1. Evaluation of germplasm resources of <italic>Sarcomyxa edulis</italic> in the Changbai Mountains of Northeast China.
- Author
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Miao, Liu, Dong, Qingsong, Jin, Yuanju, Priyashantha, A. K. Hasith, Jiang, Wanzhu, Zhang, Chunlan, and Xu, Jize
- Subjects
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GERMPLASM , *CYTOLOGICAL techniques , *CLUSTER analysis (Statistics) , *EUCLIDEAN distance , *GENETIC variation - Abstract
In this study, we evaluated 16 strains of
Sarcomyxa edulis from the Changbai Mountains, China through antagonism tests and ISSR molecular marker technique for cytological and molecular biological evaluation. Q-type and R-type cluster analyses were utilised to evaluate and classify agronomic traits according to the test guidelines for distinctness, uniformity, and stability. The tested strains showed antagonistic reactions. The differences in antagonistic reactions among different germplasm resources are helpful for evaluating genetic diversity and provide a basis for better development and utilisation of fungal germplasm resources. A total of 330 bands were amplified by 21 ISSR primers, including 321 polymorphic bands. The percentage of polymorphic bands observed was 97.07%. Using PopGen32 software, the effective number of alleles (Ne), Nei's gene diversity index (H), and Shannon's diversity index (I) were calculated, yielding average values of 1.6638, 0.3796, and 0.5480, respectively. In addition, the genetic similarity coefficient of 16 strains ranged from 0.4306–0.7778. According to the Q-type clustering, the tested strains were classified into two categories based on the Euclidean distance of 8.52. While R-type cluster analysis revealed a significant correlation between the traits. At the Euclidean distance of 6.298, the traits could be classified into three distinct categories. According to the findings, all 16 strains demonstrated high genetic diversity. The ISSR clustering results revealed that strains with shorter genetic distances were more similar in specific traits. However, there were notable differences between the results of comprehensive trait clustering (Q-type clustering) and ISSR clustering. [ABSTRACT FROM AUTHOR]- Published
- 2024
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