水稻灌浆期相关性状的动态遗传及环境互作研究
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摘要
近年来,水稻灌浆已成为生理生态研究领域的热点问题之一。对水稻灌浆特性及其影响因子的探讨对指导水稻高效生产意义重大。目前,对水稻灌浆过程的研究大多集中在生理生化机理、栽培措施调控等手段上,在基因水平上的研究相对较少。为揭示水稻灌浆过程灌浆相关数量性状的动态变化及其环境互作,本研究利用课题组由小穗小粒型品种密阳46和大穗大粒型品种FJCD建立的一个包含130个家系F10的重组自交系群体,在水稻开花后6d、12d、18d、24d和30d五个时期分别记为水稻灌浆始期,灌浆前期,灌浆中期,灌浆后期和灌浆末期(分别记为第一、二、三、四、五期),测定武夷山和莆田环境下水稻的源性状、库性状、净光合速率及灌浆速率的动态变化,进行灌浆相关QTL的定位及环境互作研究,并探讨了源库QTL互作关系。主要研究结果如下:
1.考察水稻灌浆源相关的11个性状,包括旗叶长、旗叶宽、旗叶面积、倒二叶长、倒二叶宽、倒二叶面积、倒三叶长、倒三叶宽、倒三叶长、倒三叶面积、抽穗期和株高,结果表明:各性状的频率分布均呈正态或偏正态分布,表明这些性状为数量性状,可以用于QTL定位研究。
在武夷山环境下,对11个与源相关的性状进行了QTL定位,共检测到22个加性QTL位点,分布于水稻第1、2、6、11号染色体上,解释了大部分的遗传变异。这些QTL的贡献率介于1.79%-44.55%之间,其中大于10%的位点有15个,效应值小于5%的微效QTL也检测到有3个。单个性状的QTL位点数为1-4个。检测到与抽穗期有关的QTL1个,位于6号染色体上,解释了25.63%的变异,该QTL的正加性效应由亲本FJCD的等位基因提供。
莆田种植的RIL群体,进行源相关的11个性状的QTL分析,共检测到20个加性QTL位点,分布于水稻第2、5、6、7、12号染色体上,解释了大部分的遗传变异。这些QTL的遗传效应值介于0.66%-56.75%之间,其中效应值大于10%的位点有7个,效应值小于5%的微效QTL也检测到有8个。单个性状的QTL位点数为0-5个,没有检测到与抽穗期、旗叶长、旗叶叶面积相关的QTL,其余8个性状均检测到相应的QTL位点。
GE互作分析,共检测到20个QTL,分布在水稻1、2、6、11、12号染色体上,均与环境存在互作效应。20个QTL中,加性效应对表型变异贡献率1.37%-34.98%,4个加性QTL贡献率较大,大于10%,10个表型变异小于5%的加性QTL,GE互作效应对表型贡献率0%-15.16%,4个贡献率大于10%,12个小于5%的微效GE互作。2.考察水稻灌浆库相关的5个性状,包括穗长、穗重、粒长、粒重、粒宽等,结果表明:各性状的频数分布均呈正态或偏正态分布,表明这些性状为数量性状,可以用于QTL定位研究。
武夷山环境下定位灌浆期五个时段的穗长、穗重的QTL:结果共得到21个相关QTL,分布在水稻第1、3、5、6、7、8、11号染色体上,加性效应表型贡献率0.61%-31.63%,大于10%的加性效应QTL8个,小于5%的微效QTL6个。其中,qPL-7-5和qP L-7-6两个QTL在灌浆第一、四阶段重复出现,分别在两个阶段对表型变异的贡献也差异很大。
定位控制灌浆期五个阶段千粒重的QTL:结果共得到9个相关QTL,分布在水稻第1、2、3、5、7号染色体上,加性效应表型贡献率3.03%-11.9%,大于10%的加性效应QTL1个,小于5%的微效QTL3个。其中,qGW-7-7在灌浆第四、五阶段重复出现,分别在两个阶段对表型变异的贡献为7.64%、3.82%。
定位控制灌浆期粒长的QTL:结果共得到7个与粒长相关的QTL,分布在水稻第4、6、7、10、12号染色体上,加性效应分别为-0.03、-0.03、-0.03、-0.03、0.02、-0.05、0.03,表型贡献率分别为5.69%、5.69%、8.22%、5.69%、2.53%、15.82%、5.69%。
定位控制灌浆期粒宽的QTL:结果共得到相关加性QTL7个,分别位于2、2、5、5、6、6、7号染色体上,加性效应分别为0.02、0.01、0.01、0.01、-0.02、-0.02、0.02,表型贡献率分别为5.72%、0.44%、0.44%、0.44%、1.77%、1.77%、1.77%。
莆田环境下定位灌浆期五个时段的穗长、穗重的QTL:结果共得到41个相关QTL,分布在水稻第1、2、3、4、5、6、8、9、10号染色体上,加性效应表型贡献率0.32%-16.52%,大于10%的加性效应QTL11个,小于5%的微效QTL26个。其中,控制穗长的qPL-2-11、qPL-2-12、qPL-2-13、qPL-4-2、qPL-4-3、qPL-8-3、qPL-8-4、qPL-9-4等QTL均在一个以上的阶段重复出现;控制穗重的qPW-10-3、qPW-3-2、qPW-3-3、qPW-8-8等QTL均在一个以上的阶段重复出现,说明QTL加性效应是穗长形成过程中的主要力量。
定位控制灌浆期五个阶段千粒重的QTL:结果共得到13个相关QTL,分布在水稻第1、2、4、6、7号染色体上,加性效应表型贡献率1.9%-24.78%,大于10%的加性效应QTL5个,小于5%的微效QTL1个。其中,qGW-6-7、qGW-2-1、qGW-6-8分别在灌浆第四、二、三阶段出现,又全部在灌浆第五阶段重复出现,说明QTL加性效应对水稻灌浆后期产量的形成有极其重要的作用。
定位控制灌浆期粒长的QTL:结果共得到9个与粒长相关的QTL,分布在水稻第1、2、4、5、7、10、11、12号染色体上,加性效应分别为-0.02、0.02、-0.02、-0.02、-0.03、-0.04、-0.03、0.02、-0.02,表型贡献率分别为3.58%、3.58%、3.58%、3.58%、8.06%、16.12%、8.06%、3.58%、3.58%。
定位控制灌浆期粒宽的QTL:对控制籽粒宽的QTL进行定位分析,得到相关加性QTL3个,分别位于5、5、6号染色体上,加性效应分别为0.0136、0.0061、-0.0159,表型贡献率分别为14.99%、3.02%、20.49%。
库相关性状的GE互作分析,联合该群体两地的灌浆期穗长、穗重、粒长、粒重、粒宽等5个库相关性状,定位控制相应性状的GE互作位点,共检测到46个位点,分布在水稻1、2、3、4、5、6、7、8、9、10、11号染色体上,均与环境存在显著的互作效应。46个QTL中,加性效应对表型变异贡献率0%-19.22%,加性效应的大于10%的主效QTL10个,小于5%的微效QTL29个,GE互作效应对表型贡献率0%-12.31%,大多为小于5%的微效QTL。
3.武夷山环境下定位灌浆期五个时段的净光合速率的QTL:只在灌浆期第一、四阶段检测到7加性QTL位点,分布于水稻第2、4、10、11号染色体上,解释了大部分的表型变异。这些QTL的遗传效应值介于0.34%-22.71%之间,除了两个QTL为贡献率小于5%的微效QTL外,其他QTL的效应值均在10%左右。
莆田环境下定位灌浆期五个时段的净光合速率QTL:灌浆期第五阶段仍然没有定位到相关QTL,但在第二、三阶段各定位到一个显著的加性QTL。莆田环境下共检测到15与净光合速率相关的QTL位点,分布于水稻第1、2、4、7、9、10号染色体上,解释了大部分的表型变异。这些QTL的遗传效应值介于0.36%-17.85%之间,2个QTL为贡献率小于5%的微效QTL,只有1个QTL对表型变异的贡献率大于10%。
GE互作分析,联合该群体两地的灌浆期五个阶段的净光合速率,共检测到灌浆期第一、二、四阶段的7个GE互作位点,分布在水稻2、4、9、11号染色体上,均与环境存在显著的互作效应。7个QTL中,加性效应对表型变异贡献率3.51%-16.05%,加性效应的大于10%的主效QTL1个,小于5%的微效QTL2个,GE互作效应对表型贡献率0%-20.91%,大于10%的主效QTL3个,小于5%的微效QTL3个。
4.武夷山环境下定位灌浆期五个时段的灌浆速率的QTL:在灌浆期第一、二、四、五阶段及灌浆期平均灌浆速率检测到11个加性QTL位点,分布于水稻第1、2、4、5、7、10号染色体上,解释了大部分的表型变异。这些QTL的遗传效应值介于0.92%-17.39%之间,6个QTL为贡献率小于5%的微效QTL,只有1个QTL的贡献率大于10%。
莆田环境下定位灌浆期五个时段的灌浆速率的QTL:在灌浆期第二、三、四阶段及平均灌浆速率检测到9个加性QTL位点,分布于水稻第1、2、5、6、8号染色体上,解释了大部分的表型变异。这些QTL的遗传效应值介于1.99%-24.41%之间,2个QTL为贡献率小于5%的微效QTL,3个QTL的贡献率大于10%。
GE互作分析:联合该群体两地的灌浆期五个阶段的灌浆速率及平均灌浆,共检测到灌浆期第二、四、五阶段及平均灌浆速率的7个GE互作QTL,分布在水稻1、2、4、5、6、10号染色体上,均与环境存在显著的互作效应。8个GE互作位点中,加性效应对表型变异贡献率1.1%-4.54%,均为加性效应为小于5%的微效位点,GE互作效应对表型贡献率0%-10.29%,加性效应对表型贡献率不大。5.运用SPSS软件进行相关性分析:源性状与灌浆速率的相关性分析;净光合速率与灌浆速率相关性分析;五个阶段的灌浆速率及平均灌浆速率之间的相关性分析。结果表明,灌浆期相关性之间关系复杂存在相关性,有些达到了显著相关,形成了统一的整体。
本研究从两地水稻灌浆的不同时期分别对于源、库组件进行分别定位及环境互作分析,在一定意义上揭示了水稻灌浆过程是一个基因动态表达的过程,不同基因之间、基因环境之间存在互相作用,这对水稻遗传育种的工作实践有一定的意义,但基因间的互作仅靠QTL方法作简单的分析,无法确切了解基因的时空表达特征,需要如RNA水平和蛋白质组水平进一步加以证实。
Understanding the factors that regulate grain filling is important for improving rice yield potential. However, there is disagreement as to whether improving sink size alone in rice crops will also result in an increase in leaf and whole plant photosynthesis. The grain yield in rice is the product of spikelet yield (or sink) and ripening ability (or source). The relationship between the source and sink in rice research in general level,QTL mapping and gene×environment interaction are used to research the relationship of source and sink is benefit for us. Thus a RIL including 130 offspring was built in order to study the character of dynamic QTLs during the grain-filling.
1. In our study,we got 11 characters associated with source for grain filling.Frequencies of 11 characters were close to normal pattern, and showed continuously variant,implying that they are quantitative characters. The result QTL mapping and GE interaction as below:
Under the environment of Wuyishan,we got 22 QTL of characters associated with source.They are located in 1、2、6、11 linkage group and mostly explained phenotypic variation.The range of the contribution rate is 1.79%-44.55%,and 15 QTLs exceeded 10%,3 QTLs are below 5% among them.The number of a character is 1-4.
Under the environment of Putian,we got 20 QTLs of characters associated with source.They are located in 2、5、6、7、12 linkage group and mostly explained phenotypic variation.The range of the contribution rate is 0.66%-56.75%,and 7 QTLs exceeded 10%,8 QTLs are below 5% among them.The number of a character is 0-5,8 characters were detected additive QTL expect days to heading,length of flag leaf,area of flag leaf.
We got 20 QTLs with prominent gene×environment interaction.They are located in1、2、6、11、12 linkage group.The range of the contribution rate of additive effects to phenotype were 1.37%-34.98%,and 4 QTLs exceeded 10%,10 QTLs are below 5% among them. The range of the contribution rate of GE interactions to phenotype were 0%-15.16%,and 4 QTLs exceeded 10%,12 QTLs are below 5% among them.
2. In our study,we got 5 characters associated with sink for grain filling. Frequencies of 11 characters were close to normal pattern, and showed continuously variant implying that they are quantitative characters. The result QTL mapping and GE interaction as below:
The QTLs associated with length of panicle and weight of panicle for five stages of grain filling under the environment of Wuyishan:we got 21 QTLs,which were located in 1、3、5、6、7、8、11 linkage group and mostly explained phenotypic variation.The range of the contribution rate is 0.61%-31.63%,and 8 QTLs exceeded 10%,6 QTLs are below 5% among them. Two QTL of qPL-7-5 and qP L-7-6 emerged at 1、4 stage of grain filling,but differencies of two stages were prominent.
The QTLs associated with weight of 1000 grains for five stages of grain filling under the environment of Wuyishan:we got 9 QTLs .Those QTL were located in1、2、3、5、7linkage group and mostly explained phenotypic variation.The range of the contribution rate is 3.03%-11.9%,and 1 QTLs exceeded 10%,3 QTLs are below 5% among them. The QTL of qGW-7-7 emerged at 4、5 stage of grain filling,and respectively explained 7.64%、3.82% of phenotypic variation.
The QTLs associated with length of grain for grain filling under the environment of Wuyishan:we got 7 QTLs that were located in 4、6、7、10、12 linkage group and mostly explained phenotypic variation.The values of additive effects were respectively -0.03、-0.03、-0.03、-0.03、0.02、-0.05、0.03 and contribution rates of phenotypic variation were 5.69%、5.69%、8.22%、5.69%、2.53%、15.82% and 5.69% respectively.
The QTLs associated with width of grain for grain filling under the environment of Wuyishan:we got 7 QTLs and respectively located in 2、2、5、5、6、6、7 linkage group and mostly explained phenotypic variation.The values of additive effects were respectively 0.02、0.01、0.01、0.01、-0.02、-0.02、0.02 and contribution rates of phenotypic variation were 5.72%、0.44%、0.44%、0.44%、1.77%、1.77% and 1.77% respectively.
The QTLs associated with length of panicle and weight of panicle for five stages of grain filling under the environment of Putian:we got 41 QTLs and located in 1、2、3、4、5、6、8、9、10 linkage group and mostly explained phenotypic variation.The range of the contribution rate is 0.32%-16.52%,and 11 QTLs exceeded 10%,26 QTLs are below 5% among them. The QTLs associated with length of panicle of qPL-2-11、qPL-2-12、qPL-2-13、qPL-4-2、qPL-4-3、qPL-8-3、qPL-8-4、qPL-9-4 emerged at least one time,implying the importance of additive effects to length of panicle.
The QTLs associated with weight of 1000 grains for five stages of grain filling under the environment of Putian:we got 13 QTLs which were located in1、2、4、6、7 linkage group and mostly explained phenotypic variation.The range of the contribution rate is 1.9%-24.78%,and 5 QTLs exceeded 10%,1 QTLs are below 5% among them. There QTLs of qGW-6-7、qGW-2-1、qGW-6-8 respectively emerged at 4、2、3 stage of grain filling,and all emerged at 5 stage.It showed the importance of additive effects to the form of later output.
The QTLs associated with length of grain for grain filling under the environment of Putian:we got 9 QTLs and located in 1、2、4、5、7、10、11、12 linkage group and mostly explained phenotypic variation.The values of additive effects were respectively -0.02、0.02、-0.02、-0.02、-0.03、-0.04、-0.03、0.02、-0.02 and contribution rates of phenotypic variation were 3.58%、3.58%、3.58%、3.58%、8.06%、16.12%、8.06%、3.58% and 3.58% respectively.
The QTLs associated with width of grain for grain filling under the environment of Putian.we got 3 QTLs and respectively located in 5、5、6 linkage group and mostly explained phenotypic variation.The values of additive effects were respectively 0.0136、0.0061、-0.0159 and contribution rates of phenotypic variation were 14.99%、3.02% and 20.49% respectively.
We got 46 QTLs with prominent gene×environment interaction.They are located in1、2、3、4、5、6、7、8、9、10、11 linkage group.The range of the contribution rate of additive effects to phenotype were 0%-19.22%%,and 10 QTLs exceeded 10%,29 QTLs are below 5% among them. The range of the contribution rate of GE interactions to phenotype were 0%-12.31%, QTLs are mostly below 5% among them..
3. The QTLs associated with net photosynthetic rate for five stages of grain filling under the environment of Wuyishan:we got 7 QTLs located in 1、2、3、4、5、6、8、9、10 linkage group just at 1、4 stage and mostly explained phenotypic variation.The range of the contribution rate is 0.34%-22.71%, the contribution rate of QTLs mostly exceeded 10% except 2 QTLs.
The QTLs associated with net photosynthetic rate for five stages of grain filling under the environment of Putian:we got 15 QTLs located in 1、2、4、7、9、10 linkage group at 1、2、3、4 stage and mostly explained phenotypic variation.The range of the contribution rate is 0.36%-17.85%, and just 1 QTLs exceeded 10%,2 QTLs are below 5% among them.
We got 7 QTLs with prominent gene×environment interaction at 1、2、4 stage.They are located in2、4、9、11 linkage group.The range of the contribution rate of additive effects to phenotype were 3.51%-16.05%,and 1 QTL exceeded 10%,9 QTLs are below 5% among them. The range of the contribution rate of GE interactions to phenotype were 0%-20.91%, 3 QTL exceeded 10%,3 QTLs are below 5% among them.
4. The QTLs associated with grain-filling rate for five stages of grain filling under the environment of Wuyishan:we got 11QTLs located in 1、2、4、5、7、10 linkage group at 1、2、4、5 stage and mostly explained phenotypic variation.The range of the contribution rate is 0.92%-17.39%, and just 1 QTL exceeded 10%,6 QTLs are below 5% among them.
The QTLs associated with grain-filling rate for five stages of grain filling under the environment of Putian:we got 9 QTLs located in 1、2、5、6、8 linkage group at 2、3、4 stage and mostly explained phenotypic variation.The range of the contribution rate is 1.99%-24.41%, and 3 QTLs exceeded 10%,2 QTLs are below 5% among them.
We got 7 QTLs with prominent gene×environment interaction at 2、4、5 stage.They are located in 1、2、4、5、6、10 linkage group.The range of the contribution rate of additive effects to phenotype were 1.1%-4.54%, and contribution rate of QTLs are mostly below 5% among them. The range of the contribution rate of GE interactions to phenotype were 0%-10.29%.
5. The correlation analysis by SPSS software:the correlation analysis between characters with source、net photosynthetic rates and grain-filling rates.The result indicated that the relationships of the characters during grain-filling were complex and some of them reach significant correlation.
This research work on the source and the sink carries in Wuyishan and Putian was conducted for separately QTL mapping at different stages and gene×environment interaction in rice.The result in a sense revealed that grain filing process in the rice is a gene dynamic expression process, simultaneously is also an interactive action between the gene and gene, gene and environment in the process, it was significant in rice breeding. Simultaneously the gene dynamic expression and interaction cannot only depend on the QTL method to make the simple analysis. To further understead Understanding the gene expression in space and time characteristics, this also need to do more work. In the different levels, such as RNA proteome level.
1.考察水稻灌浆源相关的11个性状,包括旗叶长、旗叶宽、旗叶面积、倒二叶长、倒二叶宽、倒二叶面积、倒三叶长、倒三叶宽、倒三叶长、倒三叶面积、抽穗期和株高,结果表明:各性状的频率分布均呈正态或偏正态分布,表明这些性状为数量性状,可以用于QTL定位研究。
在武夷山环境下,对11个与源相关的性状进行了QTL定位,共检测到22个加性QTL位点,分布于水稻第1、2、6、11号染色体上,解释了大部分的遗传变异。这些QTL的贡献率介于1.79%-44.55%之间,其中大于10%的位点有15个,效应值小于5%的微效QTL也检测到有3个。单个性状的QTL位点数为1-4个。检测到与抽穗期有关的QTL1个,位于6号染色体上,解释了25.63%的变异,该QTL的正加性效应由亲本FJCD的等位基因提供。
莆田种植的RIL群体,进行源相关的11个性状的QTL分析,共检测到20个加性QTL位点,分布于水稻第2、5、6、7、12号染色体上,解释了大部分的遗传变异。这些QTL的遗传效应值介于0.66%-56.75%之间,其中效应值大于10%的位点有7个,效应值小于5%的微效QTL也检测到有8个。单个性状的QTL位点数为0-5个,没有检测到与抽穗期、旗叶长、旗叶叶面积相关的QTL,其余8个性状均检测到相应的QTL位点。
GE互作分析,共检测到20个QTL,分布在水稻1、2、6、11、12号染色体上,均与环境存在互作效应。20个QTL中,加性效应对表型变异贡献率1.37%-34.98%,4个加性QTL贡献率较大,大于10%,10个表型变异小于5%的加性QTL,GE互作效应对表型贡献率0%-15.16%,4个贡献率大于10%,12个小于5%的微效GE互作。2.考察水稻灌浆库相关的5个性状,包括穗长、穗重、粒长、粒重、粒宽等,结果表明:各性状的频数分布均呈正态或偏正态分布,表明这些性状为数量性状,可以用于QTL定位研究。
武夷山环境下定位灌浆期五个时段的穗长、穗重的QTL:结果共得到21个相关QTL,分布在水稻第1、3、5、6、7、8、11号染色体上,加性效应表型贡献率0.61%-31.63%,大于10%的加性效应QTL8个,小于5%的微效QTL6个。其中,qPL-7-5和qP L-7-6两个QTL在灌浆第一、四阶段重复出现,分别在两个阶段对表型变异的贡献也差异很大。
定位控制灌浆期五个阶段千粒重的QTL:结果共得到9个相关QTL,分布在水稻第1、2、3、5、7号染色体上,加性效应表型贡献率3.03%-11.9%,大于10%的加性效应QTL1个,小于5%的微效QTL3个。其中,qGW-7-7在灌浆第四、五阶段重复出现,分别在两个阶段对表型变异的贡献为7.64%、3.82%。
定位控制灌浆期粒长的QTL:结果共得到7个与粒长相关的QTL,分布在水稻第4、6、7、10、12号染色体上,加性效应分别为-0.03、-0.03、-0.03、-0.03、0.02、-0.05、0.03,表型贡献率分别为5.69%、5.69%、8.22%、5.69%、2.53%、15.82%、5.69%。
定位控制灌浆期粒宽的QTL:结果共得到相关加性QTL7个,分别位于2、2、5、5、6、6、7号染色体上,加性效应分别为0.02、0.01、0.01、0.01、-0.02、-0.02、0.02,表型贡献率分别为5.72%、0.44%、0.44%、0.44%、1.77%、1.77%、1.77%。
莆田环境下定位灌浆期五个时段的穗长、穗重的QTL:结果共得到41个相关QTL,分布在水稻第1、2、3、4、5、6、8、9、10号染色体上,加性效应表型贡献率0.32%-16.52%,大于10%的加性效应QTL11个,小于5%的微效QTL26个。其中,控制穗长的qPL-2-11、qPL-2-12、qPL-2-13、qPL-4-2、qPL-4-3、qPL-8-3、qPL-8-4、qPL-9-4等QTL均在一个以上的阶段重复出现;控制穗重的qPW-10-3、qPW-3-2、qPW-3-3、qPW-8-8等QTL均在一个以上的阶段重复出现,说明QTL加性效应是穗长形成过程中的主要力量。
定位控制灌浆期五个阶段千粒重的QTL:结果共得到13个相关QTL,分布在水稻第1、2、4、6、7号染色体上,加性效应表型贡献率1.9%-24.78%,大于10%的加性效应QTL5个,小于5%的微效QTL1个。其中,qGW-6-7、qGW-2-1、qGW-6-8分别在灌浆第四、二、三阶段出现,又全部在灌浆第五阶段重复出现,说明QTL加性效应对水稻灌浆后期产量的形成有极其重要的作用。
定位控制灌浆期粒长的QTL:结果共得到9个与粒长相关的QTL,分布在水稻第1、2、4、5、7、10、11、12号染色体上,加性效应分别为-0.02、0.02、-0.02、-0.02、-0.03、-0.04、-0.03、0.02、-0.02,表型贡献率分别为3.58%、3.58%、3.58%、3.58%、8.06%、16.12%、8.06%、3.58%、3.58%。
定位控制灌浆期粒宽的QTL:对控制籽粒宽的QTL进行定位分析,得到相关加性QTL3个,分别位于5、5、6号染色体上,加性效应分别为0.0136、0.0061、-0.0159,表型贡献率分别为14.99%、3.02%、20.49%。
库相关性状的GE互作分析,联合该群体两地的灌浆期穗长、穗重、粒长、粒重、粒宽等5个库相关性状,定位控制相应性状的GE互作位点,共检测到46个位点,分布在水稻1、2、3、4、5、6、7、8、9、10、11号染色体上,均与环境存在显著的互作效应。46个QTL中,加性效应对表型变异贡献率0%-19.22%,加性效应的大于10%的主效QTL10个,小于5%的微效QTL29个,GE互作效应对表型贡献率0%-12.31%,大多为小于5%的微效QTL。
3.武夷山环境下定位灌浆期五个时段的净光合速率的QTL:只在灌浆期第一、四阶段检测到7加性QTL位点,分布于水稻第2、4、10、11号染色体上,解释了大部分的表型变异。这些QTL的遗传效应值介于0.34%-22.71%之间,除了两个QTL为贡献率小于5%的微效QTL外,其他QTL的效应值均在10%左右。
莆田环境下定位灌浆期五个时段的净光合速率QTL:灌浆期第五阶段仍然没有定位到相关QTL,但在第二、三阶段各定位到一个显著的加性QTL。莆田环境下共检测到15与净光合速率相关的QTL位点,分布于水稻第1、2、4、7、9、10号染色体上,解释了大部分的表型变异。这些QTL的遗传效应值介于0.36%-17.85%之间,2个QTL为贡献率小于5%的微效QTL,只有1个QTL对表型变异的贡献率大于10%。
GE互作分析,联合该群体两地的灌浆期五个阶段的净光合速率,共检测到灌浆期第一、二、四阶段的7个GE互作位点,分布在水稻2、4、9、11号染色体上,均与环境存在显著的互作效应。7个QTL中,加性效应对表型变异贡献率3.51%-16.05%,加性效应的大于10%的主效QTL1个,小于5%的微效QTL2个,GE互作效应对表型贡献率0%-20.91%,大于10%的主效QTL3个,小于5%的微效QTL3个。
4.武夷山环境下定位灌浆期五个时段的灌浆速率的QTL:在灌浆期第一、二、四、五阶段及灌浆期平均灌浆速率检测到11个加性QTL位点,分布于水稻第1、2、4、5、7、10号染色体上,解释了大部分的表型变异。这些QTL的遗传效应值介于0.92%-17.39%之间,6个QTL为贡献率小于5%的微效QTL,只有1个QTL的贡献率大于10%。
莆田环境下定位灌浆期五个时段的灌浆速率的QTL:在灌浆期第二、三、四阶段及平均灌浆速率检测到9个加性QTL位点,分布于水稻第1、2、5、6、8号染色体上,解释了大部分的表型变异。这些QTL的遗传效应值介于1.99%-24.41%之间,2个QTL为贡献率小于5%的微效QTL,3个QTL的贡献率大于10%。
GE互作分析:联合该群体两地的灌浆期五个阶段的灌浆速率及平均灌浆,共检测到灌浆期第二、四、五阶段及平均灌浆速率的7个GE互作QTL,分布在水稻1、2、4、5、6、10号染色体上,均与环境存在显著的互作效应。8个GE互作位点中,加性效应对表型变异贡献率1.1%-4.54%,均为加性效应为小于5%的微效位点,GE互作效应对表型贡献率0%-10.29%,加性效应对表型贡献率不大。5.运用SPSS软件进行相关性分析:源性状与灌浆速率的相关性分析;净光合速率与灌浆速率相关性分析;五个阶段的灌浆速率及平均灌浆速率之间的相关性分析。结果表明,灌浆期相关性之间关系复杂存在相关性,有些达到了显著相关,形成了统一的整体。
本研究从两地水稻灌浆的不同时期分别对于源、库组件进行分别定位及环境互作分析,在一定意义上揭示了水稻灌浆过程是一个基因动态表达的过程,不同基因之间、基因环境之间存在互相作用,这对水稻遗传育种的工作实践有一定的意义,但基因间的互作仅靠QTL方法作简单的分析,无法确切了解基因的时空表达特征,需要如RNA水平和蛋白质组水平进一步加以证实。
Understanding the factors that regulate grain filling is important for improving rice yield potential. However, there is disagreement as to whether improving sink size alone in rice crops will also result in an increase in leaf and whole plant photosynthesis. The grain yield in rice is the product of spikelet yield (or sink) and ripening ability (or source). The relationship between the source and sink in rice research in general level,QTL mapping and gene×environment interaction are used to research the relationship of source and sink is benefit for us. Thus a RIL including 130 offspring was built in order to study the character of dynamic QTLs during the grain-filling.
1. In our study,we got 11 characters associated with source for grain filling.Frequencies of 11 characters were close to normal pattern, and showed continuously variant,implying that they are quantitative characters. The result QTL mapping and GE interaction as below:
Under the environment of Wuyishan,we got 22 QTL of characters associated with source.They are located in 1、2、6、11 linkage group and mostly explained phenotypic variation.The range of the contribution rate is 1.79%-44.55%,and 15 QTLs exceeded 10%,3 QTLs are below 5% among them.The number of a character is 1-4.
Under the environment of Putian,we got 20 QTLs of characters associated with source.They are located in 2、5、6、7、12 linkage group and mostly explained phenotypic variation.The range of the contribution rate is 0.66%-56.75%,and 7 QTLs exceeded 10%,8 QTLs are below 5% among them.The number of a character is 0-5,8 characters were detected additive QTL expect days to heading,length of flag leaf,area of flag leaf.
We got 20 QTLs with prominent gene×environment interaction.They are located in1、2、6、11、12 linkage group.The range of the contribution rate of additive effects to phenotype were 1.37%-34.98%,and 4 QTLs exceeded 10%,10 QTLs are below 5% among them. The range of the contribution rate of GE interactions to phenotype were 0%-15.16%,and 4 QTLs exceeded 10%,12 QTLs are below 5% among them.
2. In our study,we got 5 characters associated with sink for grain filling. Frequencies of 11 characters were close to normal pattern, and showed continuously variant implying that they are quantitative characters. The result QTL mapping and GE interaction as below:
The QTLs associated with length of panicle and weight of panicle for five stages of grain filling under the environment of Wuyishan:we got 21 QTLs,which were located in 1、3、5、6、7、8、11 linkage group and mostly explained phenotypic variation.The range of the contribution rate is 0.61%-31.63%,and 8 QTLs exceeded 10%,6 QTLs are below 5% among them. Two QTL of qPL-7-5 and qP L-7-6 emerged at 1、4 stage of grain filling,but differencies of two stages were prominent.
The QTLs associated with weight of 1000 grains for five stages of grain filling under the environment of Wuyishan:we got 9 QTLs .Those QTL were located in1、2、3、5、7linkage group and mostly explained phenotypic variation.The range of the contribution rate is 3.03%-11.9%,and 1 QTLs exceeded 10%,3 QTLs are below 5% among them. The QTL of qGW-7-7 emerged at 4、5 stage of grain filling,and respectively explained 7.64%、3.82% of phenotypic variation.
The QTLs associated with length of grain for grain filling under the environment of Wuyishan:we got 7 QTLs that were located in 4、6、7、10、12 linkage group and mostly explained phenotypic variation.The values of additive effects were respectively -0.03、-0.03、-0.03、-0.03、0.02、-0.05、0.03 and contribution rates of phenotypic variation were 5.69%、5.69%、8.22%、5.69%、2.53%、15.82% and 5.69% respectively.
The QTLs associated with width of grain for grain filling under the environment of Wuyishan:we got 7 QTLs and respectively located in 2、2、5、5、6、6、7 linkage group and mostly explained phenotypic variation.The values of additive effects were respectively 0.02、0.01、0.01、0.01、-0.02、-0.02、0.02 and contribution rates of phenotypic variation were 5.72%、0.44%、0.44%、0.44%、1.77%、1.77% and 1.77% respectively.
The QTLs associated with length of panicle and weight of panicle for five stages of grain filling under the environment of Putian:we got 41 QTLs and located in 1、2、3、4、5、6、8、9、10 linkage group and mostly explained phenotypic variation.The range of the contribution rate is 0.32%-16.52%,and 11 QTLs exceeded 10%,26 QTLs are below 5% among them. The QTLs associated with length of panicle of qPL-2-11、qPL-2-12、qPL-2-13、qPL-4-2、qPL-4-3、qPL-8-3、qPL-8-4、qPL-9-4 emerged at least one time,implying the importance of additive effects to length of panicle.
The QTLs associated with weight of 1000 grains for five stages of grain filling under the environment of Putian:we got 13 QTLs which were located in1、2、4、6、7 linkage group and mostly explained phenotypic variation.The range of the contribution rate is 1.9%-24.78%,and 5 QTLs exceeded 10%,1 QTLs are below 5% among them. There QTLs of qGW-6-7、qGW-2-1、qGW-6-8 respectively emerged at 4、2、3 stage of grain filling,and all emerged at 5 stage.It showed the importance of additive effects to the form of later output.
The QTLs associated with length of grain for grain filling under the environment of Putian:we got 9 QTLs and located in 1、2、4、5、7、10、11、12 linkage group and mostly explained phenotypic variation.The values of additive effects were respectively -0.02、0.02、-0.02、-0.02、-0.03、-0.04、-0.03、0.02、-0.02 and contribution rates of phenotypic variation were 3.58%、3.58%、3.58%、3.58%、8.06%、16.12%、8.06%、3.58% and 3.58% respectively.
The QTLs associated with width of grain for grain filling under the environment of Putian.we got 3 QTLs and respectively located in 5、5、6 linkage group and mostly explained phenotypic variation.The values of additive effects were respectively 0.0136、0.0061、-0.0159 and contribution rates of phenotypic variation were 14.99%、3.02% and 20.49% respectively.
We got 46 QTLs with prominent gene×environment interaction.They are located in1、2、3、4、5、6、7、8、9、10、11 linkage group.The range of the contribution rate of additive effects to phenotype were 0%-19.22%%,and 10 QTLs exceeded 10%,29 QTLs are below 5% among them. The range of the contribution rate of GE interactions to phenotype were 0%-12.31%, QTLs are mostly below 5% among them..
3. The QTLs associated with net photosynthetic rate for five stages of grain filling under the environment of Wuyishan:we got 7 QTLs located in 1、2、3、4、5、6、8、9、10 linkage group just at 1、4 stage and mostly explained phenotypic variation.The range of the contribution rate is 0.34%-22.71%, the contribution rate of QTLs mostly exceeded 10% except 2 QTLs.
The QTLs associated with net photosynthetic rate for five stages of grain filling under the environment of Putian:we got 15 QTLs located in 1、2、4、7、9、10 linkage group at 1、2、3、4 stage and mostly explained phenotypic variation.The range of the contribution rate is 0.36%-17.85%, and just 1 QTLs exceeded 10%,2 QTLs are below 5% among them.
We got 7 QTLs with prominent gene×environment interaction at 1、2、4 stage.They are located in2、4、9、11 linkage group.The range of the contribution rate of additive effects to phenotype were 3.51%-16.05%,and 1 QTL exceeded 10%,9 QTLs are below 5% among them. The range of the contribution rate of GE interactions to phenotype were 0%-20.91%, 3 QTL exceeded 10%,3 QTLs are below 5% among them.
4. The QTLs associated with grain-filling rate for five stages of grain filling under the environment of Wuyishan:we got 11QTLs located in 1、2、4、5、7、10 linkage group at 1、2、4、5 stage and mostly explained phenotypic variation.The range of the contribution rate is 0.92%-17.39%, and just 1 QTL exceeded 10%,6 QTLs are below 5% among them.
The QTLs associated with grain-filling rate for five stages of grain filling under the environment of Putian:we got 9 QTLs located in 1、2、5、6、8 linkage group at 2、3、4 stage and mostly explained phenotypic variation.The range of the contribution rate is 1.99%-24.41%, and 3 QTLs exceeded 10%,2 QTLs are below 5% among them.
We got 7 QTLs with prominent gene×environment interaction at 2、4、5 stage.They are located in 1、2、4、5、6、10 linkage group.The range of the contribution rate of additive effects to phenotype were 1.1%-4.54%, and contribution rate of QTLs are mostly below 5% among them. The range of the contribution rate of GE interactions to phenotype were 0%-10.29%.
5. The correlation analysis by SPSS software:the correlation analysis between characters with source、net photosynthetic rates and grain-filling rates.The result indicated that the relationships of the characters during grain-filling were complex and some of them reach significant correlation.
This research work on the source and the sink carries in Wuyishan and Putian was conducted for separately QTL mapping at different stages and gene×environment interaction in rice.The result in a sense revealed that grain filing process in the rice is a gene dynamic expression process, simultaneously is also an interactive action between the gene and gene, gene and environment in the process, it was significant in rice breeding. Simultaneously the gene dynamic expression and interaction cannot only depend on the QTL method to make the simple analysis. To further understead Understanding the gene expression in space and time characteristics, this also need to do more work. In the different levels, such as RNA proteome level.
引文
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