Application of Simple Sequence Repeat (SSR) markers helps in distinguishing a considerably high degree of polymorphism in rice and is predominantly suitable for estimating genetic diversity among closely related genotypes. In this study, A total of twenty two microsatellite molecular markers were used across 21 rice genotypes (3 cultivars from China and 18 a new introgression lines carrying African rice genes) for their characterization and discrimination. The goal of this research is to provide genetic identity for introgression lines carrying African rice genes and detect the genetic correlations among these genotypes to construct a DNA fingerprinting database of these genotypes.
All the experiments for the microsatellite analysis were carried out at lab of MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Sciences and Technology, Huazhong Agricultural University (HZAU), Wuhan, China
In this study, Genomic DNA was extracted from young leaves of the seedlings according to the modified CTAB method, also PCR products separated on 2.0% agarose gels, stained with ethidium bromide and photographed under UV light. PCR products were separated on 6% denature polyacrylamide gel Electrophoresis. Gels were stained with silver nitrate. SSR method was employed in the PCR technique to determine the levels of polymorphism. The obtained banding pattern indicates a high level of polymorphism. The number of alleles per locus ranged from two to six and the average number of polymorphic alleles per marker was 4.1.
The results showed that there are several genetic differences between these different genotypes, which could be lead to identify them. These genotypes clustered into eight groups mainly according to their pedigree. The lowest diversity was found between ILA65 and ILA78 (similarity level 98%) that strengthen the supposition of close relationship between them. And the highest diversity was found between J23B and other genotypes used in this study at similarity level 35%. Principal component analysis was also discussed and the results showed that the PC1 contributed 65.1389% followed by PC2 7.8560% and cumulative variance of first two PCA was 72.9948%. The results were close similarity of the results obtained based on unweighted pair group method with Arithmetic mean (UPGMA). The similarity ranged from 0.2308 to 0.9780 and the average similarity among all genotypes was 0.6807. Highest similarity (0.9780 ) observed between ILA65 and ILA78, whereas lowest similarity (0.2308 ) obtained between ILA19 and J23B, ILA123 and J23B. The similarity coefficients of J23B with all the other genotypes ranged from 0.2308 to 0.4396.
In the 22 selected higher genetic polymorphism primer pairs, RM297, RM208, RM337, RM336, RM213, RM26, RM342, RM186, RM202, RM310, RM80, RM274 and RM121 were able to amplify stable, clear and easily identified bands. That will be helpful for identification and construct DNA fingerprinting database for the genotypes used in this study. Finally we could extract ID for each genotype.
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