Comparison of statistical methods for genetic similarity evaluation of maize inbred lines

Conventional breeding methods have been aided by molecular genetic techniques giving the chance for efficient improvement in creation of maize hybrids. Proper choice of statistical methods for data analysis is very important because it ensures greater reliability. The aim of this study was to determine the most suitable statistical approach for molecular marker data analysis. SSR markers were used for the analysis of 10 maize inbreds. Genetic similarity/distance was calculated using three types of data: binary, allele frequency based on densitometry and allele frequency according to band size data applying Simple matching, Jaccard's and Rogers' coefficient. Cluster analysis was performed in NTSYS, 2.11a software. The highest value for Spearman's rank of correlation (0.95) was detected between distance matrices based on binary data. The results showed that binary data Jaccard's coefficient) and allele frequency data based on fragment sizes (Rogers' coefficient) gave identical clusters by visual inspection and according to CIc index.