QTLs for days to silking in a recombinant inbred line maize population subjected to high and low nitrogen regimes

Days to silking (DTS) is one of the most important traits in maize (Zea mays). To investigate its genetic basis, a recombinant inbred line population was subjected to high and low nitrogen (N) regimes to detect quantitative trait loci (QTLs) associated with DTS. Three QTLs were identified under the high N regime; these explained 25.4% of the phenotypic variance. Due to additive effects, the QTL on chromosome 6 increased DTS up to 0.66 days; while the other two QTLs mapped on chromosome 9 (one linked with Phi061 and the other linked with Nc134) decreased DTS 0.89 and 0.91 days, respectively. Under low N regime, two QTLs were mapped on chromosomes 6 and 9, which accounted for 25.9% of the phenotypic variance. Owing to additive effects, the QTL on chromosome 6 increased DTS 0.67 days, while the other QTL on chromosome 9 decreased it 1.48 days. The QTL on chromosome 6, flanked by microsatellite markers Bnlg1600 and Phi077, was detected under both N regimes. In conclusion, we identified four QTLs, one on chromosome 6 and three on chromosome 9. These results contribute to our understanding of the genetic basis of DTS and will be useful for developing marker-assisted selection in maize breeding programs.     

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F3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).在灌溉和干旱胁迫下,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.     

粳稻蒸煮食味品质相关性状的QTL分析

以沈农265和丽江新团黑谷杂交衍生的重组自交系群体(RILs)为实验材料,对12个粳稻(Oryza sativa subsp.japonica)蒸煮食味品质相关性状进行QTL分析。共检测到29个蒸煮食味品质相关的QTLs,分布于除第8染色体外的11条染色体上,LOD值介于2.50–16.47之间,加性效应值为–132.69–471.85,单个QTL贡献率为10.36%–73.24%。在第6染色体RM508–RM253区域检测到1个蒸煮营养食味品质多效性QTL簇,其中q AC6表型贡献率最大,解释73.24%的表型变异;在第10染色体PM166–RM258区域检测到2个与蒸煮食味品质相关的QTLs,分别是控制口感的q CTS10和综合评分的q CCS10。此外,检测到15个与RVA特征谱相关的QTLs,在第6染色体RM253–RM402区域检测到3个与RVA谱特征值相关的QTLs,表型贡献率均大于12%。这些定位结果将为粳稻蒸煮食味相关品质的分子遗传机理研究奠定基础。     

High-Density Genetic Linkage Map Construction and QTL Mapping of Grain Shape and Size in the Wheat Population Yanda1817 × Beinong6

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F ₈ recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010–2014 by inclusive composite interval mapping (ICIM) (LOD≥2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82–23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.     

Molecular mapping of quantitative trait loci for drought tolerance in maize plants

Drought tolerance is one of the most important but complex traits of crops. We looked for quantitative trait loci (QTLs) that affect drought tolerance in maize. Two maize inbreds and their advanced lines were evaluated for drought-related traits. A genetic linkage map developed using RFLP markers was used to identify QTLs associated with drought-related traits. Twenty-two QTLs were detected, with a minimum of one and a maximum of nine for drought-related traits. A single-QTL was detected for sugar concentration accounting for about 52.2% of the phenotypic variation on chromosome 6. A single-QTL was also identified for each of the traits root density, root dry weight, total biomass, relative water content, and leaf abscisic acid content, on chromosomes 1 and 7, contributing to 24, 0.2, 0.4, 7, and 19% of the phenotypic variance, respectively. Three QTLs were identified for grain yield on chromosomes 1, 5, and 9, explaining 75% of the observed phenotypic variability, whereas four QTLs were detected for osmotic potential on chromosomes 1, 3, and 9, together accounting for 50% of the phenotypic variance. Nine QTLs were detected for leaf surface area on chromosomes 3 and 9, with various degrees of phenotypic variance, ranging from 25.8 to 42.2%. Four major clusters of QTLs were identified on chromosomes 1, 3, 7, and 9. A QTL for yield on chromosome 1 was found co-locating with the QTLs for root traits, total biomass, and osmotic potential in a region of about 15 cM. A cluster of QTLs for leaf surface area were coincident with a QTL for osmotic potential on chromosome 3. The QTLs for leaf area also clustered on chromosome 9, whereas QTLs for leaf abscisic acid content and relative water content coincided on chromosome 7, 10 cM apart. Co-location of QTLs for different traits indicates potential pleiotropism or tight linkage, which may be useful for indirect selection in maize improvement for drought tolerance.     

Identification and mapping of the QTL for aluminum tolerance introgressed from the new source, <Emphasis Type="SmallCaps">ORYZA RUFIPOGON</Emphasis> Griff., into indica rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

This study was conducted to identify and map the quantitative trait locus (QTL) controlling Al tolerance in rice using molecular markers. A population of 171 F(6) recombinant inbred lines (RILs) derived from the cross of Oryza sativa (IR64), the Al susceptible parent, and Oryza rufipogon, the Al tolerant parent, was evaluated for Al tolerance using a nutrient solution with and without 40 ppm of active Al(+3). A genetic map, consisting of 151 molecular markers covering 1,755 cM with an average distance of 11.6 cM between loci, was constructed. Nine QTLs were dentified including one for root length under non-stress conditions (CRL), three for root length under Al stress (SRL) and five for relative root length (RRL). O. rufipogon contributed favorable alleles for each of the five QTLs for RRL, which is a primary parameter for Al tolerance, and individually they explained 9.0-24.9% of the phenotypic variation. Epistatic analysis revealed that CRL was conditioned by an epistatic effect, whereas SRL and RRL were controlled by additive effects. Comparative genetic analysis showed that QTLs for RRL, which mapped on chromosomes 1 and 9, appear to be consistent among different rice populations. Interestingly, a major QTL for RRL, which explained 24.9% of the phenotypic variation, was found on chromosome 3 of rice, which is conserved across cereal species. These results indicate the possibilities to use marker-assisted selection and pyramiding QTLs for enhancing Al tolerance in rice. Positional cloning of such QTLs introgressed from O. rufipogon will provide a better understanding of the Al tolerance mechanism in rice and the evolutionary genetics of plant adaptation to acid-soil conditions across cereal species.     

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.     

Analysis of quantitative trait loci underlying the traits related to chlorophyll content of the flag leaf in rice

A population of 117 doubled haploid (DH) lines derived from the cross of Zhaiyeqing 8 (indica) x Jingxi 17 (japonica) was employed to map quantitative trait loci (QTL) underlying four physiological traits related to chlorophyll contents of the flag leaf. There were significantly positive correlations among chlorophyll a, chlorophyll b and chlorophyll a+ b content. Chlorophyll a/b ratio was significantly negatively correlated with chlorophyll b content. These four traits were normally distributed with transgressive segregation, suggesting that they were controlled by multiple minor genes. A total of 11 QTLs were detected for the four traits and they lay on six chromosomes. Each of them explained 9.2%-19.6% of the phenotypic variations, respectively. Of these, two QTLs controlling chlorophyll a content were mapped on chromosomes 2 and 5; four QTLs underlying chlorophyll b content were mapped on chromosomes 2, 3, 5 and 9; three QTLs underlying chlorophyll a+b amount were mapped on chromosomes 3, 5 and 9; two QTLs under-lying chlorophyll a/b ratio were mapped on chromosomes 6 and 1 1. The intrinsic relationship among the four traits and the practical implication in rice breeding are discussed.     

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.     

Mapping QTLs for heading synchrony in a doubled haploid population of rice in two environments

Simultaneous heading of plants within the same rice variety, also refer to heading synchrony, is an important factor that affects simultaneous ripening of the variety. Understanding of the genetic basis of heading synchrony may contribute to molecular breeding of rice with simultaneous heading and ripening. In the present study, a doubled haploid （DH） population, derived from a cross between Chunjiang 06 and TN1 was used to analyze quantitative trait locus （QTL） for heading synchrony related traits, i.e., early heading date （EHD）, late heading date （LHD）, heading asynchrony （HAS）, and tiller number （PN）. A total of 19 QTLs for four traits distributed on nine chromosomes were detected in two environments. One QTL, qHAS-8 for HAS, explained 27.7% of the phenotypic variation, co-located with the QTLs for EHD and LHD, but it was only significant under long-day conditions in Hangzhou, China. The other three QTLs, qHAS-6, qHAS-9, and qHAS-10, were identified under short-day conditions in Hainan, China, each of which explained about 11% of the phenotypic variation. Two of them, qHAS-6 and qHAS-9, were co-located with the QTLs for EHD and LHD. Two QTLs, qPN-4 and qPN-5 for PN, were detected in Hangzhou, and qPN-5 was also detected in Hainan. However, none of them was co-located with QTLs for EHD, LHD, and HAS, suggesting that PN and HAS were controlled by different genetic factors. The results of this study can be useful in marker assisted breeding for improvement of heading synchrony.     

Quantitative trait locus analysis for rice panicle and grain characteristics

 The development of molecular genetic maps has accelerated the identification and mapping of genomic regions controlling quantitative characters, referred to as quantitative trait loci or QTLs. A molecular map derived from an F₂ population of a tropical japonica×indica cross (Labelle/Black Gora) consisted of 116 restriction fragment length polymorphism (RFLP) markers. Composite interval mapping was used to identify the QTLs controlling six panicle and grain characteristics. Two QTLs were identified for panicle size at LOD>3.0, with one on chromosome 3 accounting for 16% of the phenotypic variation. Four loci controlling spikelet fertility accounted for 23% of the phenotypic variation. Seven, four, three and two QTLs were detected for grain length, breadth, shape and weight, respectively, with the most prominent QTLs being on chromosomes 3, 4, and 7. Grain shape, measured as the ratio of length to breadth, was mostly controlled by loci on chromosomes 3 and 7 that coincided with the most important QTLs identified for length and breadth, respectively. A model including three loci accounted for 45% of the phenotypic variation for this trait. The identification of economically important QTLs will be useful in breeding for improved grain characteristics. Received: 18 July 1997 / Accepted: 9 December 1997     

Genetic analysis of grain protein-content,grain yield and thousand-kernel weight in bread wheat

Grain yield and grain protein content are two very important traits in bread wheat. They are controlled by genetic factors, but environmental conditions considerably affect their expression. The aim of this study was to determine the genetic basis of these two traits by analysis of a segregating population of 194 F(7) recombinant inbred lines derived from a cross between two wheat varieties, grown at six locations in France in 1999. A genetic map of 254 loci was constructed, covering about 75% of the bread wheat genome. QTLs were detected for grain protein-content (GPC), yield and thousand-kernel weight (TKW). 'Stable' QTLs (i.e. detected in at least four of the six locations) were identified for grain protein-content on chromosomes 2A, 3A, 4D and 7D, each explaining about 10% of the phenotypic variation of GPC. For yield, only one important QTL was found on chromosome 7D, explaining up to 15.7% of the phenotypic variation. For TKW, three QTLs were detected on chromosomes 2B, 5B and 7A for all environments. No negative relationships between QTLs for yield and GPC were observed. Factorial Regression on GxE interaction allowed determination of some genetic regions involved in the differential reaction of genotypes to specific climatic factors, such as mean temperature and the number of days with a maximum temperature above 25 degrees C during grain filling.     

Location of genes involved in ear compactness in wheat (Triticum aestivum) by means of molecular markers

Quantitative trait loci (QTLs) for three traits related to ear morphology (spike length, number of spikelets, and compactness as the ratio between number of spikelets and spike length) in wheat (Triticum aestivum L.) were mapped in a doubled-haploid (DH) population derived from the cross between the cultivars Courtot and Chinese Spring. A molecular marker linkage map of this cross that had previously been constructed based on 187 DH lines and 380 markers was used for QTL mapping. The genome was well covered (85%) except chromosomes 1D and 4D and a set of anchor loci regularly spaced (one marker each 15.5 cM) were chosen for marker regression analysis. The presence of a QTL was declared at a significance threshold = 0.001. The population was grown in one location under field conditions during three years (1994, 1995 and 1998). For each trait, 4 to 6 QTLs were identified with individual effects ranging between 6.9% and 21.8% of total phenotypic variation. Several QTLs were detected that affected more than one trait. Of the QTLs 50% were detected in more than one year and two of them (number of spikelets on chromosome 2B, and compactness on chromosome 2D) emerged from the data from the three years. Only one QTL co-segregated with the gene Q known to be involved in ear morphology, namely the speltoid phenotype. However, this chromosome region explained only a minor part of the variation (7.5–11%). Other regions had a stronger effect, especially two previously unidentified regions located on chromosomes 1A and 2B. The region on the long arm of chromosome 1A was close to the locus XksuG34-1A and explained 12% of variation in spike length and 10% for compactness. On chromosome 2B, the QTL was detected for the three traits near the locus Xfbb121-2B. This QTL explained 9% to 22% of variation for the traits and was located in the same region as the gene involved in photoperiod response (Ppd2). Other regions were located at homoeologous positions on chromosomes 2A and 2D.     

Mapping quantitative trait loci associated with arsenic accumulation in rice (Oryza sativa)

The quantitative trait loci (QTLs) associated with arsenic (As) accumulation in rice were mapped using a doubled haploid population established by anther culture of F1 plants from a cross between a Japonica cultivar CJ06 and an Indica cultivar TN1 (Oryza sativa). Four QTLs for arsenic (As) concentrations were detected in the map. At the seedling stage, one QTL was mapped on chromosome 2 for As concentrations in shoots with 24.4% phenotypic variance and one QTL for As concentrations in roots was detected on chromosome 3. At maturity, two QTLs for As concentrations in grains were found on chromosomes 6 and 8, with 26.3 and 35.2% phenotypic variance, respectively. No common loci were detected among these three traits. Interestingly, the QTL on chromosome 8 was found to be colocated for As concentrations in grain at maturity and shoot phosphorus (P) concentrations at seedling stage. These results provide an insight into the genetic basis of As uptake and accumulation in rice, and will be useful in identifying genes associated with As accumulation.     

Quantitative Trait Loci Mapping for ChlorophyⅡ Fluorescence and Associated Traits in Wheat （ Triticum aestivum）

Parameters of chlorophyll fluorescence kinetics （PCFKs） under drought stress condition are generally used to characterize instincts for dehydration tolerance in wheat （Triticum aestivum L.）. Therefore, it is important to map quantitative trait loci （QTLs） for PCFKs in wheat genetic improvement for drought tolerance. A doubled haploid （DH） population with 150 lines, derived from a cross between two common wheat varieties, Hanxuan 10 and Lumai 14, was used to analyze the correlation between PCFKs and chlorophyll content （CHIC） and to map QTLs at the grainfilling stage under conditions of both rainfed （drought stress, DS） and well-watered （WW）, respectively. QTLs for these traits were detected by QTLMapper version 1.0 based on the composite Interval mapping method of the mixed-linear model. The results showed a very significant positive correlation between Fv, Fm, Fv/Fm and Fv/Fo. The correlation coefficients were generally higher under WW than under DS. Also, there was a significant or a highly significant positive correlation between Fv, Fm, Fv/Fm, Fv/Fo and CHIC. The correlation coefficients were higher in the DS group than the WW group. A total of 14 additive QTLs （nine QTLs detected under DS and five QTLs under WW） and 25 pairs of eplstatlc QTLs （15 pairs detected under DS and 10 pairs under WW） for PCFKs were mapped on chromosomes 6A, 7A, 1B, 3B, 4D and 7D. The contributions of additive QTLs for PCFKs to phenotype variation were from 8.40% to 72.72%. Four additive QTLs （two QTLs detected under DS and WW apiece） controlling Chic were mapped on chromosomes 1A, 5A and 7A. The contributions of these QTLs for ChIC to phenotype variation were from 7.27% to 11.68%. Several QTL clusters were detected on chromosomes 1B, 7A and 7D, but no shared chromosomal regions for them were identified under different water regimes, indicating that these QTLs performed different expression patterns under rainfed and well-watered conditions.     

Novel pleiotropic loci controlling panicle architecture across environments in japonica rice （Oryza sativa L.）

To identify quantitative trait loci (QTLs) controlling panicle architecture in japonica rice, a genetic map was constructed based on simple sequence repeat (SSR) markers and 254 recombinant inbred lines (RILs) derived from a cross between cultivars Xiushui 79 and C Bao. Seven panicle traits were investigated under three environments. Single marker analysis indicated that a total of 27 SSR markers were highly associated with panicle traits in all the three environments. Percentage of phenotypic variation explained by single locus varied from 2% to 35%. Based on the mixed linear model, a total of 40 additive QTLs for seven panicle traits were detected by composite interval mapping, explaining 1.2%-35% of phenotypic variation. Among the 9 QTLs with more than 10% of explained phenotypic variation, two QTLs were for the number of primary branches per panicle (NPB), two for panicle length (PL), two for spikelet density (SD), one for the number of secondary branches per panicle (NSB), one for secondary branch distribution density (SBD), and one for the number of spikelets per panicle (NS), respectively. qPLSD-9-1 and qPLSD-9-2 were novel pleiotropic loci, showing effects on PL and SD simultaneously. qPLSD-9-1 explained 34.7% of the phenotypic variation for PL and 25.4% of the phenotypic variation for SD, respec- tively. qPLSD-9-2 explained 34.9% and 24.4% of the phenotypic variation for PL and SD, respectively. The C Bao alleles at the both QTLs showed positive effects on PL, and the Xiushui 79 alleles at the both QTLs showed positive effects on SD. Genetic variation of panicle traits are mainly attributed to additive effects. QTL × environment interactions were not significant for additive QTLs and additive × additive QTL pairs.     

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.     

粳稻穗部结构性状的QTL分析

利用温带粳稻‘沈农265’和‘丽江新团黑谷’构建的重组自交系群体,在沈阳和哈尔滨两地对15个穗部结构性状进行了QTL分析。共检测到61个相关QTL,其中沈阳检测到的38个QTL在第1、4、6、11和12号染色体上形成了sir-QTL簇;而在哈尔滨检测到的31个QTL也在第3、9和10号染色体上形成了QTL簇。仅有8个QTL是在两地同时被检测到的,分别是控制一次枝梗数#＇．jqPBN4、控制穗长的qPL6和qPL9、控制一次枝梗实粒数的qGNPB4、控制一次枝梗颖花数的qTSNPBJ、控制结实率的qPSSIO、以及控制着粒密度qSD3和qSD9。其中,qPBN4（最高表型贡献率43．2％）、qPL9（最高表型贡献率63．2％）、qGNPB4（最高表型贡献率30．9％）和qSD9（最高表型贡献率42．9％）是主效QTL。通过进一步的分析发现控制穗长qPL9和控制着粒密度qSD94于DEPl所在区间。同时控制一次枝梗数和一次枝梗实粒数的位于第4号染色体长臂端的穗部结构主效QTL,qPBN4qGNPB4极富研究与应用价值。     

Quantitative trait loci underlying the development of seed composition in soybean (Glycine max L. Merr.).

One hundred and forty-three F2:7 recombinant inbred lines (RILs) developed from the cross of soybean cultivars 'Charleston' and 'Dongnong 594' were analyzed for the quantitative trait loci (QTLs) underlying protein or oil content at 6 different developmental stages by composite interval mapping with a mixed genetic model. The genotype x environment (GxE) interactions of the QTLs were also evaluated. Nineteen (2004) and 33 (2005) unconditional QTLs underlying seed protein or oil content at the different developmental stages were mapped onto 8 and 9 linkage groups, respectively. The proportion of phenotypic variation explained by these QTLs ranged from 6.26% to 30.52% and from 5.38% to 28.47%, respectively. Fourteen (2004) and 21 (2005) conditional QTLs underlying seed protein or oil content were mapped onto 5 and 8 linkage groups, respectively. The proportion of phenotypic variation explained by these QTLs ranged from 2.97% to 29.68% and from 5.42% to 31.96%, respectively. The numbers and types of QTLs and the genetic effect for the two traits were different at each developmental stage. However, several genomic regions that simultaneously control the development of both traits were detected. The genetic effect on protein content and oil content was opposite for loci in the marker interval Satt335-SSatt334, reflecting a negative correlation of protein content and oil content. A G x E interaction effect of some QTLs underlying protein or oil content at different growth periods was observed.