【作者】 唐绍清；

【导师】 石春海；

【作者基本信息】 浙江大学 ， 作物遗传育种， 2007， 博士

【摘要】 稻米品质改良是水稻遗传育种的重要研究领域。QTL定位和分析可为分子标记辅助选择改良稻米品质和开展相关基因的精细定位及图位克隆提供理论基础。本研究利用日本晴／Kasalath／／日本晴衍生的回交重组自交系(backcross inbred lines，BILs)群体以及245个分子标记连锁图谱，在杭州和海南两个环境对稻米蒸煮和营养品质进行QTL分析。同时在重金属胁迫环境下对籽粒重金属含量开展了糙米重金属铜、镉、铅和锌低积累材料的筛选和QTL定位。主要的研究结果如下：1．本研究在杭州和海南两个环境种植了BIL群体，同时应用WinQTLCart 1.13进行单环境的QTL定位和应用QTLMAPPER 1.6定位软件检测稻米蒸煮食用品质的主效应与上位性QTL及与环境互作效应。结果表明，在第6染色体R1962-C191B标记区间均检测到1个控制稻米直链淀粉含量(AC)和1个控制胶稠度(GC)的QTL，具有明显的主效应，贡献率分别为54.87％和62.80％，应为Wx基因的等位基因，说明胶稠度主要由Wx基因或与Wx基因紧密连锁的基因控制，这2个QTL同时存在着显著的环境互作效应。在第6染色体C1478-R2171区间则检测到1个控制碱消值(ASV)的QTL，其主效应也已达到显著水平，贡献率为59.90％，应为ALK基因的等位基因，但其环境互作效应未达显著水平。另外，还在Wx基因位点检测到1个具有遗传主效应和环境互作效应的ASV QTL，贡献率9.56％。本研究中检测到4对互作对(qAC5和qAC，qGC6和qGC9，qASV1和qASV6-1以及qASV6-2和qASV9)，但这些上位性QTL对性状表型变异的贡献率较低。AC、GC和ASV的QTLs所具有的显著环境互作效应，可以较好地解释同一品种在不同环境下稻米蒸煮品质有较大差异的遗传基础。其中位于R1962-C191B区间控制AC和GC以及位于C1478-R2171区间控制ASV的主效应QTL在杭州和海南二个环境下均能分别检测到。在杭州和海南共检测到控制AC、GC和ASV的QTL共21个，分布于第1、2、4、6、7和8染色体上，并且集中分布于第6染色体Wx基因和ALK基因所在区域。2．蛋白质含量(PC)和氨基酸含量是重要的营养品质性状。本研究利用BIL群体进行多环境的QTL联合分析，在蛋白质含量性状上检测到1个具有主效应的QTL(qPC6-3)和4对上位性QTLs(qPC1和qPC8，qPC6-1和qPC6-4，qPC6-2和qPC6-6，qPC6-5和qPC12)；在16种氨基酸中，仅在赖氨酸含量(Lys)、苏氨酸(Thr)、天冬氨酸(Asp)和甘氨酸(Gly)4种氨基酸含量中检测到6个具有主效应的QTLs(qLYS6-1，qLYS6-2，qTHR6-1，qTHR6-2，qASP6和qGLY6)，均位于第6染色体蒸煮品质性状QTLs的相同区间或邻近位点，这反映出稻米蒸煮品质和营养品质性状的表现可能具有一定相关性，这些区间在今后的研究中值得重视。还检测到3对影响氨基酸含量的上位性QTLs(qTHR3和qTHR6-2，qTHR10-1和qTHR10-2以及qGLY6和qGLY11)在杭州环境中定位到1个影响蛋白质含量、35个影响16种氨基酸含量的QTLs，而在海南环境下则定位到3个影响蛋白质含量、69个影响16种氨基酸含量的QTLs，都有集中分布的趋势，杭州环境条件下主要集中在第5、12、6和8等4条染色体，海南环境下集中分布于第1、2、6和12等4条染色体。由于氨基酸受环境条件变化的影响较大，除赖氨酸外其他氨基酸很少在二个环境检测到相同QTL位点。本研究在第6染色体定位到2个(qLYS6-1和qLYS6-2)影响赖氨酸含量的QTLs，具有显著的加性效应，可解释变异的74.64％。这2个QTL分别位于第6染色体的R1962-C191B和C1478-R2171标记区域，贡献率为27.08％和47.56％，其增加赖氨酸含量的增效基因分别来源于Kasalath和日本晴，前者与环境存在着显著的互作效应，在杭州和海南两个环境条件下均能分别检测到。3．上述利用近红外光谱(NIRS)分析技术测定稻米品质，定位稻米直链淀粉含量、胶稠度、碱消值及蛋白质含量QTL的结果，与利用相同遗传群体和其他群体已有研究结果能相互印证，证实了利用NIRS技术分析稻米品质开展品质性状QTL定位的可行性。4．在重金属污染严重的稻田，种植367份不同材料，测定糙米铜、镉、铅和锌以筛选重金属低积累的材料。结果表明，重金属含量在不同品种间差异明显，从中筛选到一批低铜、低镉、低铅和低锌含量材料，同时筛选到铜、镉、铅和锌含量均低的材料27份。利用BIL群体，共检测到13个控制重金属铜、镉、铅和锌含量的QTLs，分布在第3、4、5、6、7、9、11和12等8条染色体上，其中单个QTL的表型贡献率在7.26～15.92％之间。在第3、9和12染色体检测到3个影响Cu含量的QTLs；在第3、7和11染色体定位到3个影响Cd含量的QTLs；在第4、5和12(2个)染色体定位到4个影响Pb含量的QTLs。更多还原

【Abstract】 Grain quality improvement is a very important area of rice breeding. In this study, the backcross recombinant inbred line population derived from Nipponbare (japonica)/ Kasalath (indica)//Nipponbare and its genetic linkage map were used to map the QTLs controlling cooking and nutrient quality traits in two distinct environments, Hangzhou and Hainan. QTLs for Cu, Cd, Pb and Zn contents were also studied under heavy metal stress. Molecular markers tightly linked to the QTLs were identified based on the study and could be used for grain quality improvement by marker-assisted selection (MAS). The results are summarized as follows:1. QTL detection for rice cooking quality traitsFour main effect QTLs (qAC6, qGC6, qASV6-1 and qASV6-2) were identified in the BIL population by using of QTLMAPPER 1.6 software. Among them, qAC6, qGC6 and qASV6-l were also detected to have significant G×E interaction. In addition, 4 pairs of QTLs with additive×additive epistasis (qAC5 vs qAC6, qGC6 vs qGC9, qASV1 vs qASV6-1 and qASV6-2 vs qASV9 ) were detected, but they had no G×E interaction. Three out of four main effect QTLs detected above, qAC6, qGC6 and qASV6-2, were also detected in both two experimental locations by using of WinQTLCart 1.13a software. In Hangzhou and Hainan, totally 21 putative QTLs for cooking quality traits were identified on chromosome 1, 2, 4, 6, 7 and 8. 11 out of 21 QTLs were found near the loci of Wx and ALK genes on chromosome 6.2. QTL detected for rice nutrient quality traitsProtein content (PC) and amino acids content are very important nutrient quality trait. The identification of QTL for PC and amino acids content and analysis of possible epistasis among the QTLs and QTL×environment interaction were conducted using QTLMAPPER 1.6. For PC, one main effect QTL (qPC6-3) and 4 pairs of epistatic QTLs (qPC1 vs qPC8, qPC6-1 vs qPC6-4, qPC6-2 vs qPC6-6 and qPC6-5 vs qPC12) were identified. Among 16 types of amino acids tested, only 6 main effect QTLs (qLYS6-1, qLYS6-2, qTHR6-1, qTHR6-2, qASP6 and qGLY6 ) and 3 pairs of epistatic QTLs (qTHR3 vs qTHR6-2, qTHR10-1 vs qTHR10-2 and qGLY6 vs qGLY11 ) for 4 types of amino acids ( Lys, Thr, ASP and Gly) content were detected. Results showed that the same region near the Wx and ALK loci on chromosome 6 was also an important region for main effect QTLs of PC and the amino acids content. A total of 35 and 69 putative QTLs were detected in Hangzhou and Hainan, respectively, but very few QTLs except for lysine content were detected commonly in the two experimental locations. It can be deduced that enhancing cooking and nutrient quality simultaneously was a complicated and tough job, and further research needs to be done.Two putative QTLs (qLYS6-1 and qLYS6-2) for lysine content with genetic main effects were detected on chromosome 6, which contributed to 27.08% and 47.56% of the total phenotypic variations, respectively. The qLYS6-1 was also detected to have significant G×E interaction. The additive effects of qLYS6-1 came from Kasalath while qLYS6-2 came from Nipponbare. In addition, no QTLs with additive×additive epistasis were detected in this experiment. Both qLYS6-1 and qLYS6-2 were detected in Hangzhou and Hainan, respectively. The results could be useful for pyramiding qLYS6-1 and qLYS6-2 by MAS in breeding programs to enhance lysine content in rice grain.3. Feasibility of QTL detection by the near infrared spectroscopy (NIRS)In this study, cooking and nutrient quality traits were determined by the established near infrared spectroscopy (NIRS) predication model. Comparison QTLs controlling cooking quality traits detected in this study with other studies revealed that those QTLs controlling AC, GC and ASV with large effect were detected in the same or different populations. The results proved that mapping QTLs of grain quality traits via the use of the NIRS measurement is feasible.4. Screening genotypes with low heavy metals accumulation in grains and QTL detection for heavy metals accumulation traitsThree hundred sixty seven rice genotypes were planted in a paddy field contaminated by toxic heavy metals. Concentrations of Copper (Cu), Cadmium (Cd), plumbum (Pb) and zinc (Zn) in rice grain were analyzed. The results showed that there was a great difference in heavy metal concentrations in rice grains among the genotypes. Some rice genotypes with low Cu, Cd, Pb and Zn concentrations were identified. Among them, 27 rice genotypes were found with a jointly Cu, Cd, Pb and Zn concentrations below 0.1, 10.0, 0.2 and 50.0 mg/kg, tolerance limit of the national hygienic standards for foodstuffs, respectively.Copper (Cu), Cadmium (Cd), plumbum (Pb) and zinc (Zn) content of the parents and BIL lines were determined in rough rice powder. The quantitative trait loci (QTLs) for each trait were analyzed based on the constructed molecular linkage map of this population. Totally, 13 QTLs for accumulation of 4 heavy metals were detected on chromosome 3, 4, 5, 7, 9, 11 and 12. Their variance explained by each of them ranged from 7.26%-15.92%. Three putative QTLs for Cu content (qCu3, qCu9 and qCu12) were mapped on chromosome 3, 9 and 12, respectively. Three QTLs for Cd content (qCd3, qCd7 and qCd11) were located on chromosome 3, 7 and 11, respectively. Four QTLs for Pb content (qPb4, qPb5, qPb12-1 and qPb12-2) were identified on chromosome 4, 5 and 12, respectively. Three QTLs for Zn content (qZn4, qZn6 and qZn7) were detected on chromosome 4, 6 and 7, respectively.更多还原