Genetic parameters for gaussian and categorical traits in organic and low input dairy cattle herds based on random regression methodology

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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Genetic Parameters for Milk Yield and Persistency in Carora Dairy Cattle Breed Using Random Regression Model

Italian Journal of Animal Science ◽

10.4081/ijas.2014.3484 ◽

2014 ◽

Vol 13 (4) ◽

pp. 3484

Author(s):

Emanuela Tullo ◽

Stefano Biffani ◽

Christian Maltecca ◽

Rita Rizzi

Keyword(s):

Dairy Cattle ◽

Regression Model ◽

Milk Yield ◽

Genetic Parameters ◽

Cattle Breed ◽

Random Regression ◽

Random Regression Model

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Genetic analyses of binary longitudinal health data in small low input dairy cattle herds using generalized linear mixed models

Livestock Science ◽

10.1016/j.livsci.2014.01.021 ◽

2014 ◽

Vol 162 ◽

pp. 31-41 ◽

Cited By ~ 6

Author(s):

T. Yin ◽

B. Bapst ◽

U.U. von Borstel ◽

H. Simianer ◽

S. König

Keyword(s):

Dairy Cattle ◽

Mixed Models ◽

Generalized Linear Mixed Models ◽

Linear Mixed Models ◽

Health Data ◽

Genetic Analyses ◽

Low Input ◽

Cattle Herds

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Genetic analysis of productive and reproductive traits in multiple-breed dairy cattle populations

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2017001100017 ◽

2017 ◽

Vol 52 (11) ◽

pp. 1109-1117 ◽

Cited By ~ 4

Author(s):

Virgínia Mara Pereira Ribeiro ◽

Fernanda Albuquerque Merlo ◽

Gabriela Canabrava Gouveia ◽

Larissa Kretli Winkelstroter ◽

Luíza Rodrigues Alves Abreu ◽

...

Keyword(s):

Dairy Cattle ◽

Genetic Parameters ◽

Reproductive Traits ◽

Genetic Group ◽

Random Regression ◽

Multiple Trait ◽

Genetic Merit ◽

Lactation Length ◽

Random Regression Model ◽

Residual Variances

Abstract: The objective of this work was to determine whether the random regression model using linear splines (RRMLS) is suitable to estimate the genetic parameters for productive and reproductive traits of a multiple-breed dairy cattle population, as well as to investigate the effect of the genetic group of the progeny on the genetic merit of the sire. The multiple-trait model (MTM) and the RRMLS with one knot fitted for every genetic group were used to obtain the genetic parameters. Records of 1/2 Holstein + 1/2 Gyr (1/2HG), 5/8 Holstein + 3/8 Gyr (5/8HG), and 3/4 Holstein + 1/4 Gyr (3/4HG) crossbreed dams were considered. The RRMLS showed better fitting. The additive and residual variances estimated by the MTM and the RRMLS were similar. Heritability varied from 0.20 to 0.33 for age at first calving, from 0.09 to 0.22 for first lactation length, and from 0.15 to 0.35 for first lactation 305-day milk yield, according to the genetic composition of the dams. The RRMLS is suitable to estimate the genetic parameters for productive and reproductive traits of multiple-breed dairy cattle populations. The genetic merit of the sires is affected by the genetic group of the progeny by which they are evaluated.

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Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle

Asian-Australasian Journal of Animal Sciences ◽

10.5713/ajas.15.0768 ◽

2016 ◽

Vol 29 (12) ◽

pp. 1682-1687 ◽

Cited By ~ 2

Author(s):

Masoumeh Naserkheil ◽

Seyed Reza Miraie-Ashtiani ◽

Ardeshir Nejati-Javaremi ◽

Jihyun Son ◽

Deukhwan Lee

Keyword(s):

Dairy Cattle ◽

Legendre Polynomials ◽

Regression Models ◽

Milk Protein ◽

Genetic Parameters ◽

Random Regression

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Genetic Parameters for Functional Traits in Dairy Cattle from Daily Random Regression Models

Journal of Dairy Science ◽

10.3168/jds.s0022-0302(06)72141-5 ◽

2006 ◽

Vol 89 (2) ◽

pp. 791-798 ◽

Cited By ~ 24

Author(s):

B. Karacaören ◽

F. Jaffrézic ◽

H.N. Kadarmideen

Keyword(s):

Dairy Cattle ◽

Functional Traits ◽

Regression Models ◽

Genetic Parameters ◽

Random Regression

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Estimation of genetic parameters and trends for production traits of dairy cattle in Thailand using a multiple-trait multiple-lactation test day model

Asian-Australasian Journal of Animal Sciences ◽

10.5713/ajas.19.0141 ◽

2020 ◽

Vol 33 (9) ◽

pp. 1387-1399

Author(s):

Sayan Buaban ◽

Somsook Puangdee ◽

Monchai Duangjinda ◽

Wuttigrai Boonkum

Keyword(s):

Dairy Cattle ◽

Milk Fat ◽

Fixed Effects ◽

Genetic Parameters ◽

Genetic Evaluation ◽

Random Regression ◽

Breeding Value ◽

Production Traits ◽

Linear Quadratic ◽

Multiple Trait

Objective: The objective of this study was to estimate the genetic parameters and trends for milk, fat, and protein yields in the first three lactations of Thai dairy cattle using a 3-trait,- 3-lactation random regression test-day model.Methods: Data included 168,996, 63,388, and 27,145 test-day records from the first, second, and third lactations, respectively. Records were from 19,068 cows calving from 1993 to 2013 in 124 herds. (Co) variance components were estimated by Bayesian methods. Gibbs sampling was used to obtain posterior distributions. The model included herd-year-month of testing, breed group-season of calving-month in tested milk group, linear and quadratic age at calving as fixed effects, and random regression coefficients for additive genetic and permanent environmental effects, which were defined as modified constant, linear, quadratic, cubic and quartic Legendre coefficients.Results: Average daily heritabilities ranged from 0.36 to 0.48 for milk, 0.33 to 0.44 for fat and 0.37 to 0.48 for protein yields; they were higher in the third lactation for all traits. Heritabilities of test-day milk and protein yields for selected days in milk were higher in the middle than at the beginning or end of lactation, whereas those for test-day fat yields were high at the beginning and end of lactation. Genetics correlations (305-d yield) among production yields within lactations (0.44 to 0.69) were higher than those across lactations (0.36 to 0.68). The largest genetic correlation was observed between the first and second lactation. The genetic trends of 305-d milk, fat and protein yields were 230 to 250, 25 to 29, and 30 to 35 kg per year, respectively.Conclusion: A random regression model seems to be a flexible and reliable procedure for the genetic evaluation of production yields. It can be used to perform breeding value estimation for national genetic evaluation in the Thai dairy cattle population.

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