Analyses of growth curves of Nellore cattle by multiple-trait and random regression models

2003 ◽  
Vol 81 (4) ◽  
pp. 918-926 ◽  
Author(s):  
P. R. C. Nobre ◽  
I. Misztal ◽  
S. Tsuruta ◽  
J. K. Bertrand ◽  
L. O. C. Silva ◽  
...  
Keyword(s):  
Regression Models ◽  
Growth Curves ◽  
Random Regression ◽  
Multiple Trait ◽  
Nellore Cattle

scholarly journals Genetic evaluation of growth traits in Nellore cattle through multi-trait and random regression models

2018 ◽  
Vol 63 (No. 6) ◽  
pp. 212-221 ◽  
Author(s):  
B.B. Teixeira ◽  
R.R. Mota ◽  
R.B. Lôbo ◽  
L.P. Silva ◽  
A.P. Souza Carneiro ◽  
...  
Keyword(s):  
Random Effects ◽  
Variance Components ◽  
Regression Models ◽  
Growth Traits ◽  
Growth Curves ◽  
Genetic Evaluation ◽  
Information Criteria ◽  
Random Regression ◽  
Nellore Cattle ◽  
Heritability Estimates

We aimed to evaluate different orders of fixed and random effects in random regression models (RRM) based on Legendre orthogonal polynomials as well as to verify the feasibility of these models to describe growth curves in Nellore cattle. The proposed RRM were also compared to multi-trait models (MTM). Variance components and genetic parameters estimates were performed via REML for all models. Twelve RRM were compared through Akaike (AIC) and Bayesian (BIC) information criteria. The model of order three for the fixed curve and four for all random effects (direct genetic, maternal genetic, permanent environment, and maternal permanent environment) fits best. Estimates of direct genetic, maternal genetic, maternal permanent environment, permanent environment, phenotypic and residual variances were similar between MTM and RRM. Heritability estimates were higher via RRM. We presented perspectives for the use of RRM for genetic evaluation of growth traits in Brazilian Nellore cattle. In general, moderate heritability estimates were obtained for the majority of studied traits when using RRM. Additionally, the precision of these estimates was higher when using RRM instead of MTM. However, concerns about the variance components estimates in advanced ages via Legendre polynomial must be taken into account in future studies.

Genetic evaluation of growth in Nellore cattle by multiple-trait and random regression models

2003 ◽  
Vol 81 (4) ◽  
pp. 927-932 ◽  
Author(s):  
P. R. C. Nobre ◽  
I. Misztal ◽  
S. Tsuruta ◽  
J. K. Bertrand ◽  
L. O. C. Silva ◽  
...  
Keyword(s):  
Regression Models ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Multiple Trait ◽  
Nellore Cattle

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

2009 ◽  
Vol 66 (4) ◽  
pp. 522-528 ◽  
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.

scholarly journals PSI-1 Evaluating genetic parameters and trends of weight traits for the Hays Converter beef breed by multiple-trait and random regression models.

2018 ◽  
Vol 96 (suppl_3) ◽  
pp. 60-61
Author(s):  
R Khorshidi ◽  
M MacNeil ◽  
D Hays ◽  
M Abo-Ismail ◽  
J Crowley ◽  
...  
Keyword(s):  
Regression Models ◽  
Genetic Parameters ◽  
Random Regression ◽  
Multiple Trait ◽  
Beef Breed

scholarly journals Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes

2013 ◽  
Vol 96 (9) ◽  
pp. 5923-5932 ◽  
Author(s):  
Rusbel Raul Aspilcueta Borquis ◽  
Francisco Ribeiro de Araujo Neto ◽  
Fernando Baldi ◽  
Naudin Hurtado-Lugo ◽  
Gregório M.F. de Camargo ◽  
...  
Keyword(s):  
Milk Fat ◽  
Regression Models ◽  
Genetic Parameters ◽  
Protein Yield ◽  
Random Regression ◽  
Multiple Trait ◽  
Estimation Of Genetic Parameters
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