Genetic relationship and population genetic structure analysis of 100 accessions of grape germplasm resources based on SSR markers

Abstract:【Objective】100 accessions of grape resources were selected from the National Grape Germ- plasm Resource Center in Taigu, Shanxi through phenotypic character identification for SSR analysis in order to reveal their genetic relationship and population genetic structure and provide references for the scientific management of grape germplasm resources and molecular marker-assisted breeding.【Methods】The modified CTAB was used to extract the genomic DNA of the tested grape germplasm. Based on the SSR primers extracted from the grape genome sequencing, 33 pairs of core polymorphic primers were screened out, and the DNA was amplified according to standard molecular weight records using capillary electrophoresis technology. According to the polymorphic bands, the data matrix was obtained. The number of alleles Na (Na), the number of effective alleles (Ne), the Shannon’s information index (I), the expected heterozygosity (He), and the observed heterozygosity were calculated using Gen Al Ex6.503 software .At the same time, Gen Al Ex6.503 software were used to count the allelic composi-tion of each pair of SSR primers in 100 accessions of grape germplasm resources, including the number of rare alleles (≤1%), the number of common alleles (1%–20%), and the most common allele (20%). The site polymorphism information content (PIC) of 33 pairs of SSR primers and the Nei’s Genetic dis- tance between 100 accessions of grape germplasm resources were calculated using Power Marker soft- ware. The NJ adjacent cluster trees of 100 grape germplasm resources based on the Nei’s genetic dis- tance were constructed using MEGA7.0 software. The STRUCTURE software was used to infer the ge- netic structure of the original grape set and the core set based on the Bayesian clustering analysis meth- od. The population number (K) was set to 1-10, and each K value was simulated 10 times, and the num- ber of iterations (length of burn -in period) was set. The Markov Chain Monte Carlo (MCMC) at the be- ginning was 100 000 times, and the MCMC after no-count iterations was 1 000 000 times. Finally, the structure results were imported into the online website Structure Harvester to predict and determine the best K value (number of groups).【Results】A total of 423 alleles were detected in 33 pairs of SSR mark- ers in 100 accessions of grape germplasm resources. Each pair of primers amplified 4 to 26 bands, and the average number of amplified bands was 12.82. The number of rare alleles (≤1%) in the allele compo- sition amplified by 33 pairs of SSR primers in 100 accessions of grape germplasm resources ranged from 0 to 9, with an average of 3.39, and the total number of alleles (1%-20%) was 8.26, and the most common number of alleles (>20%) was 1.67. The Nei’s genetic distance between 100 grape germplasm resources was about 0.4 to 0.8, accounting for 97.80% of the total resources, 48.22% was about 0.6 to 0.7. 27.17% is about 0.5 to 0.6, 17.03% was about 0.7 to 0.8. The NJ genetic clustering tree was built based on the genetic distance using software. The 100accessions of grape germplasm resources were di- vided into 4 groups. In order to further understand the genetic relationship between different grape germ- plasms, a principal coordinate analysis (PCoA) was performed on 100 accessions of grape germplasm re- sources based on the genetic distance matrix. From the results, The best dividing group number of K was 2, which meant the core set of grape could be divided into two subgroups. In general, the two popula- tions were the fresh grape populations and the hybrid populations including European hybrids, American hybrids, Chinese wild species and European wine-making varieties. By the analysis of our research re- sults, the rootstocks from American species and the wild species from Shanxi were clustered together. Meanwhile, they were also clustered together with the European and American hybrids and the wine- making varieties. The reason for this might be the mix of the genetic background of the varieties due to the artificial breeding and selection for different purpose. Although the clustering results were basically consistent with the geographical origins of the various groups, there were also 1 rootstock species and 1 wild species in Eurasian species, or European and Asian hybrids in Eurasian species. The analysis was due to the expansion The process was not a complete genome sequence, so there were some varieties that just did not show up. The specific reasons need to analyze further. In addition, the first of the two clusters were mainly the table grape varieties of grape subspecies, and the second cluster mainly includ- ed four subpopulations of Eurasian wine-making varieties, European and American hybrids, American species, and Chinese wild species. From the results, it could be seen that the varieties of Eurasian species appeared in different clusters due to different usages; and several other species were also clustered in this cluster. The analysis of the reason may be due to the difference in the amount of ancient or tradition- al genes caused by the difference in the degree of evolution or the way of evolution.【Conclusion】This article provided new evidences for analysis of the genetics relationship between the wild species and the cultivated varieties through genetic relationship of genotypes and analysis of gene structures.