Genetic Diversity Analysis Of Millet Germplasm Based On SSR Molecular Markers

As one of China’s main grain and forage crops,millet occupies an extremely important position in China’s agricultural production.With the updating of the millet germplasm resources,the range of parental choices for the creation of new varieties has also been continuously narrowed,which has caused great difficulties for the work of breeding.The evaluation of genetic diversity of germplasm resources and the study of genetic distances between various germplasms can provide important basis for the selection of new millet varieties.Shanxi Province is one of the origins of millet and has abundant local breed resources.These resources contain a considerable number of excellent genes that can be used for the improvement and innovation of germplasm.In this experiment,genetic diversity and population genetic structure of 90 millet varieties in Shanxi Province were analyzed using SSR molecular markers.The experimental results are as follows:1.By using polyacrylamide gel electrophoresis,38 pairs of SSR primers were screened out from 73 pairs of SSR primers,which were clear and polymorphic,covering the whole genome of millet,which laid the foundation for the genetic diversity analysis of millet.2.In this study,the genetic diversity of 90 main grain germplasm resources and dominant cultivars in Shanxi Province was analyzed using 38 pairs of SSR primers.A total of 256 alleles were detected,with an average of 6.7 alleles per locus.The average frequency of major alleles was 0.3854,the average gene diversity was 0.7264,and the average polymorphism information content(PIC)was 0.6884,indicating that the 90 varieties tested had high genetic diversity.3.Using Power Marker V3.25 to analyze the amplification results of 38 pairs of SSR primers,the genetic distance between the two materials was obtained,which ranged from 0.2632 to 0.9737 with an average of 0.7264.Among them,the lowest genetic distance(0.2632)was from Shantong glutinous rice(L15320)and bald glutinous rice(L15322)from Wutai county in Xinzhou city of Shanxi Province.The largest genetic distance(0.9737)was from blind old cartilage from Xinzhou area(X15385)It is from Huangshan Mountain Valley(C15196)provided by Changzhi(Caozi).4.Based on the genetic distance,the UPGMA and NJ algorithms were used to cluster the 90 millet materials.The clustering results of the two clustering methods were basically the same.The 90 materials were clustered into 3 large groups and further divided into 5 small subgroups.STRUCTER software for population structure analysis,the results and cluster analysis results are basically the same,divided into five subgroups.Subgroup I contains 18 materials,mainly from counties in Xinzhou and several from Changzhi(millet);Subgroup II includes 24 materials,mainly from Changzhi,which also includes From Luliang region,Xinzhou region,and several maize cultivars from Shanxi Province provided by Ma Jianping.Subgroup III contains 10 materials,mostly from Changzhi.Only red flag 1 is the bred variety from Xintai Wutai County.Group Ⅳ contains 22 materials,including Ma Jianping provided three varieties of Shanxi millet breeding,two Luliang materials,four Xinzhou area materials,the rest are materials Changzhi City;Subgroup Ⅴ contains 16 The more complex materials and geograp Hical sources contain most of the sources of materials sourced from this study.5.By calculating the genetic diversity and genetic distance between populations,it was found that the subpopulation II population was the largest,the polymorphism information content(PIC),the number of genotypes,and the gene diversity were the highest.The subpopulation III was opposite to the subpopulation II and above indicators.The genetic distance between subgroup I and subgroup III was the highest(0.4576)The genetic distance between subgroup II and subgroup IV was the smallest(0.3488).6.Among the nine agronomic traits investigated,the highest index of genetic diversity was single ear weight,which was 2.0499;the second was kernel weight,which was 2.0053;the lowest was grain color,which was 0.061.Correlation analysis showed that the correlation between spike weight and single ear weight was the strongest in the positive correlation,the correlation coefficient was 0.94;the correlation between spike compactness and spike length was second,the correlation coefficient was 0.54;in Among the negative correlations,the correlation between spike density and stubble density was the strongest,the correlation coefficient was-0.45;the correlation between the stubble yield and the provenance and panicle length followed,and the correlation coefficients were-0.23 and-0.22,respectively.