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 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.

scholarly journals Genetic evaluation for large data sets by random regression models in Nellore cattle

2009 ◽  
Vol 61 (4) ◽  
pp. 959-967
Author(s):  
P.R.C. Nobre ◽  
A.N. Rosa ◽  
L.O.C. Silva
Keyword(s):  
Large Data ◽  
Genetic Evaluation ◽  
Large Data Sets ◽  
Random Regression ◽  
Data Sets ◽  
Multiple Trait ◽  
Contemporary Group ◽  
Random Regression Model ◽  
Nellore Cattle ◽  
Covariance Components

Expected progeny differences (EPD) of Nellore cattle estimated by random regression model (RRM) and multiple trait model (MTM) were compared. Genetic evaluation data included 3,819,895 records of up nine sequential weights of 963,227 animals measured at ages ranging from one day (birth weight) to 733 days. Traits considered were weights at birth, ten to 110-day old, 102 to 202-day old, 193 to 293-day old, 283 to 383-day old, 376 to 476-day old, 551 to 651-day old, and 633 to 733-day old. Seven data samples were created. Because the parameters estimates biologically were better, two of them were chosen: one with 84,426 records and another with 72,040. Records preadjusted to a fixed age were analyzed by a 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 REML, with five traits at a time. The RRM included the effects of age of animal, contemporary group, age of dam class, additive direct, permanent environment, additive maternal, and maternal permanent environment. Different degree of Legendre polynomials were used to describe random effects. MTM estimated covariance components and genetic parameters for weight at birth and sequential weights and RRM for all ages. Due to the fact that correlation among the estimates EPD from MTM and all the tested RM were not equal to 1.0, it is not possible to recommend RRM to genetic evaluation to large data sets.

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 Multiple trait and random regression models using linear splines for genetic evaluation of multiple breed populations

2019 ◽  
Vol 102 (1) ◽  
pp. 464-475 ◽  
Author(s):  
V.M.P. Ribeiro ◽  
F.S.S. Raidan ◽  
A.R. Barbosa ◽  
M.V.G.B. Silva ◽  
F.F. Cardoso ◽  
...  
Keyword(s):  
Regression Models ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Multiple Trait ◽  
Linear Splines

Genetic evaluation of weekly body weight in Japanese quail using random regression models

2016 ◽  
Vol 58 (1) ◽  
pp. 13-18 ◽  
Author(s):  
K. Karami ◽  
S. Zerehdaran ◽  
M. Tahmoorespur ◽  
B. Barzanooni ◽  
E. Lotfi
Keyword(s):  
Body Weight ◽  
Japanese Quail ◽  
Regression Models ◽  
Genetic Evaluation ◽  
Random Regression

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 Genetic evaluation and selection response for growth in meat-type quail through random regression models using B-spline functions and Legendre polynomials

animal ◽  
2018 ◽  
Vol 12 (4) ◽  
pp. 667-674 ◽  
Author(s):  
L.F.M. Mota ◽  
P.G.M.A. Martins ◽  
T.O. Littiere ◽  
L.R.A. Abreu ◽  
M.A. Silva ◽  
...  
Keyword(s):  
Legendre Polynomials ◽  
Regression Models ◽  
Selection Response ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Spline Functions ◽  
B Spline ◽  
Meat Type
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