Use este identificador para citar ou linkar para este item: https://locus.ufv.br//handle/123456789/23555
Tipo: Artigo
Título: Accessing marker effects and heritability estimates from genome prediction by Bayesian regularized neural networks
Autor(es): Glória, Leonardo Siqueira
Cruz, Cosme Damião
Vieira, Ricardo Augusto Mendonça
Resende, Marcos Deon Vilela de
Lopes, Paulo Sávio
Silva, Fabyano Fonseca e
Siqueira, Otávio H. G. B. Dias de
Abstract: 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: Genomic selection
Artificial neural networks
QTL
Genetic parameters
Editor: Livestock Science
Tipo de Acesso: 2016 Elsevier B.V. All rights reserved.
URI: https://doi.org/10.1016/j.livsci.2016.07.015
http://www.locus.ufv.br/handle/123456789/23555
Data do documento: Set-2016
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