Genetic Diversity Analysis of Phenotypic Traits in 41 Cultivars of Oriental Cherry

Twelve quantitative traits and 30 qualitative traits were selected from 41 cultivars of oriental cherry as experimental materials.The genetic diversity of phenotypic traits of cultivars of oriental cherry was studied by diversity analysis,correlation analysis,cluster analysis and principal component analysis,in order to provide reference for breeding of new cultivars of oriental cherry and identification of cultivar resources.The results showed that the lowest coefficient of variation of leaf shape index was 9.35%,and the highest coefficient of variation of total pedicel length was 64.20%.The petal length was significantly correlated with petal width and diameter,the correlation coefficients were0.839 and 0.863,respectively（P＜0.01）.The information index（H）and genetic diversity index（D）of 30 quality traits varied from 0 to 1.41 and 0 to 0.71,respectively.The diversity of petal number,petal shape,tree appearance,petal color and calyx tube shape was abundant.R-type cluster analysis showed that 42 phenotypic traits were divided into three groups at Euclidean distance of about 21,and each group had a strong correlation.The 41 cultivars of oriental cherry were divided into three groups by Q-cluster analysis when the Euclidean distance was about 24.The first group could be used for breeding cultivars with many petals,large flower diameter and rich colors.The second group could be used for breeding cultivars with drooping branches.The third group could be used for breeding single petal and early flowering cultivars.Principal component analysis results showed that the first six principal components accounted for 76.43%,mainly reflects flower diameter,pedicel diameter,flower period,flower shape,petal number,petal length and petal width,combined with the samples based on the first two principal components value scatter plot as a result,you could pick and choose the above these traits related evaluation.Thus,the efficiency of oriental cherry resource identification and evaluation could be improved.