02443naa a2200277 a 450000100080000000500110000800800410001902400560006010000190011624501430013526000090027852015710028765000300185865000200188865000250190865300230193365300080195665300180196470000160198270000210199870000250201970000170204470000320206170000190209377300530211220611382017-01-18       2016    bl uuuu     u00u1 u    #d7 ahttp://dx.doi.org/10.1016/j.livsci.2016.07.0152DOI1 aGLÓRIA, L. S.  aAccessing marker effects and heritability estimates from genome prediction by Bayesian regularized neural networks.h[electronic resource]  c2016  aRecently, 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.  aMarker-assisted selection  aNeural networks  aParâmetro Genético  aGenetic parameters  aQTL  aRedes neurais1 aCRUZ, C. D.1 aVIEIRA, R. A. M.1 aRESENDE, M. D. V. de1 aLOPES, P. S.1 aSIQUEIRA, O. H. G. B. D. de1 aSILVA, F. F. e  tLivestock Sciencegv. 191, p. 91-96, Sept. 2016.