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 Genetic evaluation of milk yield in Alpine goats for the first four lactations using random regression models

2014 ◽  
Vol 13 (4) ◽  
pp. 10943-10951
Author(s):  
F.G. Silva ◽  
R.A. Torres ◽  
L.P. Silva ◽  
H.T. Ventura ◽  
F.F. Silva ◽  
...  
Keyword(s):  
Milk Yield ◽  
Regression Models ◽  
Genetic Evaluation ◽  
Random Regression

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.

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

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 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 Variance component estimates applying random regression models for test-day milk yield in Caracu heifers (Bos taurus Artiodactyla, Bovidae)

2008 ◽  
Vol 31 (3) ◽  
pp. 665-673 ◽  
Author(s):  
Lenira El Faro ◽  
Vera Lucia Cardoso ◽  
Lucia Galvão de Albuquerque
Keyword(s):  
Milk Yield ◽  
Regression Models ◽  
Variance Component ◽  
Bos Taurus ◽  
Random Regression ◽  
Variance Component Estimates

scholarly journals Genetic evaluation of growth traits in beef cattle using random regression models

2012 ◽  
Vol 42 (5) ◽  
Author(s):  
FWC Neser ◽  
JB Van Wyk ◽  
MD Fair ◽  
P Lubout
Keyword(s):  
Beef Cattle ◽  
Regression Models ◽  
Growth Traits ◽  
Genetic Evaluation ◽  
Random Regression

scholarly journals Test-day or 305-day milk yield for genetic evaluation of Gir cattle

2019 ◽  
Vol 54 ◽  
Author(s):  
Rodrigo Junqueira Pereira ◽  
Denise Rocha Ayres ◽  
Mário Luiz Santana Junior ◽  
Lenira El Faro ◽  
Aníbal Eugênio Vercesi Filho ◽  
...  
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Breeding Values ◽  
Random Regression Model ◽  
The Mean ◽  
Yield Record ◽  
Genetic Evaluations ◽  
Dairy Breed

Abstract: The objective of this work was to compare genetic evaluations of milk yield in the Gir breed, in terms of breeding values and their accuracy, using a random regression model applied to test-day records or the traditional model (TM) applied to estimates of 305-day milk yield, as well as to predict genetic trends for parameters of interest. A total of 10,576 first lactations, corresponding to 81,135 test-day (TD) records, were used. Rank correlations between the breeding values (EBVs) predicted with the two models were 0.96. The percentage of animals selected in common was 67 or 82%, respectively, when 1 or 5% of bulls were chosen, according to EBVs from random regression model (RRM) or TM genetic evaluations. Average gains in accuracy of 2.7, 3.0, and 2.6% were observed for all animals, cows with yield record, and bulls (sires of cows with yield record), respectively, when the RRM was used. The mean annual genetic gain for 305-day milk yield was 56 kg after 1993. However, lower increases in the average EBVs were observed for the second regression coefficient, related to persistency. The RRM applied to TD records is efficient for the genetic evaluation of milk yield in the Gir dairy breed.

scholarly journals Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil

2012 ◽  
Vol 150 (1-3) ◽  
pp. 401-406 ◽  
Author(s):  
A.B. Bignardi ◽  
L. El Faro ◽  
M.L. Santana ◽  
G.J.M. Rosa ◽  
V.L. Cardoso ◽  
...  
Keyword(s):  
Bayesian Analysis ◽  
Milk Yield ◽  
Model Test ◽  
Regression Models ◽  
Random Regression ◽  
Holstein Cattle ◽  
B Splines

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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