Molecular markers & plant breeding
Farideh Nik khoy; Mohammadreza Shiri
Abstract
Comparing different methods of estimating the genetic diversity could define their usefulness in plant breeding programs. In this study, a total of 18 morphological traits and 20 simple sequence repeat (SSR) loci were used to study the morphological and genetic diversity among 20 maize hybrids selected ...
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Comparing different methods of estimating the genetic diversity could define their usefulness in plant breeding programs. In this study, a total of 18 morphological traits and 20 simple sequence repeat (SSR) loci were used to study the morphological and genetic diversity among 20 maize hybrids selected from different countries, and to classify the hybrids into groups based on molecular profiles and morphological traits. To collect morphological data, a field experiment was carried out using an RBCD design with three replications in Moghan, Ardabil, Iran. The highest estimates for genetic coefficients of variation were observed in anthesis-silking interval, followed by grain yields, leaf chlorophyll rates, kernel row numbers, and ear heights. The total number of PCR-amplified products was 84 bands, all of which were polymorphic. Among the studied primers,NC009,BNLG1108,BNLG1194,PHI026 and PHI057 showed the maximum polymorphism information content(PIC) and the greatest diversity. To determine the genetic relationship among maize hybrids, the cluster analysis was performed based on both morphological traits(using the Ward method) and SSR markers (using the CLINK method). The cluster analysis of morphological traits divided the maize hybrids into five groups. Furthermore, Maize hybrids were divided into seven main groups based on SSR markers. Principal coordinate analysis (PCoA) of a similarity matrix of hybrids for SSR data showed that the first 15 coordinates explained 97.21% of the total variance, whereas the first two coordinates explained only 33.14% of the total variance. Generally, results indicated that SSR markers were able to classify closely related maize hybrids more efficiently than morphological traits.