Uso de modelos de regressão aleatória para descrever a variação genética da produção de leite na raça Holandesa

Araújo, Cláudio Vieira de; Torres, Robledo de Almeida; Costa, C. N.; Filho, Rodolpho de Almeida Torres; Araújo, Simone Inoe; Lopes, Paulo Sávio; Regazzi, Adair José; Pereira, Cláubia; Sarmento, José Lindenberg Rocha

doi:10.1590/s1516-35982006000400006

Cited by 22 publications

(20 citation statements)

References 13 publications

(12 reference statements)

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“…B-spline and Wilmink showed similar estimates. Several studies in Holstein cattle using RRM reported similar trend for TD heritability estimates as obtained in this study, Olori et al (1999) and Kettunen et al (2000), and in Brazil by Cobuci et al (2005), Araújo et al (2006), Melo et al (2007) and Dorneles et al (2009).…”

Section: Resultssupporting

confidence: 87%

“…Random regression based on orthogonal Legendre polynomials has been used in most studies to model the structure of covariances for random additive genetic and permanent environmental effects (Strabel and Misztal, 1999;Olori et al, 1999;Araújo et al, 2006). Parametric functions such as the logarithmic polynomial proposed by Ali and Schaeffer (1987) and the Wilmink (1987) exponential function have also been used, with these functions providing genetic parameter estimates that differ from those obtained with orthogonal Legendre polynomials.…”

Section: Introductionmentioning

confidence: 99%

“…Brotherstone et al (2000), comparing RRM using orthogonal Legendre polynomials, the Ali and Schaeffer (1987) function and two variations of the Wilmink (1987) function to model random effects, concluded that the function used exerts a marked influence on the genetic parameter estimates and that the parametric functions of Ali and Schaeffer (1987) and Wilmink (1987) best fitted the data, despite negative correlations between test-days at the beginning and at the end of lactation. On the other hand, Araújo et al (2006), who compared RRM using orthogonal Legendre polynomials and the parametric functions of Ali and Schaeffer (1987) and Wilmink (1987) to model random effects, concluded that orthogonal Legendre polynomials provided the best data fit.…”

Section: Introductionmentioning

confidence: 99%

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Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

Bignardi

Faro²,

Torres³

et al. 2011

Genet. Mol. Res.

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ABSTRACT. We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.

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Section: Resultssupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

Bignardi

Faro²,

Torres³

et al. 2011

Genet. Mol. Res.

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“…Considering the Holstein breed in Brazil, heritability estimates for the test-day milk yield, adjusted with the Legendre polynomials, Wilmink, Ali & Schaeffer under RRM range from .11 to .31 (Cobuci et al, 2004;Cobuci et al, 2006;Araújo et al, 2006;Costa et al, 2008;Biassus, Cobuci, & Costa, 2011). These results are in agreement with heritability results observed in studies with Holsteins from other countries, ranging from 0.08 to 0.23 (Hahammi et al, 2008;Silvestre, Petim-Batista, & Colaço, 2006;Strabel & Misztal, 1999).…”

Section: Introductionsupporting

confidence: 79%

Adjustment of Lactation Curves of Holstein Cows from Herds of Minas Gerais, Brazil

Azevedo¹,

Gonçalves²,

Souza³

et al. 2018

JAS

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Random regression models (RRM) differ in terms of the functions used to describe the shape of lactation curves. The aim was to compare random regression models under different functions to describe the lactation curves from Holstein cows in herds of the state of Minas Gerais. A database of 28,118 production records was analyzed using the test-day records of 4,230 first parity cows from five herds. The Wilmink, Ali & Schaeffer and Legendre polynomial (orders 4, 5 and 6) functions were adjusted in RRM to model the mean production trend (fixed) and genetic and permanent environmental (random) effects. The residual variances were assumed to be constant throughout lactation. Analyses were performed using the AIREMLF90 program. Except for the model with the polynomial function of order 5, all models converged. The Wilmink function showed lower values for criteria based on the -2log (L), AIC and BIC. The model with the Legendre polynomial of order 6 showed lower residual variance. Heritability estimates were similar between functions, ranging from 0.07 to 0.18 and were higher from 215 days of lactation. From 155 days of lactation, genetic and permanent environmental correlations between successive controls are of high magnitude. The Wilmink function is the most suitable for the study of milk yields from primiparous Holstein cows. The selection of animals is possible from 155 days of lactation on. Permanent environmental effects have greater influence on the milk production at the end of lactation of primiparous cows and should be considered since they are important and may be cumulative throughout lactation.

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“…Hereditability estimates similar to those in the present study, obtained using Wilmink and AS functions, were reported by Herrera et al (2008) and Pereira et al (2010), with values varying from 0.15 to 0.33 for TDMY of Gyr cows. Araújo et al (2006), who studied the Dutch breed, also found heritability estimates with this amplitude by using the same functions.…”

Section: Resultsmentioning

confidence: 80%

Comparison of random regression models to estimate genetic parameters for milk production in Guzerat (Bos indicus) cows

Santos

Peixoto²,

Aspilcueta-Borquis³

et al. 2013

Genet. Mol. Res.

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ABSTRACT. Random regression models have been widely used to estimate genetic parameters that influence milk production in Bos taurus breeds, and more recently in B. indicus breeds. With the aim of finding appropriate random regression model to analyze milk yield, different parametric functions were compared, applied to 20,524 test-day milk yield records of 2816 first-lactation Guzerat (B. indicus) cows in Brazilian herds. The records were analyzed by random regression models whose random effects were additive genetic, permanent environmental and residual, and whose fixed effects were contemporary group, the covariable cow age at calving (linear and quadratic effects), and the herd lactation curve. The additive genetic and permanent environmental effects were modeled by the Wilmink function, a modified Wilmink function (with the second term divided by 100), a function that combined third-order Legendre polynomials with the last term of the Wilmink function, and the Ali and Schaeffer function. The residual variances were modeled by means of 1, 4, 6, or 10 heterogeneous classes, with the exception of the last term of the Wilmink function, for which there were 1, 3, 6, or 10 classes. The models gave similar hereditability estimates, ranging from 0.20 to 0.33. Genetic correlations between adjacent records were high values (0.83-0.99), but they declined when the interval between the test-day records increased, and were negative between the first and last records. The model employing the Ali and Schaeffer function with six residual variance classes was the most suitable for fitting the data.

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Uso de modelos de regressão aleatória para descrever a variação genética da produção de leite na raça Holandesa

Cited by 22 publications

References 13 publications

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

Adjustment of Lactation Curves of Holstein Cows from Herds of Minas Gerais, Brazil

Comparison of random regression models to estimate genetic parameters for milk production in Guzerat (Bos indicus) cows

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