Covariance functions and random regression models for cow weight in beef cattle1

Objective. Covariance functions for additive genetic and permanent environmental effects and, subsequently, genetic parameters for test-day milk (MY), fat (FY) protein (PY) yields and mozzarella cheese (MP) in buffaloes from Colombia were estimate by using Random regression models (RRM) with Legendre polynomials (LP). Materials and Methods. Test-day records of MY, FY, PY and MP from 1884 first lactations of buffalo cows from 228 sires were analyzed. The animals belonged to 14 herds in Colombia between 1995 and 2011. Ten monthly classes of days in milk were considered for test-day yields. The contemporary groups were defined as herd-year-month of milk test-day. Random additive genetic, permanent environmental and residual effects were included in the model. Fixed effects included the contemporary group, linear and quadratic effects of age at calving, and the average lactation curve of the population, which was modeled by third-order LP. Random additive genetic and permanent environmental effects were estimated by RRM using third- to- sixth-order LP. Residual variances were modeled using homogeneous and heterogeneous structures. Results. The heritabilities for MY, FY, PY and MP ranged from 0.38 to 0.05, 0.67 to 0.11, 0.50 to 0.07 and 0.50 to 0.11, respectively. Conclusions. In general, the RRM are adequate to describe the genetic variation in test-day of MY, FY, PY and MP in Colombian buffaloes.Key words: Cattle, genetics, zootechnics (Source: EuroVoc).

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Random regression models for the estimation of genetic and environmental covariance functions for growth traits in Santa Ines sheep

Genetics and Molecular Research ◽

10.4238/gmr.15025749 ◽

2016 ◽

Vol 15 (2) ◽

Author(s):

J.L.R. Sarmento ◽

R.A. Torres ◽

W.H. Sousa ◽

R.N.B. Lôbo ◽

L.G. Albuquerque ◽

...

Keyword(s):

Regression Models ◽

Growth Traits ◽

Random Regression ◽

Covariance Functions ◽

Santa Inês

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0393 Analysis of genetic residual feed intake in Danish Holstein cows by covariance functions using random regression models

Journal of Animal Science ◽

10.2527/jam2016-0393 ◽

2016 ◽

Vol 94 (suppl_5) ◽

pp. 190-191

Author(s):

C. Pfeiffer ◽

B. Li ◽

P. Lovendahl ◽

J. Lassen

Keyword(s):

Feed Intake ◽

Regression Models ◽

Residual Feed Intake ◽

Random Regression ◽

Holstein Cows ◽

Covariance Functions

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Estimates of covariance functions for growth of Anglo-Nubian goats

Revista Brasileira de Zootecnia ◽

10.1590/s1516-35982011000100015 ◽

2011 ◽

Vol 40 (1) ◽

pp. 106-114 ◽

Cited By ~ 2

Author(s):

José Ernandes Rufino de Sousa ◽

Martinho de Almeida e Silva ◽

José Lindenberg Rocha Sarmento ◽

Wandrick Hauss de Sousa ◽

Maria do Socorro Medeiros de Souza ◽

...

Keyword(s):

Bayesian Information Criterion ◽

Regression Models ◽

Genetic Effect ◽

Information Criterion ◽

Random Regression ◽

Additive Genetic Effect ◽

Ratio Test ◽

Residual Variance ◽

Covariance Functions ◽

Heritability Estimates

It was used 4,313 weight records from birth to 196 days of age from 946 Anglo-nubiana breed goats, progenies from 43 sires and 279 dams, controlled in the period from 1980 to 2005, with the objective of estimating covariance functions and genetic parameters of animals by using random regression models. It was evaluated 12 random regression models, with degrees ranging from 1 to 7 for direct additive genetic and maternal and animal permanent environment effect and residual variance adjusted by using animal age ordinary polynomial of third order. Models were compared by using likelihood ratio test and by Bayesian information criterion of Schwarz and Akaike information criterion. The model selected based on Bayesian information criterion was the one that considered the maternal and direct additive genetic effect adjusted by a quadratic polynomial and the animal permanent environmental effect adjusted by a cubic polynomial (M334). Heritability estimates for direct effect were lower in the beginning and at the end of the studied period and maternal heritability estimates were higher at 196 days of age in comparison to the other growth phases. Genetic correlation ranged from moderate to high and they decreased as the distance between ages increased. Higher efficiency in selection for weight can be obtained by considering weights close to weaning, which is a period when the highest estimates of genetic variance and heritability are obtained.

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Covariance Genetic Estimates For Features Tabapuã Growth In Brazil

Nucleus Animalium ◽

10.3738/1982.2278.1580 ◽

2016 ◽

Vol 8 (2) ◽

pp. 5-12

Author(s):

Wéverton José Lima Fonseca ◽

Amauri Felipe Evangelista ◽

Laylson Da Silva Borges ◽

Leandro De Oliveira Guerra ◽

Gleissa Mayone Silva Vogado ◽

...

Keyword(s):

Regression Models ◽

Statistical Correlation ◽

Random Regression ◽

Meat Production ◽

Continuous Processes ◽

Covariance Functions ◽

Quantitative Genetic ◽

Covariance Components ◽

Analysis Models ◽

Growth Of Animals

This review aimed to estimate covariance components for Tabapuã the growth characteristics in Brazil, using random regression models. The random regression models (MRA) make it possible to estimate coefficients of the covariance functions by the method of restricted maximum likelihood. These are suitable for longitudinal data analysis models, because of the deficiencies of conventional methods of quantitative genetic analysis, which are considered phenotypic values inherently continuous processes, as discrete processes. The covariance functions show the statistical correlation between two features of a trajectory at different points of same, ie that present in the arrangement have the same direction and magnitude. With the current concern of cutting bovinocultores regarding the growth of animals, the use of methods or techniques that provide increasingly accurate assessments of genetic parameters to be used in studying the development of animals for meat production is relevant

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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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Genetic parameters for post weaning growth of Nellore cattle using polinomyals and trigonometric functions in random regression models

Scientia Agricola ◽

10.1590/s0103-90162009000400014 ◽

2009 ◽

Vol 66 (4) ◽

pp. 522-528 ◽

Cited By ~ 2

Author(s):

Osmar Jesus Macedo ◽

Décio Barbin ◽

Gerson Barreto Mourão

Keyword(s):

Regression Models ◽

Growth Curves ◽

Information Criterion ◽

The Other ◽

Random Regression ◽

Trigonometric Functions ◽

Coefficient Estimates ◽

Covariance Functions ◽

Nellore Cattle ◽

Selection Of

Covariance functions and random regression models have been considered as an alternative for data adjustment, in sequence, stemming from the same animal along time and which presents a structured pattern of covariance. Aiming to evaluate the performance of random regression models based on the Legendre, modified Jacobi and trigonometric functions, data concerning the weights of Nellore breed animals were used from birth to the 800th day of life, in models that assumed direct additive and animal permanent environmental effects coefficients. The Schwarz Bayesian information criterion (BIC) led to the selection of the models Legendre of order six (ML6), Jacobi of order five (MJ5) and trigonometric of order six (MT6), the ML6 model presenting the lowest BIC. At the extremity of the interval, the MJ5 model presented lower variance of component estimates than those obtained through the ML6 model, however the estimates were in accordance to the medium part of the interval; while the estimates from the MT6 model were oscillating and different from those obtained through the other models. At the extremity of the interval, the heritability coefficient estimates (<img src="/img/revistas/sa/v66n4/h4_circ.gif" align="absmiddle">2) obtained through the MJ5 model were lower than those obtained through the ML6 model, however, in the medium part of the interval, they were in accordance, remaining between 0.2 and 0.3. The values obtained through the MT6 model were different from those obtained through the other models, remaining between 0.35 and 0.40 on the first 285th days and then dropping to 0.01 on the 800th days of life. The means of the estimated growth curves started to distance from the data mean tendency from the 470th days on, and in this interval, the MT6 model was the most suitable.

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