Whole Genome Sequencing and Morphological Trait-Based Evaluation of UPOV Option 2 for DUS Testing in Rice

Abstract

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.

Identifying SNP markers associated with distinctness, uniformity, and stability testing in Egyptian fenugreek genotypes

“…Therefore, the UPOV approved the application of molecular markers on the DUS test under three models. However, the potential application of the first UPOV model in fenugreek requires the development of molecular markers associated with genes underlying various agronomic and non-agronomical traits [ 27 ], which is impractical due to the limited information on the fenugreek genome [ 20 ]. Regarding the third UPOV model, there is also a big argument because the determination of distinctness at three SNPs differences can lead to an inaccurate conclusion of the uniformity and stability results [ 27 ].…”

“…However, the potential application of the first UPOV model in fenugreek requires the development of molecular markers associated with genes underlying various agronomic and non-agronomical traits [ 27 ], which is impractical due to the limited information on the fenugreek genome [ 20 ]. Regarding the third UPOV model, there is also a big argument because the determination of distinctness at three SNPs differences can lead to an inaccurate conclusion of the uniformity and stability results [ 27 ]. Previous studies on the second UPOV model found that the correlation between the molecular and phenotypic distance is based on the type, the number, and the distribution of molecular markers and enough phenotypic data with common environmental effects [ 27 , 28 ].…”

“…Regarding the third UPOV model, there is also a big argument because the determination of distinctness at three SNPs differences can lead to an inaccurate conclusion of the uniformity and stability results [ 27 ]. Previous studies on the second UPOV model found that the correlation between the molecular and phenotypic distance is based on the type, the number, and the distribution of molecular markers and enough phenotypic data with common environmental effects [ 27 , 28 ].…”