The Visual Heat Map Analysis of Correlation and Principal Component Clustering of Different Peanut Varieties
In order to evaluate and analyze the genetic diversity of different peanut cultivars,60 peanut cultivars were selected as the research objects,and 16 main agronomic traits were analyzed by variation analysis,correlation evaluation,principal component analysis and cluster analysis.The results showed that the coefficient of variation of agronomic characters ranged from 6.32％to 52.81％,and linoleic acid had the largest coefficient of variation,and 13 characters had a coefficient of variation greater than 10％,showing rich genetic information and diversity.Hundred kernel weight and growth period were positively correlated with pod yield.Main stem height and side branch length,pod yield and seed kernel yield,number of fruit branches and total branch number,hundred kernel weight and hundred fruit weight,growth period,linoleic acid content were positively correlated.There were 6 principal components with eigenvalue greater than 1,and the cumulative contribution rate was 81.64％.Cluster analysis was conducted on 11 traits screened by principal component analysis,including main stem height,lateral branch length,total number of branches,number of fruiting branches,hundred fruit weight,fruit satiation rate,and hundred kernel weight.The 11 traits were divided into 6 groups,and the 60 varieties were divided into 8 groups,and each group features outstanding traits.Groups 1 and 2 included all high oleic acid peanut varieties;the fourth group has the most aggregate varieties,which belong to the varieties with higher yield and larger pods.The objec-tive evaluation and reasonable classification can provide theoretical reference for the setting of peanut breeding target and the selection of new varieties.
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