scholarly journals Covariance Genetic Estimates For Features Tabapuã Growth In Brazil

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

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

2004 ◽  
Vol 82 (1) ◽  
pp. 54-67 ◽  
Author(s):  
J. A. Arango ◽  
L. V. Cundiff ◽  
L. D. Van Vleck
Keyword(s):  
Regression Models ◽  
Random Regression ◽  
Covariance Functions

Estimates of covariance functions for growth of Kordi sheep in Iran using random regression models

2018 ◽  
Vol 162 ◽  
pp. 69-76 ◽  
Author(s):  
Davoud Ali Saghi ◽  
Ali Reza Shahdadi ◽  
Fatemeh Kazemi Borzelabad ◽  
Kourosh Mohammadi
Keyword(s):  
Regression Models ◽  
Random Regression ◽  
Covariance Functions

Genetic evaluation using random regression models with different covariance functions for test-day milk yield in an admixture population of Thailand goats

2014 ◽  
Vol 86 (7) ◽  
pp. 655-660 ◽  
Author(s):  
Mongkol Thepparat ◽  
Wuttigrai Boonkum ◽  
Monchai Duangjinda ◽  
Sornthep Tumwasorn ◽  
Sansak Nakavisut ◽  
...  
Keyword(s):  
Milk Yield ◽  
Regression Models ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Covariance Functions ◽  
Admixture Population

scholarly journals Random regression models for milk, fat and protein in Colombian Buffaloes

2015 ◽  
pp. 4415-4426
Author(s):  
Naudin Hurtado-Lugo ◽  
Humberto Tonhati ◽  
Raul Aspilcuelta-Borquis ◽  
Cruz Enríquez-Valencia ◽  
Mario Cerón-Muñoz
Keyword(s):  
Environmental Effects ◽  
Milk Fat ◽  
Fixed Effects ◽  
Regression Models ◽  
Random Regression ◽  
Residual Effects ◽  
Covariance Functions ◽  
Lp Residual ◽  
Quadratic Effects ◽  
Residual Variances

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).

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

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

scholarly journals Estimates of covariance functions for growth of Anglo-Nubian goats

2011 ◽  
Vol 40 (1) ◽  
pp. 106-114 ◽  
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.

scholarly journals Analyses of growth curves of Nellore cattle by Bayesian method via Gibbs sampling

2003 ◽  
Vol 55 (4) ◽  
pp. 480-490 ◽  
Author(s):  
P.R.C. Nobre ◽  
P.S. Lopes ◽  
R.A. Torres ◽  
L.O.C. Silva ◽  
A.J. Regazzi ◽  
...  
Keyword(s):  
Birth Weight ◽  
Bayesian Method ◽  
Growth Curves ◽  
Random Regression ◽  
Multiple Trait ◽  
Covariance Functions ◽  
Contemporary Group ◽  
Random Regression Model ◽  
Nellore Cattle ◽  
Covariance Components

Growth curves of Nellore cattle were analyzed using body weights measured at ages ranging from 1 day (birth weight) to 733 days. Traits considered were birth weight, 10 to 110 days weight, 102 to 202 days weight, 193 to 293 days weight, 283 to 383 days weight, 376 to 476 days weight, 551 to 651 days weight, and 633 to 733 days weight. Two data samples were created: one with 79,849 records from herds that had missing traits and another with 74,601 from herds with no missing traits. Records preadjusted to a fixed age were analyzed by a multiple trait model (MTM), which included the effects of contemporary group, age of dam class, additive direct, additive maternal, and maternal permanent environment. Analyses were carried out by a Bayesian method for all nine traits. The random regression model (RRM) included the effects of age of animal, contemporary group, age of dam class, additive direct, permanent environment, additive maternal, and maternal permanent environment. Legendre cubic polynomials were used to describe random effects. MTM estimated covariance components and genetic parameters for birth weight and sequential weights and RRM for all ages. Due to the fact that covariance components based on RRM were inflated for herds with missing traits, MTM should be used and converted to covariance functions.

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

2016 ◽  
Vol 51 (7) ◽  
pp. 890-897 ◽  
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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