RAO Qinglin
Oil Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550006JIANG Min
Oil Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550006LYU Jianwei
Oil Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550006HU Tinghui
Oil Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550006CHENG Liangqiang
Oil Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550006WANG Jinhua
Oil Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550006WANG Jun
Oil Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550006Oil Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550006
Foundation projects: Science and Technology Program of Guizhou Province(ZK [2023],ZK[2022],ZK[2021]);National Peanut Industry Technology System (CARS-13);National Key Research and Development Program(2022YFD1100303);Research and Experimental Platform Construction of Characteristic Oil Resources Utilization in Guizhou Mountainous Area(Qianke zhongyindi[2020]4012)
The objective of this study was to investigate the correlation between the quality traits of peanut germplasm resources from different sources and provide a theoretical basis for the rational exploration of fresh peanut germplasm resources. We utilized genetic diversity analysis, correlation analysis, principal component analysis , clustering analysis, and comprehensive score to analyze and assess the 11 quality traits of 287 peanut germplasm resources.The results showed that the coefficient of variation of 11 quality traits ranged from 1.286% to 19.506%, and the genetic diversity index ranged from 1.046 to 2.073. The results of correlation analysis showed that the oleic acid content has an extremely significant negative correlation with proteins content and an extremely significant positive correlation with sucrose content. A total of three principal component factors were extracted from the principal component analysis, and their cumulative contribution rate reached 71.467%. Cluster analysis divided the 287 materials into 3 groups.The first group has a higher content of fat and stearic acid,which contains 100 materials;the second group has a higher oleic acid content ,which contains 61 materials;and the third group has the characteristics of high protein content and low fat content,which contains 126 materials. By assigning weights of the contribution rate of the 3 principal components, we constructed a comprehensive scoring formula: F=0.588F1+0.277F2+0.135F3. According to this formula, we selected 51 materials with a comprehensive score greater than 1, including 3 materials with a score exceeding 5.This research provides valuable insights for future studies in peanut quality breeding.