Estimates of covariance functions for growth of Anglo-Nubian goats

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.

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

Revista Brasileira de Zootecnia ◽

10.1590/s1516-35982011000400011 ◽

2011 ◽

Vol 40 (4) ◽

pp. 781-787 ◽

Cited By ~ 2

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.

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Estimates of (co)variance function for growth to yearling in Horro sheep of Ethiopia using random regression model

Archives Animal Breeding ◽

10.5194/aab-53-689-2010 ◽

2010 ◽

Vol 53 (6) ◽

pp. 689-700

Author(s):

S. Abegaz ◽

J. B. van Wyk ◽

J. J. Olivier

Keyword(s):

Fixed Effects ◽

Model Comparison ◽

Information Criterion ◽

Coefficient Matrix ◽

Random Regression ◽

Ratio Test ◽

Residual Variance ◽

Genetic Covariance ◽

Heritability Estimates ◽

One Year

Abstract. Random regression analyses of weight data from birth to 396 days were done using 22 141 weight records of 1 951 Horro lambs. Six different models formed from three different orthogonal polynomial regressions (legendre scale)orders (quadratic, cubic, quartic) of fit for both additive genetic and animals’ permanent environmental effects, with assumption of either homogeneous or heterogeneous residual variance, were compared. Fixed effects of year and type of birth, sex and age of dam were fitted along with a fourth order polynomial. Both likelihood ratio test (LRT) and Akaike's Information Criterion (AIC) were used for model comparison. Model fit improved with increased order of polynomial and assumption of heterogeneity of residual variance. Components for additive genetic and permanent environmental (co)variance increased from 0.03 and 0.09 at birth to 23.8 and 37.6 at 396 days of age, respectively. The first three eigenvalues of the coefficient matrix of the additive genetic covariance accounted for about 98 % of the sum of all the eigenvalues. Heritability estimates have shown a declining and increasing trend at different parts of the trajectory, the lowest estimate being 0.14 for weight at birth while the highest being 0.36 for weight at about 390 days of age. Higher heritability estimates in previous uni- and bi-variate models and in the current study and also strong correlation with weight at early age makes weight at one year of age the most important trait to consider in improving productivity in Horro sheep.

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

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2016000700012 ◽

2016 ◽

Vol 51 (7) ◽

pp. 890-897 ◽

Cited By ~ 2

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

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2014000500007 ◽

2014 ◽

Vol 49 (5) ◽

pp. 372-383 ◽

Cited By ~ 4

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.

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Estimates of (co)variance components and genetic parameters for growth traits in Suffolk lambs

Ciência Rural ◽

10.1590/s0103-84782013001200016 ◽

2013 ◽

Vol 43 (12) ◽

pp. 2215-2220 ◽

Cited By ~ 6

Author(s):

Priscilla Regina Tamioso ◽

Jaime Luiz Alberti Filho ◽

Laila Talarico Dias ◽

Rodrigo de Almeida Teixeira

Keyword(s):

Maternal Effects ◽

Variance Components ◽

Genetic Parameters ◽

Growth Traits ◽

Genetic Effect ◽

Additive Genetic Effect ◽

Ratio Test ◽

Effect Model ◽

Estimate Heritability ◽

Heritability Estimates

The study aimed to estimate the components of (co)variance and heritability for weights at birth (BW), weaning (WW) and 180 days of age (W180), as well as the average daily gains from birth to weaning (ADG1), birth to 180 days of age (ADG2) and weaning to 180 days of age (ADG3) in Suffolk sheep. Thus, three different single-trait animal models were fitted, considering the direct additive genetic effect (Model 1), the direct additive genetic and maternal permanent environmental effects (Model 2), and in Model 3, in addition to those in Model 2, the maternal additive genetic effect was included. After comparing models through the likelihood ratio test (LRT), model 3 was chosen as the most appropriate to estimate heritability for BW, WW and ADG1. Model 2 was considered as the best to estimate the coefficient of heritability for W180 and ADG2, and model 1 for ADG3. Direct heritability estimates were inflated when maternal effects were ignored. According to the most suitable models, the heritability estimates for BW, WW, W180, ADG1, ADG2 and ADG3 were 0.06, 0.08, 0.09, 0.07, 0.08 and 0.07, respectively, indicating low possibility of genetic gain through individual selection. The results show the importance of including maternal effects in the models to properly estimate genetic parameters even at post-weaning ages.

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Genetic parameters for growth of young beef bulls

Archives Animal Breeding ◽

10.5194/aab-55-245-2012 ◽

2012 ◽

Vol 55 (3) ◽

pp. 245-254

Author(s):

L. Vostrý ◽

Z. Veselá ◽

J. Přibyl

Keyword(s):

Fixed Effects ◽

Genetic Effect ◽

Information Criterion ◽

Ratio Test ◽

Residual Variance ◽

Multiple Trait ◽

Environment Effect ◽

Body Weights ◽

Direct Genetic Effect ◽

Maternal Heritability

Abstract. The average daily gains of young bulls on test stations (ADGT) were analysed for the most frequent breeds of beef cattle in the Czech Republic using a multiple-trait animal model. Body weights at birth (W0), at 120 days of age (W120) and at weaning at 210 days (WW) were considered in this model as pre-weaning growth. The tested models comprised some of the random effects: direct genetic effect, maternal genetic effect, permanent animal environment effect, permanent maternal environment effect, and some of the fixed effects: dam’s age, sex, herd-year-season, linear and quadratic regression on age at the beginning of the test. For optimization of the models Akaike information criterion (AIC), residual variance and likelihood ratio test were used. Coefficients of direct and maternal heritability across breeds of about 0.25 for W0, about 0.17 for W120, about 0.17 for WW and about 0.29 for ADGT were estimated by all models. All criteria selected models including the permanent animal environment effect, which was the most important effect in the model.

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Sensitivity of breeding values of meat quail as a result of tryptophan: lysine ratios in the diet

Revista Brasileira de Saúde e Produção Animal ◽

10.1590/s1519-99402018000400005 ◽

2018 ◽

Vol 19 (4) ◽

pp. 403-414

Author(s):

Rúbia Francielle Moreira Rodrigues ◽

Mariele Freitas Sousa ◽

Valdecy Aparecida Rocha Cruz ◽

Thaiza da Silva Campideli ◽

Leonardo da Silva Costa ◽

...

Keyword(s):

Genetic Effect ◽

Genotype X Environment Interaction ◽

Reaction Norms ◽

Random Regression ◽

Additive Genetic Effect ◽

Residual Variance ◽

Environment Interaction ◽

Breeding Values ◽

Genotype X Environment ◽

Best Fit

SUMMARY We aimed to evaluate the random regression models that promote the best fit of residual variance predicting the breeding values of quail body weights and the sensitivity of its breeding values to the variations of different tryptophan:lysine ratios in the diets via reaction norms. A total of 1112 meat quails from LF1 and LF2 lines with 35 days of age were evaluated. During the period of 1 to 21 days of age, birds were fed with different tryptophan:lysine ratios (0.17, 0.20, 0.23, 0.26 and 0.29%) containing 2900 kcal ME/kg and 26.10% crude protein, followed by basal diet provided up to 35 days. The best model fit for residual variance was evaluated comparing heterogeneity (2, 3 and 4 classes) and homogeneity (1 class), including sex as fixed effect and the additive genetic effect as random. The second order Legendre polynomial was used to analyze the genotype x environment interaction using reaction norms. The model considering two classes of residual variance was the one that promoted the best fit of the data, being adopted to predict the breeding values. Thus, we observed changes in the sensitivity of the breeding values, characterized by the rearrangement of the breeding values, according to the different ratios of amino acids, suggesting the genotype x environment interaction.

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

Scientia Agricola ◽

10.1590/s0103-90162009000400014 ◽

2009 ◽

Vol 66 (4) ◽

pp. 522-528 ◽

Cited By ~ 2

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.

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Genetic-Quantitative Uni- and Bi-Trait Analysis for Growth Traits in the Colombian Creole Breed Blanco Orejinegro (BON)

10.21203/rs.3.rs-1125146/v1 ◽

2021 ◽

Author(s):

Marisol Londoño-Gil ◽

Juan Carlos Rincón Flórez ◽

Albeiro López-Herrera ◽

Luis Gabriel Gonzalez-Herrera

Keyword(s):

Growth Traits ◽

Genetic Correlations ◽

Genetic Effect ◽

Cattle Breed ◽

Live Weight ◽

Daily Weight Gain ◽

Additive Genetic Effect ◽

Selection Processes ◽

Heritability Estimates ◽

Maternal Heritability

Abstract The Blanco Orejinegro (BON) is a Colombian creole cattle breed that is not genetically well characterized for growth traits. The aim of this work was to estimate genetic parameters for birth weight (BW), weaning weight (WW), yearling weight (YW), daily weight gain between birth and weaning (DWG), time to reach 120 kg of live weight (T120), and time to reach 60% of adult weight (T60%), and establish the selection criteria for growth traits in the BON population of Colombia. Genealogical and phenotypic information for BW, WW, YW, DWG, T120, and T60% traits of BON animals from 14 Colombian herds were used. These traits were analyzed with the AIREML method in a uni- and bi-trait animal model including the maternal effect for BW, WW, DWG, and T120. The direct heritability estimates values were 0.22 ± 0.059 (BW), 0.20 ± 0.057 (WW), 0.20 ± 0.153 (YW), 0.17 ± 0.07 (DWG), 0.26 (T120), and 0.44 ± 0.03 (T60%). The maternal heritability estimates values were 0.14 ± 0.040 (BW), 0.15 ± 0.039 (WW), 0.25 ± 0.06 (DWG), and 0.16 (T120). The direct genetic correlations were high (>|0.60|) among all the traits, except between T60% with BW, WW, YW, and DWG (ranged from -0.02 to -0.51), all in a favorable direction. The results showed that there is genetic variation in the growth traits associated with the additive genetic effect and they might respond to selection processes. Furthermore, genetic gains would improve through selection, especially for YW and T60% when WW is used as criterion.

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Random regression for modeling yield genetic trajectories in Jatropha curcas breeding

PLoS ONE ◽

10.1371/journal.pone.0244021 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0244021

Author(s):

Marco Antônio Peixoto ◽

Rodrigo Silva Alves ◽

Igor Ferreira Coelho ◽

Jeniffer Santana Pinto Coelho Evangelista ◽

Marcos Deon Vilela de Resende ◽

...

Keyword(s):

Jatropha Curcas ◽

Repeated Measures ◽

Variance Components ◽

High Reliability ◽

Genetic Selection ◽

Information Criterion ◽

Random Regression ◽

Ratio Test ◽

Sib Families ◽

Over Time

Random regression models (RRM) are a powerful tool to evaluate genotypic plasticity over time. However, to date, RRM remains unexplored for the analysis of repeated measures in Jatropha curcas breeding. Thus, the present work aimed to apply the random regression technique and study its possibilities for the analysis of repeated measures in Jatropha curcas breeding. To this end, the grain yield (GY) trait of 730 individuals of 73 half-sib families was evaluated over six years. Variance components were estimated by restricted maximum likelihood, genetic values were predicted by best linear unbiased prediction and RRM were fitted through Legendre polynomials. The best RRM was selected by Bayesian information criterion. According to the likelihood ratio test, there was genetic variability among the Jatropha curcas progenies; also, the plot and permanent environmental effects were statistically significant. The variance components and heritability estimates increased over time. Non-uniform trajectories were estimated for each progeny throughout the measures, and the area under the trajectories distinguished the progenies with higher performance. High accuracies were found for GY in all harvests, which indicates the high reliability of the results. Moderate to strong genetic correlation was observed across pairs of harvests. The genetic trajectories indicated the existence of genotype × measurement interaction, once the trajectories crossed, which implies a different ranking in each year. Our results suggest that RRM can be efficiently applied for genetic selection in Jatropha curcas breeding programs.

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