To evaluate the application potential of high-density SNPs in rice distinctness, uniformity, and stability (DUS) testing, we screened 37,929 SNP loci distributed on 12 rice chromosomes based on whole-genome resequencing of 122 rice accessions. These SNP loci were used to analyze the DUS testing of rice varieties based on the correlation between the molecular and phenotypic distances of varieties according to UPOV option 2. The results showed that statistical algorithms and the number of phenotypic traits and SNP loci all affected the correlation between the molecular and phenotypic distances of rice varieties. Relative to the other nine algorithms, the Jaccard similarity algorithm had the highest correlation of 0.6587. Both the number of SNPs and the number of phenotypes had a ceiling effect on the correlation between the molecular and phenotypic distances of varieties, and the ceiling effect of the number of SNP loci was more obvious. To overcome the correlation bottleneck, we used the genome-wide prediction method to predict 30 phenotypic traits and found that the prediction accuracy of some traits, such as the basal sheath anthocyanin color, glume length, and intensity of the green color of the leaf blade, was very low. In combination with group comparison analysis, we found that the key to overcoming the ceiling effect of correlation was to improve the resolution of traits with low predictive values. In addition, we also performed distinctness testing on rice varieties by using the molecular distance and phenotypic distance, and we found that there were large differences between the two methods, indicating that UPOV option 2 alone cannot replace the traditional phenotypic DUS testing. However, genotype and phenotype analysis together can increase the efficiency of DUS testing.
Simple sequence repeat (SSR) markers are highly polymorphism, good reproducibility, abundant number and co-dominant inheritance, and were considered to be one of the preferred molecular markers for DNA fingerprints in distinctness, uniformity and stability (DUS) testing. In this study,
10 representative Dimocarpus longan Lour (longan) varieties with significant differences were selected from 63 longan varieties according to the morphological characteristics. Based on PCR amplifications of the 10 selected varieties, 24 SSR primers pairs were screened from total 300
SSR primers pairs, to establish SSR fingerprints for all 63 longan varieties. The results showed that a total of 127 alleles were detected in 63 longan varieties, with an average of 5.29 alleles for each pair of primers. The Shannon’s index of the 24 pairs of SSR markers ranged from
0.64 to 1.58, with an average of 1.20. The polymorphism information content of each locus ranged from 0.32 to 0.72, with an average of 0.58. Clustering analysis indicated that most of the varieties with close genetic relationships tended to fall in the same cluster, and only a few in different
clusters or sub-clusters. The 63 longan varieties were completely identified by the optimal combination of 6 pairs of SSR primers (LY161, LY252, LY137, LY130, LY25 and LY34). Overall, DNA fingerprints of the 63 longan varieties were constructed based on these 24 pairs of SSR primers. This
study may provide a technical support for variety identification and similar variety screening in DUS detection in longan.
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