Artificial neural network analysis of genetic diversity in Carica papaya L.

Abstract

The study of genetic diversity is fundamental in the preliminary selection of accessions with superior characteristics and for a successful use of these genotypes in breeding programs. The purpose of this study was to evaluate, as a strategy for genetic diversity analysis, the bioinformatics approach called artificial neural network. Based on the average of three growing seasons, eight quantitative traits and thirty-seven papaya accessions were evaluated in a randomized complete block design, with two replications. By Anderson's discriminant analysis, 91.90 % of the accessions were correctly classified in the groups previously defined by artificial neural network. It was concluded that the technique of artificial neural network is feasible to classify the accessions. The presence of significant genetic diversity among accessions was observed.