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

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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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.

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Short communication: Single-step genomic evaluation of milk production traits using multiple-trait random regression model in Chinese Holsteins

Journal of Dairy Science ◽

10.3168/jds.2018-15090 ◽

2018 ◽

Vol 101 (12) ◽

pp. 11143-11149 ◽

Cited By ~ 5

Author(s):

H. Kang ◽

C. Ning ◽

L. Zhou ◽

S. Zhang ◽

Q. Yan ◽

...

Keyword(s):

Regression Model ◽

Milk Production ◽

Short Communication ◽

Single Step ◽

Random Regression ◽

Production Traits ◽

Milk Production Traits ◽

Genomic Evaluation ◽

Multiple Trait ◽

Random Regression Model

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GENETIC AND PHENOTYPIC TREND OF MILK PRODUCTION TRAITS AND AGE AT FIRST CALVING APPLYING RANDOM REGRESSION MODEL IN FRIESIAN CATTLE IN EGYPT

Egyptian Journal of Animal Production ◽

10.21608/ejap.2020.104021 ◽

2020 ◽

Vol 57 (2) ◽

pp. 71-79

Keyword(s):

Regression Model ◽

Milk Production ◽

Random Regression ◽

Production Traits ◽

Milk Production Traits ◽

Age At First Calving ◽

Random Regression Model ◽

Friesian Cattle

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Estimation of Environmental Sensitivity of Genetic Merit for Milk Production Traits Using a Random Regression Model

Journal of Dairy Science ◽

10.3168/jds.s0022-0302(03)73982-4 ◽

2003 ◽

Vol 86 (11) ◽

pp. 3756-3764 ◽

Cited By ~ 68

Author(s):

M.P.L. Calus ◽

R.F. Veerkamp

Keyword(s):

Regression Model ◽

Milk Production ◽

Random Regression ◽

Production Traits ◽

Milk Production Traits ◽

Environmental Sensitivity ◽

Genetic Merit ◽

Random Regression Model

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Linear and threshold model genetic parameters for disease, fertility and milk production in dairy cattle

Animal Science ◽

10.1017/s1357729800055338 ◽

2000 ◽

Vol 71 (3) ◽

pp. 411-419 ◽

Cited By ~ 123

Author(s):

H. N. Kadarmideen ◽

R. Thompson ◽

G. Simm

Keyword(s):

Dairy Cattle ◽

Milk Production ◽

Fixed Effects ◽

Genetic Parameters ◽

Threshold Model ◽

Production Traits ◽

Milk Production Traits ◽

Model Framework ◽

Data Set ◽

Fertility Traits

AbstractThis study provides estimates of genetic parameters for various diseases, fertility and 305-day milk production traits in dairy cattle using data from a UK national milk recording scheme. The data set consisted of 63891 multiple lactation records on diseases (mastitis, lameness, milk fever, ketosis and tetany), fertility traits (calving interval, conception to first service, number of services for a conception, and number of days to first service), dystocia and 305-day milk, fat and protein yield. All traits were analysed by multi-trait repeatability linear animal models (LM). Binary diseases and fertility traits were further analysed by threshold sire models (TM). Both LM and TM analyses were based on the generalized linear mixed model framework. The LM included herd-year-season of calving (HYS), age at calving and parity as fixed effects and genetic, permanent environmental and residual effects as random. The TM analyses included the same effects as for LM, but HYS effects were treated as random to avoid convergence problems when HYS sub-classes had 0 or 100% incidence. Because HYS effects were treated as random, herd effects were fitted as fixed effects to account for effect of herds in the data. The LM estimates of heritability ranged from 0•389 to 0•399 for 305-day milk production traits, 0•010 to 0•029 for fertility traits and 0•004 to 0•038 for diseases. The LM estimates of repeatability ranged from 0•556 to 0•586 for 305-day milk production traits, 0•029 to 0•086 for fertility traits and 0•004 to 0•100 for diseases. The TM estimates of heritabilities and repeatabilities were greater than LM estimates for binary traits and were in the range 0•012 to 0•126 and 0•013 to 0•168, respectively. Genetic correlations between milk production traits and fertility and diseases were all unfavorable: they ranged from 0•07 to 0•37 for milk production and diseases, 0•31 to 0•54 for milk production and poor fertility and 0•06 to 0•41 for diseases and poor fertility. These results show that future selection programmes should include disease and fertility for genetic improvement of health and reproduction and for sustained economic growth in the dairy cattle industry.

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