玉米雌雄开花间隔天数、结穗率与产量的数量性状位点（QTL）分析

采用SSR标记连锁图谱和复合区间作图法在山西灌溉和干旱胁迫条件下,对玉米(Zea mays L.)自交系黄早四×掖107组合的F_3群体雌雄开花间隔天数(ASI)、结穗率和籽粒产量进行了数量性状位点(QTL)定位及基因效应分析。结果表明,在两种水分处理下,ASI、结穗率与籽粒产量的相关性均达到显著水平(P<0.05)。在灌溉和干旱胁迫卜,分别检测到3个和2个控制ASI的QTL,位于第1、2、3和第2、5染色体上。在灌溉条件下,在第3和第6染色体上各检测到1个控制结穗率的QTL,基因作用方式呈加性或部分显性,可解释19.9％的表型变异;在干旱条件下,在第3、7、10染色体上共检测到4个控制结穗率的QTL,基因作用方式为显性或部分显性,可解释60.4％的表型变异。在灌溉和干旱胁迫下,控制产量的QTL分别定位在第3、6、7和第1、2、4、8染色体上,基因作用方式均以加性或部分显性为主,可解释的表型变异为7.3％～22.0％。在干旱条件下,借助连锁分子标记和基因效应分析,可构建包含ASI、结穗率和产量QTL的选择指数,用于分子标记辅助育种。     

干旱胁迫和正常灌溉条件下玉米开花相关性状的QTL分析

干旱是影响玉米生产的重要限制因素,特别是花期对干旱胁迫非常敏感.本研究通过对玉米L050× B73的180个F2:3家系进行开花期干旱胁迫处理和分子标记鉴定,重点对开花相关性状进行了数量性状位点(QTL)分析.结果表明,在干旱胁迫处理条件下,存在与出苗到抽雄天数有关的6个QTL,位于第1、6、9染色体上各1个,位于第3染色体上有3个,共可解释的表现型变异为55.0%;基于出苗到散粉天数检测到4个QTL,其中两个位于第3染色体上,位于第1、2染色体上各1个,共可解释的表型变异为52.8%;对出苗到吐丝天数检测到分别位于第3、6染色体上的2个QTL,共可解释的表现型变异为20.4%;对抽雄至吐丝间隔天数(ASI)只检测位于第6染色体上的1个QTL,可解释6.5%的表现型变异.而正常灌溉环境下,检测到出苗到抽雄天数检测到1个QTL,位于第9染色体上,可解释的变异为15.0%;对出苗到散粉天数检测到3个QTL,位于第1、3、9染色体上,共可解释的变异为55.0%;对出苗到吐丝天数检测到4个QTL,分别位于第1、2、3、7染色体上.共可解释表现型变异的46.8%;对ASI检测到分别位于第2、6染色体上的2个QTL,可解释的变异为15.5%.这些QTL的基因效应以显性与超显性为主.     

不同水分条件下玉米株高和穗位高的QTL分析

干旱是影响玉米产量的重要因素.在干旱条件下,玉米株高和穗位高往往受到影响,因此是研究耐旱性的重要指标.本研究利用A188×91黄15的F2∶3家系,进行株高和穗位高的数量性状位点(QTL)分析.结果表明,在水分胁迫条件下,分别各有10个QTL与株高和穗位高有关;在水分充足条件下,则检测到各有6个QTL与株高和穗位高有关.各QTL解释的表型变异在7.3%～53.9%之间.位于第8染色体上的QTL个数占总QTL近50%,LOD值均大于4.6,推测该染色体存在控制玉米株高和穗位高QTL的重要区域.本研究在bnlg1812标记附近检测到在水分胁迫下同时控制株高和穗位高的QTL,解释的表型变异在20%以上,该QTL是值得进一步研究和利用的位点.     

利用三倍体胚乳遗传模型定位玉米籽粒淀粉含量QTL

在两种环境条件下种植以普通玉米自交系丹232和爆裂玉米自交系N04为亲本构建的259个F2:3家系群体, 采用SSR标记构建了包含183个标记的玉米遗传连锁图谱, 覆盖玉米基因组1 762.2 cM, 标记间平均距离为9.6 cM。利用三倍体胚乳遗传模型和区间作图方法对籽粒淀粉含量进行了QTL定位和遗传效应分析, 春、夏播条件下共检测到10个QTL, 春播条件下检测到的QTL在夏播均被检测到, 分别位于第1、3、4、5、7染色体上,可解释淀粉的表型总变异分别为36.84%和72.65%, 单个QTL解释表型变异介于4.74%~11.26%。在检测到的 QTL中, 有2个QTL的遗传作用方式在春播均表现为超显性, 而夏播分别为加性和部分显性; 其他2个为加性, 1个为部分显性, 5个为超显性。3个QTL的增效基因来自丹232, 其余QTL的增效基因均来自N04。     

干旱胁迫和正常灌溉条件下玉米产量性状的QTL分析

产量及其产量因子是衡量玉米耐旱能力的重要性状。本研究利用Lo1067×Y i72的F2∶3家系进行产量性状的数量性状位点(QTL)的分析。结果表明,在正常水分条件和开花期干旱胁迫条件下,分别有14个QTL与产量性状穗重、粒重、轴重、百粒重、穗数、穗粒数有关。此外,还检测到7个与抗旱指数(TI)相关的QTL。各QTL所解释的表型变异在1%~78%;这些QTL以部分显性和超显性为主。不同胁迫条件下检测到的QTL不一致,说明存在显著的QTL与环境互作。     

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.     

玉米优异早熟种质单330开花相关性状的QTL分析

玉米开花相关性状与玉米的成熟期和产量有密切的联系。通过对玉米CN165×单330(早熟种质)群体的130个F2:3家系开花相关性状在3个环境下进行分子鉴定和数量性状位点(QTL)分析,结果表明,在3个环境中检测到控制抽雄天数的10个QTL,分别位于第2、3、4、5、7、8染色体上,在第8染色体上同一区域在3种环境下都检测到了QTL;检测到控制散粉天数的10个QTL,分别位于第1、2、3、5、7、8染色体上,在第8染色体上同一区域在2种环境下都检测到了QTL;检测到控制吐丝天数的4个QTL,分别位于第4、5、8染色体上,在第8染色体不同环境下都检测到了2个QTL;仅仅在一个环境中检测到控制ASI的2个QTL,分别位于第6、9染色体上。这些QTL的基因效应以部分显性和超显性为主。研究表明,第8染色体上ph i060-um c2401区域(8.03~8.04)是一个研究开花相关性状的重要基因组区段,涉及到的标记可以作为分子标记辅助选择的重要候选标记。     

不同发育时期小麦粒重性状QTL的动态分析

利用6044×01-35构建的重组自交系(RIL)群体为试验材料,对小麦粒重性状进行发育动态QTL分析。结果表明,在小麦花后子粒灌浆的7个不同时期,两个试验点共检测到16个与粒重性状相关的QTL。其中开花后20d检测到的单穗粒重QTL位于2A染色体上,解释率达12%,遗传效应超过10;两环境下控制千粒重QTL在7个时期均被检测到。花后的各个时期均能在Xgwm448-Xgpw7399标记区间定位到千粒重QTL。其中花后10d检测到1个千粒重QTL,位于2A染色体的Xgwm448-Xgpw7399标记区间,解释较大的表型变异,达到18%。Qtl8、Qtl13和Qtl14均定位在Xgwm448-Xgpw7399标记区间的同一位置,共同解释11%的表型变异。花后20d和花后25d均检测到1个QTL,位于2A染色体的Xgwm372-Xgwm95标记区间的不同位点,均能解释4%的表型变异。花后40d检测到1个QTL,位于1D染色体的Xwmc93-Xgpw2224标记区间,解释1%的表型变异。从连锁群的位置上看,控制千粒重的QTL主要集中在2A染色体的Xgwm448-Xgpw7399标记区间,这是一个控制千粒重QTL的富集区域,以期进行精细定位和图位克隆。     

利用置换系检测棉花第16染色体的产量、纤维品质QTLs

陆地棉(Gossypium hirsutum L.)和海岛棉(Gossypium barbadense L.)是两个栽培四倍体棉种.前者产量高、适应性广,后者纤维品质优良.置换了海岛棉一对染色体的陆地棉置换系是研究海陆杂种此对染色体上基因互作的优异材料.在对第16染色体的置换系(简称Sub 16)进行遗传评价的基础上,利用(TM-1×Sub 16)F2∶3家系对位于第16染色体上的重要农艺性状进行遗传分析,发现第16染色体上有铃重、衣分、衣指、纤维长度、第一果枝节位的QTLs 各2个,纤维伸长率、开花天数的QTL各 1个,没有检测到子指、纤维强度、麦克隆值的QTL.在构建第16染色体的RAPD、SSR分子标记连锁图基础上,利用分子标记对相应重要农艺性状进行区间作图,检测到铃重、开花天数、纤维长度、纤维伸长率的QTL各1个,在F2∶3株系群体中能解释的表型变异分别为15.2%、12.1%、19.7%和11.7%;检测到2个衣指QTLs,在F2∶3株系群体中能解释的表型变异分别为11.6%和41.9%;检测到3个衣分QTLs,在F2∶3株系群体中能解释的表型变异分别为8.7%、9.6%和29.2%.单标记检测到铃重、开花天数的QTL各1个,在F2∶3株系群体中能解释的表型变异分别为1.60%和4.63%.证明了第16染色体与铃重、衣分、衣指、纤维长度、纤维伸长率、开花天数等性状的关系.     

水稻籽粒锌含量的QTL 定位

锌元素的营养失衡已成为影响人类健康的最重要因素之一, 籽粒锌含量的QTL(quantitative trait loci)定位对研究富锌水稻的遗传育种具有重要的意义。以水稻(Oryza sativa L.)亲本奉新红米和明恢100杂交的145个株系的F2群体为实验材料, 利用92个SSR(simple sequence repeat)标记对水稻籽粒锌含量进行了QTL定位, 共检测到3个QTLs , 分别定位于第3、6和11染色体上, 对表型变异的贡献率分别为4.97%、12.75%和7.74%。其中位于第3染色体上的分子标记RM186和RM168之间的QZN3对表型变异的贡献率最大, 其增效等位基因来自亲本明恢100, 表现为部分显性。3个QTLs 的联合贡献率为25.46%, 具有基因累加效应。该研究结果有利于深入理解水稻锌含量的遗传基础, 为锌含量的QTL精细定位、基因克隆和分子标记辅助选择提供依据。     

Identification of quantitative trait loci under drought conditions in tropical maize. 2. Yield components and marker-assisted selection strategies

In most maize-growing areas yield reductions due to drought have been observed. Drought at flowering time is, in some cases, the most damaging. In the experiment reported here, trials with F₃ families, derived from a segregating F₂ population, were conducted in the field under well-watered conditions (WW) and two other water-stress regimes affecting flowering (intermediate stress, IS, and severe stress, SS). Several yield components were measured on equal numbers of plants per family: grain yield (GY), ear number (ENO), kernel number (KNO), and 100-kernel weight (HKWT). Correlation analysis of these traits showed that they were not independent of each other. Drought resulted in a 60% decrease of GY under SS conditions. By comparing yield under WW and SS conditions, the families that performed best under WW conditions were found to be proportionately more affected by stress, and the yield reductions due to SS conditions were inversely proportional to the performance under drought. Moreover, no positive correlation was observed between a drought-tolerance index (DTI) and yield under WW conditions. The correlation between GY under WW and SS conditions was 0.31. Therefore, in this experiment, selection for yield improvement under WW conditions only, would not be very effective for yield improvement under drought. Quantitative trait loci (QTLs) were identified for GY, ENO and KNO using composite interval mapping (CIM). No major QTLs, expressing more then 13% of the phenotypic variance, were detected for any of these traits, and there were inconsistencies in their genomic positions across water regimes. The use of CIM allowed the evaluation of QTL-by-environment interactions (Q×E) and could thus identify “stable” QTLs CIMMYT, Apartado Postal 6-641, 06600 Mexico D.F., Mexico across drought environments. Two such QTLs for GY, on chromosomes 1 and 10, coincided with two stable QTLs for KNO. Moreover, four genomic regions were identified for the expression of both GY and the anthesis-silking interval (ASI). In three of these, the allelic contributions were for short ASI and GY increase, while for that on chromosome 10 the allelic contribution for short ASI corresponded to a yield reduction. From these results, we hypothesize that to improve yield under drought, marker-assisted selection (MAS) using only the QTLs involved in the expression of yield components appears not to be the best strategy, and neither does MAS using only QTLs involved in the expression of ASI. We would therefore favour a MAS strategy that takes into account a combination of the “best QTLs” for different traits. These QTLs should be stable across target environments, represent the largest percentage possible of the phenotypic variance, and, though not involved directly in the expression of yield, should be involved in the expression of traits significantly correlated with yield, such as ASI.     

Two consensus quantitative trait loci clusters controlling anthesis–silking interval, ear setting and grain yield might be related with drought tolerance in maize

Unravelling the molecular basis of drought tolerance will provide novel opportunities for improving crop yield under water-limited conditions. The present study was conducted to identify quantitative trait loci (QTLs) controlling anthesis–silking interval (ASI), ear setting percentage (ESP) and grain yield (GY). The mapping population included 234 F₂ plants derived from the cross X178 (drought tolerant) × B73 (drought susceptible). The corresponding F_2:3 progenies, along with their parents, were evaluated for the above-mentioned traits under both well-watered and water-stressed field conditions in three different trials carried out in central and southern China. Interval mapping and composite interval mapping identified 45 and 65 QTLs for the investigated traits, respectively. Two QTL clusters influencing ASI and ESP on chromosomes 1 (bin 1.03) and 9 (bins 9.03–9.05) were identified in more than two environments, showing sizeable additive effects and contribution to phenotypic variance; these two QTL clusters influenced GY only in one environment. No significant interaction was detected between the two genomic regions. A comparative analysis of these two QTL clusters with the QTLs controlling maize drought tolerance previously described in three mapping populations confirmed and extended their relevance for marker-assisted breeding to improve maize production under water-limited conditions.     

Identification of quantitative trait loci under drought conditions in tropical maize. 1. Flowering parameters and the anthesis-silking interval

Drought is an important climatic phenomenon which, after soil infertility, ranks as the second most severe limitation to maize production in developing countries. When drought stress occurs just before or during the flowering period, a delay in silking is observed, resulting in an increase in the length of the anthesis-silking interval (ASI) and in a decrease in grain yield. Selection for reduced ASI in tropical open-pollinated varieties has been shown to be correlated with improved yields under drought stress. Since efficient selection for drought tolerance requires carefully managed experimental conditions, molecular markers were used to identify the genomic segments responsible for the expression of ASI, with the final aim of developing marker-assisted selection (MAS) strategies. An F₂population of 234 individuals was genotyped at 142 loci and F₃ families were evaluated in the field under several water regimes for male flowering (MFLW), male sterility (STER), female flowering (FFLW) and ASI. The genetic variance of ASI increased as a function of the stress intensity, and the broad-sense heritabilites of MFLW, FFLW and ASI were high under stress conditions, being 86%, 82% and 78%, respectively. Putative quantitative trait loci (QTLs) involved in the expression of MFLW and/or FFLW under drought were detected on chromosomes 1, 2, 4, 5, 8, 9 and 10, accounting for around 48% of the phenotypic variance for both traits. For ASI, six putative QTLs were identified under drought on chromosomes 1, 2, 5, 6, 8 and 10, and together accounted for approximately 47% of the phenotypic variance. Under water stress conditions, four QTLs were common for the expression of MFLW and FFLW, one for the expression of ASI and MFLW, and four for the expression of ASI and FFLW. The number of common QTLs for two traits was related to the level of linear correlation between these two traits. Segregation for ASI was found to be transgressive with the drought-susceptible parent contributing alleles for reduced ASI (4 days) at two QTL positions. Alleles contributed by the resistant line at the other four QTLs were responsible for a 7-day reduction of ASI. These four QTLs represented around 9% of the linkage map, and were stable over years and stress levels. It is argued that MAS based on ASI QTLs should be a powerful tool for improving drought tolerance of tropical maize inbred lines.     

Detection of Quantitative Trait Loci for Yield and Drought Tolerance Traits in Soybean Using a Recombinant Inbred Line Population

To investigate the genetic basis of drought tolerance in soybean ( Glycine max L. Merr.) a recombinant inbred population with 184 F_2:7:11 lines developed from a cross between Kefeng1 (drought tolerant) and Nannong1138-2 (drought sensitive) were tested under water-stressed and well-watered conditions in field and greenhouse trials. Traits measured included leaf wilting coefficient, excised leaf water loss and relative water content as indicators of plant water status and seed yield. A total of 40 quantitative trait loci (QTLs) were identified: 17 for leaf water status traits under drought stress and 23 for seed yield under well-watered and drought-stressed conditions in both field and greenhouse trials. Two seed yield QTLs were detected under both well-watered and drought-stressed conditions in the field on molecular linkage group H and D1b, while two seed yield QTLs on molecular linkage group C2 were found under greenhouse conditions. Several QTLs for traits associated with plant water status were identified in both field and greenhouse trials, including two leaf wilting coefficient QTLs on molecular linkage group A2 and one excised leaf water loss QTL on molecular linkage group H. Phenotypic correlations of traits suggested several QTLs had pleiotropic or location-linked associations. These results will help to elucidate the genetic basis of drought tolerance in soybean, and could be incorporated into a marker-assisted selection breeding program to develop high-yielding soybean cultivars with improved tolerance to drought stress.     

Quantitative trait locus analysis of drought tolerance and yield in Maize in China

Drought accounts for significant yield losses in crops. Maize (Zea mays L.) is particularly sensitive to water stress at reproductive stages, and breeding to improve drought tolerance has been a challenge. By use of a linkage map with 121 single sequence repeat (SSR) markers, quantitative trait loci (QTLs) for grain yield and yield components were characterized in the population of the cross X178×B73 under water-stressed and well-watered conditions. Under the well-watered regime, 2, 4, 4, 1, 2, 2, and 3 QTLs were identified for grain yield, 100-kernel weight, kernel number per ear, cob weight per ear, kernel weight per ear, ear weight, and ear number per plant, respectively, whereas under the water-stressed conditions, 1, 5, 2, 6, 1, 3, and 2 QTLs, respectively, were found. The significant phenotypic correlations among yield and yield components to some extent were observed under both water conditions, and some overlaps between the corresponding QTLs were also found. QTLs for grain yield and kernel weight per ear under well-watered conditions and ear weight under both well-watered and water-stressed conditions over-lapped, and all were located on chromosome 1.03 near marker bnlg176. Two other noticeable QTL regions were on chromosome 9.05 and 9.07 near markers umc1657 and bnlg1525; the first corresponded to grain yield, kernel weight per ear, and ear weight under well-watered conditions and kernel number per ear under both water conditions, and the second to grain yield and cob weight per ear under water-stressed conditions and ear number per plant under both water conditions. A comparative analysis of the QTLs herein identified with those described in previous studies for yield and yield components in different maize populations revealed a number of QTLs in common. These QTLs have potential use in molecular marker-assisted selection.     

Drought stress and tropical maize: QTL-by-environment interactions and stability of QTLs across environments for yield components and secondary traits

A recombinant inbred line (RIL) population was evaluated in seven field experiments representing four environments: water stress at flowering (WS) and well-watered (WW) conditions in Mexico and Zimbabwe. The QTLs were identified for each trait in each individual experiment (single-experiment analysis) as well as per environment, per water regime across locations and across all experiments (joint analyses). For the six target traits (male flowering, anthesis-to-silking interval, grain yield, kernel number, 100-kernel fresh weight and plant height) 81, 57, 51 and 34 QTLs were identified in the four step-wise analyses, respectively. Despite high values of heritability, the phenotypic variance explained by QTLs was reduced, indicating epistatic interactions. About 80, 60 and 6% of the QTLs did not present significant QTL-by-environment interactions (QTL × E) in the joint analyses per environment, per water regime and across all experiments. The expression of QTLs was quite stable across years at a given location and across locations under the same water regime. However, the stability of QTLs decreased drastically when data were combined across water regimes, reflecting a different genetic basis of the target traits in the drought and well-watered trials. Several clusters of QTLs for different traits were identified by the joint analyses of the WW (chromosomes 1 and 8) and WS (chromosomes 1, 3 and 5) treatments and across water regimes (chromosome 1). Those regions are clear targets for future marker-assisted breeding, and our results confirm that the best approach to breeding for drought tolerance includes selection under water stress.     

Quantitative trait loci for yield and correlated traits under high and low soil nitrogen conditions in tropical maize

The first objective of this study was to map and characterize quantitative trait loci (QTL) for grain yield (GY) and for secondary traits under varying nitrogen (N) supply. To achieve this objective, a segregating F_2:3 population previously developed for QTL mapping under water-limited conditions was used. The population was evaluated in Mexico under low N conditions in the dry winter season and under low and high N conditions in the wet summer season. From eight QTLs identified for GY under low N conditions, two were also detected under high N conditions. Five QTLs were stable across the two low N environments and five co-localized with QTLs identified for the anthesis-silking interval (ASI) or for the number of ears per plant (ENO) under low N conditions. The percentage of the phenotypic variance expressed by all QTLs for ASI and ENO was quite different when evaluated under low N conditions during the dry winter (40% for ASI and 22% for ENO) and the wet summer seasons (22% for ASI and 46% for ENO). The results suggest optimizing different breeding strategies based on selection index depending on the growing season. Good QTL colocalization was observed for ASI (four QTLs) and ENO (three QTLs) when looking at QTL identified under low N and water-limited conditions in the same population. The results suggest that that both secondary traits can be used in breeding programs for simultaneous improvement of maize against low N and drought stresses.     

QTL analysis of shading sensitive related traits in maize under two shading treatments

During maize development and reproduction, shading stress is an important abiotic factor influencing grain yield. To elucidate the genetic basis of shading stress in maize, an F(2:3) population derived from two inbred lines, Zhong72 and 502, was used to evaluate the performance of six traits under shading treatment and full-light treatment at two locations. The results showed that shading treatment significantly decreased plant height and ear height, reduced stem diameter, delayed day-to-tassel (DTT) and day-to-silk (DTS), and increased anthesis-silking interval (ASI). Forty-three different QTLs were identified for the six measured traits under shading and full light treatment at two locations, including seven QTL for plant height, nine QTL for ear height, six QTL for stem diameter, seven QTL for day-to-tassel, six QTL for day-to-silk, and eight QTL for ASI. Interestingly, three QTLs, qPH4, qEH4a, and qDTT1b were detected under full sunlight and shading treatment at two locations simultaneously, these QTL could be used for selecting elite hybrids with high tolerance to shading and high plant density. And the two QTL, qPH10 and qDTS1a, were only detected under shading treatment at two locations, should be quit for selecting insensitive inbred line in maize breeding procedure by using MAS method.     

Identification of QTLs for Yield and Associated Traits in F₂ Population of Rice

Identification of quantitative trait loci (QTLs) controlling yield and yield-related traits in rice was performed in the F₂ mapping population derived from parental rice genotypes DHMAS and K343. A total of 30 QTLs governing nine different traits were identified using the composite interval mapping (CIM) method. Four QTLs were mapped for number of tillers per plant on chromosomes 1 (2 QTLs), 2 and 3; three QTLs for panicle number per plant on chromosomes 1 (2 QTLs) and 3; four QTLs for plant height on chromosomes 2, 4, 5 and 6; one QTL for spikelet density on chromosome 5; four QTLs for spikelet fertility percentage (SFP) on chromosomes 2, 3 and 5 (2 QTLs); two QTLs for grain length on chromosomes 1 and 8; three QTLs for grain width on chromosomes1, 3 and 8; three QTLs for 1000-grain weight (TGW) on chromosomes 1, 4 and 8 and six QTLs for yield per plant (YPP) on chromosomes 2 (3 QTLs), 4, 6 and 8. Most of the QTLs were detected on chromosome 2, so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety. Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection (MAS) breeding. Further, the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.