Análise de fatores e componentes principais genéticos para características de crescimento, carcaça e qualidade da carne em bovinos da raça Nelore
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Data
2017-06-14
Autores
Sales, Lucas Henrique Brito [UNESP]
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Universidade Estadual Paulista (Unesp)
Resumo
A análise de componentes principais (PCA) e análise de fatores (FA) são amplamen-te utilizadas no processamento de dados visando a redução da dimensionalidade. No entanto, poucos são os estudos que empregou estes métodos em um conjunto de características importantes em bovinos da raça nelore com a finalidade de mode-lar a estrutura de (co)variância dos dados. O objetivo deste estudo foi estimar pa-râmetros genéticos para as características de crescimento, carcaça e qualidade da carne em machos Nelore, utilizando diferentes modelos multi-características visando a redução da dimensionalidade dos dados. Um total de 3.865; 4.121; 4.109; 4.188; 4.213; 3.320 e 16.575 registros de peso da carcaça quente (PCQ), área de olho de lombo (AOL), espessura de gordura subcutânea (EGS), escore de marmorização (MARM), maciez, mensurada pela força de cisalhamento (SF), lipídios (LP) e peso ao sobreano (PS), respectivamente, foram usados. As análises foram realizadas por meio de cinco modelos: multi-característica padrão, três modelos de posto reduzido ajustando os primeiros três (PC3), quatro (PC4) e cinco (PC5) componentes princi-pais genéticos e um modelo utilizando análise de fatores com três (FA3) fatores. Os modelos foram comparados pelo Critério de Informação de Akaike (AIC) e Critério Bayesiano de Schwarz (BIC), levando em consideração o número de parâmetros. Foram considerados para todos os modelos os efeitos aleatórios genético aditivo direto e residual, e os efeitos fixos do grupo de contemporâneos e o efeito linear da idade do animal ao abate como covariável, exceto para peso ao sobreano. Os mo-delos de análise de fatores e componentes principais genéticos selecionados por ambos os critérios de seleção foram o PC3 (BIC), PC4 (AIC) e o FA3 (AIC e BIC), com 46, 50 e 53 parâmetros, respectivamente. No entanto, o modelo que mais se aproximou do modelo multi-característica padrão foi o PC4, sendo então, o mais indicado devido a semelhança das estimativas de (co)variância. As estimativas de herdabilidade variaram de 0,06 (LP) a 0,31 (AOL) com MV e 0,04 (LP) a 0,30 (AOL) com o PC4. As maiores estimativas de correlações genéticas foram entre as carac-terísticas PCQ x SF, PCQ x PS e MARM x LP, sendo similar nos modelos MV e PC4. As maiores estimativas de correlações fenotípicas foram entre as característi-cas de peso e AOL (PS x SF), (PS x AOL) e (SF x AOL). Em geral, foram necessá-rios quatro componentes principais para modelar a estrutura das (co)variâncias ge-néticas para as características de crescimento, carcaça e qualidade da carne. O modelo de posto reduzido indicado pelo o AIC (PC4) reduziu o número de parâme-tros a serem estimados em 21,4%, sem diminuir a qualidade do ajuste.
Principal component (PCA) and factor analysis (FA) are widely used in data processing to reduce dimensionality. However, few studies have used these methods in a set of important traits in Nelore cattle for the purpose of modeling the covariance structure data. The aim of this study was to estimate genetic parameters for growth, carcass and meat quality traits in Nellore males, using different multivariate models to reduce the dimensionality data. A total of 3.865; 4.121; 4.109; 4.188; 4.123 3.320 and 16.575 records, hot carcass weight (HCW), 12-13th rib eye area (REA), backfat thickness (BF), marbling score (MS), warner-wraztler wear force (WBSF), lipid content (LC), and weight at 550 days (W550), respectively, were used. The analyzes were performed using five models: standard multi-characteristic, three reduced-rank models, and adjusted the first three (PC3), four (PC4) and five (PC5) genetic main components, and a model using factor analysis with three (FA3) factors. The models were compared by the Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (BIC), taking into account the parameters number. The random effects of the direct and residual additive genetic and the fixed effects of the contemporaneous group and the linear effect of animal age on slaughter were considered as covariate, except for yearling weight. The analysis models of genetic factors and main components selected by both selection criteria were PC3 (BIC), PC4 (AIC) and FA3 (AIC and BIC), with 46, 50 and 53 parameters, respectively. However, the model that most approached the standard multi-characteristic model was PC4, and is therefore most appropriate due to the similarity of covariance estimates. Estimates of heritability ranged from 0.06 (LP) to 0.31 (AOL) with MV and 0.04 (LP) to 0.30 (AOL) with PC4. The highest estimates of genetic correlations were among the characteristics PCQ x SF, PCQ x PS and MARM x LP, being similar in the MV and PC4 models. The highest estimates of phenotypic correlations were among the weight and AOL (PS x SF), (PS x AOL) and (SF x AOL) traits. In general, four main components were required to model the structure of the genetic covariances for growth, carcass and meat quality traits. The reduced rank model indicated by the AIC (PC4) reduced the parameters number to be estimated by 21.4%, without decreasing the quality of the adjustment.
Principal component (PCA) and factor analysis (FA) are widely used in data processing to reduce dimensionality. However, few studies have used these methods in a set of important traits in Nelore cattle for the purpose of modeling the covariance structure data. The aim of this study was to estimate genetic parameters for growth, carcass and meat quality traits in Nellore males, using different multivariate models to reduce the dimensionality data. A total of 3.865; 4.121; 4.109; 4.188; 4.123 3.320 and 16.575 records, hot carcass weight (HCW), 12-13th rib eye area (REA), backfat thickness (BF), marbling score (MS), warner-wraztler wear force (WBSF), lipid content (LC), and weight at 550 days (W550), respectively, were used. The analyzes were performed using five models: standard multi-characteristic, three reduced-rank models, and adjusted the first three (PC3), four (PC4) and five (PC5) genetic main components, and a model using factor analysis with three (FA3) factors. The models were compared by the Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (BIC), taking into account the parameters number. The random effects of the direct and residual additive genetic and the fixed effects of the contemporaneous group and the linear effect of animal age on slaughter were considered as covariate, except for yearling weight. The analysis models of genetic factors and main components selected by both selection criteria were PC3 (BIC), PC4 (AIC) and FA3 (AIC and BIC), with 46, 50 and 53 parameters, respectively. However, the model that most approached the standard multi-characteristic model was PC4, and is therefore most appropriate due to the similarity of covariance estimates. Estimates of heritability ranged from 0.06 (LP) to 0.31 (AOL) with MV and 0.04 (LP) to 0.30 (AOL) with PC4. The highest estimates of genetic correlations were among the characteristics PCQ x SF, PCQ x PS and MARM x LP, being similar in the MV and PC4 models. The highest estimates of phenotypic correlations were among the weight and AOL (PS x SF), (PS x AOL) and (SF x AOL) traits. In general, four main components were required to model the structure of the genetic covariances for growth, carcass and meat quality traits. The reduced rank model indicated by the AIC (PC4) reduced the parameters number to be estimated by 21.4%, without decreasing the quality of the adjustment.
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Palavras-chave
Bovinos de corte, Modelo de posto reduzido, Parâmetros genéticos, Beef cattle, Genetic parameters, Reduced rank models