Data/hora: 02/06/2024 03:43:11

Biblioteca(s): 	Embrapa Florestas.	
Data corrente: 	18/01/2017	
Data da última atualização: 	18/01/2017	
Autoria: 	GLÓRIA, L. S.; CRUZ, C. D.; VIEIRA, R. A. M.; RESENDE, M. D. V. de; LOPES, P. S.; SIQUEIRA, O. H. G. B. D. de; SILVA, F. F. e.	
Afiliação: 	Leonardo Siqueira Glória, UFV; Cosme Damião Cruz, UFV; Ricardo Augusto Mendonça Vieira, Universidade Estadual do Norte Fluminense; MARCOS DEON VILELA DE RESENDE, CNPF; Paulo Sávio Lopes, UFV; Otávio H. G. B. Dias de Siqueira, UFV; Fabyano Fonseca e Silva, UFV.	
Título: 	Accessing marker effects and heritability estimates from genome prediction by Bayesian regularized neural networks.	
Ano de publicação: 	2016	
Fonte/Imprenta: 	Livestock Science, v. 191, p. 91-96, Sept. 2016.	
DOI: 	http://dx.doi.org/10.1016/j.livsci.2016.07.015	
Idioma: 	Inglês	
Conteúdo: 	Recently, there is an increasing interest on semi- and non-parametric methods for genome-enabled prediction, among which the Bayesian regularized artificial neural networks (BRANN) stand. We aimed to evaluate the predictive performance of BRANN and to exploit SNP effects and heritability estimates using two different approaches (relative importance-RI, and relative contribution-RC). Additionally, we aimed also to compare BRANN with the traditional RR-BLUP and BLASSO by using simulated datasets. The simplest BRANN (net1), RR-BLUP and BLASSO methods outperformed other more parameterized BRANN (net2, net3, ? net6) in terms of predictive ability. For both simulated traits (Y1 and Y2) the net1 provided the best h2 estimates (0.33 for both, being the true h2=0.35), whereas RR-BLUP (0.18 and 0.22 for Y1 and Y2, respectively) and BLASSO (0.20 and 0.26 for Y1 and Y2, respectively) underestimated h2. The marker effects estimated from net1 (using RI and RC approaches) and RR-BLUP were similar, but the shrinkage strength was remarkable for BLASSO on both traits. For Y1, the correlation between the true fifty QTL effects and the effects estimated for the SNPs located in the same QTL positions were 0.61, 0.60, 0.60 and 0.55, for RI, RC, RR-BLUP and BLASSO; and for Y2, these correlations were 0.81, 0.81, 0.81 and 0.71, respectively. In summary, we believe that estimates of SNP effects are promising quantitative tools to bring discussions on chromosome regions contributing most effectively to the phenotype expression when using ANN for genomic predictions.	
Palavras-Chave: 	Genetic parameters; QTL; Redes neurais.	
Thesagro: 	Parâmetro Genético.	
Thesaurus Nal: 	Marker-assisted selection; Neural networks.	
Categoria do assunto: 	G Melhoramento Genético	
Registro original: 	Embrapa Florestas (CNPF)	
Exemplares: 	CNPF - (55564-1) - UPC - AP - DD -  -  -  - DIS