Data/hora: 08/06/2024 12:34:30

Biblioteca(s): 	Embrapa Café.	
Data corrente: 	10/01/2023	
Data da última atualização: 	10/01/2023	
Autoria: 	EVANGELISTA, J. S. P. C.; PEIXOTO, M. A.; COELHO, I.; ALVES, R.; RESENDE, M. D. V. de; SILVA, F. F. e; LAVIOLA, B.; BHERING, L. L.	
Afiliação: 	JENIFFER SANTANA PINTO COELHO EVANGELISTA, UNIVERSIDADE FEDERAL DE VIÇOSA; MARCOS ANTONIO PEIXOTO, UNIVERSIDADE FEDERAL DE VIÇOSA; IGOR COELHO, UNIVERSIDADE FEDERAL DE VIÇOSA; RODRIGO ALVES, UNIVERSIDADE FEDERAL DE VIÇOSA; MARCOS DEON VILELA DE RESENDE, CNPCa; FABYANO FONSECA E SILVA, UNIVERSIDADE FEDERAL DE VIÇOSA; BRUNO LAVIOLA, EMBRAPA AGROENERGIA; LEONARDO LOPES BHERING, UNIVERSIDADE FEDERAL DE VIÇOSA.	
Título: 	Genetic evaluation and selection in jatropha curcas through frequentist and bayesian inferences.	
Ano de publicação: 	2022	
Fonte/Imprenta: 	Bragantia, v. 81, 2022.	
Páginas: 	12 p.	
DOI: 	https://doi.org/10.1590/1678-4499.20210262	
Idioma: 	Inglês	
Conteúdo: 	An accurate and efficient statistical method for genetic evaluation is a key requirement for progress in any breeding program. Thus, the present study aimed to evaluate the performance of Frequentist and Bayesian inferences for repeated measures analysis in Jatropha curcas breeding. To this end, 730 individuals from 73 half-sib families were evaluated for grain yield trait, over six crop years. Frequentist and Bayesian analyses were made considering repeatability models with different residual variance structures. Variance components were estimated through restricted maximum likelihood (REML) and Markov Chain Monte Carlo (MCMC). Genetic values were predicted through best linear unbiased prediction (BLUP) and estimated through MCMC. Variance components and genetic and non-genetic parameters estimated by the Frequentist inference presented values similar to those estimated by the Bayesian inference. The selective accuracy presented high magnitude (0.84) by the Frequentist and Bayesian inferences, indicating high reliability. Confidence and highest posterior density (HPD) intervals were similar for the genetic parameters, however the HPD intervals range was slightly short. This study highlighted the importance of testing the residual variance structure and pointed out that the Frequentist and Bayesian inferences presented similar results when using non-informative prior. Then, both inferences can be efficiently applied in Jatropha curcas breeding.	
Thesaurus Nal: 	Bayesian theory; Genetic variance; Jatropha; Plant breeding; Plant selection guides.	
Categoria do assunto: 	G Melhoramento Genético	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1150826/1/Genetic-evaluation-and-selection.pdf	
Registro original: 	Embrapa Café (CNPCa)	
Exemplares: 	CNPCa - (1666-1) - UPC - AP - DD -  -  -  - DIS