scholarly journals Covariance functions under B-spline polynomials to model Polled Nellore cattle growth

2021 ◽  
Vol 42 (6supl2) ◽  
pp. 3977-3990
Author(s):  
Diego Helcias Cavalcante ◽  
◽  
Carlos Syllas Monteiro Luz ◽  
Marcelo Richelly Alves de Oliveira ◽  
Wéverton José Lima Fonseca ◽  
...  
Keyword(s):  
Random Effects ◽  
Growth Curve ◽  
Fixed Effects ◽  
Random Regression ◽  
Spline Functions ◽  
Average Growth ◽  
B Spline ◽  
Covariance Functions ◽  
Model Animal ◽  
Nellore Cattle

B-spline functions have been used in random regression models (RRM) to model animal weight from birth to adulthood because they are less vulnerable to common difficulties of other methods. However, its application to model growth traits of Polled Nellore cattle has been little studied. Therefore, this study aimed to evaluate polynomial functions of different orders and segment numbers to model effects associated with the Polled Nellore cattle growth curve. For this purpose, we used 15,148 weight records of 3,115 animals aged between 1 and 660 days and reared in northern Brazil and born between 1995 and 2010. Random effects were modeled using B-spline polynomials. As random effects, we considered the direct and maternal genetic additives, as well as direct and maternal permanent environments. As fixed effects were included contemporary group, cow age at calving (linear and quadratic) and fourth-order Legendre polynomials to represent average growth curve. The residue was modeled by considering seven age classes. The bestfitted model was the one that considered cubic B-spline functions with four knots for direct additive genetic effects and three knots for maternal genetic, animal permanent environment, and maternal permanent environment effects (C6555). Therefore, covariance functions under B-spline polynomials are efficient and can be used to model the growth curve of Polled Nellore cattle from birth to 660 days of age.

scholarly journals Random regression analyses using B-spline functions to model growth of Nellore cattle

animal ◽  
2012 ◽  
Vol 6 (2) ◽  
pp. 212-220 ◽  
Author(s):  
A.A. Boligon ◽  
M.E.Z. Mercadante ◽  
R.B. Lôbo ◽  
F. Baldi ◽  
L.G. Albuquerque
Keyword(s):  
Random Regression ◽  
Spline Functions ◽  
Regression Analyses ◽  
B Spline ◽  
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 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

scholarly journals Reaction norms in weights at 365 days old in nellore bulls in northern Brazil

2015 ◽  
Vol 36 (6Supl2) ◽  
pp. 4613 ◽  
Author(s):  
Jorge Luís Ferreira ◽  
Alliny Souza de Assis ◽  
Fernando Brito Lopes ◽  
Thomas Wayne Murphy ◽  
Marcelo Corrêa da Silva ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Reaction Norm ◽  
Genotype By Environment Interaction ◽  
Random Regression ◽  
Residual Effects ◽  
Breeding Value ◽  
Environment Interaction ◽  
Nellore Cattle ◽  
Regression Techniques ◽  
Northern Brazil

<p>Genotype by environment interaction (GxE) studies are of particular interest in Brazil because of the regional diversity of environmental effects and the wide variety of management systems. The present study evaluates GxE effects on 365 d weight (365W) of Nellore cattle raised on pasture in northern Brazil. The analysis utilized random regression techniques to model the reaction norm. Fixed effects consisted of sex, contemporary group, and the covariate of age of cow at calving. The environmental gradient, defined by the concatenation of a bull and the state in which the calf was born, was modeled by second order Legendre polynomials. Direct additive genetic and residual effects were fit as random. Results showed differences in the magnitude of expression of genotype in proportion to decreasing favorability of the environment. As the environment became more unfavorable, the correlation of breeding value to different environments decreased. The correlations between the intercept and the level slope for 365W feature were of moderate magnitude, predominantly indicating the reclassification of sires in different environments. Reaction standard model was coherent from a technical and biological view point and enabled the perception of GxE in the genetic evaluation of Nellore cattle in the states of Maranhão, Pará and Tocantins.</p><p> </p>

scholarly journals Covariance function of Legendre polynomials for the modeling of Polled Nellore cattle growth in northern Brazil

2019 ◽  
Vol 40 (2) ◽  
pp. 781
Author(s):  
Diego Helcias Cavalcante ◽  
Severino Cavalcante Sousa Júnior ◽  
Luciano Pinheiro Silva ◽  
Carlos Henrique Mendes Malhado ◽  
Raimundo Martins Filho ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Growth Parameters ◽  
Random Effect ◽  
Growth Curves ◽  
Random Regression ◽  
Analytical Models ◽  
Residual Variance ◽  
Age Related ◽  
Nellore Cattle ◽  
Northern Brazil

This study aimed to compare random regression models fitted by Legendre orthogonal polynomials and determine which best fits changes in Nellore cattle growth parameters. Age polynomial functions of different orders were evaluated using a random-effect modeling associated with a genetic study of cattle growth curves. For this purpose, weight records (15,148) were performed in Polled Nellore bovines (3,115), aged between 1 and 660 days, reared in northern Brazil and born between 1995 and 2010. The fixed effects of analytical models comprised age-matched groups, heifer calving age (linear and quadratic), and fourth-order Legendre age polynomial (cubic), depicting the mean growth curve. Besides, different order functions were considered for random effects, so that (co) variance associated with genetic effects (direct and maternal) and permanent environmental effects (animal and maternal) could be modeled. Residual variance was fitted by six heterogeneous classes throughout the analyzed period. According to AIC and BIC criteria, the model 6333 allowed the fitting of changes in variance and covariance over time (genetic and environmental). Thus, this model can be used to describe age-related changes in Polled Nellore cattle reared in northern Brazil.

scholarly journals Random regression models for milk, fat and protein in Colombian Buffaloes

2015 ◽  
pp. 4415-4426
Author(s):  
Naudin Hurtado-Lugo ◽  
Humberto Tonhati ◽  
Raul Aspilcuelta-Borquis ◽  
Cruz Enríquez-Valencia ◽  
Mario Cerón-Muñoz
Keyword(s):  
Environmental Effects ◽  
Milk Fat ◽  
Fixed Effects ◽  
Regression Models ◽  
Random Regression ◽  
Residual Effects ◽  
Covariance Functions ◽  
Lp Residual ◽  
Quadratic Effects ◽  
Residual Variances

Objective. Covariance functions for additive genetic and permanent environmental effects and, subsequently, genetic parameters for test-day milk (MY), fat (FY) protein (PY) yields and mozzarella cheese (MP) in buffaloes from Colombia were estimate by using Random regression models (RRM) with Legendre polynomials (LP). Materials and Methods. Test-day records of MY, FY, PY and MP from 1884 first lactations of buffalo cows from 228 sires were analyzed. The animals belonged to 14 herds in Colombia between 1995 and 2011. Ten monthly classes of days in milk were considered for test-day yields. The contemporary groups were defined as herd-year-month of milk test-day. Random additive genetic, permanent environmental and residual effects were included in the model. Fixed effects included the contemporary group, linear and quadratic effects of age at calving, and the average lactation curve of the population, which was modeled by third-order LP. Random additive genetic and permanent environmental effects were estimated by RRM using third- to- sixth-order LP. Residual variances were modeled using homogeneous and heterogeneous structures. Results. The heritabilities for MY, FY, PY and MP ranged from 0.38 to 0.05, 0.67 to 0.11, 0.50 to 0.07 and 0.50 to 0.11, respectively. Conclusions. In general, the RRM are adequate to describe the genetic variation in test-day of MY, FY, PY and MP in Colombian buffaloes.Key words: Cattle, genetics, zootechnics (Source: EuroVoc).

scholarly journals Models for evaluation of growth of performance tested bulls

2008 ◽  
Vol 53 (No. 2) ◽  
pp. 45-54 ◽  
Author(s):  
J. Přibyl ◽  
H. Krejčová ◽  
J. Přibylova ◽  
I. Misztal ◽  
S. Tsuruta ◽  
...  
Keyword(s):  
Growth Curve ◽  
Variance Components ◽  
Legendre Polynomials ◽  
Performance Test ◽  
Live Weight ◽  
Random Regression ◽  
Mild Depression ◽  
Average Growth ◽  
Linear Spline

Before being used for insemination, young bulls of Czech Fleckvieh (CF) are tested for growth at performance-test stations. While at stations, the bulls are weighed monthly. Evaluation included 7 448 bulls with 82 676 records of weight measured from 6 to 520 days of life. In the station-year-period (HYS), which can be prolonged up to 3 months, different groups were tested according to the beginning of growth curve and according to test-days of weighing. Weight analyses were used to handle heterogeneous variability based on age. Legendre Polynomials (LP) with 5 parameters described the average growth curve for HYS classes. Deviations from average curves were decomposed into genetic (G), animal’s permanent environment (PE) and residual (RES) components. Functions of (G) and (PE) were tested using LP random regression (RR) methodology with 5 or 3 parameters and Linear Spline (SP) function with 5 knots. Variance increases with the age of the animals. From 100 to 400 days, heritability was nearly the same with a mild depression in the middle of the period. The average was <I>h</I><sup>2</sup> = 0.31 and ended with <I>h</I><sup>2</sup> = 0.36. Results were similar for variance components, heritability, genetic, environmental and phenotype correlations from different models with different LP and SP functions. Higher RES variability occurred only for LP with 3 parameters. For traits like live weight, the RR should have at least 3 parameters and SP function should be used.

scholarly journals Modeling of average growth curve in Santa Ines sheep using random regression models

2011 ◽  
Vol 40 (2) ◽  
pp. 314-322 ◽  
Author(s):  
José Lindenberg Rocha Sarmento ◽  
Robledo de Almeida Torres ◽  
Wandrick Hauss de Sousa ◽  
Lucia Galvão de Albuquerque ◽  
Raimundo Nonato Braga Lôbo ◽  
...  
Keyword(s):  
Growth Curve ◽  
Regression Models ◽  
Random Regression ◽  
Average Growth ◽  
Breeding Values ◽  
Cubic Polynomial ◽  
Linear Fit ◽  
Santa Inês ◽  
The Difference ◽  
Cubic Function

Polynomial functions of age of different orders were evaluated in the modeling of the average growth trajectory in Santa Ines sheep in random regression models. Initially, the analyses were performed not considering the animal effect. Subsequently, the random regression analyses were performed including the random effects of the animal and its mother (genetic and permanent environment). The linear fit was lower, and the other orders were similar until near 100 days of age. The cubic function provided the closest fit of the observed averages, mainly at the end of the curve. Orders superior to this one tended to present incoherent behavior with the observed weights. The estimated direct heritabilities, considering the linear fit, were higher to those estimated by considering other functions. The changes in animal ranking based on predicted breeding values using linear fit and superior orders were small; however, the difference in magnitude of the predicted breeding values was higher, reaching values 77% higher than those obtained with the cubic function. The cubic polynomial function is efficient in describing the average growth curve.

scholarly journals Reaction norms in weights at 365 days old in nellore bulls in northern Brazil

2015 ◽  
Vol 36 (6Supl2) ◽  
pp. 4613
Author(s):  
Jorge Luís Ferreira ◽  
Alliny Souza de Assis ◽  
Fernando Brito Lopes ◽  
Thomas Wayne Murphy ◽  
Marcelo Corrêa da Silva ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Reaction Norm ◽  
Genotype By Environment Interaction ◽  
Random Regression ◽  
Residual Effects ◽  
Breeding Value ◽  
Environment Interaction ◽  
Nellore Cattle ◽  
Regression Techniques ◽  
Northern Brazil

Genotype by environment interaction (GxE) studies are of particular interest in Brazil because of the regional diversity of environmental effects and the wide variety of management systems. The present study evaluates GxE effects on 365 d weight (365W) of Nellore cattle raised on pasture in northern Brazil. The analysis utilized random regression techniques to model the reaction norm. Fixed effects consisted of sex, contemporary group, and the covariate of age of cow at calving. The environmental gradient, defined by the concatenation of a bull and the state in which the calf was born, was modeled by second order Legendre polynomials. Direct additive genetic and residual effects were fit as random. Results showed differences in the magnitude of expression of genotype in proportion to decreasing favorability of the environment. As the environment became more unfavorable, the correlation of breeding value to different environments decreased. The correlations between the intercept and the level slope for 365W feature were of moderate magnitude, predominantly indicating the reclassification of sires in different environments. Reaction standard model was coherent from a technical and biological view point and enabled the perception of GxE in the genetic evaluation of Nellore cattle in the states of Maranhão, Pará and Tocantins.

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