| There are only a few reports on the development and application of SSR markers and SNP markers have not been studied because the genome is still unavailable in sweetpotato, Ipomoea batatas (L.) Lam.. In the present study, SSR, SNP and morphological markers were used to analyze the genetic diversity of a sweetpotato collection. The main results were as follows:1. A total of 70 SSR pairs were screened and 30 of them showed good quality polymorphism. These 30 SSR pairs were used to amplify the 380 sweetpotato accessions and generated 122 alleles, ranging from 1 to 9 per pair, with an average of 4.07. PCA analysis based on SSR markers classified the 380 accessions into 3 polulations, namely P1, P2 and P3. The different accessions from the same region could not be clustered together, in accordance with the results of phylogenetic tree analysis. Phylogenetic tree analysis indicated the mean genetic distance among landraces> the mean genetic distance between modern cultivars and landraces>the mean genetic distance among modern cultivars. Genetic diversity index varied from 0 to 0.39,0 to 0.30, and 0 to 0.49 in PI, P2 and P3, respectively. PIC values changed from 0 to 0.31,0 to 0.31, and 0 to 0.37 in PI, P2 and P3, respectively. AMOVA analysis revealed that 16.47%(P< 0.001) of total molecular variance were attributed to genetic differentiation among populations and 83.53%(P< 0.001) attributed to variations within populations.2. Based on SLAF-seq technology, SNP markers were, for the first time, developed in sweetpotato. A total of 13 744 polymorphic SNP sites were discovered and the number of sites changed from 1 to 122 per accession, with an average of 48.39. The SNP sites classified the 300 sweetpotato accessions into 9 populations, which distinguished modern cultivars and landraces. Phylogenetic tree analysis indicated the mean genetic distance between modern cultivars and landraces> the mean genetic distance among landraces> the mean genetic distance among modern cultivars. The gentic diversity was different within different regions and the maximum genetic diversity (0.03-0.90) was found in Guangdong. Most of the regions exhibited the similar PIC value of 0.05-0.50. AMOVA analysis indicated that 0.05%(P< 0.001) of molecular variance were attributed to genetic differentiation among populations,0.56%(P< 0.001) attributed to variations among accessions within populations, and 99.39%(P< 0.001) attributed to variations within accessions.3. A total of 16 traits, including general inmature leaf outline, number of inmature leaf lobes, type of inmature leaf lobes, inmature leaf color, general mature leaf outline, number of mature leaf lobes, type of mature leaf lobes, mature leaf outline, mature leaf color, base of leaf vine pigmentation, base of petiole pigmentation, predominant color of petiole, predominant color of vine, predominant color of leaf vine, abaxial leaf vine pigmentation, yield and dry matter content, were used to analyze the genetic diversity of the 123 sweetpotato accessions. Except for inmature leaf color, mature leaf color, predominant color of leaf vine and predominant color of petiole, the remaining traits had more genetic variations. Person correlation analysis indicated that different degrees of correlations were observed between different traits and the highest correlation (r=0.848) was found between base of leaf vine pigmentation and leaf vine color, followed by the correlation (r=0.832) between base of leaf vine pigmentation and base of petiole pigmentation. Abaxial leaf vein pigmentation was poorly correlated with other traits. Principle components analysis demonstrated that the extracted 5 principle components contributed 66.05% of the total variability. The cluster analysis based on the 5 principal components classified the 123 sweetpotato accessions into the 3 populations and accessions from the same region could not be clustered together.4. Based on the results of PCA, phylogenetic tree and genetic diversity analyses, the assessing efficiency was SNP markers>SSR markers>morphological markers. |
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