Data/hora: 26/05/2024 00:41:57

Biblioteca(s): 	Embrapa Florestas.	
Data corrente: 	18/01/2017	
Data da última atualização: 	18/01/2017	
Autoria: 	ALMEIDA FILHO, J. E. de; GUIMARÃES, J. F. R.; SILVA, F. F. e; RESENDE, M. D. V. de; MUÑOZ, P.; KIRST, M.; RESENDE JUNIOR, M. F. R.	
Afiliação: 	J. E. de Almeida Filho, University of Florida; J. F. R. Guimarães, University of Florida; F. F. e SILVA, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; P. Muñoz, University of Florida; M. Kirst, University of Florida; M. F. R. Resende JUnior, RAPiD Genomics LLC.	
Título: 	The contribution of dominance to phenotype prediction in a pine breeding and simulated population.	
Ano de publicação: 	2016	
Fonte/Imprenta: 	Heredity, v. 117, p. 33-41, July 2016.	
DOI: 	10.1038/hdy.2016.23	
Idioma: 	Inglês	
Conteúdo: 	Pedigrees and dense marker panels have been used to predict the genetic merit of individuals in plant and animal breeding, accounting primarily for the contribution of additive effects. However, nonadditive effects may also affect trait variation in many breeding systems, particularly when specific combining ability is explored. Here we used models with different priors, and including additive-only and additive plus dominance effects, to predict polygenic (height) and oligogenic (fusiform rust resistance) traits in a structured breeding population of loblolly pine (Pinus taeda L.). Models were largely similar in predictive ability, and the inclusion of dominance only improved modestly the predictions for tree height. Next, we simulated a genetically similar population to assess the ability of predicting polygenic and oligogenic traits controlled by different levels of dominance. The simulation showed an overall decrease in the accuracy of total genomic predictions as dominance increases, regardless of the method used for prediction. Thus, dominance effects may not be accounted for as effectively in prediction models compared with traits controlled by additive alleles only. When the ratio of dominance to total phenotypic variance reached 0.2, the additive?dominance prediction models were significantly better than the additive-only models. However, in the prediction of the subsequent progeny population, this accuracy increase was only observed for the oligogenic trait.	
Thesagro: 	Árvore conífera.	
Categoria do assunto: 	G Melhoramento Genético	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/153479/1/2016-M.Deon-H-TheContribution.pdf	
Registro original: 	Embrapa Florestas (CNPF)	
Exemplares: 	CNPF - (55558-1) - UPC - AP - DD -  -  -  - DIS