Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2017-03-07T17:37:49Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) lorena_tavares_oliveira.pdf: 1039032 bytes, checksum: 99b6c1aac4a55a13ac7523067c7e14af (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2017-03-30T14:58:02Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) lorena_tavares_oliveira.pdf: 1039032 bytes, checksum: 99b6c1aac4a55a13ac7523067c7e14af (MD5) / Made available in DSpace on 2017-03-30T14:58:02Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) lorena_tavares_oliveira.pdf: 1039032 bytes, checksum: 99b6c1aac4a55a13ac7523067c7e14af (MD5) Previous issue date: 2016 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / Funda??o de Amparo ? Pesquisa do Estado de Minas Gerais (FAPEMIG) / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (CNPq) / Objetivou-se com este estudo estimar par?metros gen?ticos e fenot?picos para probabilidade de prenhez nas idades 15, 19, 23, 27, 31, 35, 39 e 43 meses (PP_15, PP_19, PP_23, PP_27, PP_31, PP_35, PP_39 e PP_43), em novilhas da ra?a Sindi, via modelo de limiar em regress?o aleat?ria utilizando os polin?mios ortogonais de Legendre de segunda, terceira e quarta ordem, considerando a homogeneidade e heterogeneidade de vari?ncia residual. Os componentes de covari?ncia foram estimados por abordagem Bayesiana, utilizando o programa THRGIBBS3F90. Foram inclu?dos os efeitos fixos de criador, fazenda do criador, ano de nascimento, esta??o de nascimento e como covari?vel (de efeito linear) foi considerado o peso da vaca na idade em que ela concebeu. Al?m desses efeitos para todas as idades estudadas, foram considerados os efeitos aleat?rios gen?ticos aditivos, de ambiente permanente e residual. O melhor modelo para a an?lise RRTM foi o LEG_4441 que obteve as seguintes estimativas de herdabilidade 0,34; 0,41; 0,45; 0,41; 0,37; 0,32; 0,32; 0,33 para PP_15, PP_19, PP_23, PP_27, PP_31, PP_35, PP_39, PP_43, respectivamente. As estimativas das correla??es gen?ticas obtidas entre PP_15 com PP_19, PP_23, PP_27, PP_31, PP_35, PP_39 e PP_43 foram 0,1; -0,39; -0,65; -0,83; -0,93; -0,81; -0,71, respectivamente. As estimativas das correla??es fenot?picas para PP_15 com PP_19, PP_23, PP_27, PP_31, PP_35, PP_39 e PP_43 foram respectivamente 0,12; -0,09; -0,17; -0,22; -0,26; -0,25; 0,40. A an?lise que utilizou modelo LEG_4441 foi a mais indicada para estimar os par?metros gen?ticos para a probabilidade de prenhez em diferentes idades em novilhas da ra?a Sindi, indicando que a prenhez precoce possui variabilidade gen?tica para ser inclu?da como caracter?stica alvo de sele??o em programas de melhoramento, com potencial para se obter ganhos gen?ticos satisfat?rios. / Disserta??o (Mestrado) ? Programa de P?s-Gradua??o em Zootecnia, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2016. / Our aim with this study was estimating genetic and phenotypic parameters for pregnancy probability at 15, 19, 23, 27, 31, 35, 39 and 43 month old (PP_15, PP_19, PP_23, PP_27, PP_31, PP_35, PP_39 and PP_43) in Sindhi heifers, by using threshold model in a random regression framework, adopting Legendre orthogonal polynomials of second, third and fourth order, considering homogeneity and heterogeneity of residual variance. Covariance components were estimated by Bayesian approach using THRGIBBS3F90 program. The model included the fixed effects of herd, herd of the farm, year of birth, birth season and as a covariate (with linear effect) was considered the weight of the cow at the age when she calved. In addition to these effects for all ages studied, additive genetic, permanent environmental and residual random effects were considered. The best model for the analysis was the RRTM LEG_4441 which obtained the following heritability estimates: 0.34; 0.41; 0.45; 0.41; 0.37; 0.32; 0.32; 0.33 to PP_15, PP_19, PP_23, PP_27, PP_31, PP_35, PP_39 and PP_43, respectively. Genetic correlation estimates between PP_15 with PP_19, PP_23, PP_27, PP_31, PP_35, PP_39 and PP_43 were 0.1; -0.39; -0.65; -0.83; -0.93; -0.81; -0.71, respectively. Phenotypic correlation estimates for PP_15 with PP_19, PP_23, PP_27, PP_31, PP_35, PP_39 and PP_43 were respectively 0.12; -0.09; -0.17; -0.22; -0.26; -0.25; 0.40. The analysis by using LEG_4441 model was the most appropriate to estimate genetic parameters for pregnancy probability at different ages in Sindhi heifers, indicating that early pregnancy has genetic variability to be included as a goal trait selection in breeding programs, with the potential to achieve satisfactory genetic gains.