Comparação de modelos de regressão aleatória para estimação de parâmetros genéticos em caprinos leiteiros

Sarmento, José Lindenberg Rocha; Albuquerque, Lúcia Galvão de; Torres, Robledo de Almeida; Rodrigues, Marcelo Teixeira; Lopes, Paulo Sávio; Filho, João Cruz Reis

doi:10.1590/s1516-35982008001000011

Cited by 9 publications

(12 citation statements)

References 18 publications

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“…Heritability presented with similar behavior to additive genetic variance, i.e., greater in the final third of lactation, which is in contrast to the results found by Menezes et al (2010) who found higher values of additive genetic variance and heritability at the beginning of the lactation curve and greater values for the permanent environment at the end of lactation using an F3A4EP6H6 model. Sarmento et al (2008) reported higher heritability estimates in the middle of the lactation curve. According to them, the animals were more susceptible to environmental changes in the tests at the beginning and end of the lactation period.…”

Section: Resultsmentioning

confidence: 91%

Random regression models using Legendre orthogonal polynomials to evaluate the milk production of Alpine goats

Silva

Torres²,

Brito³

et al. 2013

Genet. Mol. Res.

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ABSTRACT. The objective of this study was to identify the best random regression model using Legendre orthogonal polynomials to evaluate Alpine goats genetically and to estimate the parameters for test day milk yield. On the test day, we analyzed 20,710 records of milk yield of 667 goats from the Goat Sector of the Universidade Federal de Viçosa. The evaluated models had combinations of distinct fitting orders for polynomials (2-5), random genetic (1-7), and permanent environmental (1-7) fixed curves and a number of classes for residual variance (2, 4, 5, and 6). WOMBAT software was used for all genetic analyses. A random regression model using the best Legendre orthogonal polynomial for genetic evaluation of milk yield on the test day of Alpine goats considered a fixed curve of order 4, curve of genetic additive effects of order 2, curve of permanent environmental effects of order 7, and a minimum of 5 classes of residual variance because it was the most economical model among those that were equivalent to the complete model by the likelihood ratio test. Phenotypic variance and heritability were higher at the end of the lactation period, indicating that the length RR using LOP to evaluate milk production of Alpine goats of lactation has more genetic components in relation to the production peak and persistence. It is very important that the evaluation utilizes the best combination of fixed, genetic additive and permanent environmental regressions, and number of classes of heterogeneous residual variance for genetic evaluation using random regression models, thereby enhancing the precision and accuracy of the estimates of parameters and prediction of genetic values.

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Section: Resultsmentioning

confidence: 91%

Random regression models using Legendre orthogonal polynomials to evaluate the milk production of Alpine goats

Silva

Torres²,

Brito³

et al. 2013

Genet. Mol. Res.

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“…Other authors also emphasized the need to consider Heterogeneous Residual Variance models to assess growth rate curves (Meyer, 2000;Sousa et al, 2008), for milk production on the control day (El faro & Albuquerque, 2003; Sarmento et al, 2008) and sensitivity studies of genotypes in meat quails (Felipe, 2010).…”

Section: Resultsmentioning

confidence: 99%

Genetic evaluation of European quails by random regression models

et al. 2012

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-The objective of this study was to compare different random regression models, defined from different classes of heterogeneity of variance combined with different Legendre polynomial orders for the estimate of (co)variance of quails. The data came from 28,076 observations of 4,507 female meat quails of the LF1 lineage. Quail body weights were determined at birth and 1, 14, 21, 28, 35 and 42 days of age. Six different classes of residual variance were fitted to Legendre polynomial functions (orders ranging from 2 to 6) to determine which model had the best fit to describe the (co)variance structures as a function of time. According to the evaluated criteria (AIC, BIC and LRT), the model with six classes of residual variances and of sixth-order Legendre polynomial was the best fit. The estimated additive genetic variance increased from birth to 28 days of age, and dropped slightly from 35 to 42 days. The heritability estimates decreased along the growth curve and changed from 0.51 (1 day) to 0.16 (42 days). Animal genetic and permanent environmental correlation estimates between weights and age classes were always high and positive, except for birth weight. The sixth order Legendre polynomial, along with the residual variance divided into six classes was the best fit for the growth rate curve of meat quails; therefore, they should be considered for breeding evaluation processes by random regression models.

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“…Todavia, em caprinos leiteiros, poucos estudos têm sido desenvolvidos, o que reflete a necessidade de pesquisas buscando os devidos ajustes em função das particularidades intrínsecas à espécie, como intervalos de tempo entre as medidas, definição de subclasses para a curva fixa e da ordem de ajuste da regressão aleatória (Sarmento et al, 2008).…”

Section: Introductionunclassified

Modelos de regressão aleatória na avaliação da produção de leite em cabras da raça Saanen

Menezes¹,

Torres²,

Sarmento³

et al. 2011

R. Bras. Zootec.

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RESUMO -Foram utilizados 10.238 registros semanais de produção de leite no dia do controle leiteiro provenientes de 388 primeiras lactações de cabras da raça Saanen visando comparar diferentes modelos de regressão aleatória (MRA).Primeiramente, foram comparados cinco modelos, cujos termos exponenciais da função de Wilmink assumiram os seguintes valores -0,0350; -0,0500; -0,0565; -0,0680 e -0,1000 (W0350, W0500, W0565, W0680 e W1000, respectivamente), Palavras-chave: função de Wilmink, leite, modelagem, produção de leite no dia do controle, variância residual Random regression models in the milk yield evaluation in Saanen goatsABSTRACT -It was used 10,238 weekly test day records from 388 first lactations of Saanen goats with the objective of comparing random regression models (RRM). Firstly, it was compared five models, whose exponential terms of Wilmink function assumed the following values: -0.0350; -0.0500; -0.0565; -0.0680 and -0.1000 (W0350, W0500, W0565, W0680 and W1000, respectively) by considering homogeneity of residual variance over the lactation period. The value -0.0500 was kept in the model W0500 whereas models W0350, W0565, W0680 and W1000 used values -0.0350; -0.0565; -0.0680 and -0.1000, respectively, replacing the value -0.0500, proposed by the original model used in dairy cattle. After choosing the best model according to ln L, homogeneity and heterogeneity for residual variance: homogeneity, two classes, three classes, four classes, five classes and six classes along the lactation were evaluated by using AIC, BIC and ln L criteria. According to criterion used, W0350 presents the best fit among the evaluated models. With regard to residual variance, the use of six classes over lactation is indicated by AIC, BIC, ln L and likelihood ratio test. Heritability estimates over lactation, for the best model, ranges from 0.07 (2 nd lactation week) to 0.25 (20 th lactation week).

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Comparação de modelos de regressão aleatória para estimação de parâmetros genéticos em caprinos leiteiros

Cited by 9 publications

References 18 publications

Random regression models using Legendre orthogonal polynomials to evaluate the milk production of Alpine goats

Random regression models using Legendre orthogonal polynomials to evaluate the milk production of Alpine goats

Genetic evaluation of European quails by random regression models

Modelos de regressão aleatória na avaliação da produção de leite em cabras da raça Saanen

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