Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (2): 310-316.DOI: 10.3969/j.issn.1004-1524.2022.02.12

• Horticultural Science • Previous Articles     Next Articles

Genetic diversity analysis of 151 cherry tomato resources in Guizhou Province

PEI Yun1,2(), XU Xiuhong1,2, LU Jinbiao1, CHEN Amin1, ZHANG Wanping1,*()   

  1. 1. College of Agriculture, Guizhou University, Guiyang 550025, China
    2. Institute of Vegetable Research, Guizhou University, Guiyang 550025, China
  • Received:2020-09-15 Online:2022-02-25 Published:2022-03-02
  • Contact: ZHANG Wanping

Abstract:

In this study, agronomic traits (13 quality traits and 11 quantitative traits) of 151 local cherry tomato resources in Guizhou were evaluated by variation level, genetic diversity, principal component analysis and cluster analysis. The results showed that genetic diversity index of the quality traits was lower than quantitative traits, among the quality traits, Shannon-Wiener diversity index of leaf color was the highest.The highest quantitative trait was the longitudinal diameter of the fruit, the coefficient of variation of ventricular number was the highest (32.47%). The relationships between different traits were complex, the accumulative contribution rate of the first 7 main inflorescence types, fruit number of single inflorescence, stalk length, fruit longitudinal diameter, fruit transverse diameter, fruit mass and leaf width was 67.633%, which included most of the information of all indexes. Based on phenotypic traits, 151 resources were divided into 9 groups at a genetic distance of 3.0 by systematic cluster intergroup polymerization. The first group contained 36 resources, which were mainly characterized by round fruit, early ripening and very early ripening. In group 8, No. 30 and No. 76 had strong growth ability and excellent performance; group 9 contained 17 resources, which were characterized by low single fruit weight and medium and late maturity.

Key words: cherry tomato, agronomic traits, genetic diversity analysis, principal component analysis, cluster analysis

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