水稻抽穗期QTL定位及候选基因分析

水稻(Oryza sativa)抽穗期是决定产量和品质的重要性状,在育种、制种及引种驯化过程中发挥重要作用。将热研2号(O. sativa subsp. japonica cv.‘Nekken2’)和华占(O. sativa subsp. indica cv.‘HZ’)杂交获得F₁代,经连续多代自交得到120个重组自交系(RILs)群体。在常规水肥管理条件下,对120个RILs株系的抽穗时间进行统计分析。利用已构建好的高密度遗传图谱,对水稻抽穗期相关性状进行QTL定位分析,结果共检测到11个QTLs,分别位于第1、3、4、5、6、8和12号染色体上,其中1个LOD值高达5.75。通过分析QTLs区间内的候选基因,筛选出可能影响两亲本抽穗期的相关基因,并利用实时定量PCR进行基因表达量分析,发现LOC＿Os03g03070、LOC＿Os03g50310、LOC＿Os03g55389、LOC＿Os04g55510、LOC＿Os08g07740和LOC＿Os08g01670共6个基因在双亲间的表达量差异显著,其中LOC＿Os03g50310在Nekken2中的表达量比H...     

水稻穗部性状QTL定位及候选基因分析

水稻(Oryza sativa)穗部性状与产量直接相关,其相关基因的挖掘与功能解析对于保障国家粮食安全意义重大。以籼稻华占(HZ)和粳稻热研2号(Nekken2)及构建的120个重组自交系(RILs)为实验材料,测定了穗长、每穗粒数、结实率、柱头外露率及一次枝梗数等穗部性状。结合高密度分子遗传图谱进行QTL定位,结果共检测到31个QTLs,分别位于第1、2、3、4、5、6、10和11号染色体上,其中2个位点的LOD值分别高达5.45与5.28。通过分析筛选QTL区间内可能影响穗部性状的相关基因,并利用qRT-PCR进行基因表达检测,发现LOC＿Os05g05490、LOC＿Os05g06150、LOC＿Os03g11700、LOC＿Os03g12430、LOC＿Os05g28720、LOC＿Os05g30890、LOC＿Os05g31740和LOC＿Os02g17880在双亲间的表达水平差异显著。其中,前5个基因编码三角状五肽重复蛋白,而后3个基因编码糖基转移酶。研究挖掘到31个与穗部性状相关的QTLs,为进一步定位和克隆相关基因,从而选育高产水稻新品种奠定理论基础。     

水稻籽粒镉积累QTL定位及候选基因分析

水稻(Oryza sativa)是全世界重要的经济作物之一, 稻田镉(Cd)污染和镉积累问题严重威胁世界水稻的产量和品质以及人类健康, 如何降低水稻中镉积累已成为热点问题。以籼稻品种华占(HZ)为父本、粳稻品种热研2号(Nekken2)为母本, 连续自交多代后得到120个重组自交系群体, 对其镉积累进行检测和分析, 同时利用遗传图谱进行QTL作图。结果共检测到7个QTLs, 分别位于水稻第2、3、9和12号染色体上, 其中1个LOD值高达4.97。对这些QTL区间内与耐金属离子胁迫相关的候选基因进行定量分析, 发现LOC_Os02g50240、LOC_Os02g52780、LOC_Os09g31200、LOC_Os09g35030和LOC_Os09g37949这5个基因在双亲间的表达量差异显著, 结合亲本对不同金属离子的浓度积累数据, 推测LOC_ Os02g50240、LOC_Os09g31200及LOC_Os09g35030的高表达可能极大地提高了水稻对镉离子的吸收和胁迫耐受能力。通过QTL挖掘和分析, 发现这些基因与水稻籽粒的镉积累有关, 可能影响水稻耐镉胁迫的能力。研究结果为进一步筛选和培育耐镉胁迫的水稻品种创造了条件, 为阐明水稻镉积累的分子调控机制奠定了基础。     

水稻光合功能相关性状QTL分析

利用粳稻Kinmaze／籼稻DV85杂交后代单粒传衍生的81个F11家系所组成的重组自交系（Recombinant Inbred Lines,RILs）群体,研究水稻光合功能相关性状的数量性状基因座（QTL）。在水稻抽穗后7d测定叶片全氮含量（TLN）、叶绿素a／b比值（Chl．a：b）和叶绿素含量（Chl）。共检测到6个QTL,各QTL的LOD值为2．66～4．81,贡献率为11．2％-29．6％,其中,在第1、2和11染色体上检测到3个与全氮含量相关的QTL,相应贡献率为17．3％、15．3％、13．7％;在第3和4染色体检测到2个与叶绿素a／b比值相关的QTL,贡献率为13．8％和29．6％;在第1染色体检测到1个与叶绿素含量相关的QTL,贡献率为11．2％。4个QTL为本研究新检测的基因座。有趣的是,控制叶绿素含量的qCC-1位于第1染色体上RFLP标记C122附近,与已报道的NADH-谷氨酸合成酶基因位置一致,而叶绿素合成始于谷氨酸,暗示该基因座与水稻光合功能关系极为密切。然而,对抽穗后30d叶绿素含量进行QTL分析,结果未检测到与其相关的QTL,表明控制叶绿素含量qCC-1效应随水稻叶片的衰老而降低。     

水稻产量相关QTL研究现状

产量是最为复杂的数量性状,对它的遗传机理了解甚微。近15年来,许多学者利用随机分离群体定位了许多影响水稻产量及其组分的QTL,即以QTL定位的方法对产量潜力进行遗传剖析。试验证明上位性效应对产量及其组分性状遗传变异起着重要作用,但目前大多数QTL研究仍侧重于发掘和克隆单个主效QTL,然而对单一基因／QTL的深入了解还不足以诠释复杂性状遗传基础的全貌,还没有为育种家提供足够的可应用于分子标记辅助育种的遗传信息并用于提高水稻产量。笔者认为今后的数量性状研究尚需加强复杂性状QTL遗传网络的发掘,在改良水稻品种性状的同时发展并完善QTL研究。     

水稻籽粒维生素E QTL挖掘及候选基因分析

维生素E (V_E)是稻米营养品质的重要指标。水稻(Oryza sativa)是我国种植最广泛的粮食作物, 增加其籽粒的V_E含量是实现国民营养强化的一条便捷有效的途径。该研究以籼稻华占(HZ)为父本, 粳稻热研2号(Nekken2)为母本, 构建120个重组自交系(RILs)群体。采用高效液相色谱法(HPLC)对RILs群体的V_E各组分含量进行测定, 并基于构建的高密度分子遗传图谱进行QTL定位, 谱系分析后挖掘到122个V_E总量和分量相关QTLs, 分布在12条染色体上。其中qT3α/to2-1的LOD值高达10.32, qT3α2-1的LOD值高达9.91, 另有多个控制各异构体含量的主效QTLs, 且区间内包含OsGGR1、OsGGR2、OsTC及OsγTMT等V_E生物合成基因。通过qRT-PCR检测亲本中V_E合成基因的表达量, 发现在华占中候选基因的表达量均极显著高于热研2号, 推测这些基因的高表达是华占生育酚及生育三烯酚含量高于热研2号的原因。研究挖掘到的QTL数目较多, LOD值也较大, 为进一步筛选和培育高V_E含量的水稻新品种奠定了分子基础, 同时为揭示水稻V_E生物合成的分子调控机制提供了重要基因资源。     

候选基因策略在植物遗传学中的应用

随着遗传学研究技术的快速发展和后基因组学时代的来临,候选基因的概念和研究方法越来越多地被人们所应用,成 为功能克隆、图位克隆、表型克隆、插入突变等方法之外的又一重要基因克隆策略。候选基因策略的基本原理和主要步骤,以 及其在植物遗传学中具有重要的应用实例。     

甘蓝型油菜种子发芽率QTL定位及相关生理性状

利用GH06×P174组合衍生的183个重组自交系进行种子发芽率遗传分析及种子发芽期间种子生理性状分析。用复合区间作图法对在室温下保存两年的种子(STY)、保存1年的种子(SOY)与新收获种子(FS)发芽率进行QTL定位。另外对保存两年的种子及新收获的种子萌发期间脂肪酶活性、电导率、还原糖含量、总糖含量及根系活力进行了测定, 并对结果进行分析。结果表明3批种子的发芽率QTL位点各不相同, 保存两年的种子在第9、14、17条连锁群上分别检测到1个位点, 保存1年的种子在第5、9条连锁群上各检测到1个位点, 新收获的种子在第4、18条连锁群上分别测检到1个位点。研究发现, 3批种子的发芽率相关性不显著, 发芽率差异达到极显著水平, 同时保存不同年份种子的发芽率QTL各不相同, 这表明甘蓝型油菜发芽率受很多不同因素所控制。保存两年及新收获种子的发芽率与电导率之间的相关性均达到极显著负相关, 表明可以通过电导率的测定估测发芽率, 电导率的研究对种子发芽率的研究具有重要意义。     

水稻DH群体籽粒充实度的QTL定位

籽粒充实度较差是当前水稻亚种间杂种优势利用中所面临的最大障碍之一。研究采用籼粳交(圭630×02428)来源的DH群体对水稻籽粒充实度进行QTL分析,检测到1个主效应QTL(qGP-7),该QTL位于第7染色体RZ978～RG404a～RG404c区间的大约26cM的染色体区段上,对籽粒充实度的贡献率为10%～15%。发现了2对"加性×加性"效应的互作QTL,对籽粒充实度的贡献率皆为20%左右,表明QTL的上位性是控制籽粒充实度的重要遗传基础之一。还对亚种间杂交稻育种中"以饱攻饱"的亲本选配策略作了讨论。     

水稻叶绿素含量的QTL定位

利用由两个籼稻品种Acc8558和H359杂交构建的一个包含131个株系(F19)的重组自交系群体,及其相应的包含147个RFLP和78个AFLP标记的遗传图谱,采用多性状复合区间定位方法,对控制水稻叶绿素含量的QTL进行了定位分析。对叶绿素a和叶绿素b含量各检测到6个QTL,其中5个QTL在两性状问是相同的。这些QTL主要分布在第1和第4染色体上,因此这两条染色体对叶绿素含量是重要的。QTL qChlAlc／qChlBlb(二者位置相同)在4个观测时期均表现较大效应,且在最后的剑叶期贡献最大,因此对叶绿素含量最为重要。另两个QTL(qCh-LA4a／qChlB4a和qChlA4b／qChlB4b)只在第2次观测时期效应显著,表明它们只在特定发育阶段发挥作用。     

Identification of candidate markers associated with agronomic traits in rice using discriminant analysis

Plant genetic mapping strategies routinely utilize marker genotype frequencies obtained from progeny of controlled crosses to declare presence of a quantitative trait locus (QTL) on previously constructed linkage maps. We have evaluated the potential of discriminant analysis (DA), a multivariate statistical procedure, to detect candidate markers associated with agronomic traits among inbred lines of rice (Oryza sativa L.). A total of 218 lines originating from the US and Asia were planted in field plots near Alvin, Texas, in 1996 and 1997. Agronomic data were collected for 12 economically important traits, and DNA profiles of each inbred line were produced using 60 SSR and 114 RFLP markers. Model-based methods revealed population structure among the lines. Marker alleles associated with all traits were identified by DA at high levels of correct percent classification within subpopulations and across all lines. Associated marker alleles pointed to the same and different regions on the rice genetic map when compared to previous QTL mapping experiments. Results from this study suggest that candidate markers associated with agronomic traits can be readily detected among inbred lines of rice using DA combined with other methods described in this report.N. Zhang and Y. Xu contributed equally to work and considered as first authors.Approved for publication by the Director of the Louisiana Agricultural Experiment Station as paper no. 04-14-0335.     

A novel functional gene associated with cold tolerance at the seedling stage in rice

Identification and cloning of cold‐tolerant genes that can stably express under different cold environments are crucial for molecular rice breeding for cold tolerance. In the previous study, we identified a cold‐tolerant QTL at the seedling stage, qCTS‐9 which could be detected under different cold environments using a recombinant inbred line (RIL) population derived from a cold‐tolerant variety Lijiangxintuanheigu (LTH) and a cold‐sensitive variety Shanhuangzhan 2 (SHZ‐2). In this study, eight candidate genes within the qCTS‐9 interval were identified through integrated analysis of QTL mapping with genomewide differential expression profiling of LTH. The qRT‐PCR assay showed that only Os09g0410300 exhibited different expression patterns between LTH and SHZ‐2 during cold stress, and significantly positive correlation was found between cold induction of Os09g0410300 and seedling cold tolerance in the RI lines. Five SNPs and one InDel in the promoters of Os09g0410300 were detected between LTH and SHZ‐2, and the InDel marker ID410300 designed based on the insertion–deletion polymorphism in the promoter was significantly associated with seedling cold tolerance in RIL population. Further, Os09g0410300 over‐expression plants exhibited enhanced cold tolerance at the seedling stage compared with the wild‐type plants. Thus, our results suggest that Os09g0410300 is the functional gene underlying qCTS‐9. To our knowledge, it is a novel gene contributed to enhance cold tolerance at the seedling stage in rice. Identification of the functional gene underlying qCTS‐9 and development of the gene‐specific marker will facilitate molecular breeding for cold tolerance at the seedling stage in rice through transgenic approach and marker‐assisted selection (MAS).     

Mapping quantitative trait loci associated with days to flowering and photoperiod sensitivity in rice (Oryza sativa L.)

This study was undertaken to identify putative quantitative trait loci (QTLs) associated with days to flowering (DTF) and photoperiod response in rice. A population of 143 recombinant inbred lines derived from a cross between CO39 and Moroberekan was grown under greenhouse conditions and exposed to two different photoperiod regimes. DTF of individual plants was evaluated under 10 h and 14 h day lengths, and loci associated with photoperiod sensitivity were identified based on the delay in flowering under the 14 h photoperiod (DTF at 14 h minus DTF at 10 h). An RFLP data set consisting of 127 markers provided the basis for the QTL analysis. Both single marker and interval analysis were used and interactions between putative QTLs were estimated based on two-way ANOVA. Out of 15 QTLs associated with DTF, only 4 were identified as influencing the response to photoperiod. Interactions between flowering QTLs indicated the complex nature of the control of flowering in rice. The effectiveness of using a single recombinant inbred population to study a variety of complex phenotypes is discussed in relation to practical plant breeding.     

Mapping QTL for Biomass Yield and Its Components in Rice (Oryza sativa L.)

Addicive effects, additive by additive epistatic effects, and their environmental interactions of QTLs are important genetic components of quantitative traits. Genetic architecture underlying rice biomass yield and its two component traits (straw yield and grain yield) were analyzed for a population of 125 DH lines from an inter-subspecific cross of IR64/Azucena. The mixed-model based composite interval mapping approach (MCIM) was used to detect QTLs, There were 12 QTLs detected with additive main effects, 27 QTLs involved in digenic interaction with aa and/or aae effects, and 18 QTLs affected by environments with ae and/or aae effects. It was revealed that epistatic effects and QE interaction effects existed on biomass yield and its component traits in rice. In addition, the genetic basis of relationships among these traits were investigated. Four QTLs and one pair of epistatic QTLs were detected to be responsible for the positive correlation between biomass yield and straw yield. Three QTLs might be responsible for the negative correlation between straw yield and grain yield. This result could partially explain the genetic basis of correlation among the three traits, and provide useful information for genetic improvement of these traits by marker-assisted selection.     

水稻QTL分析的研究进展

水稻许多重要的性状是由多基因控制的数量性状,经典的数量遗传学只能把数量性状作为一个整体进行研究。近年来．高密度分子标记连锁图的构建和有效的生物统计学方法的发展使人们对数量性状遗传基础的研究出现了革命性的变化。通过对不同群体内的个体或品系的分子标记基因型和表型数据的共分离分析,能对QTL进行检测和定位。本文对QTL定位的原理和方法进行了介绍,从QTL的数目和效应、上位性效应、QTL基因型与环境的互作、相关性状的QTL以及个体发育不同阶段的QTL等方面对水稻QTL分析的研究进展进行了综述。水稻基因组测序计划已经完成,本文还对基因组时代水稻QTL精细定位和克隆的方法进行了探讨,对QTL分析在水稻育种中的应用前景进行了展望。     

水稻生物学产量及其构成性状的QTL定位

QTL的加性效应、加性×加性上位性效应及它们与环境的互作效应是数量性状的重要遗传分量.利用IR64/Azucena的125个DH品系为群体,分析了水稻生物学产量及其两个构成性状干草产量和谷粒产量的遗传组成.用基于混合模型的复合区间作图(MCIM)方法进行QTL定位.检测到12个位点有加性主效应,27个位点涉及双位点互作,18个位点存在环境互作.结果表明水稻生物学产量和它的两个构成性状普遍存在上位性效应和QE互作效应.此外,还探讨了性状间相关的遗传基础.发现4个QTLs和一对上位性QTLs可能与生物学产量与干草产量之间的正相关有关.3个QTL可能与干草产量与谷粒产量之间的负相关有关.这些结果可能部分地解释了这3个性状相关的遗传原因.通过对水稻生物学产量及其两个构成性状所定位QTL的分析,加深了对数量性状QTL的认识.首先,QTL的上位性效应和QE互作效应是普遍存在的;其次,QTL的多效性或紧密连锁可能是遗传相关的原因,当QTL对两个性状作用的方向相同时可导致正向遗传相关,反之则为负向遗传相关,当有些QTL表现为同向作用而另一些QTL表现为反向作用时,则可削弱性状间的遗传相关性;第三,复合性状的QTL效应可分解为其组成性状的QTL效应,如果QTL对各组成性状的效应方向相反而相互抵消,可使复合性状的QTL效应不易被检测;第四,加性效应的QTL常参预构成上位性效应,而具有上位性效应的QTL并非都有加性主效应,表明忽略上位性的QTL定位方法会降低检测QTL的功效;最后,鉴别不同类型的QTL效应有利于指导育种实践,选择主效QTL适用于多环境,QE互作QTL适用于特定环境,对上位性QTL应强调选择基因组合而并非单个基因.     

Mapping quantitative trait loci underlying the cooking and eating quality of rice using a DH population

The cooking and eating quality of rice has attracted more attention recently. In a comprehensive effort to unravel its genetic basis, we conducted a genome-wide analysis of six traits representing the cooking and eating quality of rice grain, namely, amylose content (AC), gel consistency (GC), gelatinization temperature (GT), water absorption (WA), cooked rice elongation (CRE) and volume expansion (VE) using a DH population derived from the anther culture of an F₁ hybrid between WYJ 2 (japonica) and Zhenshan 97B (indica). For each trait, one to three quantitative trait loci (QTL) were found, which were located on chromosomes 1, 2, 3, 6, 11. QTL analysis revealed a major QTL specifying GT, located at the interval RM276-RM121, which should be the same locus as the alkali degeneration gene (alk), while for each of the remaining five traits the QTL explaining the largest proportion of variance was located on the short arm of chromosome 6, centered at RM190 (found in the waxy gene). Our results, in combination with previous reports, further confirmed that either the waxy gene itself or a genomic region tightly linked to it plays a major role in determining the cooking and eating quality of rice.