三种供氮水平下控制水稻产量及其构成因子的分子遗传基础剖析
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摘要
随着人们对资源和环境问题严峻性认识的不断加深,以节省资源、保护环境为前提的农业可持续发展热潮波及全球。作物遗传改良的目标和重点不仅仅是提高作物的产量潜力,而且还要高产、高效、优质、抗逆兼顾。
水稻是我国主要的粮食作物之一,水稻的高产、稳产直接关系到我国乃至世界的粮食安全。近年来由于稻田长期大量施用氮肥,虽然水稻产量水平得到了较大幅度的提高,但随之而来的是水稻生产成本逐年提高,效益递减现象严重,同时氮肥的过度施用,还带来肥料利用率降低、环境污染等一系列问题,在过去几十年间,人们通过大量施用氮肥来提高粮食产量达到了一定的效果。但随着氮营养元素施用水平的提高,逐渐表现出氮肥施用量与产量的提高并不成正比例增长,反而有负增长的趋势。挖掘和利用水稻自身的潜力、培育和利用氮素利用效率高和耐低氮条件的水稻品种是解决这一问题最有效的途径。
近年来,随着基因组学的发展和生物统计软件的完善,特别是分子标记技术在遗传育种领域的广泛应用,以连锁不平衡(linkage disequilibrium, LD)为基础的关联分析(association analysis)方法为水稻数量性状遗传的研究提供了新途径,也为作物的分子设计育种提供了新的思路,它与基于连锁作图的数量性状位点(quantitative trait loci, QTL)定位结果可相互验证、相互补充。
本研究以一套水稻重组自交系群体(RILs)和一套中国栽培稻微核心种质为材料,分别在3种氮肥水平(N300、N150、NO)、两种生态环境(上海雨季、海南旱季)下对控制水稻产量及其构成因子等田间表型特性进行调查分析,并借助相应的分子连锁图谱和群体的基因型等信息,进行基于连锁作图的QTL定位分析和基于连锁不平衡的关联分析,以期探讨不同氮肥水平下控制水稻产量及其构成因子的分子遗传基础,为水稻氮高效育种提供有用的种质资源和标记信息,并应用于水稻氮高效分子聚合育种。主要结果如下：
1.分别在N300、N150和NO三种氮肥水平下,对来源于HR5/ZS97的重组自交系群体(RIL,共188个株系),进行基于单株产量(GYPP)及产量构成因子(单株穗数PNPP、单穗实粒数GNPP、单穗总粒数FGPP和百粒重HGW)的联合QTL定位分析,共检测到57个主效QTL,涉及到41个染色体区间,其中N300、N150和NO水平下各检测到的主效QTL数分别为15个、23个和19个,每个QTL解释的表型变异都较小最大的QTL解释表型效应的13.3%。另外,在NO水平下检测到2个主效QTL(qGYPP-4b和qGNPP-12),分别解释表型变异的10.9%和10.2%。
2.共检测到30对控制产量及其构成的上位性QTL,这30对上位性互作位点涉及除SFP以外的其他5个性状,包括11条染色体上的45个位点,其中N300、N150和NO水平下分别为11对、11对和8对,其中大部分上位性QTL互作位点发生在非主效QTL之间。
3.相关分析和通径分析表明,在N300和N150条件下,SFP对YPP的贡献最大,直接通径系数分别为0.809和0.783,相应的相关系数为0.549**和0.605**。而在低氮条件下(N3),虽然SFP与YPP的相关系数最大(R=0.553**),但SFP主要是通过FGPP对YPP产生影响,FGPP对YPP的直接通径系数达0.816。
4.基于全基因组218个多态性位点,考虑群体结构和亲缘关系,采用混合线性模型,在P≤0.01水平下,分别在E5(2008,上海)和E6(2009,海南)两环境下,N300、N150和NO三种氮肥水平下,对一套中国栽培稻微核心种质(160份材料)的田间产量相关性状：单株穗数(PNPP)、穗长(PL)、单穗实粒数(GNPP)、单穗总粒数(FGPP)、结实率(SFP)、百粒重(HGW)、单株产量(GYPP)、地上部生物学产量(SDW)和植株含氮量(NCP)等9个性状表型值与多态性位点进行关联分析。每次试验中各性状在全基因组范围内检测到6-26个显著关联的标记位点,分布于全部12条染色体上。这些显著关联位点中,大部分位于其他研究中定位到的QTL或基因所在区间。
5.本研究通过关联定位和QTL分析,检测出大量的在三种氮肥水平下特异表达的位点/QTL,同时也检测到一些共同表达的QTL/位点。大多数QTL/位点只在一种氮肥水平下检测到,表明水稻产量及其构成因子在不同的氮肥条件下生长是受不同的基因调节控制的。同时也发现少数主效QTL/位点被定位在第1、2、3、5、7、10染色体上的相同区域,这些区域可能存在控制和调节水稻产量及其构成的关键基因。研究得到的这些材料标记信息和定位结果可应用于水稻氮高效分子聚合育种。
As people deeply aware the serious problem of resources and environments, the hotspot for sustainable development of agriculture based on conserving resources and protecting environments has spread around the world. The objectives and key points for crops genetic improvement are not only to enhance the yield potential, but also to balance the high-yielding, high efficiency, good quality and high resistance.
Rice is one of the most important food crops in China. The high-yielding and stability of rice is closely related with food security in our country even in the world. In recent years, through the rice yield is increased by applying excess nitrogen fertilizer, the production costs are also increased year by year and the phenomenon of diminishing utility is serious. Moreover, the application of excessive nitrogen fertilizers in rice cultivation has reduced the efficiency of fertilizer nitrogen use and increased the production cost and environmental pollution. With the increase of nitrogen fertilizer application levels, the nitrogen fertilizer input rates do not rise steadily with the rice yield, but negatively increase with the rice yield. The reduction of nitrogen fertilizer application in fertile soils and increased productivity in sterile soils both require the improvement of the nitrogen use efficiency (NUE), including uptake and utilization efficiency. Therefore, the strategy for exploiting and using of the genotypic potential rice varieties with high NUE would be of economic benefit to farmers and help to reduce environmental contamination.
Recently, with the improvements of genomics and bio-statistics analysis software, especially the application of molecular marker technique in genetics and breeding, the association analysis method based on linkage disequilibrium has provided a new access to genetically research quantitative traits in rice and offered a new idea for molecular design breeding in crops. The results from this method and from quantitative trait loci (QTL) can testify and complement each other.
To understand the performance of yield and its components to different nitrogen fertilizer levels, a set of recombinant inbred lines (RILs) from a cross of Zhenshan97(indica) and HR5(indica) and a set of Chinese cultivated mini-core germplasm rice were planted in two different growing seasons (rainy season in Shanghai and dry season in Hainan) at three nitrogen fertilizer input levels (i.e. N300,300kg urea/ha, N150,150kg urea/ha and NO,0kg urea/ha) for detection of quantitative trait loci on grain yield and its related components consisting of grain yield per plant (GYPP), panicle number per plant (PNPP), grain number per panicle (GNPP), filled grains per panicle (FGPP), spikelet fertility percentage (SFP) and100-grain weight (HGW). Analysis on genetic components including main effects, epistatic effects of the quantitative trait locus (QTL) and QTL by environment interactions (Q×E) were carried out. The identification of genomic regions associated with yield and its components at different nitrogen levels will be useful to improve nitrogen use efficiency of rice by marker-assisted selection. The main results are as follows:
1. A total of57main-effect QTLs for GYPP, PNPP, GNPP, FGPP and HGW were identified from the recombinant inbred line (RIL) population of HR5/ZS97(including188lines) under the three N levels (N300, N150and NO), which distributed at41chromosomal regions. Among them,15,23and19QTLs were identified under the N300, N150and NO, respectively. The contribution rate of a single QTL was small, with the maximum phenotypic variance of13.3%. Under the NO condition, two main-effect QTLs (qGYPP-4b and qGNPP-12) were declared, which explained10.9%and10.2%phenotypic variances, respectively.
2. Additionally,30pairs of epistatic effect QTLs controlled yield and its components were detected, except for SFP, which involved in45loci on11choromosomes. Under the N300, N150and NO levels,11,11and8pairs were respectively detected, and most of them were found between two non-main effect QTLs.
3. Correlation and path analysis indicated that SFP had the greatest contribution to GYPP at the N300and N150levels with the direct path coefficients of0.809and0.783, and correlation coefficients of0.549**and0.605**, respectively. Under the NO level, although the correlation coefficient of SFP and GYPP was the greatest (R=0.553**), SFP affected GYPP through FGPP, and FGPP contributed the most to GYPP (P=0.816).
4. Based on200polymorphic loci in whole genome and the population structure and their kinship, the relationships of nine phenotypic values including PNPP, PL, GNPP, FGPP, SFP, HGW, GYPP, SDW and NCP with polymorphic loci were analyzed by using the mixed linear model (MLM) under two environments (Shanghai in2008and Hainan in 2009) at the three different nitrogen levels. In each experiment,6to26significantly related loci for each trait were detected in whole genome, which distributed on all the12chromosomes. Among these related loci, most of them were located at the known chromosomal regions of mapped QTLs or genes.
5. Numerous specially expressed loci/QTLs under the three nitrogen application levels and some co-expression loci/QTLs were declared by the association analysis and QTL mapping. Most of them were identified at one nitrogen application level, indicating that the traits of yield and its components are controlled by different genes under different nitrogen levels. Also, several main-effect QTLs/loci were located at the same regions on chromosomes1,2,3,5,7and10, suggesting that there are some key genes controlling or regulating rice yield and its components. On the whole, the identification of genomic regions associated with yield and its components at different nitrogen levels will be useful to improve NUE of rice by molecular marker-assisted approach. This information together with association results can be applied in breeding programs.
水稻是我国主要的粮食作物之一,水稻的高产、稳产直接关系到我国乃至世界的粮食安全。近年来由于稻田长期大量施用氮肥,虽然水稻产量水平得到了较大幅度的提高,但随之而来的是水稻生产成本逐年提高,效益递减现象严重,同时氮肥的过度施用,还带来肥料利用率降低、环境污染等一系列问题,在过去几十年间,人们通过大量施用氮肥来提高粮食产量达到了一定的效果。但随着氮营养元素施用水平的提高,逐渐表现出氮肥施用量与产量的提高并不成正比例增长,反而有负增长的趋势。挖掘和利用水稻自身的潜力、培育和利用氮素利用效率高和耐低氮条件的水稻品种是解决这一问题最有效的途径。
近年来,随着基因组学的发展和生物统计软件的完善,特别是分子标记技术在遗传育种领域的广泛应用,以连锁不平衡(linkage disequilibrium, LD)为基础的关联分析(association analysis)方法为水稻数量性状遗传的研究提供了新途径,也为作物的分子设计育种提供了新的思路,它与基于连锁作图的数量性状位点(quantitative trait loci, QTL)定位结果可相互验证、相互补充。
本研究以一套水稻重组自交系群体(RILs)和一套中国栽培稻微核心种质为材料,分别在3种氮肥水平(N300、N150、NO)、两种生态环境(上海雨季、海南旱季)下对控制水稻产量及其构成因子等田间表型特性进行调查分析,并借助相应的分子连锁图谱和群体的基因型等信息,进行基于连锁作图的QTL定位分析和基于连锁不平衡的关联分析,以期探讨不同氮肥水平下控制水稻产量及其构成因子的分子遗传基础,为水稻氮高效育种提供有用的种质资源和标记信息,并应用于水稻氮高效分子聚合育种。主要结果如下：
1.分别在N300、N150和NO三种氮肥水平下,对来源于HR5/ZS97的重组自交系群体(RIL,共188个株系),进行基于单株产量(GYPP)及产量构成因子(单株穗数PNPP、单穗实粒数GNPP、单穗总粒数FGPP和百粒重HGW)的联合QTL定位分析,共检测到57个主效QTL,涉及到41个染色体区间,其中N300、N150和NO水平下各检测到的主效QTL数分别为15个、23个和19个,每个QTL解释的表型变异都较小最大的QTL解释表型效应的13.3%。另外,在NO水平下检测到2个主效QTL(qGYPP-4b和qGNPP-12),分别解释表型变异的10.9%和10.2%。
2.共检测到30对控制产量及其构成的上位性QTL,这30对上位性互作位点涉及除SFP以外的其他5个性状,包括11条染色体上的45个位点,其中N300、N150和NO水平下分别为11对、11对和8对,其中大部分上位性QTL互作位点发生在非主效QTL之间。
3.相关分析和通径分析表明,在N300和N150条件下,SFP对YPP的贡献最大,直接通径系数分别为0.809和0.783,相应的相关系数为0.549**和0.605**。而在低氮条件下(N3),虽然SFP与YPP的相关系数最大(R=0.553**),但SFP主要是通过FGPP对YPP产生影响,FGPP对YPP的直接通径系数达0.816。
4.基于全基因组218个多态性位点,考虑群体结构和亲缘关系,采用混合线性模型,在P≤0.01水平下,分别在E5(2008,上海)和E6(2009,海南)两环境下,N300、N150和NO三种氮肥水平下,对一套中国栽培稻微核心种质(160份材料)的田间产量相关性状：单株穗数(PNPP)、穗长(PL)、单穗实粒数(GNPP)、单穗总粒数(FGPP)、结实率(SFP)、百粒重(HGW)、单株产量(GYPP)、地上部生物学产量(SDW)和植株含氮量(NCP)等9个性状表型值与多态性位点进行关联分析。每次试验中各性状在全基因组范围内检测到6-26个显著关联的标记位点,分布于全部12条染色体上。这些显著关联位点中,大部分位于其他研究中定位到的QTL或基因所在区间。
5.本研究通过关联定位和QTL分析,检测出大量的在三种氮肥水平下特异表达的位点/QTL,同时也检测到一些共同表达的QTL/位点。大多数QTL/位点只在一种氮肥水平下检测到,表明水稻产量及其构成因子在不同的氮肥条件下生长是受不同的基因调节控制的。同时也发现少数主效QTL/位点被定位在第1、2、3、5、7、10染色体上的相同区域,这些区域可能存在控制和调节水稻产量及其构成的关键基因。研究得到的这些材料标记信息和定位结果可应用于水稻氮高效分子聚合育种。
As people deeply aware the serious problem of resources and environments, the hotspot for sustainable development of agriculture based on conserving resources and protecting environments has spread around the world. The objectives and key points for crops genetic improvement are not only to enhance the yield potential, but also to balance the high-yielding, high efficiency, good quality and high resistance.
Rice is one of the most important food crops in China. The high-yielding and stability of rice is closely related with food security in our country even in the world. In recent years, through the rice yield is increased by applying excess nitrogen fertilizer, the production costs are also increased year by year and the phenomenon of diminishing utility is serious. Moreover, the application of excessive nitrogen fertilizers in rice cultivation has reduced the efficiency of fertilizer nitrogen use and increased the production cost and environmental pollution. With the increase of nitrogen fertilizer application levels, the nitrogen fertilizer input rates do not rise steadily with the rice yield, but negatively increase with the rice yield. The reduction of nitrogen fertilizer application in fertile soils and increased productivity in sterile soils both require the improvement of the nitrogen use efficiency (NUE), including uptake and utilization efficiency. Therefore, the strategy for exploiting and using of the genotypic potential rice varieties with high NUE would be of economic benefit to farmers and help to reduce environmental contamination.
Recently, with the improvements of genomics and bio-statistics analysis software, especially the application of molecular marker technique in genetics and breeding, the association analysis method based on linkage disequilibrium has provided a new access to genetically research quantitative traits in rice and offered a new idea for molecular design breeding in crops. The results from this method and from quantitative trait loci (QTL) can testify and complement each other.
To understand the performance of yield and its components to different nitrogen fertilizer levels, a set of recombinant inbred lines (RILs) from a cross of Zhenshan97(indica) and HR5(indica) and a set of Chinese cultivated mini-core germplasm rice were planted in two different growing seasons (rainy season in Shanghai and dry season in Hainan) at three nitrogen fertilizer input levels (i.e. N300,300kg urea/ha, N150,150kg urea/ha and NO,0kg urea/ha) for detection of quantitative trait loci on grain yield and its related components consisting of grain yield per plant (GYPP), panicle number per plant (PNPP), grain number per panicle (GNPP), filled grains per panicle (FGPP), spikelet fertility percentage (SFP) and100-grain weight (HGW). Analysis on genetic components including main effects, epistatic effects of the quantitative trait locus (QTL) and QTL by environment interactions (Q×E) were carried out. The identification of genomic regions associated with yield and its components at different nitrogen levels will be useful to improve nitrogen use efficiency of rice by marker-assisted selection. The main results are as follows:
1. A total of57main-effect QTLs for GYPP, PNPP, GNPP, FGPP and HGW were identified from the recombinant inbred line (RIL) population of HR5/ZS97(including188lines) under the three N levels (N300, N150and NO), which distributed at41chromosomal regions. Among them,15,23and19QTLs were identified under the N300, N150and NO, respectively. The contribution rate of a single QTL was small, with the maximum phenotypic variance of13.3%. Under the NO condition, two main-effect QTLs (qGYPP-4b and qGNPP-12) were declared, which explained10.9%and10.2%phenotypic variances, respectively.
2. Additionally,30pairs of epistatic effect QTLs controlled yield and its components were detected, except for SFP, which involved in45loci on11choromosomes. Under the N300, N150and NO levels,11,11and8pairs were respectively detected, and most of them were found between two non-main effect QTLs.
3. Correlation and path analysis indicated that SFP had the greatest contribution to GYPP at the N300and N150levels with the direct path coefficients of0.809and0.783, and correlation coefficients of0.549**and0.605**, respectively. Under the NO level, although the correlation coefficient of SFP and GYPP was the greatest (R=0.553**), SFP affected GYPP through FGPP, and FGPP contributed the most to GYPP (P=0.816).
4. Based on200polymorphic loci in whole genome and the population structure and their kinship, the relationships of nine phenotypic values including PNPP, PL, GNPP, FGPP, SFP, HGW, GYPP, SDW and NCP with polymorphic loci were analyzed by using the mixed linear model (MLM) under two environments (Shanghai in2008and Hainan in 2009) at the three different nitrogen levels. In each experiment,6to26significantly related loci for each trait were detected in whole genome, which distributed on all the12chromosomes. Among these related loci, most of them were located at the known chromosomal regions of mapped QTLs or genes.
5. Numerous specially expressed loci/QTLs under the three nitrogen application levels and some co-expression loci/QTLs were declared by the association analysis and QTL mapping. Most of them were identified at one nitrogen application level, indicating that the traits of yield and its components are controlled by different genes under different nitrogen levels. Also, several main-effect QTLs/loci were located at the same regions on chromosomes1,2,3,5,7and10, suggesting that there are some key genes controlling or regulating rice yield and its components. On the whole, the identification of genomic regions associated with yield and its components at different nitrogen levels will be useful to improve NUE of rice by molecular marker-assisted approach. This information together with association results can be applied in breeding programs.
引文
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