Random regression models in the evaluation of the growth curve of Simbrasil beef cattle

Abstract. Title of the paper: Index construction with restrictions in random regression models to change the pattern of the growth curve Random regression models provide estimated breeding values (EBV) for the complete growth curve for any target age. The animal-specific curves can be described as the weighted sum of continuous covariates with random regression coefficients. By using the covariance matrix K of the additive genetic regression coefficients the response to index selection can be calculated for any age or time of the test period. In this study selection indexes with equality restrictions based on the eigenvectors of matrix K were used to modify the growth curve of the population. In order to demonstrate the index construction a matrix K was used, estimated from repeated measurements for body weight of bulls by using Legendre polynomials as covariates. Indexes for high and low growth rate until age at the reflection point were derived subject to the restriction of zero gain for initial and final body weight. Selection strategies for improving body weight at the end of the test period while holding the daily gain in a certain time interval on a desired level were compared. By using so-called "restrictive economic values", an aggregate breeding value for body weight was derived from EBV for individual growth curve.

Download Full-text

APPLICATION OF RANDOM REGRESSION MODELS FOR GROWTH TRAITS OF NELLORE CATTLE IN BRAZIL

Nucleus Animalium ◽

10.3738/1982.2278.1657 ◽

2016 ◽

Vol 8 (2) ◽

pp. 45-54

Author(s):

Wéverton José Lima Fonseca ◽

Amauri Felipe Evangelista ◽

Laylson Da Silva Borges ◽

Gleissa Mayone Silva Vogado ◽

Carlos Syllas Monteiro Luz ◽

...

Keyword(s):

Longitudinal Data ◽

Beef Cattle ◽

Regression Models ◽

Growth Traits ◽

Standard Procedure ◽

Random Regression ◽

Nellore Cattle ◽

Cattle Breeders ◽

Estimate Variance ◽

Genetic Evaluations

The purpose of this review is to show the increase in number of researches on covariance components and genetic evaluation using random regression models (RRM) for growth traits of Nellore cattle. Random regression models, also known as infinite-dimension models have been used to estimate variance components and genetic parameters for weight of beef cattle. In addition, those models are a standard alternative for genetic analyses of longitudinal data, however, the availibility of computational resources for performing genetic evaluations widely is an obstacle. Traits related to animal growth are adopted as selection criteria in beef cattle breeding programs, because the remuneration of cattle breeders is made based on the weight of carcasses. In recent years, RRM have been adopted as standard procedure in relation to the analysis of longitudinal data in animal breeding.

Download Full-text

Random regression models to explore genetic variation and genetic variability in the growth curve of Baluchi lambs

Meta Gene ◽

10.1016/j.mgene.2018.09.011 ◽

2018 ◽

Vol 18 ◽

pp. 195-201 ◽

Cited By ~ 1

Author(s):

Farhad Ghafouri-Kesbi ◽

Mohsen Gholizadeh

Keyword(s):

Genetic Variation ◽

Genetic Variability ◽

Growth Curve ◽

Regression Models ◽

Random Regression

Download Full-text

Weight evaluation of Tabapuã cattle raised in northeastern Brazil using random-regression models

Arquivo Brasileiro de Medicina Veterinária e Zootecnia ◽

10.1590/1678-4162-9070 ◽

2017 ◽

Vol 69 (2) ◽

pp. 457-464 ◽

Cited By ~ 3

Author(s):

M.R. Oliveira ◽

D.M. Azevêdo ◽

C. Malhado ◽

L. Pires ◽

R. Martins Filho ◽

...

Keyword(s):

Birth Weight ◽

Regression Model ◽

Growth Curve ◽

Regression Models ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Northeastern Brazil ◽

Random Regression ◽

Phenotypic Correlation ◽

Random Regression Model

ABSTRACT The objective of this study is to compare random-regression models used to describe changes in evaluation parameters for growth in Tabapuã bovine raised in the Northeast of Brazilian. The M4532-5 random-regression model was found to be best for estimating the variation and heritability of growth characteristics in the animals evaluated. Estimates of direct additive genetic variance increased with age, while the maternal additive genetic variance demonstrated growth from birth to up to nearly 420 days of age. The genetic correlations between the first four characteristics were positive with moderate to large ranges. The greatest genetic correlation was observed between birth weight and at 240 days of age (0.82). The phenotypic correlation between birth weight and other characteristics was low. The M4532-5 random-regression model with 39 parameters was found to be best for describing the growth curve of the animals evaluated providing improved selection for heavier animals when performed after weaning. The interpretation of genetic parameters to predict the growth curve of cattle may allow the selection of animals to accelerate slaughter procedures.

Download Full-text

Modeling of average growth curve in Santa Ines sheep using random regression models

Revista Brasileira de Zootecnia ◽

10.1590/s1516-35982011000200012 ◽

2011 ◽

Vol 40 (2) ◽

pp. 314-322 ◽

Cited By ~ 4

Author(s):

José Lindenberg Rocha Sarmento ◽

Robledo de Almeida Torres ◽

Wandrick Hauss de Sousa ◽

Lucia Galvão de Albuquerque ◽

Raimundo Nonato Braga Lôbo ◽

...

Keyword(s):

Growth Curve ◽

Regression Models ◽

Random Regression ◽

Average Growth ◽

Breeding Values ◽

Cubic Polynomial ◽

Linear Fit ◽

Santa Inês ◽

The Difference ◽

Cubic Function

Polynomial functions of age of different orders were evaluated in the modeling of the average growth trajectory in Santa Ines sheep in random regression models. Initially, the analyses were performed not considering the animal effect. Subsequently, the random regression analyses were performed including the random effects of the animal and its mother (genetic and permanent environment). The linear fit was lower, and the other orders were similar until near 100 days of age. The cubic function provided the closest fit of the observed averages, mainly at the end of the curve. Orders superior to this one tended to present incoherent behavior with the observed weights. The estimated direct heritabilities, considering the linear fit, were higher to those estimated by considering other functions. The changes in animal ranking based on predicted breeding values using linear fit and superior orders were small; however, the difference in magnitude of the predicted breeding values was higher, reaching values 77% higher than those obtained with the cubic function. The cubic polynomial function is efficient in describing the average growth curve.

Download Full-text