中国农业科学 ›› 2017, Vol. 50 ›› Issue (4): 599-611.doi: 10.3864/j.issn.0578-1752.2017.04.001

• 作物遗传育种·种质资源·分子遗传学 •    下一篇

稻米蒸煮品质性状与分子标记关联研究

高维维,陈思平,王丽平,陈立凯,郭涛,王慧,陈志强   

  1. 华南农业大学国家植物航天育种工程技术研究中心,广州 510642
  • 收稿日期:2016-08-22 出版日期:2017-02-16 发布日期:2017-02-16
  • 通讯作者: 陈志强,E-mail:chenlin@scau.edu.cn。郭涛,E-mail:guoguot@scau.edu.cn
  • 作者简介:高维维,E-mail:18819266044@163.com。陈思平,E-mail:867716697@qq.com。高维维和陈思平为同等贡献作者。
  • 基金资助:
    国家高技术研究发展计划(2016YFD0102102)、广东省科技计划(2015B020231011)、国家现代农业产业技术体系建设专项(CARS-01-12)

Association Analysis of Rice Cooking Quality Traits with Molecular Markers

GAO WeiWei, CHEN SiPing, WANG LiPing, CHEN LiKai, GUO Tao, WANG Hui, CHEN ZhiQiang   

  1. National Engineering Research Centre of Plant Space Breeding, South China Agricultural University, Guangzhou 510642
  • Received:2016-08-22 Online:2017-02-16 Published:2017-02-16

摘要: 【目的】调查分析代表性水稻种质重要的品质性状和淀粉RVA谱变异,筛选与性状显著关联的分子标记,为稻米品质改良提供依据。【方法】以48份国内外水稻多样性种质为材料,进行稻米品质性状变异调查分析;利用快速淀粉粘滞测定仪(rapid visco analyzer)鉴定材料淀粉RVA谱。利用已报道的稻米淀粉合成相关基因等位标记及稻米籽粒相关QTL连锁分子标记对种质材料进行基因分型。利用GLM模型进行分子标记与品质性状的关联检测,并对显著关联标记进行逐步回归分析;评估等位基因及其组合对目标性状的表型影响效应,同时鉴别对应优异等位基因型及载体品种。【结果】供试材料在直链淀粉含量(AC)、胶稠度(GC)和碱消值(ASV)等表现出广泛的表型变异和多样性,变异系数为26.5%—36.3%。RVA谱检测表明崩解值、消减值和回复值等在材料间具有明显差异,能较好地反映不同种质的淀粉糊化特性。相关分析表明,AC与冷胶黏度、消减值和回复值呈显著正相关,与最高黏度和崩解值呈显著负相关;GC同时与消减值和回复值呈显著负相关。利用154个多态性标记共检测到491个等位变异,基因多样性平均为0.447,多态信息含量(PIC)平均为0.390。性状-标记关联检测共获得22个与稻米品质性状显著关联的位点,单个关联标记位点解释的表型变异(R2)范围为14.11%—75.62%。GBSSI是影响AC和GC的主效基因,分子标记Wx-G/T对AC和GC的表型变异解释率分别为61.44%和41.87%。SSIIa是影响ASV的主效基因,alk-GC/TT和SSIIa-F对ASV的表型变异解释率分别为75.62%和74.46%。利用显著关联标记构建AC、GC和ASV的回归模型方程,决定系数分别为85.30%、40.62%和80.38%。【结论】水稻淀粉RVA谱与AC、GC和ASV密切相关,利用RVA谱可更全面地评价稻米品质性状。利用稻米品质表型-分子标记关联,共鉴定出22个与品质性状显著关联的位点,其中5个位点同时与AC和GC关联。回归模型表明标记的组合可产生不同的表型效应。

关键词: 水稻, 稻米品质, 性状-标记关联, 等位变异

Abstract: 【Objective】 In this study, the variation of major grain-quality traits and RVA profiles of representative rice germplasm were investigated, molecular markers significantly associated with the traits were identified, aiming to provide an important foundation for improvement of grain quality of rice. 【Method】 Global grain-quality properties and their differences were surveyed with a rice panel consisted of 48 diverse germplasms collected from both China and abroad. Starch RVA profile was examined with a Rapid Visco Analyzer. The markers reported to be related to starch biosynthesizing genes and QTLs for development of rice grain were used for genotyping. Trait-marker association for grain-quality properties was detected using the general linear model with Tassel 3.0 software. Moreover, stepwise regression analysis was performed with these detected markers significantly associated with grain-quality traits. The phenotypic effects of the alleles and allelic combinations were estimated and both elite alleles and typical carrier genotypes were identified. 【Result】There were wide phenotypic variance and diversity in amylose content (AC), gel consistency (GC) and alkali spreading value (ASV), and the coefficient of variation (CV.) ranged from 26.5% to 36.3%. Based on inspection of RVA profile, significant differences were found in breakdown (BDV), setback (SBV) and consistence (CSV), and these parameters have preferably reflected the diverse starch paste properties among different rice varieties. Correlation analysis showed the AC was positively correlated with cool paste viscosity (CPV), SBV and CSV, while negatively correlated with peak viscosity (PKV) and BDV. The GC was negatively correlated with SBV and CSV. A total of 491 alleles were identified with 154 polymorphic markers, with an average gene diversity of 0.447 and an average polymorphic information content of 0.390. Based on the analysis of trait-marker association, a total of 22 markers were detected to associate with grain-quality traits, which explained the phenotypic variance ranged from 14.11% to 75.62%. The GBSSI gene majorly affected the properties of AC and GC, and the SNP marker of Wx-G/T explained up to 61.44% and 41.87% of the phenotypic variation for AC and GC, respectively. While the SSIIa was the major gene affecting ASV, and the phenotypic variation of ASV explained by the markers of alk-GC/TT and SSIIa-F was up to 75.62% and 74.46%. The model equations based on stepwise regression analysis of AC, GC and ASV were developed using the significant markers, whose determination coefficients were 85.30%, 40.62%, and 80.38%, respectively. 【Conclusion】The starch RVA profile wass closely related to AC, GC and ASV. The RVA profile can be used to evaluate rice quality traits more comprehensively. With trait-marker association analysis, 22 markers were detected to be associated with grain-quality traits, and five of these sites were associated with both AC and GC. Regression models showed allelic combination can produce different phenotypic effects.

Key words: rice, grain-quality traits, trait-marker association, allelic variation