scholarly journals Estimation of (co)variance components and genetic parameters for weights of red-winged tinamou using random regression models

2011 ◽  
Vol 40 (4) ◽  
pp. 781-787 ◽  
Author(s):  
P. Tholon ◽  
S.A. Queiroz
Keyword(s):  
Body Weight ◽  
Environmental Effects ◽  
Regression Models ◽  
Genetic Parameters ◽  
Genetic Effect ◽  
Information Criterion ◽  
Random Regression ◽  
Additive Genetic Effect ◽  
First Eigenvalue ◽  
Permanent Effect

The objective of this work was to determine genetic parameters for body weight of tinamou in captivity. It was used random regression models in analyses of data by considering the direct additive genetic (DA) and permanent environmental effects of the animal (PE) as random effects. Residual variances were modeled by using a fifth-order variance function. The mean population growth curve was fitted by sixth-order Legendre orthogonal polynomials. Direct additive genetic effects and animal environmental permanent effect were modeled by using Legendre polynomials of order two to nine. The best results were obtained by models with orders of fit of 6 for direct additive genetic effect and of order 3 for permanent effect by Akaike information criterion and of order 3 for both additive genetic effect and permanent effect by Schwarz Bayesian information criterion and likelihood ratio test. Heritability estimates ranged from 0.02 to 0.57. The first eigenvalue explained 94% and 90% of the variation from additive direct and permant environmental effects, respectively. Selection of tinamou for body weight is more effective after 112 days of age.

scholarly journals Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

2014 ◽  
Vol 49 (5) ◽  
pp. 372-383 ◽  
Author(s):  
Maria Gabriela Campolina Diniz Peixoto ◽  
Daniel Jordan de Abreu Santos ◽  
Rusbel Raul Aspilcueta Borquis ◽  
Frank Ângelo Tomita Bruneli ◽  
João Cláudio do Carmo Panetto ◽  
...  
Keyword(s):  
Milk Production ◽  
Legendre Polynomial ◽  
Legendre Polynomials ◽  
Regression Models ◽  
Environmental Effect ◽  
Genetic Parameters ◽  
Genetic Effect ◽  
Random Regression ◽  
Additive Genetic Effect ◽  
Fifth Order

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

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 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 Estimation of direct and maternal effects on body weight in Mehraban sheep using random regression models

2008 ◽  
Vol 51 (3) ◽  
pp. 235-246
Author(s):  
F. G. Kesbi ◽  
M. Eskandarinasab ◽  
M. H. Shahir
Keyword(s):  
Body Weight ◽  
Environmental Effects ◽  
Fixed Effects ◽  
Regression Models ◽  
Error Variance ◽  
Random Regression ◽  
Growth Data ◽  
Measurement Error Variance ◽  
Heritability Estimates ◽  
Additive Genetic Effects

Abstract. In the present study the growth data of Mehraban sheep were used to estimate direct and maternal additive genetic effects together with direct and maternal permanent environmental effects on body weight from birth to 270 days of age using random regression models. The fixed effects of the model were age of dam, type of birth and contemporary groups. Animal, dam, animal and maternal permanent environmental effects were considered as random effects. The models were fitted to the data using Legendre polynomials for age of lambs. Changes in residual (measurement error) variance with age were modeled by a variance function. Direct heritability estimates for the later ages with the least records tended to be overestimated, particularly heritability beyond 180 days. Maternal heritability estimates increased after birth to a maximum around 120 days of age and decreased thereafter. The results showed that covariance between weights of lambs for a considerable range of ages can be modelled properly using random regression.

Genetic parameters for body weight in buffaloes (Bubalus bubalis) in Colombia using random regression models

2013 ◽  
Vol 158 (1-3) ◽  
pp. 40-49 ◽  
Author(s):  
D.M. Bolívar ◽  
M.F. Cerón-Muñoz ◽  
A.A. Boligon ◽  
M.A. Elzo ◽  
A.C. Herrera
Keyword(s):  
Body Weight ◽  
Regression Models ◽  
Genetic Parameters ◽  
Bubalus Bubalis ◽  
Random Regression

scholarly journals Random regression models with B-splines to estimate genetic parameters for body weight of young bulls in performance tests

2018 ◽  
Vol 47 (0) ◽  
Author(s):  
Daiane Cristina Becker Scalez ◽  
Breno de Oliveira Fragomeni ◽  
Dalinne Chrystian Carvalho dos Santos ◽  
Tiago Luciano Passafaro ◽  
Maurício Mello de Alencar ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Models ◽  
Genetic Parameters ◽  
Random Regression ◽  
Performance Tests ◽  
B Splines

scholarly journals Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling

2011 ◽  
Vol 40 (3) ◽  
pp. 557-567 ◽  
Author(s):  
Jaime Araújo Cobuci ◽  
Claudio Napolis Costa ◽  
José Braccini Neto ◽  
Ary Ferreira de Freitas
Keyword(s):  
Multiple Regression ◽  
Milk Yield ◽  
Regression Models ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Information Criterion ◽  
Multiple Regression Model ◽  
Random Regression ◽  
Fixed Curve ◽  
Best Fit

Records of test-day milk yields of the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters for milk yield by using two alternatives of definition of fixed regression of the random regression models (RRM). Legendre polynomials of fourth and fifth orders were used to model regression of fixed curve (defined based on averages of the populations or multiple sub-populations formed by grouping animals which calved at the same age and in the same season of the year) or random lactation curves (additive genetic and permanent enviroment). Akaike information criterion (AIC) and Bayesian information criterion (BIC) indicated that the models which used multiple regression of fixed lactation curves of lactation multiple regression model with fixed lactation curves had the best fit for the first lactation test-day milk yields and the models which used a single regression of fixed curve had the best fit for the second and third lactations. Heritability for milk yield during lactation estimates did not vary among models but ranged from 0.22 to 0.34, from 0.11 to 0.21, and from 0.10 to 0.20, respectively, in the first three lactations. Similarly to heridability estimates of genetic correlations did not vary among models. The use of single or multiple fixed regressions for fixed lactation curves by RRM does not influence the estimates of genetic parameters for test-day milk yield across lactations.

scholarly journals Estimation of genetic parameters for body weight traits in Mazandaran native breeder hens by random regression models

Genetika ◽  
2019 ◽  
Vol 51 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Yousef Naderi
Keyword(s):  
Body Weight ◽  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Information Criterion ◽  
The Body ◽  
Random Regression ◽  
Likelihood Method ◽  
Primary Concern ◽  
Environmental Correlations

The primary concern of this study is to investigate appropriate random regression model for estimate genetic parameters body weight at hatch (BW1), eight (BW8), twelve (BW12) and thirty two (BW32) weeks of ages by the restricted maximum likelihood method. The body weight records set included 39872 during 16 generations of hens kept at the Mazandaran Breeding Center of Iran. Random regression were modelled using generation-hatch as a fixed effect and additive genetic and permanent environmental effects as random effects Residual variances were modeled through a step function with 1 and 3 classes. The model was considered to be the most appropriate with the highest significant log likelihood ratio test (LRT) and the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC). Heritability values increased from 0.21 for BW1, to 0.40 for BW32. Genetic correlations of body weight at different record keeping were often higher than permanent environmental correlations. Genetic correlations between pairs of body weight measures were moderate to high with a range from 0.25 to 0.97. The largest genetic correlation, as well as permanent environmental correlation, was observed between BW12and BW32. High and moderate broad sense heritability values for all studied traits shows that these traits are less influenced by residual effects which make them effectively transmitted to the progeny. Findings show that genetic improvement for body weight can be achieved by selection. The Heritability of body weight at thirty two weeks of ages and its relatively high genetic correlation with all other ages showed that it could be the most appropriate period for selection. Also, the genetic trend estimates for body weight traits showed that selection decisions made during the breeding program effectively improved the growth performance.

scholarly journals Genetic analysis of weight, fat and muscle depth in growing lambs using random regression models

2006 ◽  
Vol 82 (1) ◽  
pp. 13-22 ◽  
Author(s):  
T. M. Fischer ◽  
J. H. J. van der Werf ◽  
R. G. Banks ◽  
A. J. Ball ◽  
A. R. Gilmour
Keyword(s):  
Orthogonal Polynomials ◽  
Environmental Effects ◽  
Repeated Measures ◽  
Regression Models ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Error Variance ◽  
Random Regression ◽  
Eye Muscle ◽  
Management Group

AbstractGenetic parameters were estimated using uni- and bi-variate random regression models for weight, eye-muscle depth and fat depth measures between 60 and 360 days of age. Each trait was measured up to five times in 50-day intervals following weaning on approximately 4000 Australian Poll Dorset Sheep. The model accounted for rearing type, dam age, management group and age of recording. The model used for analysing weight included quadratic, orthogonal polynomials for direct genetic and environmental effects, a linear polynomial for maternal genetic effects and heterogeneous error variance across ages. The fat and muscle analysis used linear orthogonal polynomials for direct genetic and environmental effects and heterogeneous error variance. Throughout the 300-day trajectory heritability for weight traits ranged from 0·20 to 0·31, while heritability for fat depth ranged from 0·24 to 0·34 and heritability for eye-muscle depth ranged from 0·24 to 0·40. Genetic correlations between repeated measures of the same trait at different ages were positive and declined as the age interval increased, to minimum values of 0·60, 0·31 and 0·50 for weight, fat and muscle respectively between 60 and 360 days of age. Genetic correlations between weight and fat and weight and eye muscle were moderate to high (0·6 to 0·8) and positive but decreased slightly with age. The genetic correlations between fat and muscle were moderate to high (0·5 to 0·7) throughout the 300-day trajectory. In all cases, the estimates produced in this study were reasonably consistent with the limited number of studies that exist in the reported literature. This study demonstrated the relationships that exist between repeated measures of weight, fat and muscle measures over time, which is of interest to prime lamb producers looking to select for specific breeding objectives or market end points requiring precise weight, fat and muscle combinations at certain ages.

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