SSR-based genetic analysis of sweet corn inbred lines using artificial neural networks

Abstract

Studies on genetic diversity and population structure provide basic information at the molecular level, which is a key input for breeding programs of crop species. This study evaluated the genetic diversity of 12 elite lines of sweet corn, using 20 microsatellite markers. To determine the genetic differentiation among lines, we used an artificial neural network with the self-organizing map (SOM) algorithm. This algorithm identified three genetically differentiated groups and produced relatively more accurate results than UPGMA, according to the indices of Davies-Bouldin and RMSSTD (Root Mean Square Standard Deviation). The expected heterozygosity was high (He>0.5) for 90% and the polymorphism information content high (PIC>0.6) for 40% of the SSR loci, indicating their potential to detect genetic differences among lines. The high genetic differentiation, detected by the neural network procedure, would allow the selection of promising divergent sweet corn genotypes.