Validation of qGSIO, a quantitative trait locus for grain size on the long arm of chromosome 10 in rice (Oryza sativa L.)

Grain size is a major determinant of grain weight and a trait having important impact on grain quality in rice. The objective of this study is to detect QTLs for grain size in rice and identify important QTLs that have not been well characterized before. The QTL mapping was first performed using three recombinant inbred line populations derived from indica rice crosses Teqing/IRBB lines, Zhenshan 97/Milyang 46, Xieqingzao/Milyang 46. Fourteen QTLs for grain length and 10 QTLs for grain width were detected, including seven shared by two populations and 17 found in one population. Three of the seven com- mon QTLs were found to coincide in position with those that have been cloned and the four others remained to be clarified. One of them, qGSIO located in the interval RM6100-RM228 on the long arm of chromosome 10, was validated using F2：3 populations and near isogenic lines derived from residual heterozygotes for the interval RM6100-RM228. The QTL was found to have a considerable effect on grain size and grain weight, and a small effect on grain number. This region was also previously detected for quality traits in rice in a number of studies, providing a good candidate for functional analysis and breeding utilization.

Fine-Mapping of qTGW1.2a, a Quantitative Trait Locus for 1000-Grain Weight in Rice

Thousand-grain weight (TGW) is a key component of grain yield in rice. This study was conducted to validate and fine-map qTGW1.2a, a quantitative trait locus for grain weight and grain size previously located in a 933.6-kb region on the long arm of rice chromosome 1. Firstly, three residual heterozygotes (RHs) were selected from a BC2F11 population of the indica rice cross Zhenshan 97 (ZS97)///ZS97//ZS97/Milyang 46. The heterozygous segments in these RHs were arranged successively in physical positions, forming one set of sequential residual heterozygotes (SeqRHs). In each of the populations derived, non-recombinant homozygotes were identified to produce near isogenic lines (NILs) comprising the two homozygous genotypes. The NILs were tested for grain weight, grain length and grain width. QTL analyses for the three traits were performed. Then, the updated QTL location was followed for a new run of SeqRHs identification-NIL development-QTL mapping. Altogether, 11 NIL populations derived from four sets of SeqRHs were developed and used. qTGW1.2a was finally delimitated into a 77.5-kb region containing 13 annotated genes. In the six populations segregating this QTL, which were in four generations and were tested across four years, the allelic direction of qTGW1.2a remained consistent and the genetic effects were stable. For TGW, the additive effects ranged from 0.23 to 0.38 g and the proportions of phenotypic variance explained ranged from 26.15% to 41.65%. These results provide a good foundation for the cloning and functional analysis of qTGW1.2a.

Quantitative Trait Locus Analysis for Rice Yield Traits under Two Nitrogen Levels

A recombinant inbred line population derived from a super hybrid rice Xieyou 9308（Xieqingzao B/Zhonghui 9308） and its genetic linkage map were used to detect quantitative trait loci（QTLs） for rice yield traits under the low and normal nitrogen（N） levels. A total of 52 QTLs for yield traits distributed in 27 regions on 9 chromosomes were detected, with each QTL explaining 4.93%–26.73% of the phenotypic variation. Eleven QTLs were simultaneously detected under the two levels, and 30 different QTLs were detected under the two N levels, thereby suggesting that the genetic bases controlling rice growth under the low and normal N levels were different. QTLs for number of panicles per plant, number of spikelets per panicle, number of filled grains per panicle, and grain density per panicle under the two N levels were detected in the RM135–RM168 interval on chromosome 3. QTLs for number of spikelets per panicle and number of filled grains per panicle under the two N levels, as well as number of panicles per plant and grain density per panicle, under the low N level, were detected in the RM5556–RM310 interval on chromosome 8. The above described QTLs shared similar regions with previously reported QTLs for rice N recycling.

A major quantitative trait locus controlling phosphorus utilization efficiency under different phytate-P conditions at vegetative stage in barley

Organic phosphorus（P） is an important component of the soil P pool, and it has been proven to be a potential source of P for plants. The phosphorus utilization efficiency（PUE） and PUE related traits（tiller number（TN）, shoot dry weight（DW）, and root dry weight） under different phytate-P conditions（low phytate-P, 0.05 mmol L^-1 and normal phytate-P, 0.5 mmol L^-1） were investigated using a population consisting of 128 recombinant inbred lines（RILs） at the vegetative stage in barley. The population was derived from a cross between a P-inefficient genotype（Baudin） and a P-efficient genotype（CN4027, a Hordeum spontaneum accession）. A major locus（designated Qpue.sau-3 H） conferring PUE was detected in shoots and roots from the RIL population. The quantitative trait locus（QTL） was mapped on chromosome 3 H and the allele from CN4027 confers high PUE. This locus explained up to 30.3 and 28.4% of the phenotypic variance in shoots under low and normal phytate-P conditions, respectively. It also explains 28.3 and 30.7% of the phenotypic variation in root under the low and normal phytate-P conditions, respectively. Results from this study also showed that TN was not correlated with PUE, and a QTL controlling TN was detected on chromosome 5 H. However, dry weight（DW） was significantly and positively correlated with PUE, and a QTL controlling DW was detected near the Qpue.sau-3 H locus. Based on a covariance analysis, existing data indicated that, although DW may affect PUE, different genes at this locus are likely involved in controlling these two traits.

QTL analysis for some quantitative traits in bread wheat

Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) sug- gested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.

Mapping of qTGW1.1,a Quantitative Trait Locus for 1000-Grain Weight in Rice (Oryza sativa L.)

1000-grain weight （ TGW） is one ot the three component traits ot the grain yiela in rice （Oryza sativa L）. This study was conducted to validate and fine-map qTGWl. 1, a minor QTL for TGW which was previously located in a 3.7-Mb region on the long arm of rice chromosome 1. Five sets of near isogenic lines （NILs） were developed from two BC2F4 populations of the indica rice cross Zhenshan 973/Milyang 46 The NIL sets consisted of two homozygous genotypic groups differing in the regions RM11448-RM11522, RM11448-RM11549, RM1232-RM11615, RM11543-RM11554 and RM11569-RM11621, respectively. Four traits, including TGW, grain length, grain width and heading date, were measured. Phenotypic difference between the two genotypic groups in each NIL population was analyzed using SAS procedure GLM. Significant QTL effects were detected on TGW with the Zhenshan 97 allele increasing grain weight by 0.12 g to 0.14 g and explaining 8.30% to 15.19% of the phenotypic variance. Significant effects were also observed for grain length and width, whereas no significant effect was found for heading date. Based on comparison among the five NILs on the segregating regions and the results of QTL analysis, qTGWl. 1 was delimited to a 376.9-kb region flanked by DNA markers Wn28382 and RMl1554. Our results indicate that the effects of minor QTLs could be steadily detected in a highly isogenic background and suggest that such QTLs could be utilized in the breeding of high-yielding rice varieties.

Dissection of Genetic Effects of Quantitative Trait Loci(QTL) in Transgenic Cotton

When alien DNA inserts into cotton genome in multi-copy manner,several QTL in cotton genome are disrupted,which are called dQTL in this study.Transgenic mutant line is near-isogenic to its recipient which is divergent for the dQTL from remaining QTL.So,a set of data from a

利用双向导入系定位再生稻外观品质的QTL

再生稻具有较好的外观品质。为解析再生稻外观品质的遗传基础,本研究利用籼稻明恢63和粳稻02428构建的双向导入系为材料,连续两年在湖北荆州考察了双向导入系头季和再生季的外观品质。结果表明,明恢63头季和再生季的外观品质均显著优于02428,双向导入系的所有性状均表现为连续分布。同一性状在头季和再生季间表现为显著正相关,同一季节内不同性状间也表现出显著的相关性,其中粒宽对外观品质的影响最大。结合双向导入系已有的4568个Bin的高密度基因型数据,共定位到57个影响再生稻外观品质的数量性状点位(QTL),位于全部12条染色体上。其中25个QTL在两年间稳定表达,17个QTL在两个遗传背景下被重复鉴定到。此外,第3号染色体16.28~17.33 Mb、第5号染色体3.35~4.28 Mb、第7号染色体24.68~25.46 Mb和第11号染色体6.19~6.97 Mb这4个区间同时影响4个以上性状,来自明恢63的等位基因在这4个区间均可提高外观品质。最后,利用分离群体验证了第11号染色体6.19~6.97 Mb具有真实性。本研究结果为分子育种改良再生稻的外观品质提供了遗传基础。

基于高密度遗传图谱的芝麻籽粒品质相关性状QTL定位

芝麻是我国重要的优质油料作物,对芝麻籽粒品质性状进行数量性状位点(QTL)定位对定向培育高品质芝麻品种具有重要意义。以豫芝4号为母本、孟加拉小籽为父本,分别构建F2、F2:3、BC1和BC1F2群体,结合特异位点扩增片段(SLAF)标记和简单重复序列(SSR)标记构建F2和BC1遗传图谱,以F2:3、BC1和BC1F23个群体的表型数据为基础,进行脂肪、蛋白质、芝麻素、芝麻林素含量等4个品质性状的QTL作图分析。结果表明,在F2:3群体中共检测到16个QTL,解释表型变异的5.08%~27.12%,其中仅有1个主效QTL qOC_10-1在2个环境中被重复检测到,分别解释表型变异的9.62%和27.12%。在BC1和BC1F2群体中共检测到35个QTL,其中3个主效QTL qOC_4-1、qOC_10-2和qSmin_7-2在3个环境中被重复检测到,分别解释表型变异的8.08%~12.42%、11.95%~12.60%和4.24%~10.56%;3个主效QTL qSmin_8、qSmol_5-2和qSmol_7-2在2个环境被重复检测到,分别解释表型变异的13.36%~26.75%、11.44%~14.33%和5.77%~12.38%。经过对2个图谱进行整合和比对分析,共发现10个QTL簇,其中QTL簇loci4、loci7、loci8和loci10均与多个性状相关联,并且均至少包含1个主效QTL,其对应的最大表型变异解释率分别为12.42%、12.38%、26.75%和27.12%。

花生蔗糖含量与蛋白质和含油量的相关性分析及蔗糖含量QTL定位

随着食用型花生市场规模的日益扩大,食用花生的品质特性备受关注。甜味是影响食用花生风味和口感的重要指标,而花生甜味主要来源于蔗糖。提高蔗糖含量是培育食用型花生的关键,定位蔗糖含量相关的QTL,将为花生高糖分子育种奠定基础。但提高蔗糖可能对蛋白质和含油量有影响。本研究以徐花13和中花6号为亲本构建的重组自交系群体(RIL)为材料,分析了蔗糖与蛋白质和含油量的关系,并对蔗糖含量进行了QTL定位。结果表明,蔗糖含量与蛋白质含量无显著相关性,而与含油量呈极显著负相关;RIL群体中蔗糖含量变异广泛,变异范围为14.33~61.42mg g^(-1);筛选出2份高蛋白低脂肪中等含糖量的材料。通过该群体的遗传连锁图,在3个环境下共检测到10个QTL,分布在6条染色体上,贡献率为4.56%~12.25%。其中,qSUCA07能在3个环境下被重复检测到。本研究为蔗糖含量精细定位和培育性状优良的食用型花生品种提供了重要信息。

水稻和陆稻分化相关性状的QTL分析及水旱不同栽培条件下连续选择的影响

本研究以来自云南的地方品种Ch5-10和Ch6-11为亲本,后代用SSD方法而得到的RIL9群体为试验材料,对水、陆稻差异性状(始穗期、剑叶宽、株高等)进行了考察,并利用SSR分子标记构建遗传连锁图谱,进行了各性状的QTL定位;在自然和人工选择的过程中,栽培稻无论是形态上还是基因组上,都发生多种多样的变化。利用F 2群体(父、母本和前述的SSD群体相同)采用Bulk混合选择的方法,在F 4代开始进行水、旱田两种条件下种植,以模拟水陆稻的分化条件,经过5代的种植后繁殖成株系,分别得到CA、CB两个群体。对这两个群体的水陆稻差异性状(始穗期、最高分蘖数、有效分蘖数、剑叶宽、株高等)进行了考察。结果表明,利用SSD方法而得到的RIL9群体对始穗期、剑叶宽、株高等3个性状进行QTL分析,两年共检测到19个QTL,分别位于1,2,3,7,8,9,11号染色体上。其中株高qPH-1效应最大,贡献率为22%(2009年安徽),16%(2009年北京),16%(2010年北京),表现为来自亲本Ch5-10的等位基因增加株高;利用Bulk方法而得到的CA群体(经过水田筛选5代、289个株系)和CB群体(经过旱田筛选5代、332个株系),考察其最高分蘖数、有效分蘖数、始穗期、株高、剑叶宽等5个性状。2009年安徽和北京两地试验发现:两群体间除安徽始穗期和北京剑叶宽差异不显著外,其他性状差异均达到极显著水平。根据氯酸钾抗性和低温发芽力的QTL定位结果,选择qSLtr-2、qSLratio-2、qRL28-2-1和qRL28-4附近的标记RM166、RM5472、RI05751和RM5979,及未定位到QTL的标记RM259对CA群体和CB群体进行基因型分析,两群体间t测验结果表明,RM5472、RI05751和RM5979差异均达到极显著水平,RM259和RM166差异不显著。说明在人工模拟水、陆稻选择的条件下这2个群体有了明显的分化。

含野生种血缘花生品种ICGV 86699超长链脂肪酸QTL定位

超长链脂肪酸(VLCFAs)在工业、医学和食品领域具有很好的应用价值,前期研究发现花生野生种超长链脂肪酸含量高于栽培种。本研究利用含有野生种血缘的花生品种ICGV86699和中花5号杂交构建的重组自交系群体,对超长链脂肪酸进行测定。结果表明,RIL群体中超长链脂肪酸总量变异广泛,变异范围为4.99%~10.33%。同时也发现了5份超长链脂肪酸总量高(≥8.50%)的家系,其中ZJ011家系高达9.82%。利用前期构建的遗传图谱,对单个超长链脂肪酸及总量进行QTL检测,能被重复检测到的QTL有20个,贡献率为3.72%~15.66%,主要集中在A04、B04和B06染色体上。本结果为油脂品质改良奠定了分子和材料基础。

Identification of quantitative trait loci for the dead leaf rate and the seedling dead rate under alkaline stress in rice

The quantitative trait loci （QTLs） for the dead leaf rate （DLR） and the dead seedling rate （DSR） at the different rice growing periods after transplanting under alkaline stress were identified using an F2：3 population, which included 200 individuals and lines derived from a cross between two japonica rice cultivars Gaochan 106 and Changbai 9 with microsatellite markers. The DLR detected at 20 days to 62 days after transplanting under alkaline stress showed continuous normal or near normal distributions in F3 lines, which was the quantitative trait controlled by multiple genes. The DSR showed a continuous distribution with 3 or 4 peaks and was the quantitative trait controlled by main and multiple genes when rice was grown for 62 days after transplanting under alkaline stress. Thirteen QTLs associated with DLR were detected at 20 days to 62 days after transplanting under alkaline stress. Among these, qDLR9-2 located in RM5786-RM160 on chromosome 9 was detected at 34 days, 41 days, 48 days, 55 days, and 62 days, respectively; qDLR4 located in RM3524-RM3866 on chromosome 4 was detected at 34 days, 41 days, and 48 days, respectively; qDLR7-1 located in RM3859-RM320 on chromosome 7 was detected at 20 days and 27 days; and qDLR6-2 in RM1340-RM5957 on chromosome 6 was detected at 55 days and 62 days, respectively. The alleles of both qDLR9-2 and qDLR4 were derived from alkaline sensitive parent ＂Gaochanl06＂. The alleles of both qDLR7-1 and qDLR6-2 were from alkaline tolerant parent Changbai 9. These gene actions showed dominance and over dominance primarily. Six QTLs associated with DSR were detected at 62 days after transplanting under alkaline stress. Among these, qDSR6-2 and qDSR8 were located in RM1340-RM5957 on chromosome 6 and in RM3752-RM404 on chromosome 8, respectively, which were associated with DSR and accounted for 20.32% and 18.86% of the observed phenotypic variation, respectively; qDSR11-2 and qDSR11-3 were located in RM536-RM479 and RM2596-RM286 on chromosome 11, respectively, which were associated with DSR explaining 25.85% and 15.41% of the observed phenotypic variation, respectively. The marker flanking distances of these QTLs were quite far except that of qDSR6-2, which should be researched further.

Characterization and mapping of QTLs on chromosome 2D for grain size and yield traits using a mutant line induced by EMS in wheat

Production of mutants with altered phenotypes is a powerful approach for determining the biological functions of genes in an organism. In this study, a high-grain-weight mutant line M8008 was identified from a library of mutants of the common wheat cultivar YN15 treated with ethylmethane sulfonate(EMS). F2 and F2:3generations produced from crosses of M8008 × YN15(MY) and M8008 × SJZ54(MS) were used for genetic analysis. There were significant differences between M8008 and YN15 in plant height(PH), spike length(SL),fertile spikelet number per spike(FSS), grain width(GW), grain length(GL), GL/GW ratio(GLW), and thousand-grain weight(TGW). Most simple correlation coefficients were significant for the investigated traits, suggesting that the correlative mutations occurred in M8008. Approximately 21% of simple sequence repeat(SSR) markers showed polymorphisms between M8008 and YN15, indicating that EMS can induce a large number of mutated loci. Twelve quantitative trait loci(QTLs) forming QTL clusters(one in MY and two in MS) were detected. The QTL clusters coinciding with(MY population) or near(MS population) the marker wmc41 were associated mainly with grain-size traits, among which the M8008 locus led to decreases in GW, factor form density(FFD), and TGW and to increases in GLW. The cluster in the wmc25–barc168 interval in the MS population was associated with yield traits, for which the M8008 locus led to decreased PH, spike number per plant(SN), and SL.

Detection of QTLs for Important Agronomic Traits and Analysis of Their Stabilities Using SSSLs in Rice

Single segment substitution lines （SSSLs） each with a single chromosome segment from a donor under the same genetic background as the recipient were developed in rice by advanced backcrossing and molecular marker-assisted selection. Using the SSSLs, the QTLs for the important agronomic traits in rice would be detected under different environmental conditions. Detection of the QTLs controlling 22 important traits in rice was done with 32 SSSLs by the randomized block design in 2-4 cropping seasons. 59 QTLs were detected and distributed on chromosomes 1, 2, 3, 4, 6, 7, 8, 10, and 11, of which 18 QTLs were detected more than twice. Only 30.5% of the QTLs were detected repeatedly in different cropping seasons. Most of the QTLs of important agronomic traits were of little additive effects and instability. The QTLs controlling the traits, such as grain weight, grain length, ratio of grain length to width, and heading date were relatively stable. The stable QTLs usually had larger additive effects and were less affected by environment. The QTLs for the important agronomic traits were detected using the SSSLs in rice with high resolution under different environmental conditions. The instability of the QTLs may be the basis of the variation of rice plants during growth and development. It would be the genetic basis for improving yield and quality in rice cultivars by farming methods.

Identification of Quantitative Trait Loci for Grain Traits in Japonica Rice

Quantitative trait loci（QTLs） of grain traits were detected to provide theoretical basis for fine mapping and molecular marker-assisted breeding of grain traits in japonica rice.Using an F2 population including 200 individuals derived from a cross combination between two japonica rice DL115 with large grain and XL005 with small grain,the grain length,grain width,grain thickness,ratio of grain length to width and 1 000-grain weight were evaluated in Beijing;and the quantitative trait loci for above five grain traits were identified by composite interval mapping using SSR markers.The results showed that the five grain traits exhibited a normal continuous distribution in F2 population,indicating they were quantitative traits controlled by multiple genes.A total of 16 QTLs conferring the five grain traits were detected on chromosomes 2,3,5 and 12,respectively.Eight QTLs,namely qGL3a,qGW2,qGW5,qGT2,qRLW2,qRLW3,qGWT2 and qGWT3,were major QTLs and explained 15.42,40.89,13.54,33.43,13.82,13.61,12.51 and 10.1% of the observed phenotypic variance,respectively.Among them,qGW2,qGT2,qRLW2 and qGWT2 were mapped in same interval RM12776-RM324 on chromosome 2.The marker interval RM12776-RM324 on chromosome 2 was common marker intervals of four major QTLs,and the two SSR markers RM12776 and RM324 would be used in molecular markerassisted breeding in japonica rice.The modes of gene action were mainly additive and partial dominance.Four QTLs＇ alleles were derived from small grain parent XL005,and other 12 QTLs＇ alleles were derived from large grain parent DL115.The alleles from larger parent were showed significant effects to grain length,grain width,grain thickness and 1 000-grain weight.

Dissection of QTLs for Yield Traits on the Short Arm of Rice Chromosome 6

This study was undertaken to dissect quantitative trait loci （QTLs） controlling yield traits on the short arm of rice chromosome 6. A residual heterozygous line that carries a heterozygous segment extending from RM587 to RM19784 on the short arm of rice chromosome 6 was selected from an F7 population of the indica rice cross Zhenshan 97B/Milyang 46. An F2：3 population consisting of 221 lines was derived and grown in two trial sites. Six yield traits including number of panicles per plant, number of filled grains per panicle, total number of spikelets per panicle, spikelet fertility, 1 000-grain weight, and grain yield per plant were measured. An SSR marker linkage map was constructed and employed to determine QTLs for yield traits with Windows QTL Cartographer 2.5. QTLs were detected in the target interval for all the traits analyzed except NP, with phenotypic variance explained by a single QTL ranging between 6.3% and 35.2%. Most of the QTLs for yield components acted as additive QTLs, while the three QTLs for grain yield had dominance degrees of 1.65, 0.84, and -0.42, respectively. It was indicated that three or more QTLs for yield traits were located in the target region. The genetic action mode, the direction of the QTL effect, and the magnitude of the QTL effect varied among different QTLs for a given trait, and among QTLs for different traits that were located in the same interval.

葡萄遗传图谱构建与抗病QTL定位研究进展

葡萄在生长发育过程中易遭受多种病原菌的侵染,影响果实品质和产量,制约葡萄产业的发展。生产中多采用杀菌剂对病原菌进行防治,增加了投资成本,且会对环境造成污染,因此培育高品质抗病品种对葡萄生产具有重要意义。葡萄抗病性为多基因控制的数量性状,QTL定位是研究数量性状的一种有效方法,遗传图谱的构建是检测QTLs和克隆基因的基础。多年来,研究人员通过构建遗传图谱鉴定了一系列霜霉病、白粉病、皮尔斯病等抗性遗传位点,同时,根据抗性位点的基因组区域,开发了多个连锁标记,并应用到葡萄抗病性遗传研究中,加速了葡萄的育种进程。对葡萄遗传连锁图谱的构建和抗病相关QTL定位研究进展进行了综述,分析了目前研究中存在的问题并提出建议,为今后葡萄抗病基因定位和分子标记辅助选择育种提供借鉴。

Quantitative Trait Loci for Heading Date and Their Relationship with Genetic Control of Yield Traits in Rice (Oryza sativa)

Grain yield and heading date are key factors determining the commercial potential of a rice variety. Mapping of quantitative trait loci （QTLs） in rice has been advanced from primary mapping to gene cloning, and heading date and yield traits have always attracted the greatest attention. In this review, genomic distribution of QTLs for heading date detected in populations derived from intra-specific crosses of Asian cultivated rice （Oryza sativa） was summarized, and their relationship with the genetic control of yield traits was analyzed. The information could be useful in the identification of QTLs for heading date and yield traits that are promising for the improvement of rice varieties.