Self-organizing maps in the study of genetic diversity among irrigated rice genotypes

Abstract

This study presents self-organizing maps (SOM) as an alternative method to evaluate genetic diversity in plant breeding programs. Twenty-five genotypes were evaluated in two environments for 11 phenotypic traits. The genotypes were clustered according to the SOM technique, with variable topology and numbers of neurons. In addition to the SOM analysis, unweighted pair group method with arithmetic mean clustering (UPGMA) was performed to observe the behavior of the clustering when submitted to these techniques and to evaluate their complementarities. Genotype ordering according to SOM was consistent with UPGMA results, evidenced by the basic structure of UPGMA groups being preserved in each group of the maps. Regarding genotype arrangement and the group neighbors, maps involving five neurons presented inferior organization efficiency compared to the six-map arrangements in both environments. It was observed that the organization pattern among the rice genotypes evaluated by the maps was complementary to the UPGMA approach, as observed in all scenarios. It can be concluded that self-organizing maps have the potential to be useful for genetic diversity studies in breeding programs.

Keywords

• Oriza sativa L.
• computational intelligence
• clustering technique
• SOM.