Multilayer perceptron applied to genotypes classification in diallel studies

Abstract

In the last decades, a new trend to use more refined analytical procedures, such as artificial neural networks (ANN), has emerged to be most accurate, efficient, and extensively applied for mining and data prediction in different contexts, including plant breeding. Thus, this study was developed to establish a new classification proposal for targeting genotypes in breeding programs to approach classical models, such as a complete diallel and modern prediction techniques. The study was based on the standard deviation values of an interpopulation diallel and it also verified the possibility of training a neural network with the standardized genetic parameters for a discrete scale. We used 12 intercrossed maize populations in a complete diallel scheme (66 hybrids), evaluated during the 2005/2006 crop season in three different environments in southern Brazil. The implemented MLP architecture and other associated parameters allowed the development of a generalist model of genotype classification. The MLP neural network model was efficient in predicting parental and interpopulation hybrid classifications from average genetic components from a complete diallel, regardless of the evaluation environment.

Additive and Non-Additive Effects on the Control of Key Agronomic Traits in Popcorn Lines under Contrasting Phosphorus Conditions

“…According to Sprague and Tatum [36], informed by Inocente et al [37], the GCA corresponds to the average behavior of a line in a series of hybrid combinations, and is expressed by the ĝi estimate. For Cruz and Vencovsky [38], a low value of ĝi suggests that the average of the hybrids in which line i participates does not differ much from the overall average of the diallel, meaning that when there are high values, positive or negative, parent i is superior or inferior to the other parents in the diallel when compared to the average of their hybrids.…”