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两种施氮条件下大豆品质性状QTL分析
QTL Analysis of Soybean Quality Traits under Two Nitrogen Application Conditions

DOI: 10.12677/HJAS.2021.119117, PP. 877-889

Keywords: 大豆，品质性状，QTL分析
Soybean, Quality Traits, QTL Analysis

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Abstract:

大豆是全球范围内食用蛋白和食用油脂的主要来源，同时也是动物饲料蛋白的最为重要的来源。大豆生长过程中，氮素的缺乏会严重影响大豆生长和发育，进而影响大豆产量和品质。目前关于氮素对大豆产量和品质的影响，已经有了大量研究报道，但是缺乏不施氮处理下大豆品质性状QTL分析的相关报道。本研究利用杂交组合“东农L13 × 黑河36”、“东农L13 × 合农60”衍生的两个重组自交系群体RIL3613和RIL6013为材料，在哈尔滨、阿城和双城3个地点进行正常施用氮肥和不施用氮肥两种条件种植，对大豆品质性状进行加性和上位性QTL定位，并对各性状进行氮肥响应QTL定位和候选基因预测，旨在剖析不同氮肥水平下大豆品质性状的遗传基础，发掘相关基因位点，为不同氮肥施用条件下的大豆分子设计育种提供技术和理论支撑，在RIL3613和RIL6013群体中共检出4个蛋白质含量加性QTL、2个油分含量加性QTL，单个加性QTL可解释0.20%~8.70%、7.23%~7.50%的表型变异。其中有1个油分含量加性QTL是新发现的。在RIL3613和RIL6013群体中共检出4对蛋白质含量上位性QTL，上位性QTL可解释4.04%~5.50%的表型变异。在RIL3613和RIL6013群体中共检出2个蛋白质含量氮肥响应QTL，单个氮肥响应QTL可解释8.88%~12.49%的表型变异，均是新发现的。利用SoyBase在线程序获得的标记信息，在4个一致QTL区间中，利用GO和KEGG数据库对1215个候选基因进行了筛选和注释。KEGG通路中，Ko04075参与植物激素信号转导途径，包括赤霉素、生长素、脱落酸等激素，在调节茎生长、植物生长和种子发育方面起着重要作用。基于Pathway分析和功能注释，最终鉴定出15个候选基因，被注释为囊泡转运v-SNARE家族蛋白基因、带有beta亚基和-2亚基基因的协同分子、SAUR样生长素应答蛋白家族基因、蔗糖非发酵相关蛋白激酶(SnRK)等，均是植物产量和品质形成等多种发育途径所必需的。
Soybean is the main source of edible protein and edible oil worldwide, and the most important source of animal feed protein. In the process of soybean growth, nitrogen deficiency will seriously affect the growth and development of soybean, and then affect the yield and quality of soybean. At present, there have been a lot of research reports on the effects of nitrogen on soybean yield and quality, but there is a lack of related reports on QTL analysis of soybean quality traits under nitrogen application. In this study, two recombinant inbred lines (RIL3613 and RIL6013) derived from hybrid combinations “Dongnong L13 × Heihe 36” and “Dongnong L13 × Henong 60” were used as materials to plant under two conditions of normal application of nitrogen fertilizer and no application of nitrogen fertilizer at three locations in Harbin, Acheng and Shuangcheng. For soybean quality traits, additive and epistatic QTL mapping, QTL mapping and the various characters are nitrogen response and candidate gene prediction, which aims to analyze the genetic basis of soybean quality traits under different nitrogen levels, explore related gene loci, for different conditions of soybean nitrogen molecular design breeding to provide technical and theoretical support. Four additive QTLs for protein content and two additive QTLs for oil content were detected in RIL3613 and RIL6013 populations, and a single additive QTL could explain 0.20%~8.70% and 7.23%~7.50% of the phenotypic variation. One additive QTL for oil content was newly discovered. A total of 4 pairs of epistatic QTLs for protein content were detected in RIL3613 and RIL6013 populations, and epistatic QTLs could

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