Interação genótipo ambiente para perímetro escrotal em bovinos da raça Nelore utilizando normas de reação

Pós-graduação em Zootecnia - FCAV

Estimativas de parâmetros genéticos para pesos do nascimento aos dois anos de idade para bovinos da raça Brahman utilizando modelos de regressão aleatória

Pós-graduação em Zootecnia - FCAV

Estimativas de (co) varância genética de pesos do nascimento até a maturidade em rebanhos da raça Nelore usando modelos de regressão aleatória e de características múltiplas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Interação genótipo ambiente sobre características reprodutivas de fêmeas da raça Nelore

Pós-graduação em Genética e Melhoramento Animal - FCAV

Pertubação orbital devida a um terceiro corpo com distribuição não uniforme de massa e em órbita elíptica

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modelos genéticos para a produção no dia do controle em bovinos Gir Leiteiro (Bos indicus)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo genético quantitativo do fluxo lácteo em bovinos da raça Holandesa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelos de regressão aleatória para a estimação de parâmetros genéticos da produção e constituintes do leite de búfalas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimação de parâmetros genéticos para características de crescimento em bovinos da raça Canchim com modelos de dimensão finita e infinita

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Interação genotipo-ambiente para idade ao primeiro parto, perímetro escrotal e peso ao sobreano em bovinos da raça Nelore utilizando normas de reação

Pós-graduação em Genética e Melhoramento Animal - FCAV

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

The objective of this research was to estimate (co) variance functions and genetic parameters for body weight in Colombian buffalo populations using random regression models with Legendre polynomials. Data consisted of 34,738 weight records from birth to 900 days of age from 7815 buffaloes. Fixed effects in the model were contemporary group and parity order of the mother. Random effects were direct and maternal additive genetic, as well as animal and maternal permanent environmental effects. A cubic orthogonal Legendre polynomial was used to model the mean curve of the population. Eleven models with first to sixth order polynomials were used to describe additive genetic direct and maternal effects, and animal and maternal permanent environmental effects. The residual was modeled considering five variance classes. The best model included fourth and sixth order polynomials for direct additive genetic and animal permanent environmental effects, respectively, and third-order polynomials for maternal genetic and maternal permanent environmental effects. The direct heritability increased from birth until 120 days of age (0.32 +/- 0.05), decreasing thereafter until one year of age (0.18 +/- 0.04) and increased again, reaching 0.39 +/- 0.09, at the end of the evaluated period. The highest maternal heritability estimates (0.11 +/- 0.05), were obtained for weights around weaning age (weaning age range is between 8 and 9.5 months). Maternal genetic and maternal permanent environmental variances increased from birth until about one year of age, decreasing at later ages. Direct genetic correlations ranged from moderate (0.60 +/- 0.060) to high (0.99 +/- 0.001), maternal genetic correlations showed a similar range (0.41 +/- 0.401 and 0.99 +/- 0.003), and all of them decreased as time between weighings increased. Direct genetic correlations suggested that selecting buffalos for heavier weights at any age would increase weights from birth through 900 days of age. However, higher heritabilities for direct genetic weights effects after 600 days of age suggested that selection for these effects would be more effective if done during this age period. A greater response to selection for maternal ability would be expected if selection used maternal genetic predictions for weights near weaning. (C) 2013 Elsevier B.V. All rights reserved.

Application of Reactions Norms in Study of Genotype Environmental Interaction for Milk Yield of Buffaloes

The aim of this study was to estimate genetic parameters for milk yield (MY) in buffaloes using reaction norms. Model included the additive direct effect as random and contemporary group (herd and year of birth) were included as fixed effects and cow age classes (linear) as covariables. The animal additive direct random effect was modeled through linear Legendre polynomials on environment gradient (EG) standardized means. Mean trends were taken into account by a linear regression on Legendre polynomials of environmental group means. Residual variance was modeled trough 6 heterogeneity classes (EG). These classes of residual variance was formed : EG1: mean = 866,93 kg (621,68 kg-1011,76 kg); EG2: mean = 1193,00 kg (1011,76 kg-1251,49 kg); EG3: mean = 1309,37 kg (1251,49 kg -1393,20 kg); EG4: mean = 1497,59 kg (1393,20 kg-1593,53 kg); EG5: mean = 1664,78 kg (1593,53 kg -1727,32kg) e EG6: mean = 1973,85 kg (1727,32 kg -2422,19 kg).(Co) variance functions were estimated by restricted maximum likelihood (REML) using the GIBBS3F90 package. The heritability estimates for MY raised as the environmental gradient increased, varying from 0.20 to 0.40. However, in intermediate to favorable environments, the heritability estimates obtained with Considerable genotype-environment interaction was found for MY using reaction norms. For genetic evaluation of MY is necessary to consider heterogeneity of variances to model the residual variance.

Predicting breeding values for milk yield of Guzera (Bos indicus) cows using random regression models

Given the importance of Guzera breeding programs for milk production in the tropics, the objective of this study was to compare alternative random regression models for estimation of genetic parameters and prediction of breeding values. Test-day milk yields records (TDR) were collected monthly, in a maximum of 10 measurements. The database included 20,524 records of first lactation from 2816 Guzera cows. TDR data were analyzed by random regression models (RRM) considering additive genetic, permanent environmental and residual effects as random and the effects of contemporary group (CG), calving age as a covariate (linear and quadratic effects) and mean lactation curve as fixed. The genetic additive and permanent environmental effects were modeled by RRM using Wilmink, All and Schaeffer and cubic B-spline functions as well as Legendre polynomials. Residual variances were considered as heterogeneous classes, grouped differently according to the model used. Multi-trait analysis using finite-dimensional models (FDM) for testday milk records (TDR) and a single-trait model for 305-days milk yields (default) using the restricted maximum likelihood method were also carried out as further comparisons. Through the statistical criteria adopted, the best RRM was the one that used the cubic B-spline function with five random regression coefficients for the genetic additive and permanent environmental effects. However, the models using the Ali and Schaeffer function or Legendre polynomials with second and fifth order for, respectively, the additive genetic and permanent environmental effects can be adopted, as little variation was observed in the genetic parameter estimates compared to those estimated by models using the B-spline function. Therefore, due to the lower complexity in the (co)variance estimations, the model using Legendre polynomials represented the best option for the genetic evaluation of the Guzera lactation records. An increase of 3.6% in the accuracy of the estimated breeding values was verified when using RRM. The ranks of animals were very close whatever the RRM for the data set used to predict breeding values. Considering P305, results indicated only small to medium difference in the animals' ranking based on breeding values predicted by the conventional model or by RRM. Therefore, the sum of all the RRM-predicted breeding values along the lactation period (RRM305) can be used as a selection criterion for 305-day milk production. (c) 2014 Elsevier B.V. All rights reserved.

Random regression models using different functions to model milk flow in dairy cows

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Identificação de forças de excitação em sistemas rotativos utilizando funções ortogonais

Pós-graduação em Engenharia Mecânica - FEIS