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燕麦籽粒多组分营养性状的相关性分析
引用本文:赵丽那,严俊,薛文韬,阮景军,程剑平,邝刘辉,赵钢. 燕麦籽粒多组分营养性状的相关性分析[J]. 江西农业大学学报, 2016, 0(4): 623-630. DOI: 10.13836/j.jjau.2016089
作者姓名:赵丽那  严俊  薛文韬  阮景军  程剑平  邝刘辉  赵钢
作者单位:1. 贵州大学 麦作研究中心,贵州 贵阳,550025;2. 成都大学 药学与生物工程学院,四川 成都,610106
基金项目:国家科技部国际科技合作专项(2013DFA32200),贵州省国际科技合作计划项目[黔科合外 G 字(2012)7011] Project supported by International Cooperation Projects of Ministry of Science and Technology(2013DFA32200),Guizhou Province,the International Science and Technology Cooperation Program((2012)7011)
摘    要:为了挖掘利用燕麦籽粒高营养物质含量的基因和种质资源,以引进的意大利78份野生和栽培燕麦为研究材料,种植于四川成都崇州市羊马镇、安徽来安县广大乡汪庄、安徽来安县广大乡红旗组共3个环境并对收获的籽粒总类黄酮、总酚、植酸、无机磷、氨基和可溶性蛋白质6个营养性状的含量进行测定。分析发现,6个营养性状可分为微量、常量和大量;网络相关性分析表明燕麦籽粒单粒重与各营养性状间均呈显著负相关;主成分分析(PCA)显示燕麦籽粒总类黄酮、总酚具有特殊性。对3个环境的燕麦籽粒营养成分基因型筛选发现,基因型ITAO2、ITAO3等燕麦籽粒营养组分含量较高,而基因型ITAO6、ITAO11等燕麦籽粒营养组分含量较低。本研究可为燕麦的开发利用提供理论依据,并且在遗传育种方面具有重要的意义。

关 键 词:燕麦  籽粒营养性状  网络相关性分析  主成分分析

Correlation Analysis on Multiple Nutrition Traits in Oat Grain
Abstract:To explore the genetic resources for improving nutrient content in oat grain.A set of 78 wild and cultivated oat accessions derived from Italy were planted in Sichuan and Anhui Province,respectively. The multi?nutrition components of total flavonoid ( TF),total phenols( TP),phytate ( PHY),total soluble phos?phorus(PI),ammonium (NH2 ) and total soluble protein(TSP) were detected.The analysis revealed that the six nutritional traits could be divided into micro?constituent,macro?constituent and major?constituent.Correla?tion network analysis exhibited that there was a significant negative correlation between the grain nutrient?relat?ed traits and oat seeds single grain weight.Principal component analysis (PCA) proved that there was a partic?ularity between TF and TP of oat grain.The genotype screening of oat grain nutrition composition found that nu?trients of genotype ITAO6,ITAO11 were relatively low in the three environments.This study provides theoretical basis for the development and utilization of oats and has a vital significance for oat genetic breeding.
Keywords:oat  grain nutrition characters  correlation network analysis  principal component analysis
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