QTL mapping for biomass and physiological parameters linked to resistance mechanisms to ferrous iron toxicity in rice

Lowland rice is often subject to iron toxicity which may lead to yield reduction. In order to cope with this nutrient disorder, plants have developed resistance strategies. The aim of this research was to assess morphological and physiological parameters linked to iron toxicity resistance mechanisms and to identify quantitative trait loci (QTLs) involved in their genetic determinism. A segregating population consisting of 164 recombinant inbred lines (RILs) derived from a cross between Azucena and IR64 was tested twice in hydroponics at the vegetative stage at 0 and 250 mg Fe²⁺ l⁻¹. Morphological traits were measured on all 164 RILs. Physiological traits, which were too time-consuming to allow their measurement on all the population, were measured on the two parents and extreme individuals only, selected on the basis of their leaf bronzing index and shoot dry weight. A total of 24 putative QTLs was identified on chromosomes 1, 2, 3, 4, 7 and 11 for leaf bronzing index, shoot water content, shoot and root dry weight, relative variation of shoot and root dry weight, shoot iron concentration, stomatal resistance and chlorophyll content index. Several QTLs were detected in overlapping regions for different parameters. The pertinence of phenotyping extreme RILs only for a QTL analysis is discussed in this study. The QTL analysis allowed to better understand the physiological response of rice in the presence of an excess of ferrous iron, inclusive the relations existing between the stomata closure, the shoot water content reduction and the oxidative stress linked to these growth conditions.     

水稻光合作用及相关生理性状的QTL分析

为了探讨光合作用及相关生理性状的遗传规律，利用由籼稻品种IR24 和粳稻品种Asominori杂交衍生的65个染色体片段置换系（Chromosome Segment Substitution Lines, CSSL）为材料，研究了水稻光合作用及相关生理性状的QTL。在水稻抽穗后7 d测定叶片光合速率（Pn）、蒸腾速率（G_s）、气孔导度（T_r）、细胞间隙CO₂浓度（C_i）、叶绿素含量（CHL）、全氮含量（TLN）。共检测到10个QTLs，分布于第1、3、4、5、7、8和10染色体上，LOD值在2.77~8.42之间，贡献率为9.5%~46.5%。其中仅有控制气孔导度的qGs-8 与控制叶绿素含量的qCHL-8以及第10染色体上控制气孔导度的qGs-10与控制细胞间隙CO₂浓度的qCi-10位置相同，分别位于第8染色体上标记R727和第10染色体上标记C1166附近。其他QTL在染色体上的位置不同，暗示了水稻光合功能遗传规律的复杂性。     

Quantitative trait loci mapping for yield-related traits under low and high planting densities in maize (Zea mays)

Average maize yield per hectare has increased significantly because of the improvement in high-density tolerance, but little attention has been paid to the genetic mechanism of grain yield response to high planting density. Here, we used a population of 301 recombinant inbred lines (RILs) derived from the cross YE478 × 08–641 to detect quantitative trait loci (QTLs) for 16 yield-related traits under two planting densities (57,000 and 114,000 plants per ha) across four environments. These yield-related traits responded differently to high-density stress. A total of 110 QTLs were observed for these traits: 33 QTLs only under low planting density, 50 QTLs under high planting density and 27 QTLs across both densities. Only two major QTLs, qCD6 and qWKEL2-2, were identified across low- and high-density treatments. Seven environmentally stable QTLs were also observed containing qED6, qWKEL3, qRN3-3, qRN7-2, qRN9-2 and qRN10 across both densities, as well as qRN9-1 under low density. In addition, 16 and eight pairs of loci with epistasis interaction (EPI) were detected under low and high planting densities, respectively. Additionally, nine and 17 loci showed QTL × environment interaction (QEI) under low- and high-density conditions, respectively. These interactions are of lesser importance than the main QTL effects. We also observed 26 pleiotropic QTL clusters, and the hotspot region 3.08 concentrated nine QTLs, suggesting its great importance for maize yield. These findings suggested that multiple minor QTLs, loci with EPI and QEI, pleiotropy and the complex network of “crosstalk” among them for yield-related traits were greatly influenced by plant density, which increases our understanding of the genetic mechanism of yield-related traits for high-density tolerance.     

Molecular marker assisted tagging of morphological and physiological traits under two contrasting moisture regimes at peak vegetative stage in rice (Oryza sativa L.)

Root morphology under well-watered conditions sampled on two occasions and under low-moisture stress was studied in a randomly chosen subset of 56 doubled haploid lines derived from a cross between IR64 and Azucena at two growth stages during the vegetative stage. A molecular map of the same population served as the basis for locating QTLs controlling root morphology and associated traits. The region flanking the RFLP markers RZ730 and RZ801 on chromosome 1 were associated with plant height in all three sampling environments. This position corresponds to sd-1 a semi-dwarfing gene. A total of 15 QTL were detected at the two developmental stages, of which only three QTL were common. Region flanked by RG157 and RZ318 (chromosome 2) contained QTL for root thickness under two different developmental stages. In total, 21 QTL for different traits were detected under low-moisture stress condition. While two QTL for plant height on chromosomes 1 and 3 were common, none of the loci for root morphological traits was common between the two different moisture regimes. The chromosomal segment between RG171 and RG157 contained QTL controlling tiller number per plant, total root length, root volume and total root number per plant. Absence of common QTL for root traits between two developmental stages and two moisture regimes suggests the existence of parallel genetic pathways operating at different growth stages and moisture regimes. Root volume and total root number per plant decreased significantly under stress, whereas maximum root length and plant height exhibited non-significant increases under stress. This revised version was published online in July 2006 with corrections to the Cover Date.     

QTL mapping of seedling traits in cucumber using recombinant inbred lines

Seedling traits are important for development, flower bud differentiation, fruit production and fruit quality of cucumber (Cucumis sativus L.). In this study, 160 recombinant inbred lines (RILs), derived from crossing wild cucumber inbred line PI 183967 (C. sativus var. hardwickii) with ‘931’ northern China cultivated cucumber inbred line 931, were employed to identify quantitative trait loci (QTLs) of cotyledon length (Cl), cotyledon width (Cw), hypocotyl length (Hl), first true leaf length (Fll), first true leaf width (Flw), aboveground fresh biomass (Afb) and aboveground dry biomass (Adb) at seedling stage. A genetic map including 307 SSR markers was developed which spanned 993.3 cM, with an average genetic distance of 3.23 cM between adjacent markers. 36 QTLs associated with the seven traits were detected on chromosomes 1, 2, 3, 5 and 6 in four environments (spring and autumn of 2012 and 2013), explaining 6.1 to 23.6% of the observed phenotypic variations. Among the 36 QTLs, 21 were responsible for more than 10% of observed phenotypic variations. We obtained 2, 2, 1 and 3 QTL loci for the traits of Fll, Flw, Afw and Adw, respectively. In addition, genes in the genetic region spanned by SSR15321‐SSR07711 on chr. 5 may contribute to Flw, Afw and Adw.     

Mapping of Quantitative Trait Loci Associated with Reproductive‐Stage Salt Tolerance in Rice

Salinity tolerance in rice varies with the state of growth, with the seedling and reproductive stages being the most sensitive. However, association between tolerances at the two stages is poor, suggesting that they are regulated by different processes and genes. Tolerance at the reproductive stage is the most crucial as it determines grain yield. An F₂ mapping population was developed from two rice genotypes contrasting in tolerance: Cheriviruppu and Pusa Basmati 1 (PB1). Cheriviruppu is highly tolerant at the reproductive stage, while PB1 is highly sensitive at both seedling and reproductive stages. One hundred and thirty‐one microsatellite markers polymorphic between the parents were used to construct a linkage map of 1458.5 cM (Kosambi), with a mean intermarker distance of 11.1 cM. Sixteen QTLs with LOD values ranging from 3.2 to 22.3 were identified on chromosomes 1, 7, 8 and 10, explaining 4–47 % of the phenotypic variation. The maximum number of QTL clusters for different component traits was colocalized on the long arm of chromosome 1 and chromosome 7. We identified several significant epistatic interactions, including three inter‐QTL interactions, using MapManager. The results suggest that pollen fertility, Na⁺ concentration and Na/K ratio in the flag leaf are the most important mechanisms controlling salt tolerance at the reproductive stage in rice. The study reports the construction of a genetic map for reproductive‐stage salt tolerance in rice and demonstrates its utility for molecular mapping of QTLs controlling salinity tolerance‐related traits, which will be useful in marker‐assisted selection in the future.     

Analysis of quantitative trait loci underlying the period of reproductive growth stages in soybean (<Emphasis Type="Italic">Glycine max</Emphasis> [L.] Merr.)

Quantitative trait loci (QTLs) underlying reproductive growth stages are important for molecular breeding of soybeans [Glycine max (L.) Merr.]. Most of these QTLs identified so far derived from a single environment, and thus may be influenced by specific environmental conditions. In this study (from 2004 to 2005), analysis of QTLs underlying the period to reach a given reproductive growth stage was performed in three different environments (Harbin, Heilongjiang Province, China). QTL analysis was achieved with a recombination inbred line (RIL) population consisting of 153 lines. The RIL population derived from a cross between an American semi-dwarf cultivar (cv. Charleston) and a Chinese line with a short growth stage (cv. Dongnong 594). The growth stage data of soybean was recorded for each day. QTLs for all eight reproductive growth stages of soybean (R1 to R8) were analyzed by a composite interval mapping method combined with a mixed genetic model. Fifty-four QTLs displayed main effects and 56 QTL pairs showed epistatic effects. Two marker intervals (Satt173–Satt581, Satt402–Satt267), located on the linkage group O and D1a respectively, strongly influenced plant developmental processes during reproductive growth stages. The findings of this study open the possibility to modulate the structure of soybean growth stages by marker-assisted selection and pyramiding QTL analysis. H.-M. Qiu and D.-W. Xin contributed equally to this work.     

Relationships between water-use traits and photosynthesis in Brassica oleracea resolved by quantitative genetic analysis

The genetic control of water‐use and photosynthetic traits in Brassica oleracea is resolved by genetic analysis of quantitative trait loci (QTL). Variations in leaf conductance, photosynthetic assimilation rate, leaf thickness and leaf nitrogen content were assessed in a segregating population of F₁‐derived doubled haploid (DH) B. oleracea lines. In addition, stable carbon isotope ratios in leaf organic material were used as a surrogate measure of plant water‐use efficiency. Analysis of an existing linkage map for the population revealed significant QTL on seven linkage groups. Single significant QTL explained between 3.4% and 36.6% of the phenotypic variance in each of the traits measured. The locations of QTL for several traits were found to coincide in a physiologically meaningful way; stable carbon isotope discrimination had QTL co‐locating with leaf level water‐use efficiency, photosynthetic capacity with leaf thickness and nitrogen content and stomatal density with leaf thickness. Taken together, these results suggest that single genes or clusters of genes at these loci may have an influence on the expression of physiologically related traits controlling water‐use and photosynthesis.     

Molecular Marker-Assisted Dissection of Quantitative Trait Loci for seven Morphological Traits in Rice (Oryza Sativa L.)

Summary The genetic dissection of morphological traits can helpful to evaluate their potential values as markers for rice genetic improvement. In this study, a RI population derived from a cross from Zhenshan97 and IRAT109 was used to dissect the genetic bases of seven morphological traits such as leaf sheath color (LSC), grain apiculus color (GAC), grain hairiness density (GHD), grain awn length (GAL), ratio of leaf length to width (RLW), leaf erectness (LER) and natural leaf rolling status (NLR). Totally, 26 main-effect QTLs and 22 epistatic QTLs were detected. Of them, 11 main-effect and 3 epistatic QTLs expressed environmental interactions. GAC controlled by a single gene could be regarded as the most useful marker. LSC controlled by two major interacted main-effect QTLs, but with no environmental interaction, is suitable to become morphological marker. LSC will be a very efficient morphological marker for identification of hybrid plants at rice seedling stage when the two major QTLs are introduced into male sterile line and restorer line separately. GHD controlled by a major QTL and a few minor QTLs with comparative low QEIs could also be used as marker. The traits GAL, NLR, RLW and LER, which were controlled by a number of minor effect QTLs and affected by environmental conditions could not be used as marker. But the QTLs with large effects, such as nrl8, can be targeted for corresponding trait improvement through marker-aided selection in rice breeding.     

Anatomical and biochemical trait network underlying genetic variations in tomato fruit texture

Firmness is an indicator of fruit freshness and a main component of tomato (Solanum lycopersicum) fruit texture. In this work, the genetic variability in fruit firmness and stiffness was analyzed in pre- and postharvest periods and underlying anatomical and biochemical traits were identified. Three tomato contrasted parental lines and six derived quantitative trait loci (QTL)-NILs harboring texture QTL on chromosome 4 (QTL4) and 9 (QTL9) were analyzed; the seasonal variability was assessed on two distant trusses. Firmness and stiffness were measured by compression and puncture tests at harvest and after 7-day storage at 20 °C. QTL4 poorly influenced the textural variables, on the contrary to QTL9 which increased firmness measured by puncture test and had similar effects in the two genetic backgrounds. According to this test, firmness increased along the season, but ranks among genotypes and QTL effects were hardly affected. Only some of the QTL effects were still significant after storage and firmness losses were not predicted by firmness at harvest. Fruit firmness and stiffness measured by puncture tests correlated with both morphological (locular number, R = −0.89), histological (cell size, R < −0.80) and biochemical (dry matter (R > 0.82) and soluble sugar content (R < −0.74)) fruit traits. In contrast, compression test values hardly correlated with any of the measured traits. This work provided an original comprehensive approach to analyse fleshy fruit firmness and paves the way for a future predictive model.     

Identification of quantitative trait loci for leaf‐related traits in an IBM Syn10 DH maize population across three environments

Leaf‐related traits (leaf length, leaf width, leaf area and leaf angle) are very important for the yield of maize (Zea mays L) due to their influence on plant type. Therefore, it is necessary to identify quantitative trait loci (QTLs) for leaf‐related traits. In this report, 221 doubled haploid lines (DHLs) of an IBM Syn10 DH population were provided for QTL mapping. In total, 54 QTLs were detected for leaf‐related traits in single environments using a high‐density genetic linkage map. Among them, only eight common QTLs were identified across two or three environments, and the common QTLs for the four traits explained 4.38%–19.99% of the phenotypic variation. qLL‐2‐1 (bin 2.09), qLW‐2‐2 (bin 2.09), qLW‐6‐3 (bin 6.07) and qLA‐5‐2 (bin 2.09) were detected in previous studies, and qLL‐1‐1, qLAr‐1‐1, qLAr‐2‐1 and qLA‐7‐1 may be new QTLs. Notably, qLW‐6‐3 and qLA‐5‐2 were found to be major QTLs explaining 19.99% and 10.96% of the phenotypic variation, respectively. Interestingly, we found three pairs of QTLs (qLW‐2‐2 and qLAr‐2‐1, qLW‐8‐1 and qLL‐8‐2, qLL‐3‐3 and qLAr‐3‐3) that control different traits and that were located on the same chromosome or in a nearby location. Moreover, nine pairs of loci with epistatic effects were identified for the four traits. These results may provide the foundation for QTL fine mapping and for an understanding of the genetic basis of variation in leaf‐related traits.     

Analysing genetic control of cooked grain traits and gelatinization temperature in a double haploid population of rice by quantitative trait loci mapping

Cooking quality in rice grains is a complex trait which requires improvement. Earlier reports show varying genetic influence on these traits, except for a common agreement on waxy (Wx) and alkali degeneration (Alk) loci on chromosome 6. The present study involved 86 doubled haploid lines derived from an indica × japonica cross involving IR64 and Azucena. Grain parameters viz., raw grain length (RGL), raw grain breadth (RGB), cooked grain length (CGL), cooked grain breadth (CGB), gelatinization temperature (GT), grain shape (RGS), length elongation ratio (LER) and breadth expansion ratio (BER) were subjected to mixed model mapping of quantitative trait loci (QTL). Segregation data of 175 markers covering a distance of 2395.5 cM spanning the entire genome were used. Fifteen main effect QTLs were detected spread over the genome, except on chromosomes 4, 8 and 11. Thirty epistatic interactions significantly influencing the traits were detected. Twelve of the main effect QTLs were involved in epistatic interactions. One main effect QTL associated with LER was detected near Alk locus. QTLs located for grain length on chromosomes 9 and 10 are reported for the first time. Detection of many epistatic loci and involvement of main effect QTLs in interactions demand for judicious selection of QTLs in marker-assisted selection programmes.     

QTL mapping for six ear leaf architecture traits under water‐stressed and well‐watered conditions in maize (Zea mays L.)

Morphological traits for ear leaf are determinant traits influencing plant architecture and drought tolerance in maize. However, the genetic controls of ear leaf architecture traits remain poorly understood under drought stress. Here, we identified 100 quantitative trait loci (QTLs) for leaf angle, leaf orientation value, leaf length, leaf width, leaf size and leaf shape value of ear leaf across four populations under drought‐stressed and unstressed conditions, which explained 0.71%–20.62% of phenotypic variation in single watering condition. Forty‐five of the 100 QTLs were identified under water‐stressed conditions, and 29 stable QTLs (sQTLs) were identified under water‐stressed conditions, which could be useful for the genetic improvement of maize drought tolerance via QTL pyramiding. We further integrated 27 independent QTL studies in a meta‐analysis to identify 21 meta‐QTLs (mQTLs). Then, 24 candidate genes controlling leaf architecture traits coincided with 20 corresponding mQTLs. Thus, new/valuable information on quantitative traits has shed some light on the molecular mechanisms responsible for leaf architecture traits affected by watering conditions. Furthermore, alleles for leaf architecture traits provide useful targets for marker‐assisted selection to generate high‐yielding maize varieties.     

Identification of quantitative trait loci for cold tolerance during the germination and seedling stages in rice (Oryza sativa L.)

Low temperature is a serious abiotic stress affecting rice production in subtropical and temperate areas. In this study, cold tolerance of rice at the germination and seedling stages were evaluated using one recombinant inbred line (RIL) population derived from a cross between Daguandao (japonica), with highly cold-tolerant at the seedling stage, and IR28 (indica), with more cold-tolerant at the germination stage, and the quantitative trait loci (QTL) mapping was conducted using the multiple interval mapping (MIM) approach. Continuous segregation in low temperature germinability (LTG) and cold tolerance at the seedling stage (CTS) were observed among the RIL populations. Most RILs were moderately susceptible or tolerant at the germination stage, but were susceptible at the seedling stage. No significant relationship was found in cold tolerance between the germination and seedling stages. A total of seven QTLs were identified with limit of detection (LOD) >3.0 on chromosomes 3, 8, 11 and 12, and the amount of variation (R ²) explained by each QTL ranged from 5.5 to 22.4%. The rice LTG might be regulated by two minor QTLs, with the CTS controlled by one major QTL [qCTS8.1 (LOD = 16.1, R ² = 22.4%)] and several minor loci. Among these loci, one simultaneously controls LTG (qLTG11.1) and CTS (qCTS11.1). Several cold-tolerance-related QTLs identified in previous studies were found to be near the QTLs detected here, and three QTLs are novel alleles. The alleles from Daguandao at six QTLs increased cold tolerance and could be good sources of genes for cold tolerance. In addition, only one digenic interaction was detected for CTS, with a R ² value of 6.4%. Those major or minor QTLs could be used to significantly improve cold tolerance by marker-assisted selection (MAS) in rice.     

A limited set of starch related genes explain several interrelated traits in potato

To understand the molecular basis of potato starch related traits and the underlying starch biosynthesis and degradation, a Quantitative Trait Locus (QTL) analysis in combination with a candidate gene approach was performed. The diploid mapping population C × E, consisting of 249 individuals, was assayed over two consecutive years, for chipping colour, cold induced sweetening, starch content, starch granule size, starch gelling temperature, starch enthalpy, amylose content and degree of starch phosphorylation. QTLs were observed for all traits, except enthalpy on eight out of the twelve potato chromosomes. Several QTLs were found to be consistent over 2 years. Clustering of co-localizing QTLs was observed on some chromosomes, indicating common genetic factors for the different traits. On chromosome 2, Soluble Starch Synthase 2 mapped on the same position as QTLs for starch phosphorylation, starch gelling temperature and amylose content. α-glucan, water dikinase co-localizes on chromosome 5 together with QTLs for starch phosphorylation and cold induced sweetening. Furthermore, the genes coding for two phosphorylases (StPho1a and StPho2) coincide with QTLs for starch gelling temperature, chipping colour and starch granule size on chromosome 2 and a QTL for starch phosphorylation on chromosome 9, respectively. The results suggest allelic variation acting on the genetics of the different traits.     

QTL mapping of bio-energy related traits in Sorghum

Plant height (PHT), stem and leaf fresh weight (SLFW), juice weight (JW) and sugar content of stem (Brix) are important traits for biofuel production in sweet Sorghum. QTL analysis of PHT, SLFW, JW and Brix was conducted with composite interval mapping using F₂ and F_2:3 populations derived from the cross between grain Sorghum (Shihong137) × sweet Sorghum (L-Tian). Three QTLs controlling PHT were mapped on SBI-01, SBI-07 and SBI-09 under four different environments. These QTLs could explain 10.16 to 45.29% of the phenotypic variance. Two major effect QTLs on SBI-07 and SBI-09 were consistently detected under four environments. Eight QTLs controlling SLFW were mapped across three environments and accounted for 5.49–25.36% of the phenotypic variance. One major QTL on SBI-09 located between marker Sb5-206 and SbAGE03 was observed under three environments. Four QTLs controlling Brix were identified under two environments and accounted for 11.03–17.65% of the phenotypic variance. Six QTLs controlling JW were detected under two environments, and explained 6.63–23.56% of the phenotypic variance. QTLs for JW on SBI-07 and SBI-09 were consistent in two environments showing higher environmental stability. In addition, two chromosome regions on SBI-07 and SBI-09 were identified in our study having major effect on PHT, SFLW and JW. The results would be useful for the genetic improvement of sweet Sorghum to be used for biofuel production.     

玉米光周期敏感相关性状发育动态QTL定位

玉米是短日照作物,大多数热带种质对光周期非常敏感。光周期敏感性限制了温、热地区间的种质交流。研究玉米光周期敏感性的分子机理,有利于玉米种质的扩增、改良、创新,提高玉米品种对不同光周期变化的适应性。本研究以对光周期钝感的温带自交系黄早四和对光周期敏感的热带自交系CML288为亲本配置的组合衍生的一套207个重组自交系为材料,在长日照环境条件下对不同发育时期的叶片数、株(苗)高变化进行QTL分析。结果表明,双亲间的最终可见叶片数和株高差异很大;发育初期CML288的叶片数和苗高都低于黄早四,而发育后期CML288的叶片数和株高都明显高于黄早四;测定各时期F₇重组自交系间也存在显著差异。利用包含237个SSR标记、图谱总长度1 753.6 cM、平均图距7.40 cM的遗传连锁图谱,采用复合区间作图法,分别检测到控制叶片数和株(苗)高发育的QTL 11个和20个。但是,没有一个条件QTL 能在测定的几个时期都有效应。在长日照条件下,控制叶片数与株(苗)高的非条件与条件QTL主要集中在第1、9和10染色体上,特别是在第10染色体的标记umc1873附近均检测到了影响这两个性状的QTL,且在不同的发育时期单个条件和非条件QTL所解释的表型变异分别为4.34%~25.74%和10.02%~22.57%,表明这一区域可能包含光周期敏感性关键基因。     

基于元分析的大豆生育期QTL的整合

共搜集整理了12年来已经报道的与大豆生育期有关的98个QTL,通过BioMercator2.1和公共标记映射整合到大豆公共遗传连锁图谱soymap2上,并利用元分析技术推断QTL位置,计算提取真正有效的QTL。发掘出大豆两个重要生育时期,共9个“真实QTL”及其连锁标记,其中与开花期(R1)相关的有7个,与成熟期(R8)相关的有2个,建立了QTL的一致性图谱,其中L连锁群上的一个定位区间包含一个已发表的有关R1的基因。在5个连锁群上共发现10个控制多个生育时期的QTL。本研究结果为大豆生育期QTL精细定位和基因克隆奠定了基础。     

Detection of epistatic and environmental interaction QTLs for leaf orientation‐related traits in maize

Leaf architecture traits in maize are quantitative and have been studied by quantitative trait loci (QTLs) mapping. However, additional QTLs for these traits require mapping and the interactions between mapped QTLs require studying because of the complicated genetic nature of these traits. To detect common QTLs and to find new ones, we investigated the maize traits of leaf angle, leaf flagging‐point length, leaf length and leaf orientation value using a set of recombinant inbred line populations and single nucleotide polymorphism markers. In total, 19 QTLs contributed 4.13–13.52% of the phenotypic effects to the corresponding traits that were mapped, and their candidate genes are provided. Common and major QTLs have also been detected. All of the QTLs showed significant additive effects and non‐significant additive × environment effects in combined environments. The majority showed additive × additive epistasis effects and non‐significant QTL × environment effects under single environments. Common and major QTLs provided information for fine mapping and gene cloning, and SNP markers can be used for marker‐assisted selection breeding.     

Trait identification and QTL validation for reproductive stage drought resistance in rice using selective genotyping of near flowering RILs

Drought resistance is becoming an indispensable character for rice improvement due to the dwindling global water resources. Genetic improvement for drought resistance is achieved through physiological dissection and genetic analysis of independent component traits associated with crop productivity under stress. A subset mapping population of 93 near flowering recombinant inbred lines with uniform phenology was constituted for genetic analysis of reproductive stage drought resistance. The population was phenotyped for 22 physio-morphological traits under two contrasting water regimes imposed at reproductive stage. Broad sense heritabilities of morphological traits were lower under stress than irrigated. Predominant association of plant height, panicle exsertion and harvest index with grain yield were observed under stress. The sustenance of panicle exsertion through maintaining growth during moisture stress was found as a significant trait associated with the grain yield through minimizing spikelet sterility. Selective genotyping was carried out with 23 polymorphic microsatellite markers of the established target genomic regions for drought resistance. The study validated the association of a QTL region on the long arm of chromosome 1 with plant height, panicle length, panicle exsertion, biological yield and stomatal conductance under stress. This region, flanked by markers RM246 and RM315, was known to possess the semi-dwarf gene, sd-1. Role of another major interval lying between RM256 and RM149 on chromosome 8 in defining the drought resistance could be established through identification of QTLs associated with leaf rolling, panicle exsertion, plant height, panicle length, senescence and biological yield under moisture stress condition. Few other QTLs were also identified.