. The range for the estimates of heritability (0.27 - 0.42) was similar for all models and was higher in mid lactation. There were only slight differences between the models in both genetic and permanent environmental correlations. Genetic correlations decreased for near unity between adjacent days to values as low as 0.24 between early and late lactation. A five parameter LP to model both genetic and permanent environmental effects and assuming a homogeneous residual variance would be a parsimonious option to fit TD yields of Holstein cows in Brazil.

scholarly journals Genetic Parameters for Tunisian Holsteins Using a Test-Day Random Regression Model

2008 ◽  
Vol 91 (5) ◽  
pp. 2118-2126 ◽  
Author(s):  
H. Hammami ◽  
B. Rekik ◽  
H. Soyeurt ◽  
A. Ben Gara ◽  
N. Gengler
Keyword(s):  
Regression Model ◽  
Genetic Parameters ◽  
Random Regression ◽  
Random Regression Model

scholarly journals Genetic Parameters for Litter Size in Pigs Using a Random Regression Model

2006 ◽  
Vol 20 (2) ◽  
pp. 160-165 ◽  
Author(s):  
Z. Lukovic ◽  
M. Uremovic ◽  
M. Konjacic ◽  
Z. Uremovic ◽  
D. Vincek
Keyword(s):  
Regression Model ◽  
Litter Size ◽  
Genetic Parameters ◽  
Random Regression ◽  
Random Regression Model

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

Genetic parameters for production traits of dairy Gyr (Bos indicus)×Holstein cattle estimated with a random regression model

2013 ◽  
Vol 158 (1-3) ◽  
pp. 24-31 ◽  
Author(s):  
Rodrigo Junqueira Pereira ◽  
Denise Rocha Ayres ◽  
Lenira El Faro ◽  
Aníbal Eugênio Vercesi Filho ◽  
Rui da Silva Verneque ◽  
...  
Keyword(s):  
Regression Model ◽  
Genetic Parameters ◽  
Bos Indicus ◽  
Random Regression ◽  
Holstein Cattle ◽  
Production Traits ◽  
Random Regression Model

scholarly journals Comparison of different polynomial functions in Random Regression Model for milk production traits of Iranian Holstein dairy cattle

2014 ◽  
Vol 14 (1) ◽  
pp. 55-68 ◽  
Author(s):  
Ali Mohammadi ◽  
Sadegh Alijani ◽  
Hossein Daghighkia
Keyword(s):  
Dairy Cattle ◽  
Regression Model ◽  
Milk Production ◽  
Genetic Parameters ◽  
Random Regression ◽  
Polynomial Functions ◽  
Early Lactation ◽  
Production Traits ◽  
Milk Production Traits ◽  
Random Regression Model

Abstract The aim of this research was to compare different polynomial functions including Legendre polynomials (LP), Wilmink (WRR) and Ali-Schaeffer (ARR) functions, in random regression model (RRM) for estimation of genetic parameters for milk production traits of Iranian Holstein dairy cattle. For this purpose the performance records obtained from test-day (TD) regarding milk yield, fat and protein contents of the cows calving for the first time were used. The numbers of records for the above mentioned traits were 701212, 657004, and 560775, respectively. These records were collected from the years 2006 to 2010 by the National Breeding Center of Iran. The genetic parameters were estimated using Restricted Maximum Likelihood (REML) method by applying RRM. Residual variances were considered homogeneous over the lactation period. To compare the model, different criteria (-2Logl, AIC, BIC and RV) were used for considered traits. Based on the results obtained, for all traits, RRM with LP function (2,5) were chosen as the best model. Considering residual variance (RV), LP (2,2) was proved to be a model which has the lowest performance, while using -2Logl, AIC, BIC criteria, RRM with ARR function was the worst model. According to the results, it is recommended to use LP with low orders for the additive genetic effects and with more orders for the permanent environment effects in the RRM for Iranian Holstein cattle. Permanent environment variance was higher in early lactation than during lactation and additive genetic variance in the early lactation was lower than at the end of lactation. Heritability range of milk yield, fat and protein contents was estimated to be from 0.08 to 0.23, 0.05 to 0.20 and 0.08 to 0.14, respectively. Phenotypic variance of the considered traits during lactation was not constant and it was higher at the beginning and the end of lactation. The additive genetic correlation between adjacent test days was higher than between distant test days.

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