玉米(Zea mays)出籽率

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2017-04-21 17:46:04 转载

玉米(Zea mays)出籽率作为典型的数量性状，受微效多基因控制，且易受环境因素影响，因而在育种实践中限制了对其进行遗传改良的能力。为挖掘不同环境条件下稳定遗传的控制玉米出籽率性状的QTL，同时分析QTL与环境的互作效应。本研究以许178×K12衍生的150个重组自交系(recombinant inbred lines, RIL)群体为实验材料，通过2年3点的田间实验，分别利用单环境分析、多环境联合分析和上位性分析方法，对玉米籽粒出籽率性状的表型数据和最佳线性无偏估计值(best linear unbiased prediction, BLUP)值进行QTL分析。采用出籽率表型值进行单环境分析，共检测到13个QTL位点，分布在第1，3，5，6，7，8和9染色体上，单个QTL可解释6.74%~22.18%的表型变异；利用最佳线性无偏估计值BLUP值进行QTL分析，共定位到4个位点，且均在单环境中被检测到。多环境联合分析检测到8个QTL位点，加性效应贡献率范围0.78%~2.31%，互作贡献率范围为0.21%~1.96%；上位性分析共检测到15对加性×加性互作位点，分布在所有染色体上。本研究通过多环境对玉米出籽率进行QTL定位，共筛选出4个能够稳定遗传出籽率性状的的染色体区域即Bin1.06~1.07，Bin6.01~6.02，Bin8.07和Bin9.03~9.05。同时发现，出籽率性状与环境存在着复杂的互作效应，上位性效应也是影响玉米出籽率性状的重要遗传基础。本研究结果将为今后种质资源的改良以及分子标记辅助育种(marker assisted selection, MAS)提供理论指导。	服务把本文推荐给朋友加入我的书架加入引用管理器E-mail AlertRSS作者相关文章常立国何坤辉崔婷婷薛吉全刘建超
关键词：玉米, 多环境, 出籽率, 最佳线性无偏估计值(BLUP), 上位性互作
Abstract：Kernel ratio in maize (Zea mays) is a typical quantitative trait, which is affected by the minor-gene and is susceptible to environmental effects, thus limiting the ability of genetic improvement in breeding. Multi-environment experiment was conducted at 3 locations for 2 years in order to explore QTLs which controlled kernel ratio traits in maize stably inheriting under different environments, and analyze the interaction effects between environments and QTLs. In this study, 150 recombinant inbred lines (RIL) derived from Xu178×K12 were used for the experimental materials. QTL analysis was conducted through single environment analysis, joint analysis and epistatic analysis, respectively. Results showed that 13 QTLs were detected through single environmental QTL analysis, which distributed on Chr.1, Chr.3, Chr.5, Chr.6, Chr.7, Chr.8 and Chr.9, respectively. The phenotypic variance explained by these QTLs ranged from 6.74% to 22.18%. Four QTLs were detected by using best linear unbiased prediction (BLUP) data, all of which were also detected through single environment. The phenotypic variance explained by interaction of additive effect for single QTL ranged from 0.78% to 2.31%, Eight QTLs were identified through joint analysis, the phenotypic variance explained by interaction of additive×environment for single QTL ranged from 0.21% to 1.96%. Fifteen pairs of QTLs with epistatic effect were detected through epistatic analysis in total, which distributed on all chromosomes. Four key areas, including Bin1.06~1.07, Bin6.01~6.02, Bin8.07 and Bin9.03~9.05 controlling kernel ratio were filtered through multi-environments QTL analysis. Besides, our results showed that the interaction effect between kernel ratio trait and environments was complicated. Epistatic effect was also an important genetic basis for the performance of kernel ratio in maize except for additive effect. The results of this study will be instructive for improvement of germplasm resources and molecular marker assisted selection (MAS) in further study.