Dynamic Quantitative Trait Locus Analysis of Seed Vigor at Three Maturity Stages in Rice

Seed vigor is an important characteristic of seed quality. In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed vigor, including the germination potential, germination rate, germination index and time for 50% of germination, at 4 (early), 5 (middle) and 6 weeks (late) after heading in two years. A total of 24 additive and 9 epistatic quantitative trait loci (QTL) for seed vigor were identified using QTL Cartographer and QTLNetwork program respectively in 2012; while 32 simple sequence repeat (SSR) markers associated with seed vigor were detected using bulked segregant analysis (BSA) in 2013. The additive, epistatic and QTL × development interaction effects regulated the dry maturity developmental process to improve seed vigor in rice. The phenotypic variation explained by each additive, epistatic QTL and QTL × development interaction ranged from 5.86 to 40.67%, 4.64 to 11.28% and 0.01 to 1.17%, respectively. The QTLs were rarely co-localized among the different maturity stages; more QTLs were expressed at the early maturity stage followed by the late and middle stages. Twenty additive QTLs were stably expressed in two years which might play important roles in establishment of seed vigor in different environments. By comparing chromosomal positions of these stably expressed additive QTLs with those previously identified, the regions of QTL for seed vigor are likely to coincide with QTL for grain size, low temperature germinability and seed dormancy; while 5 additive QTL might represent novel genes. Using four selected RILs, three cross combinations of seed vigor for the development of RIL populations were predicted; 19 elite alleles could be pyramided by each combination.     

Dynamic Quantitative Trait Loci Analysis of Seed Reserve Utilization during Three Germination Stages in Rice

In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed reserve utilization during the early (day 6), middle (day 10) and late (day 14) germination stages. The seedling dry weight (SDW) and weight of the mobilized seed reserve (WMSR) were increased, while the seed reserve utilization efficiency (SRUE) decreased, during the process of seed germination. The SDW and WMSR were affected by the seed weight, while the SRUE was not affected by the seed weight. A total of twenty unconditional and twenty-one conditional additive QTLs and eight epistatic QTLs were identified at three germination stages, and the more QTLs were expressed at the late germination stage. Among them, twelve additive and three epistatic QTLs for SDW, eight additive and three epistatic QTLs for WMSR and thirteen additive and two epistatic QTLs for SRUE were identified, respectively. The phenotypic variation explained by each additive QTL, epistatic QTL and QTL × development interaction ranged from 6.10 to 23.91%, 1.79 to 6.88% and 0.22 to 2.86%, respectively. Two major additive QTLs qWMSR7.1 and qSRUE4.3 were identified, and each QTL could explain more than 20% of the total phenotypic variance. By comparing the chromosomal positions of these additive QTLs with those previously identified, eleven QTLs might represent novel genes. The best four cross combinations of each trait for the development of RIL populations were selected. The selected RILs and the identified QTLs might be applicable to improve rice seed reserve utilization by the marker-assisted selection approach.     

Identification of QTLs with Additive,Epistatic, and QTL × Seed Maturity Interaction Effects for Seed Vigor in Rice

Seed maturity is a critical process of seed vigor establishment. In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed vigor, including the germination potential (GP), germination rate (GR), germination index (GI), and time for 50 % of germination (T₅₀), at 4, 5, and 6 weeks after heading in 2 years. Significant differences of seed vigor were observed among two parents and RIL population; the heritability of four traits was more than 90 % at three maturity stages. A total of 19 additive and 2 epistatic quantitative trait loci (QTL) for seed vigor were identified using QTL Cartographer and QTLNetwork program, respectively, in 2012, while 16 simple sequence repeat (SSR) markers associated with seed vigor were detected using bulked segregant analysis (BSA) in 2013. The phenotypic variation explained by each additive, epistatic QTL, and QTL × seed maturity interaction ranged from 9.19 to 22.94 %, 7.23 to 7.75 %, and 0.05 to 0.63 %, respectively. Ten additive QTLs were stably expressed in 2 years which might play important roles in establishment of seed vigor in different environments. By comparing chromosomal positions of ten stably expressed additive QTLs with those previously identified, they might be true QTLs for seed vigor; the regions of QTLs for seed vigor are likely to coincide with QTLs for seed dormancy, seed reserve mobilization, low-temperature germinability, and seedling growth. Using four selected RILs, three cross-combinations were predicted to improve seed vigor; 9 to 10 elite alleles could be pyramided by each combination. The selected RILs and the identified QTLs might be applicable for the improvement of seed vigor by marker-assisted selection (MAS) in rice.     

Mapping quantitative trait loci controlling seed longevity in rice ( Oryza sativa L.)

Quantitative trait loci (QTLs) controlling seed longevity in rice were identified using 98 backcross inbred lines (BILs) derived from a cross between a japonica variety Nipponbare and an indica variety Kasalath. Seeds of each BIL were kept for 12 months at 30 degrees C in dry conditions to promote loss of viability. To measure seed longevity, we performed an additional aging-processing treatment for 2 months at 30 degrees C maintaining seeds at 15% moisture content. We measured the germination percent of these treated seeds at 25 degrees C for 7 days as the degree of seed longevity. The germination of BILs ranged from 0 to 100% with continuous variation. Three putative QTLs for seed longevity, qLG-2, qLG-4 and qLG-9, were detected on chromosome 2, 4 and 9, respectively. Kasalath alleles increased the seed longevity at these QTLs. The QTL with the largest effect, qLG-9, explained 59.5% of total phenotypic variation in BILs. The other two QTLs, qLG-2 and qLG-4, explained 13.4 and 11.6% of the total phenotypic variation, respectively. We also verified the effect of the Kasalath allele of qLG-9 using chromosome segment substitution lines. Furthermore, QTLs for seed dormancy were identified on chromosomes 1, 3, 5, 7 and 11. Based on the comparison of the chromosomal location of QTLs for seed longevity and seed dormancy, these traits seem to be controlled by different genetic factors.     

Dynamic quantitative trait locus analysis of seed dormancy at three development stages in rice

In this study, a rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed dormancy (SD) at 4 (early), 5 (middle) and 6 (late) weeks after heading stages. Dynamic analysis showed that the indica IR28 variety tended to have deeper dormancy than the japonica Daguandao at the middle and late development stages. The level of SD decreased with the process of seed development. The significant interaction between heading date (HD) and SD occurred only in those seeds collected at the early development stage. A total of nine additive quantitative trait loci (QTLs) and eight epistatic QTLs for SD were identified at three seed development stages. Of them, one additive and four epistatic QTLs were identified for the early stage, six additive and one epistatic QTL for the middle stage and two additive and three epistatic QTLs for the late stage. The phenotypic variation explained by each additive and epistatic QTL ranged from 5.8 to 30.6 % and from 3.8 to 13.1 %, respectively. Compared with the additive QTLs, epistatic interactions were much more important for SD at the early and late development stages. Two major additive QTLs, qSD3.1 and qSD4.1, were identified; each QTL could explain more than 20 % of the total phenotypic variance and each dormancy-enhancing allele could decrease the germination percentage by about 10 %. By comparing the chromosomal positions of these additive QTLs with those previously identified, five additive QTLs, qSD1.2, qSD2.1, qSD3.2, qSD4.1 and qSD9.1, might represent novel genes. One QTL identified here, qHD1, and nine QTLs identified in previous studies for HD were co-located with our QTLs for SD, which indicated that the significant correlation between SD and HD might be due to the linkage of QTLs for SD and HD. Four RILs with deep dormancy at development stages but non-dormancy after post-ripening under different germination conditions were selected. Using the selected RILs, three cross combinations of SD for the development of RIL populations were predicted. The selected RILs and the identified QTLs might be applicable for the improvement of pre-harvest sprouting tolerance by marker-assisted selection in rice.     

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。     

Mapping quantitative trait loci controlling seed dormancy and heading date in rice, Oryza sativa L., using backcross inbred lines

 To detect quantitative trait loci (QTLs) controlling seed dormancy, 98 BC₁F₅ lines (backcross inbred lines) derived from a backcross of Nipponbare (japonica)/Kasalath (indica)//Nipponbare were analyzed genetically. We used 245 RFLP markers to construct a framework linkage map. Five putative QTLs affecting seed dormancy were detected on chromosomes 3, 5, 7 (two regions) and 8, respectively. Phenotypic variations explained by each QTL ranged from 6.7% to 22.5% and the five putative QTLs explained about 48% of the total phenotypic variation in the BC₁F₅ lines. Except for those of the QTLs on chromosome 8, the Nipponbare alleles increased the germination rate. Five putative QTLs controlling heading date were detected on chromosomes 2, 3, 4, 6 and 7, respectively. The phenotypic variation explained by each QTL for heading date ranged from 5.7% to 23.4% and the five putative QTLs explained about 52% of the total phenotypic variation. The Nipponbare alleles increased the number of days to heading, except for those of two QTLs on chromosomes 2 and 3. The map location of a putative QTL for heading date coincided with that of a major QTL for seed dormancy on chromosome 3, although two major heading-date QTLs did not coincide with any seed dormancy QTLs detected in this study. Received: 10 October 1997 / Accepted: 12 January 1998     

Analysis of natural allelic variation of Arabidopsis seed germination and seed longevity traits between the accessions Landsberg erecta and Shakdara, using a new recombinant inbred line population

Quantitative trait loci (QTL) mapping was used to identify loci controlling various aspects of seed longevity during storage and germination. Similar locations for QTLs controlling different traits might be an indication for a common genetic control of such traits. For this analysis we used a new recombinant inbred line population derived from a cross between the accessions Landsberg erecta (Ler) and Shakdara (Sha). A set of 114 F9 recombinant inbred lines was genotyped with 65 polymerase chain reaction-based markers and the phenotypic marker erecta. The traits analyzed were dormancy, speed of germination, seed sugar content, seed germination after a controlled deterioration test, hydrogen peroxide (H2O2) treatment, and on abscisic acid. Furthermore, the effects of heat stress, salt (NaCl) stress, osmotic (mannitol) stress, and natural aging were analyzed. For all traits one or more QTLs were identified, with some QTLs for different traits colocating. The relevance of colocation for mechanisms underlying the various traits is discussed.     

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。     

甘蓝型油菜种子发芽率QTL定位及相关生理性状

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

Interval mapping of QTLs controlling yield-related traits and seed protein content in Pisum sativum

A linkage map of garden pea was constructed on the basis of 114 plants (F2 generation) derived from a cross combination Wt10245 x Wt11238. The map, consisting of 204 morphological, isozyme, AFLP, ISSR, STS, CAPS and RAPD markers, was used for interval mapping of quantitative trait loci (QTLs) controlling seed number, pod number, 1000-seed weight, 1000-yield, and seed protein content. Characterization of each QTL included identification of QTL position with reference to the flanking markers, estimation of the part of variance explained by this QTL, and determination of its gene action. The yield-related traits were measured in F2 plants and in F4 recombinant inbred lines (RILs). The interval mapping revealed two to six QTLs per trait, demonstrating linkage to seven pea chromosomes. A total of 37 detected QTLs accounted for 9.1-55.9% of the trait's phenotypic variation and showed different types of gene action. As many as eight and ten QTLs influencing the analysed traits were mapped in linkage groups III and V, respectively, indicating an important role of these regions of the pea genome in the control of yield and seed protein content.     

Genetic dissection of interactions between wheat flour starch and its components in two populations using two QTL mapping methods

Starch content and its components are important for determining wheat end-use quality and yield. However, little information is available about their interactions at the QTL/gene level in more than one population using different QTL mapping methods. Therefore, to dissect these interactions, two mapping populations from two locations over 2 years were used. The QTLs for the populations were analyzed by unconditional and conditional QTL mapping by two different analysis methods. In the two populations, there were a total of 24 unconditional additive QTLs detected for flour amylose (FAMS), flour amylopectin (FAMP), flour total starch (FTSC), and the ratio of FAMS to FAMP using ICIMapping4.1 methods, but 26 unconditional QTLs were found using QTLNetwork2.0 methods. Of these QTLs, 10 stable major additive QTLs were identified in more than one environment, mainly distributed on chromosomes 3B, 4A, 5A, and 7D. The maximum percentage of phenotypic variance explained (PVE) reached 54.31%. Two new unconditional major additive QTLs on chromosome 3B (Qftsc3B and Qfamp3B) were found. A total of 23 and 19 conditional additive QTLs were identified in the two populations using two different methods, respectively. Of which, eight and six stable major conditional QTLs were detected on chromosomes 3B, 4A, and 7D, respectively. New repressed QTLs were identified, such as Qftsc/fams5B-1 and Qftsc/fams5B-2. There were 20 epistatic unconditional and 15 conditional QTLs detected. In all, important QTLs on chromosomes 3B, 4A, and 7A were found in both populations. However, the number of important QTLs in the special recombinant inbred line (RIL) population was higher than that in the double haploid (DH) population, especially on chromosomes 7D and 5B. Moreover, the QTLs on chromosomes 4A, 7A, and 7D were close to the Wx-1 loci in the RIL population. These indicated better results can be obtained by a special population to target traits than by a common population. The important QTLs on key chromosomes can always be detected no matter what kinds of populations are used, such as the QTLs on chromosome 4A. In addition, QTL clusters were found on chromosomes 4A, 3B, 7A, 7D, and 5A in the two populations, indicating these chromosome regions were very important for starch biosynthesis.     

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.     

QTL analysis of seed germination and pre-emergence growth at extreme temperatures in <Emphasis Type="Italic">Medicago truncatula</Emphasis>

Enhancing the knowledge on the genetic basis of germination and heterotrophic growth at extreme temperatures is of major importance for improving crop establishment. A quantitative trait loci (QTL) analysis was carried out at sub- and supra-optimal temperatures at these early stages in the model Legume Medicago truncatula. On the basis of an ecophysiological model framework, two populations of recombinant inbred lines were chosen for the contrasting behaviours of parental lines: LR5 at sub-optimal temperatures (5 or 10°C) and LR4 at a supra-optimal temperature (20°C). Seed masses were measured in all lines. For LR5, germination rates and hypocotyl growth were measured by hand, whereas for LR4, imbibition and germination rates as well as early embryonic axis growth were measured using an automated image capture and analysis device. QTLs were found for all traits. The phenotyping framework we defined for measuring variables, distinguished stages and enabled identification of distinct QTLs for seed mass (chromosomes 1, 5, 7 and 8), imbibition (chromosome 4), germination (chromosomes 3, 5, 7 and 8) and heterotrophic growth (chromosomes 1, 2, 3 and 8). The three QTL identified for hypocotyl length at sub-optimal temperature explained the largest part of the phenotypic variation (60% together). One digenic interaction was found for hypocotyl width at sub-optimal temperature and the loci involved were linked to additive QTLs for hypocotyl elongation at low temperature. Together with working on a model plant, this approach facilitated the identification of genes specific to each stage that could provide reliable markers for assisting selection and improving crop establishment. With this aim in view, an initial set of putative candidate genes was identified in the light of the role of abscissic acid/gibberellin balance in regulating germination at high temperatures (e.g. ABI4, ABI5), the molecular cascade in response to cold stress (e.g. CBF1, ICE1) and hypotheses on changes in cell elongation (e.g. GASA1, AtEXPA11) with changes in temperatures based on studies at the whole plant scale.     

Detection of seed dormancy QTL in multiple mapping populations derived from crosses involving novel barley germplasm

Seed dormancy is one of the most important traits in germination process to control malting and pre-harvest sprouting in barley (Hordeum vulgare L.). EST based linkage maps were constructed on seven recombinant inbred (RI) and one doubled haploid (DH) populations derived from crosses including eleven cultivated and one wild barley strains showing the wide range of seed dormancy levels. Seed dormancy of each RI and DH line was estimated from the germination percentage at 5 and 10 weeks post-harvest after-ripening periods in 2003 and 2005. Quantitative trait loci (QTLs) controlling seed dormancy were detected by the composite interval mapping procedure on the RI and DH populations. A total of 38 QTLs clustered around 11 regions were identified on the barley chromosomes except 2H among the eight populations. Several QTL regions detected in the present study were reported on similar positions in the previous QTL studies. The QTL on at the centromeric region of long arm of chromosome 5H was identified in all the RI and DH populations with the different degrees of dormancy depth and period. The responsible gene of the QTL might possess a large allelic variation among the cross combinations, or can be multiple genes located on the same region. The various loci and their different effects in dormancy found in the barley germplasm in the present study enable us to control the practical level of seed dormancy in barley breeding programs. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

小麦幼苗耐热性的QTL定位分析

以小麦DH群体(‘旱选10号’ב鲁麦14’)为材料,在高温(热胁迫)及常温(对照)两种条件下考察小麦幼苗的根干重、苗干重、幼苗生物量、叶片叶绿素含量、叶绿素荧光参数及其耐热指数,并应用基于混合线性模型的复合区间作图法分析幼苗性状及其耐热指数QTL的数量、染色体分布及表达情况,以及QTL与环境的互作效应。结果显示:(1)亲本‘旱选10号’的耐热性明显优于‘鲁麦14’,且杂交后代的耐热性出现超亲分离。(2)控制幼苗耐热相关性状的QTL位点在染色体2D、6B、3A、4A、5A和7A上分布较多,而控制幼苗性状耐热指数的QTL在染色体6A、6B、3A、2D、5A和7A上分布较多,QTL位点在染色体上的分布有区域化的趋势。(3)控制幼苗性状的单个加性QTL和上位性QTL解释的表型变异分别平均为2.48%和2.65%;而控制耐热指数的单个加性QTL和上位性QTL解释的表型变异分别平均为8.84%和1.98%。(4)在热胁迫和对照条件下共检测到与幼苗性状及其耐热指数有关的加性效应QTL 13个和上位性效应QTL 28对,分布在除4D和6D以外的19条染色体上。研究表明,控制幼苗性状的QTL以上位性效应为主,而其耐热指数的QTL以加性效应为主。     

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.     

Identification of quantitative trait loci for cadmium tolerance and accumulation in wheat

Quantitative trait loci (QTL) for Cadmium (Cd) tolerance and accumulation in wheat (Triticum aestivum L.) were identified, using 103 recombinant inbred lines (RILs) derived from a cross of Ch × Sh at germination and seedling stages. The traits of germination, growth and physiology were measured. Cd tolerance indexes (TI) were calculated for plants under Cd stress relative to control conditions. Cd concentrations in both root and shoot were determined and the amount of Cd accumulation and translocation calculated. The phenotypic variation of the above traits showed a continuous distribution pattern among the RILs. Twenty-six QTLs were detected, (16 of which were designated for the traits under the control and Cd stress, 8 for Cd tolerance and 2 for root Cd accumulation). These 26 QTLs individually could explain 7.97–60.16% of the phenotypic variation. Fourteen QTLs were positive (with the additive effects coming from Ch) while the remaining 12 QTLs were negative (with the additive effects contributed by Sh). No QTL were detected in the same region on the chromosomes of wheat. The results indicated that genetic mechanisms controlling the traits of Cd tolerance were independent from each other. Therefore, in this study, the properties of Cd tolerance and accumulation showed to be independent traits in wheat.     

QTL Analysis of Na+ and K+ Concentrations in Roots and Shoots under Different Levels of NaCl Stress in Rice (Oryza sativa L.)

The key to plant survival under NaCl salt stress is maintaining a low Na⁺ level or Na⁺/K⁺ ratio in the cells. A population of recombinant inbred lines (RILs, F_2∶9) derived from a cross between the salt-tolerant japonica rice variety Jiucaiqing and the salt-sensitive indica variety IR26, was used to determine Na⁺ and K⁺ concentrations in the roots and shoots under three different NaCl stress conditions (0, 100 and 120 mM NaCl). A total of nine additive QTLs were identified by QTL Cartographer program using single-environment phenotypic values, whereas eight additive QTLs were identified by QTL IciMapping program. Among these additive QTLs, five were identified by both programs. Epistatic QTLs and QTL-by-environment interactions were detected by QTLNetwork program in the joint analyses of multi-environment phenotypic values, and one additive QTL and nine epistatic QTLs were identified. There were three epistatic QTLs identified for Na⁺ in roots (RNC), three additive QTLs and two epistatic QTLs identified for Na⁺ in shoots (SNC), four additive QTLs identified for K⁺ in roots (RKC), four additive QTLs and three epistatic QTLs identified for K⁺ in shoots (SKC) and one additive QTL and one epistatic QTL for salt tolerance rating (STR). The phenotypic variation explained by each additive, epistatic QTL and QTL×environment interaction ranged from 8.5 to 18.9%, 0.5 to 5.3% and 0.7 to 7.5%, respectively. By comparing the chromosomal positions of these additive QTLs with those previously identified, five additive QTLs, qSNC9, qSKC1, qSKC9, qRKC4 and qSTR7, might represent novel salt tolerance loci. The identification of salt tolerance in selected RILs showed that a major QTL qSNC11 played a significant role in rice salt tolerance, and could be used to improve salt tolerance of commercial rice varieties with marker-assisted selection (MAS) approach.     

水稻花药培养力的遗传分析及基因定位

在栽培稻的籼粳亚种间,花药培养力存在显著差异,这一差异主要是由遗传因素引起的。以适合籼粳稻杂种花药培养的SK_3培养基,经花药培养获得了一个籼粳交F_1代的加倍单倍体(DH)群体,对该群体的110个株系用同一种培养基进行花药培养,利用该群体构建的分子图谱进行有关水稻花药培养力的数量性状基因座位(QTLs)的分析。结果表明,与水稻花药培养力有关的4个性状在DH群体中均表现为连续分布,愈伤组织诱导率与绿苗分化率之间不存在相关性,而绿苗产率与愈伤组织诱导率和绿苗分化率均显著相关。在第6、7、8、10和12 5条染色体上分别检测到与愈伤组织诱导率有关的5个QTLs,其加性效应均为正。在第1和第9染色体上检测到与绿苗分化率有关的2个QTLs,这两个性状间的QTs不存在连锁。在第9染色体上有一个主效基因与白苗分化率有关,对绿苗产率则没有检测到特有的QTL。