Artificial neural networks have shown great potential when applied to breeding programs. In this study, we propose the use of artificial neural networks as a viable alternative to conventional prediction methods. We conduct a thorough evaluation of the efficiency of these networks with respect to the prediction of breeding values. Therefore, we considered eight simulated scenarios, and for the purpose of genetic value prediction, seven statistical parameters in addition to the phenotypic mean in a network designed as a multilayer perceptron.
The study of quantitative trait effects is of great significance for molecular marker-assisted breeding. The accuracy of quantitative trait loci (QTL) mapping is the key factor affecting marker-assisted breeding, and is extremely significant. The effect of different heritability rates (10, 30, 50, 70, and 90%) on the accuracy of QTL mapping of five recombinant inbred lines (RILs) were analyzed via computer simulation. RILs display additive and epistatic genetic effects.