干旱胁迫下水稻叶片光合速率与叶绿素含量的相关性及其基因定位

以水稻重组自交系珍汕97B×IRAT109 F9代群体195个株系为材料,用213个简单重复系列（SSR）标记构建了基于该群体的连锁图谱,对水稻叶片叶绿素含量和光合速率在干旱和正常条件下的数量性状位点（QTL）和双基因互作进行了分析,同时分析了叶绿素含量与光合速率的相关关系. 结果表明：叶绿素含量与光合速率在正常供水下呈极显著正相关（r=0.185　7,表示在1%水平上显著）,但在干旱下则表现无关（r=0.076　6）.控制叶绿素含量的基因很复杂,主效QTL有13个,位于1、2、3、4、5、6、10号染色体上;其中,在干旱处理下检测到的主效QTL有6个,位于1、2、3、4、5号染色体上;在正常供水下检测到的主效QTL有7个,位于2、3、4、6、10号染色体上.在干旱和正常条件下它们分别解释了47.39%和56.19%的表型变异;在2种处理下均检出的主效QTL是2、3、4号染色体上的qCC2a、qCC2b、qCC3a、qCC3c、 qCC4a、 qCC4b; 它们位于同一染色体的相同区段.在干旱和正常条件下检测到4个QTL与光合速率有关;其中干旱下有3个(qPR2、 qPR10、 qPR11),正常条件下1个(qPR10).它们分别被定位于2、10、11号染色体,共解释13.94%的表型变异. 叶绿素含量互作效应位点有16对,涉及除10号染色体外的所有染色体;干旱下,有4对互作基因,共解释1857%的表型变异,分别位于1-7、2-4、5-8、6-12号染色体上;正常供水下,有12对互作基因,共解释38.49%的表型变异,分别位于1-3、1-4、1-8、2-4、2-5、3-5、4-11、4-12、5-9、7-12、8-11 号染色体上,其中3-5号染色体不同区段上有两对互作效应位点.     

小麦重组自交系(RILs)群体碳同位素分辨率的QTL分析

为了探知小麦水分利用效率(WUE)碳同位素分辨率(Δ)的遗传机理,以小麦重组自交系(RILs)群体为材料,在不同水分条件下研究Δ的遗传规律,并进行QTL定位。结果表明:(1)RILs群体的Δ值呈正态分布,Δ属于数量性状遗传。(2)共检测到11个主效QTL,主要位于2B、3B、7B、1D和3D染色体上,表型贡献率在10.83%~46.87%之间,有9个加性QTL(A-QTL)与环境发生互作,互作贡献率在1.02%~3.15%之间。(3)检测到5对影响Δ的上位QTL(AA-QTL),其中3对AA-QTL与环境发生互作,互作贡献率在0.86%~2.01%之间。(4)加性效应及贡献率大于上位性效应及贡献率,A-QTL与环境互作贡献率大于AA-QTL与环境互作贡献率,表明RILs群体中Δ遗传变异主要受加性效应影响,控制Δ的主效基因作用较大,受水分环境影响较小。     

水稻生育后期叶绿素含量的QTLs及其与环境的互作分析

利用Dular和Lemont杂交后代单粒传衍生的123个F12家系所组成的重组自交系（RILs）群体,研究水稻剑叶叶绿素含量的数量性状基因座（QTL）.分别在2005年和2006年考察该RIL群体齐穗期剑叶叶绿素含量,并进行QTL定位和上位性分析及其与环境的互作效应分析.结果表明：在4对染色体上共检测到10个控制叶绿素含量的加性QTLs,共解释了73.51%的遗传变异,单个QTL的表型贡献率为2.08%～20.14%,其中6个和环境存在显著互作;同时也检测到13对影响叶绿素含量的加性×加性上位性互作,其中6对具有显著的上位性环境互作效应.     

大豆叶绿素含量QTL定位及候选基因预测

叶绿素是调节光合作用的关键色素,对籽粒形成有着重要作用。本研究以美国半矮秆大豆Charleston为母本,东北地区主栽品种东农594为父本杂交衍生的147个重组自交系群体为材料,基于经SLAF测序获得的大豆高密度遗传图谱,利用复合区间作图法(CIM)、多重区间作图法(MIM)和完备区间作图法(ICIM)对大豆叶绿素含量进行QTL联合定位分析,并结合大豆基因组基因注释信息对QTL区段内的候选基因进行预测。利用CIM算法定位出2个QTL,表型遗传贡献率分别为6%和9.3%。利用MIM算法定位到了1个QTL,表型遗传贡献率为8.1%。利用ICIM算法定位到了1个QTL,表型遗传贡献率为7.76%。其中qchl-G-1被CIM和MIM两种算法同时检测到。在上述3个QTL区段内共含有151个基因,根据大豆基因组基因注释信息,筛选到了3个与叶绿素相关的候选基因,这些结果为叶绿素含量的遗传剖析和标记辅助育种提供理论基础,有利于分子辅助育种的发展。     

水稻柱头外露率QTLs定位及其互作分析

以协青早B/密阳46所构建的RIL群体及其相应分子遗传图谱,设置海南和杭州两地遗传试验,应用基于混合线性模型检测QTL主效应、上位性效应和G×E互作效应的遗传分析方法,对水稻柱头外露率(%)进行QTL联合分析．结果表明,该性状明显表现出海南较高(21．83%)而杭州较低(8．35%)的趋势．试验检测到1个主效应QTL(qSE6-1),其LOD值高达28．16,对性状表型的贡献率为14．14%,增效等位基因来自于母本,加性效应为5．10%,不存在显著的GE互作．试验还检测到3对显著的加性×加性双基因互作,上位性互作性效应和贡献率相对较小,且与环境不存在显著的互作．     

稻米粒形的QTL定位及上位性和QE互作分析

利用'广陆矮4号'×'佳辐占'水稻重组自交系构建了SSR标记的遗传图谱.联合2007年和2008年获得的两组稻米粒长(GL)、粒宽(GW)、长宽比(L/W)数据应用混合线性模型方法进行QTL定位,并作加性效应、加性×加性上位互作效应以及加性QTL、上位性QTL与环境的互作效应分析.结果显示;(1)在加性效应分析中两个群体共检测到4个控制粒长的QTL,4个控制粒宽的QTL,5个控制长宽比的QTL,贡献率分别为13.81%、15.36%和 16.29%.(2)在上位互作效应分析中两个群体共检测到2对控制粒长的互作QTL,1对控制粒宽的互作QTL,3对控制长宽比的互作QTL,贡献率分别为5.77%、2.59%和7.42%.(3)环境互作检测中,发现共有13个加性QTL和4对QTL的加性×加性上位性与环境产生了互作效应.结果表明,上位性效应和加性效应都影响稻米粒形遗传,QE互作效应也对粒形有着显著的影响.     

水稻耐储藏特性三年动态鉴定与QTL分析

种子耐储藏特性是粮食作物的特殊农艺性状之一, 耐储藏性能对种子生产和种质资源保存有重要意义。以粳型超级稻龙稻5 (LD5)和高产籼稻中优早8 (ZYZ8)杂交衍生的重组自交系(RILs)群体(共180个株系)为实验材料, 自然高温高湿条件下放置1年、2年和3年后, 对不同储藏时段种子发芽率进行比较, 并利用223个分子标记的遗传图谱进行动态QTL鉴定。结果表明, 不同储藏时段龙稻5的发芽率均显著低于中优早8, 株系间耐储性存在较大差异; 不同储藏时段发芽率显著相关, 相邻存储时段发芽率关系紧密。共检测到17个耐储性相关的QTLs, 3个老化时段分别检测到5、4和3个, 检测到5个动态条件QTLs, 单一QTL解释5.60%-32.76%的表型变异, 加性效应在-16.78%-16.95%范围内。主效QTL簇qSSC2、qSSC6、qSSC7和qSSC8能调控不同储藏时段的发芽率, qSSC6具有明显降低发芽率的效应。共检测到26对上位性互作位点, 主效QTL qSS1和qSS4参与上位性互作, 这表明上位性互作是调控耐储藏性状的重要遗传组成。研究结果为水稻(Oryza sativa)耐储性相关QTL的精细定位奠定基础, 同时丰富了耐储性分子标记辅助选择育种的基因资源。     

水稻耐储藏特性三年动态鉴定与QTL分析

种子耐储藏特性是粮食作物的特殊农艺性状之一, 耐储藏性能对种子生产和种质资源保存有重要意义。以粳型超级稻龙稻5 (LD5)和高产籼稻中优早8 (ZYZ8)杂交衍生的重组自交系(RILs)群体(共180个株系)为实验材料, 自然高温高湿条件下放置1年、2年和3年后, 对不同储藏时段种子发芽率进行比较, 并利用223个分子标记的遗传图谱进行动态QTL鉴定。结果表明, 不同储藏时段龙稻5的发芽率均显著低于中优早8, 株系间耐储性存在较大差异; 不同储藏时段发芽率显著相关, 相邻存储时段发芽率关系紧密。共检测到17个耐储性相关的QTLs, 3个老化时段分别检测到5、4和3个, 检测到5个动态条件QTLs, 单一QTL解释5.60%-32.76%的表型变异, 加性效应在-16.78%-16.95%范围内。主效QTL簇qSSC2、qSSC6、qSSC7和qSSC8能调控不同储藏时段的发芽率, qSSC6具有明显降低发芽率的效应。共检测到26对上位性互作位点, 主效QTL qSS1和qSS4参与上位性互作, 这表明上位性互作是调控耐储藏性状的重要遗传组成。研究结果为水稻(Oryza sativa)耐储性相关QTL的精细定位奠定基础, 同时丰富了耐储性分子标记辅助选择育种的基因资源。     

水稻叶片叶绿素和过氧化氢含量的QTL检测及上位性分析

研究水稻叶片叶绿素和过氧化氢含量的遗传规律,对探讨光合代谢产物遗传规律和开展高产育种具有重要指导意义。利用由日本晴／Kasalath∥日本晴的杂交组合衍生的98个回交重组自交家系(BC1F9)所组成的BIL(backcross inbred lines)群体,在第1、2、3和10染色体上分别检测出5个与叶绿素含量相关的QTL和2个影响剑叶过氧化氢含量的QTL,其中位于第1染色体的RFLP标记C86和C813之间的q-Chll对叶绿素含量的影响最为显著,对表型变异的贡献率达22％,其增效基因来自粳稻品种日本晴;同时在该区间检测到1个与剑叶过氧化氢含量相关的QTL:q-H2O2I,对过氧化氢含量的减效基因来自日本晴品种。上位性分析结果显示影响叶绿素含量及过氧化氢含量的非等位QTL之间存在显著的上位性效应。具有上位性效应的QTL分布于第2、6、11和12染色体上,未检测到与q-Chll或q-H2O2I互作的位点。暗示日本晴品种的RFLP标记C86和C813之间存在1个能够提高叶绿素含量,同时又能降低过氧化氢含量的主效QTL,其加性效应显著而不存在上位性效应。     

小麦籽粒品质性状的QTL分析

利用小麦中国春(母本)和兰考大粒(父本)F2群体构建了169个标记的分子遗传图谱,将F2∶3家系分别种植于3个环境中,利用基于完备区间混合模型的单环境作图模型和多环境作图模型对小麦籽粒容重、硬度、蛋白含量和结合水含量性状进行了QTL分析。结果显示:(1)两种作图模型下,检测到容重的6个共同QTL(QTW-6B-6、QTW-7B-6、QTW-7B-9、QTW-5D-2、QTW-6D-1、QTW-6D-4),单环境模型下遗传贡献率为1.99%~6.57%,多环境模型下遗传贡献率为3.66%~20.07%,其中QM TW-7B-9、QM TW-6D-1和QM TW-6D-4在多环境模型中表现为主效QTL。(2)检测到硬度的3个共同QTL(QHD-4A-5、QHD-7A-1和QHD-7B-9),单环境模型下的遗传贡献率为6.00%~6.95%,多环境模型中遗传贡献率为5.43%~9.64%。(3)检测到蛋白含量1个共同QTL(QPR-6D-1),单环境模型下的遗传贡献率为5.39%,多环境模型中遗传贡献率为10.06%,表现为主效QTL。(4)检测到籽粒结合水含量1个共同QTL(QMO-1B-4),单环境模型下的遗传贡献率为39.20%,多环境模型下的遗传贡献率为75.01%,均表现为主效QTL。(5)1B染色体上存在同时控制籽粒容重、硬度、蛋白和结合水含量的QTL,说明1B染色体对小麦品质的影响可能很大。研究表明,小麦容重、硬度、蛋白含量、结合水含量的遗传主要受加性效应控制。该研究初步定位的一些重要QTL可为进一步精细定位、基因挖掘和育种早代品质性状分子标记辅助选择提供依据。     

干旱胁迫下水稻柱头外露率加性、上位性效应和Q×E互作

在耐旱性筛选设施内对一套水稻重组自交系群体(共185个株系)进行两年的水分胁迫和非胁迫处理,调查每穗颖花数(sNP)、单边柱头外露率(PSES)、双边柱头外露率(PDES)和柱头总外露率(PES)等4个开花相关性状.方差分析结果显示年份、株系和水分处理,以及相互间互作的效应均达显著水平.表型相关以PSES和PES间最高(r=0.9752***),其次为PDES和PES (r=0.7150***),最次为PSES和PDES间(r=0.5424***).利用203个SSR标记建立的连锁图,胁迫和非胁迫条件下各检测到6个SNP的主效QTL,3～4个PSES、PDES和PES的主效QTL;检测到1～9对上位性QTL影响颖花数和柱头外露率.大部分加性和上位性效应的贡献率较低(0.76%～9.92%),仅有少数QTL或上位性QTL解释总方差的10%以上.一些主效和上位性QTL在PSES、PDES和PES间被共同检测到,解释了不同柱头外露率指标间高度正相关关系.几乎没有在水分胁迫和非胁迫两种条件下都检测到的QTL,暗示着干旱对颖花数和柱头外露率有严重的影响.     

Dissection of Additive, Epistatic Effect and Q × E Interaction of Quantitative Trait Loci Influencing Stigma Exsertion Under Water Stress in Rice

Four flowering related traits, spikelet number per panicle (SNP), percentage of single exserted stigma (PSES), dual exserted stigma (PDES) and total exserted stigma (PES) of a RI population with 185 lines under water stress and non-stress conditions for 2 years, were investigated in a drought tolerance screening facility. ANOVA results showed high significance between years, lines, and water stress treatments, together with interactions among them in pairs. Highest phenotypic correlation was found between PSES and PES (r = 0.9752^***), followed by PDES and PES (r = 0.7150^***), and PSES and PDES (r = 0.5424^***). Based on a linkage map of 203 SSR markers, six main effect QTLs were detected for SNP and three or four main effect QTLs were associated with PSES, PDES and PES under stress or non-stress conditions. There were one to nine pairs of epistatic QTLs influencing SNP and stigma exsertion. The contribution rates of additive and epistatic effects seemed to be in a low magnitude for most cases (0.76%-9.92%) while a few QTLs or QTL pairs explained more than 10% of total variance. Some main effect QTL and epistasis were commonly detected among PSES, PDES and PES, explaining the high positive correlation between them. Few QTLs were detected under both water stress and non-stress condition, implying that drought had severe impact on the genetic behaviors of both spikelet number and stigma exsertion.     

Correlation and Quantitative Trait Loci Analyses of Total Chlorophyll Content and Photosynthetic Rate of Rice (Oryza sativa) under Water Stress and Well-watered Conditions

In order to explore the relevant molecular genetic mechanisms of photosynthetic rate (PR) and chlorophyll content (CC) in rice ( Oryza sativa L.), we conducted a series of related experiments using a population of recombinant inbred lines (Zhenshan97B × IRAT109). We found a significant correlation between CC and PR ( R = 0.19**) in well-watered conditions, but no significant correlation during water stress ( r = 0.08). We detected 13 main quantitative trait loci (QTLs) located on chromosomes 1, 2, 3, 4, 5, 6, and 10, which were associated with CC, including six QTLs located on chromosomes 1, 2, 3, 4, and 5 during water stress, and seven QTLs located on chromosomes 2, 3, 4, 6, and 10 in well-watered conditions. These QTLs explained 47.39% of phenotypic variation during water stress and 56.19% in well-watered conditions. We detected four main QTLs associated with PR; three of them ( qPR2 , qPR10 , qPR11 ) were located on chromosomes 2, 10, and 11 during water stress, and one ( qPR10 ) was located on chromosome 10 in well-watered conditions. These QTLs explained 34.37% and 18.41% of the phenotypic variation in water stress and well-watered conditions, respectively. In total, CC was largely controlled by main QTLs, and PR was mainly controlled by epistatic QTL pairs.     

Mapping QTLs for Plant Phenology and Production Traits Using Indica Rice (Oryza sativa L.) Lines Adapted to Rainfed Environment

Drought is a major abiotic stress limiting rice production and yield stability in rainfed ecosystems. Identifying quantitative trait loci (QTL) for rice yield and yield components under water limited environments will help to develop drought resilient cultivars using marker assisted breeding (MAB) strategy. A total of 232 recombinant inbred lines of IR62266/Norungan were used to map QTLs for plant phenology and production traits under rainfed condition in target population of environments. A total of 79 QTLs for plant phenology and production traits with phenotypic variation ranging from 4.4 to 72.8% were detected under non-stress and drought stress conditions across two locations. Consistent QTLs for phenology and production traits were detected across experiments and water regimes. The QTL region, RM204-RM197-RM217 on chromosome 6 was linked to days to 50% flowering and grain yield per plant under both rainfed and irrigated conditions. The same genomic region, RM585-RM204-RM197 was also linked to harvest index under rainfed condition with positive alleles from Norungan, a local landrace. QTLs for plant production and drought resistance traits co-located near RM585-RM204-RM197-RM217 region on chromosome 6 in several rice genotypes. Thus with further fine mapping, this region may be useful as a candidate QTL for MAB, map-based cloning of genes and functional genomics studies for rainfed rice improvement.     

QTLs linked to leaf epicuticular wax,physio-morphological and plant production traits under drought stress in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Drought stress is the major constraint to rice (Oryza sativa L.) production and yield stability in rainfed ecosystems. Identifying genomic regions contributing to drought resistance will help to develop rice cultivars suitable for rainfed regions through marker-assisted breeding. Quantitative trait loci (QTLs) linked to leaf epicuticular wax, physio-morphological and plant production traits under water stress and irrigated conditions were mapped in a doubled haploid (DH) line population from the cross CT9993-5-10-1-M/IR62266-42-6-2. The DH lines were subjected to water stress during anthesis. The DH lines showed significant variation for epicuticular wax (EW), physio-morphological and plant production traits under stress and irrigated conditions. A total of 19 QTLs were identified for the various traits under drought stress and irrigated conditions in the field, which individually explained 9.6%–65.6% of the phenotypic variation. A region EM15_10-ME8_4-R1394A-G2132 on chromosome 8 was identified for leaf EW and rate of water loss i.e., time taken to reach 70% RWC from excised leaves in rice lines subjected to drought stress. A large effect QTL (65.6%) was detected on chromosome 2 for harvest index under stress. QTLs identified for EW, rate of water loss from excised leaves and harvest index under stress in this study co-located with QTLs linked to shoot and root-related drought resistance traits in these rice lines and might be useful for rainfed rice improvement.     

QTLs for drought escape and tolerance identified in a set of random introgression lines of rice

A large set of 254 introgression lines in an elite indica genetic background were evaluated for grain yield (GY) and related traits under the irrigated (control) and drought (stress) conditions in two consecutive years for genetic dissection of adaptive strategies of rice to water stress. A total of 36 quantitative trait loci (QTLs) affecting heading date (HD), plant height (PH), GY and yield components were identified and most QTLs showed pronounced differential expression either qualitatively or quantitatively in response to drought. These QTLs could be grouped into three major types based on their behaviors under control and stress conditions. Type I included 12 QTLs that expressed under both the stress and non-stress conditions. Type II comprised 17 QTLs that expressed under irrigation but not under stress. Type III included seven QTLs that were apparently induced by stress. The observation that the Lemont (japonica) alleles at all HD QTLs except QHd5 resulted in early heading under stress appeared to be responsible for the putative adaptation of Lemont to drought by escaping, whereas the Teqing (indica) alleles at most PH/GY QTLs were consistently associated with increased yield potential and trait stability and thus contributed to DT. Our result that most DT QTLs were non-allelic with QTLs for drought escaping suggests that the two adaptive strategies in the parental lines are under possible negative regulation of two largely non-overlapping genetic systems.     

Genetic analysis for drought resistance of rice at reproductive stage in field with different types of soil

Drought resistance of rice is a complex trait and is mainly determined by mechanisms of drought avoidance and drought tolerance. The present study was conducted to characterize the genetic basis of drought resistance at reproductive stage in field by analyzing the QTLs for drought response index (DRI, normalized by potential yield and flowering time), relative yield, relative spikelet fertility, and four traits of plant water status and their relationships with root traits using a recombinant inbred population derived from a cross between an indica rice and upland rice. A total of 39 QTLs for these traits were detected with individual QTL explained 5.1–32.1% of phenotypic variation. Only two QTLs for plant water status were commonly detected in two environments, suggesting different mechanisms might exist in two types of soil conditions. DRI has no correlation with potential yield and flowering time under control, suggesting that it can be used as a good drought resistance index in field conditions. The co-location of QTLs for canopy temperature and delaying in flowering time suggested a usefulness of these two traits as indexes in drought resistance screening. Correlation and QTL congruence between root traits and putative drought tolerance traits revealed that drought avoidance (via thick and deep root traits) was the main genetic basis of drought resistance in sandy soil condition, while drought tolerance may play more role in the genetic basis of drought resistance in paddy soil condition. Therefore, both drought mechanisms and soil textures must be considered in the improvement of drought resistance at reproductive stage in rice.     

QTL analysis of floral traits of rice (Oryza Sativa L.) under well-watered and drought stress conditions

Three floral traits, spikelet number per panicle (SNP), percentage of single exserted stigma (PSES) and dual exserted stigma (PDES) of a RI population with 185 lines under water stress and non-stress conditions for two years were investigated in a drought tolerance screening facility. ANOVA results showed high significance between years, lines, and water stress treatments, together with interactions among them in pairs. High phenotypic correlation was found between PSES and PDES (r=0.5424***). Based on a linkage map of 203 SSR markers, when under well-watered condition, six QTLs (qSNP-3b, qSNP-4, qSNP-11 qSNP-2, qSNP-5 andqSNP-9) were detected for SNP. Half of them had significant Q × E interactions. Three QTLs (qPSES-1, qPSES-2, qPSES-5) were found to influence PSES, including one locus (qPSES-2) having Q × E interaction. And three QTLs (qPDES-2, qPDES-5 andqPDES-8) were also detected to influence PDES.qPDES-5 was found to have Q × E interaction. The contribution rate of a single QTL varied from 0.80% to 8.83% for additive effect, and 1.86% to 15.25% for Q × E interactions. Under drought stress, six QTLs (qSNP-3a, qSNP-4, qSNP-7a, qSNP-7b, qSNP-8 andqSNP-9) were associated with SNP, includingqSNP-3a andqSNP-4 with Q × E interaction. Three QTLs (qPSES-1, qPSES-10 andqPSES-12) were located on rice chromosome 1, 10 and 12 for PSES. Four QTLs (qPDES-1a, qPDES-1b, qPDES-4 andqPDES-9) were detected for PDES, includingqPDES-9 with Q × E interaction. The additive effect of single QTL can only explain 1.16% to 5.84% of total variance while Q × E interaction of four loci can explain 4.25% to 11.54% of total variance for each locus. There were one to nine pairs of epistatic QTLs influencing SNP and stigma exsertion. The contribution rates of additive and epistatic effects seemed to be in a low magnitude for most cases (0.76%≈9.92%) while a few QTLs or QTL pairs explained more than 10% of total variance. Some main effect QTL and epistasis were commonly detected among PSES and PDES, explaining the high positive correlation between them. Few QTLs were detected under both water stress and non-stress conditions, indicating that drought had severe impact on the genetic behaviors of both spikelet number and stigma exsertion.     

Comparison of QTLs for rice seedling morphology under different water supply conditions

The variation of seedling characteristics under different water supply conditions is strongly associated with drought resistance in rice (Oryza sativa L.) and a better elucidation of its genetics is helpful for improving rice drought resistance. Ninety-six doubled-haploid (DH)rice lines of an indica and japonica cross were grown in both flooding and upland conditions and QTLs for morphological traits at seedling stage were examined using 208 restriction fragment length polymorphism (RFLP) and 76 microsatellite (SSR) markers. A total of 32 putative QTLs were associated with the four seedling traits: average of three adventitious root lengths (ARL), shoot height (SH), shoot biomass (SW), and root to shoot dry weight ratio (RSR). Five QTLs detected were the same under control and upland conditions. The ratio between the mean value of the seedling trait under upland and flooding conditions was used for assessing drought tolerance. A total of six QTLs for drought tolerance were detected. Comparative analysis was performed for the QTLs detected in this case and those reported from two other populations with the same upland rice variety Azucena as parent. Several identical QTLs for seedling elongation across the three populations with the positive alleles from the upland rice Azucena were detected, which suggests that the alleles of Azucena might be involved in water stress-accelerated elongation of rice under different genetic backgrounds. Five cell wall-related candidate genes for OsEXP1, OsEXP2, OsEXP4, EXT, and EGase were mapped on the intervals carrying the QTLs for seedling traits.