Evaluation of genetic diversity among Korean wheat using RAPD and ISSR analysis.
Won Cheol Yim, Kang MoonSeok, Kwon YoungUp, Jang CheolSeong, Seo YongWeon, Sung-Don Lim, Kyung‐Hee Kim, Moon JunCheol, Sang-Kyu Lee, Byung‐Moo Lee
Abstract
Won Cheol Yim, Kang MoonSeok, Kwon YoungUp, Jang CheolSeong, Seo YongWeon, Sung-Don Lim, Kyung‐Hee Kim, Moon JunCheol, Sang-Kyu Lee, Byung‐Moo Lee
Abstract
The genetic diversity was evaluated using RAPD and ISSR among natural populations and Korean wheat cultivars (Triticum aestivum). Understanding the genetic diversity of putative parental and wild stocks would be useful in wheat breeding programs. Ninety three populations were evaluated with fifty RAPD and three ISSR primers. A total of 185 RAPD and ISSR polymorphism were produced. These markers were considered to estimate the genetic distance among accessions. The genetic similarity ranged from 0.41 to 0.86. The dendrogram were constructed by using the UPGMA clustering algorithm based on genetic similarity. The genetic diversity within and among accession was assessed through Principal Component Analysis (PCA) for statistics analysis. In cluster analysis, four groups were clustered and 17 accessions were not clustered. The PCA was corresponded well to the result. This study provides basic information about the genetic relationships for breeding purposes.
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The genetic diversity was evaluated using RAPD and ISSR among natural populations and Korean wheat cultivars (Triticum aestivum). Understanding the genetic diversity of putative parental and wild stocks would be useful in wheat breeding programs. Ninety three populations were evaluated with fifty RAPD and three ISSR primers. A total of 185 RAPD and ISSR polymorphism were produced. These markers were considered to estimate the genetic distance among accessions. The genetic similarity ranged from 0.41 to 0.86. The dendrogram were constructed by using the UPGMA clustering algorithm based on genetic similarity. The genetic diversity within and among accession was assessed through Principal Component Analysis (PCA) for statistics analysis. In cluster analysis, four groups were clustered and 17 accessions were not clustered. The PCA was corresponded well to the result. This study provides basic information about the genetic relationships for breeding purposes.
Key concepts: RAPD, UPGMA, Genetic diversity, Dendrogram, Biology, Principal component analysis, Genetic similarity, Genetic distance