scholarly journals Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials

2018 ◽  
Vol 31 (5) ◽  
pp. 636-642 ◽  
Author(s):  
Hafedh Ben Zaabza ◽  
Abderrahmen Ben Gara ◽  
Boulbaba Rekik
Keyword(s):  
Genetic Analysis ◽  
Milk Production ◽  
Legendre Polynomials ◽  
Random Regression ◽  
Production Traits ◽  
Milk Production Traits ◽  
Regression Test

scholarly journals Definition of subgroups for fixed regression in the test-day animal model for milk production ofHolsteincattle in theCzech Republic

2011 ◽  
Vol 50 (No. 1) ◽  
pp. 7-13 ◽  
Author(s):  
L. Zavadilová ◽  
E. Němcová ◽  
J. Přibyl ◽  
J. Wolf
Keyword(s):  
Animal Model ◽  
Milk Production ◽  
Legendre Polynomials ◽  
Environmental Effect ◽  
Random Regression ◽  
Breeding Values ◽  
Regression Test ◽  
Days Open ◽  
Calving Season ◽  
Definition Of

The investigation was based on roughly 3.9, 2.7 and 1.7 million test-day records from first, second and third lactation, respectively, sampled from 596 200 Czech Holstein cows between the years 1991 and 2002. Breeding values were estimated from multi-lactation random-regression test-day models which contained the fixed effect of herd-test day, fixed regression on days in milk and random regressions on the animal level and the permanent environmental effect. Third degree Legendre polynomials (with four coefficients) were used for both the fixed and random regressions. The models differed in fixed regression. In Analysis I, 96 subclasses were defined according to age at calving, season and year of calving within lactation. In Analysis II, days open were additionally included as a grouping factor resulting in 480 subclasses. Rank correlations over 0.98 between both analyses were observed for breeding values for sires. Grouping according to Analysis I was recommended.  

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.

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

2014 ◽  
Vol 30 (1) ◽  
pp. 15-28 ◽  
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.

Genetic Analysis of DGAT1 Loci Related to Milk Production Traits in Native Sahiwal Cattle

2018 ◽  
Vol 8 (5) ◽  
pp. 136
Author(s):  
Vijay Agrawal ◽  
Gyan Gahlot ◽  
Mohammad Ashraf ◽  
Amitosh Kumar ◽  
Govind Dhakad
Keyword(s):  
Genetic Analysis ◽  
Milk Production ◽  
Production Traits ◽  
Milk Production Traits ◽  
Sahiwal Cattle
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