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

文献类型： 外文期刊

作者： Liu, Hong 1 ; Rao, Dehua 2 ; Guo, Tao 1 ; Gangurde, Sunil S. 3 ; Hong, Yanbin 6 ; Chen, Mengqiang 2 ; Huang, Zhanquan 2 ; Jiang, Yuan 2 ; Xu, Zhenjiang 2 ; Chen, Zhiqiang 1 ;

作者机构： 1.South China Agr Univ, Natl Engn Res Ctr Plant Space Breeding, Guangzhou, Guangdong, Peoples R China

2.South China Agr Univ, Coll Agr, Guangzhou, Guangdong, Peoples R China

3.Int Crops Res Inst Semi Arid Trop, Hyderabad, India

4.USDA ARS, Crop Protect & Management Res Unit, Tifton, GA USA

5.Univ Georgia, Dept Plant Pathol, Tifton, GA USA

6.Guangdong Acad Agr Sci, Crops Res Inst, Guangdong Prov Key Lab Crops Genet Improvement, Guangzhou, Peoples R China

关键词： rice; genotype; phenotype; SNP; correlation analysis; DUS; distinctness; genomic prediction

期刊名称：FRONTIERS IN GENETICS （ 影响因子：4.772； 五年影响因子：4.933 ）

ISSN：

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： 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.

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