143 resultados para Reml


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Com o objetivo de estimar herdabilidades para ganho de peso médio diário da desmama ao sobreano (GMDDS) e para perímetro escrotal ao sobreano (PES) e tendências genética e fenotípica para GMDDS, foram utilizados 47.668 registros de peso e de ganho de peso de uma população multirracial Nelore-Angus, coletados entre 1991 e 2001 em diversas regiões do Brasil. Os dados foram analisados pelo método REML e as estimativas das (co)variâncias foram obtidas por meio de um modelo animal, no qual foram considerados fixos os efeitos da composição racial do animal (obtida pela concatenação do percentual da raça Nelore do próprio animal, de seu pai e de sua mãe) e do grupo de contemporâneos pós-desmama (animais nascidos no mesmo rebanho, ano, época e pertencentes ao mesmo sexo e grupo de manejo) e, como aleatórios, os efeitos genético aditivo direto e residual. A herdabilidade para PES foi estimada utlizando-se o mesmo modelo, acrescido dos efeitos fixos do peso e da idade do animal ao sobreano (covariáveis). As médias para idade nas pesagens foram 215 e 528 dias para a desmama e o sobreano, respectivamente. A herdabilidade estimada para GMDDS foi 0,44 ± 0,02 e para PES, 0,22 ± 0,08. A tendência genética anual predita para GMDDS foi decrescente até 1996 e crescente a partir desse período. A tendência fenotípica anual foi de 9,4 g/dia/ano.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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This paper deals with the effects of hair coat characteristics on the sweating rate of Brazilian Braford cows and estimation of heritabilities and genetic correlations of these traits. Data (n=1607) on hair length, coat thickness, hair diameter, number of hairs per unit area, coat reflectance and sweating rate were recorded from heifers and cows of a commercial herd managed on range under extensive system. The data were analyzed considering the following effects on the model for hair coat traits: classes of sires and contemporary groups; linear effects of month and genotype; linear and quadratic effects of age. The effect of sire was important (P<0.05) for all hair coat traits, except for number of hairs; contemporary groups affected (P<0.05) all hair coat traits; the effect of sampling month was important (P<0.05) for hair length and reflectance; genotype affected (P<0.05) hair length, diameter and coat reflectance; the quadratic effect of age was important (P<0.05) only for coat reflectance. Two models were used to analyze the sweating rate. The first model considered the following fixed effects: classes of contemporary groups and sires; linear effect of genotype, coat thickness, hair length, hair diameter, number of hairs, coat reflectance; linear and quadratic effects of time of day, age, air temperature, partial vapour pressure and radiant heat load. The second model used for the sweating rate considered the same fixed effects for the first model, except that the hair coat characteristics were adjusted for important effects used in the models to analyze hair coat traits. All meteorological factors and contemporary groups were important (P<0.05) on variation of sweating rate in both models. The Restricted Maximum. Likelihood (REML) method was used to estimate variance and covariance components under the sire model. Results included heritability estimates in narrow (h(2)) and broad (H) sense for single-trait analyzes: hair thickness (h(2)=0.16; H-2=0.26); hair length (h(2)=0.18; H-2=0.39); number of hairs (h(2)=0.08 +/- 0.07; H-2=0.08 +/- 0.07); hair diameter (h(2)=0.12 +/- 0.07; H-2=0.12 +/- 0.07); coat reflectance (h(2)=0.30; H-2=0.42); and sweating rate (h(2)=0.10 +/- 0.07; H-2=0.10 +/- 0.07). In general, the genetic correlations between the adaptive traits were favorable as for the direction to select for adaptation in tropical environment; however, they presented high standard errors. The results of this study imply that hair coat characteristics and sweating ability are important for the adaptability to heat stress and they must be better studied and further considered for selection for genetic progress of adaptation in tropical environment. (C) 2007 Elsevier B.V All rights reserved.

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The kidding intervals (IDP) of dairy goats raised in Southeastern Brazil were studied to quantify the influence of environmental factors and to estimate genetic parameters by means of least squares (MMQ) and restricted maximum likelihood (REML). The data analyzed were obtained from five farms and three breeds (Alpine, Saanen, and Toggenburg). The overall mean and standard error of IDP, as estimated by MMQ, were 339 +/- 12.70 days. The interaction of year x season of parturition influenced IDP. In two of the years studied, goats kidding at the end of the kidding season showed a shorter IDP when compared to those that were kidding after the end of the season. A quadratic trend of IDP over years was observed across the three kidding periods. For the three seasons. IDP increased from 1986 until mid-1989 and decreased thereafter. The heritability and repeatability of IDP, as estimated by MMQ and REML, were: 0.046 +/- 0.071 and 0.103 +/- 0.043, and 0.00026 and 0.08411, respectively. These estimates indicate that little genetic gain can be expected from selection for this trait.

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This study aimed to: a) to compare the covariance components obtained by Restricted Maximum Likelihood (REML) and by bayesian inference (BI): b) to run genetic evaluations for weights of Canchim cattle measured at weaning (W240) and at eighteen months of age (W550), adjusted or not to 240 and 550 days of age, respectively, using the mixed model methodology with covariance components obtained by REML or by BI; and c) to compare selection decisions from genetic evaluations using observed or adjusted weights and by REML or BI. Covariance components, heritabilities and genetic correlation for W240 and W550 were estimated and the predicted breeding values were used to select 10% and 50% of the best bulls and cows, respectively. The covariance components obtained by REML were smaller than the a posteriori means obtained by Bl. Selected animals from both procedures were not the same, probably because the covariance components and genetic parameters were different. The inclusion of age of animal at weighing as a covariate in the statistical model fitted by BI did not change the selected bulls and cows.

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Milk, fat, and protein yields of Holstein cows from the States of New York and California in the United States were used to estimate (co)variances among yields in the first three lactations, using an animal model and a derivative-free restricted maximum likelihood (REML) algorithm, and to verify if yields in different lactations are the same trait. The data were split in 20 samples, 10 from each state, with means of 5463 and 5543 cows per sample from California and New York. Mean heritability estimates for milk, fat, and protein yields for California data were, respectively, 0.34, 0.35, and 0.40 for first; 0.31, 0.33, and 0.39 for second; and 0.28, 0.31, and 0.37 for third lactations. For New York data, estimates were 0.35, 0.40, and 0.34 for first; 0.34, 0.44, and 0.38 for second; and 0.32, 0.43, and 0.38 for third lactations. Means of estimates of genetic correlations between first and second, first and third, and second and third lactations for California data were 0.86, 0.77, and 0.96 for milk; 0.89, 0.84, and 0.97 for fat; and 0.90, 0.84, and 0.97 for protein yields. Mean estimates for New York data were 0.87, 0.81, and 0.97 for milk; 0.91, 0.86, and 0.98 for fat; and 0.88, 0.82, and 0.98 for protein yields. Environmental correlations varied from 0.30 to 0.50 and were larger between second and third lactations. Phenotypic correlations were similar for both states and varied from 0.52 to 0.66 for milk, fat and protein yields. These estimates are consistent with previous estimates obtained with animal models. Yields in different lactations are not statistically the same trait but for selection programs such yields can be modelled as the same trait because of the high genetic correlations.

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Data on age at first kidding (IPP) were collected on seven farms in the Brazilian Southeastern region which explored breds. Least squares (LS) were used to evaluate the effects of environmental factors and to estimate variance components, and the derivative-free restricted maximum likelihood (DFREML) method was used to estimate the variance components of IPP and to genetically evaluate the goats used in the southest region of Brazil. The LS mean and standard error of IPP were 607.18 +/- 17.09 days. The interaction of year x kidding season had a significantly influenced IPP, indicating that management conditions varied among the seasons within each specific year, with a direct influence on body weight which is the main criterion adopted by farmers to decide when the animal is ready to breed. The effect of farm-breed combination influenced the IPP. The compararison among levels of farm-breed were done by cluster analysis. The results indicated that the individual goat management within each farm had a greater influence than breed, since goats of different breeds showed high and similar values on those farms having a high mean IPP. Heritability estimates obtained by LS using intraclass correlations among paternal half-sibs and those obtained by REML were 0.220 and 0.369, respectively.

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The objectives of this study were to estimate genetic parameters for non-standardized weights at nursing (PR120), at weaning (PR240), at yearling (PR365) and at post yearling (PR550), and to predict EPD's (expected progeny differences) for these traits using records from 29,769 Nellores. Covariance components and genetic parameters were estimated by mixed-model methodology, REML, using an animal model. Models for PR120, PR240, PR365 and PR455 included the random direct and maternal animal effects, the dam permanent environmental effect and the error. Fixed effects were contemporary group (CG) and age of cow at parturition (CIVP) and the covariate age of the calf at measuring. Two additional models for PR365, PR455 and PR550 analyses were used: the first included CG and CIVP, animal and maternal direct effect, residual and age of the calf (as covariate), and the second included CG and CIVP (as fixed effects), animal direct effect, residual and age of calf at measuring. Observed means±standard deviations were: 127±25kg (PR120); 191±34kg (PR240); 225±42kg (PR365); 266±51kg (PR455) and 310±56kg (PR550). From single-trait analyses, direct and maternal heritabilities for PR120, PR240, PR365 and PR455 were, respectively, .23 and .08; .19 and .10; .24 and .04; .30 and .04. Direct heritabilities were .39; .44 and .43, respectively, for PR365, PR455 and PR550. In the model without permanent effect, direct and maternal heritabilities for PR365, PR455 and PR550 were .25 and .08; .32 and .07; .38 and .03, respectively. When the estimates for standardized traits at the same period were compared, no differences in magnitude were found. Rank correlation had important changes when standardized and non-standardized traits were compared.

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The objective was to determine whether there is a genotype x environment interaction for age at first calving (AFC) in Holstein cattle in Brazil and Colombia. Data included 51,239 and 25,569 first-lactation records from Brazil and Colombia, respectively. Of 4230 sires in the data, 530 were North American sires used in both countries. Analyses were done using the REML bi-trait animal model, and AFC was considered as a distinct characteristic in each country. Fixed effects of contemporary group (herd-calving year), sire genetic group, and cow genetic group, and random effects of animal and residual variation were included in the model. Average AFC in Brazil and Colombia were 29.5 ± 4.0 and 32.1 ± 3.5 mo, respectively. Additive and residual genetic components and heritability coefficient for AFC in Brazil were 2.21 mo 2, 9.41 mo 2, and 0.19, respectively, whereas for Colombia, they were 1.02 mo 2, 6.84 mo 2, and 0.13, respectively. The genetic correlation of AFC between Brazil and Colombia was 0.78, indicating differences in ranking of sires consistent with a genotype x environment interaction. Therefore, in countries with differing environments, progeny of Holstein sires may calve at relatively younger or older ages compared with contemporary herdmates in one environment versus another.

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Descriptive herd variables (DVHE) were used to explain genotype by environment interactions (G x E) for milk yield (MY) in Brazilian and Colombian production environments and to develop a herd-cluster model to estimate covariance components and genetic parameters for each herd environment group. Data consisted of 180,522 lactation records of 94,558 Holstein cows from 937 Brazilian and 400 Colombian herds. Herds in both countries were jointly grouped in thirds according to 8 DVHE: production level, phenotypic variability, age at first calving, calving interval, percentage of imported semen, lactation length, and herd size. For each DVHE, REML bivariate animal model analyses were used to estimate genetic correlations for MY between upper and lower thirds of the data. Based on estimates of genetic correlations, weights were assigned to each DVHE to group herds in a cluster analysis using the FASTCLUS procedure in SAS. Three clusters were defined, and genetic and residual variance components were heterogeneous among herd clusters. Estimates of heritability in clusters 1 and 3 were 0.28 and 0.29, respectively, but the estimate was larger (0.39) in Cluster 2. The genetic correlations of MY from different clusters ranged from 0.89 to 0.97. The herd-cluster model based on DVHE properly takes into account G x E by grouping similar environments accordingly and seems to be an alternative to simply considering country borders to distinguish between environments.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Data from purebred Simmental, Nellore and Canchim cattle breeds obtained from the respective Brazilian Associations of Breeders were used to estimate variance components and to predict genetic values for 365 days weight. The results obtained by Bayesian inference were compared to those from Restricted Maximum Likelihood (REML) and Best Linear Unbiased Prediction (BLUP), which are the most commonly used methods of estimation and prediction in animal breeding. The two methods presented similar point estimates but the study of the marginal posterior distributions in the Bayesian approach yields more detailed information about the parameters and other unknowns in the model.

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The aims of this study were to evaluate the variation and to estimate genetic parameters for silvicultural anatomic wood traits for genetic breeding of a Myracrodruon urundeuva (Engler) Fr. Allem, population from Selvíria-MS. For this, from samples of a progeny test established in the Fazenda de Ensino, Pesquisa e Extensão da Faculdade de Engenharia de Ilha Solteira/UNESP, macroscopic anatomic wood traits of M. urundeuva (tangential diameter and vases frequency per mm2) and growth traits were measured (height, DBH and stem form). Genetic parameters were estimated in 28 open-pollinated progenies, in three replications and 10 plants per plot, using a REML/BLUP approach. Between the analysed traits, the DBH is the most indicated for selection for timber production, because it presented the highest values of coefficient of genetic variation, heritabilities and selective accuracy. Between the anatomic traits, the vessels frequency in the pith showed the highest values for genetic parameters. For pulp yield, based on the multi-effect index, the strategy of selecting the best trees for vessels frequency in the pith, independent of the progeny, permitted to obtain substantial gains by mass selection, without progeny test.

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The objective of this study was to determine whether there is a genotype by environment interaction (GxE) for dairy buffaloes in Brazil and Colombia. The (co)variance components were estimated by using a bi-trait repeatability animal model with the REML method. Each trait consisted in the milk yield obtained in both countries. Contemporary group (herd, year and season of parity) and age at parity (linear and quadratic covariate) fixed effects, along with the additive genetic, permanent environment, and the residual random effects were included in the model. Genetic, permanent environmental and residual variance and heritabilities were different for both countries. The genetic correlations for milk yield between Brazil and Colombia were low (between 0.10 and 0.13), indicating a GxE interaction between both countries. Knowing that this interaction influences the genetic progress of buffalo populations in Brazil and Colombia, we recommend choosing sires tested in the country they will be used, along with conducting joint genetic evaluations that consider GxE interaction effects.