Random regression models to estimate genetic parameters for test-day milk yield in Brazilian Murrah buffaloes

Abstract. Title of the paper: Estimation of genetic parameters for test day results of milk performance in East Friesian milk sheep using Bayesian methods for longitudinal data The objectives of this study were to estimate genetic parameters of milk performance traits in East Friesian milk sheep by using test day models with random regresssion. The analysis was based on 7545 test day records of 918 East Friesian milk sheep with 1380 lactation records. The data were provided by the sheep breeding organizations of Lower-Saxony, Westphalia, and Bavaria. The milk recordings were collected in the years 1992 to 2000. The average values of the heritability estimates by using random regression models were for the milk yield h2 = 0.25 ± 0.03, for the fat content h2 = 0.46 ± 0.09, and for the protein content h2 = 0.63 ± 0.12. The range of heritability estimates in dependence of the days in milk was for milk yield h2 = 0.03 to 0.70, for fat content h2 = 0.30 to 0.70, and for protein content h2 = 0.44 to 0.92.

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Estimation of genetic parameters for first-lactation test-day milk yield in Holstein Friesian cows fitting random regression models

International Journal of Genetics and Molecular Biology ◽

10.5897/ijgmb2015.0111 ◽

2015 ◽

Vol 7 (4) ◽

pp. 25-31

Author(s):

S Meseret ◽

B Tamir ◽

E Negussie

Keyword(s):

Milk Yield ◽

Regression Models ◽

Genetic Parameters ◽

Random Regression ◽

Holstein Friesian ◽

Estimation Of Genetic Parameters ◽

Friesian Cows

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Genetic Parameters for Milk Yield and Lactation Persistency Using Random Regression Models in Girolando Cattle

Asian-Australasian Journal of Animal Sciences ◽

10.5713/ajas.14.0620 ◽

2015 ◽

Vol 28 (10) ◽

pp. 1407-1418 ◽

Cited By ~ 6

Author(s):

Ali William Canaza-Cayo ◽

Paulo Sávio Lopes ◽

Marcos Vinicius Gualberto Barbosa da Silva ◽

Robledo de Almeida Torres ◽

Marta Fonseca Martins ◽

...

Keyword(s):

Milk Yield ◽

Regression Models ◽

Genetic Parameters ◽

Random Regression

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Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling

Revista Brasileira de Zootecnia ◽

10.1590/s1516-35982011000300013 ◽

2011 ◽

Vol 40 (3) ◽

pp. 557-567 ◽

Cited By ~ 12

Author(s):

Jaime Araújo Cobuci ◽

Claudio Napolis Costa ◽

José Braccini Neto ◽

Ary Ferreira de Freitas

Keyword(s):

Multiple Regression ◽

Milk Yield ◽

Regression Models ◽

Genetic Parameters ◽

Genetic Correlations ◽

Information Criterion ◽

Multiple Regression Model ◽

Random Regression ◽

Fixed Curve ◽

Best Fit

Records of test-day milk yields of the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters for milk yield by using two alternatives of definition of fixed regression of the random regression models (RRM). Legendre polynomials of fourth and fifth orders were used to model regression of fixed curve (defined based on averages of the populations or multiple sub-populations formed by grouping animals which calved at the same age and in the same season of the year) or random lactation curves (additive genetic and permanent enviroment). Akaike information criterion (AIC) and Bayesian information criterion (BIC) indicated that the models which used multiple regression of fixed lactation curves of lactation multiple regression model with fixed lactation curves had the best fit for the first lactation test-day milk yields and the models which used a single regression of fixed curve had the best fit for the second and third lactations. Heritability for milk yield during lactation estimates did not vary among models but ranged from 0.22 to 0.34, from 0.11 to 0.21, and from 0.10 to 0.20, respectively, in the first three lactations. Similarly to heridability estimates of genetic correlations did not vary among models. The use of single or multiple fixed regressions for fixed lactation curves by RRM does not influence the estimates of genetic parameters for test-day milk yield across lactations.

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Estimation of genetic parameters for test-day milk yield in Khuzestan buffalo

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2016000700012 ◽

2016 ◽

Vol 51 (7) ◽

pp. 890-897 ◽

Cited By ~ 2

Author(s):

Mostafa Madad ◽

Navid Ghavi Hossein-Zadeh ◽

Abdol Ahad Shadparvar

Keyword(s):

Genetic Correlation ◽

Milk Yield ◽

Environmental Effects ◽

Regression Models ◽

Genetic Parameters ◽

Information Criterion ◽

Step Function ◽

Random Regression ◽

Residual Variance ◽

Covariance Functions

Abstract: The objective of this work was to estimate covariance functions for additive genetic and permanent environmental effects, as well as to obtain genetic parameters for buffalo test-day milk yield using random regression models on Legendre polynomials (LPs). A total of 2,538 test-day milk yield (TDMY) records from 516 first lactation records of Khuzestan buffalo, calving from 1993 to 2009 and belonging to 150 herds located in the state of Khuzestan, Iran, were analyzed. The residual variances were modeled through a step function with 1, 5, 6, 9, and 19 classes. The additive genetic and permanent environmental random effects were modeled by LPs of days in milk using quadratic to septic polynomial functions. The model with additive genetic and animal permanent environmental effects adjusted by cubic and third order LP, respectively, and with the residual variance modeled through a step function with nine classes was the most adequate one to describe the covariance structure. The model with the highest significant log-likelihood ratio test (LRT) and with the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC) was considered to be the most appropriate one. Unexpected negative genetic correlation estimates were obtained between TDMY records of the twenty-fifth and thirty-seventh week (-0.03). Genetic correlation estimates were generally higher, close to unity, between adjacent weeks during the middle of lactation. Random regression models can be used for routine genetic evaluation of milk yield in Khuzestan buffalo.

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