利用小麦关联RIL群体定位产量相关性状QTL

为定位控制小麦产量相关性状的QTL位点,获得与重要位点连锁的分子标记和染色体区段,以分别含有229和485个家系的关联重组自交系(RIL)群体WY和WJ为材料,在4个环境中,用完备区间作图法(ICIM)对产量相关性状进行了QTL定位分析。结果表明,产量相关性状QTL分布在小麦21条染色体上。在WY群体中检测到每穗小穗数、主茎穗粒数、单株穗数、千粒重和单株产量的QTL分别有9、9、4、7和5个,其中16个(55.2%)解释大于10%的表型变异;在WJ群体中检测到这5个性状的QTL分别有20、16、11、14和9个,其中只有3个(6.7%)在单个环境中解释超过10%的表型变异。在WY群体中有5个QTL在2个环境中被重复检测到;在WJ群体中,有11个QTL在2个或2个以上环境中被重复检测到。在2个群体中均检测到产量相关性状的QTL在染色体上形成了含有一因多效或紧密连锁QTL的染色体区段,并在2个群体检测到可能相同的9对QTL和2个染色体区段。     

基于DArTseq标记的人工合成小麦农艺性状QTL定位

人工合成小麦拥有丰富的有利遗传变异,可用于普通小麦的遗改良。本研究选用两个人工合成小麦改良品系构建了由284个单株组成的F2群体,基于1 671具有染色体位置信息的多态性DAr Tseq标记构建遗传图谱,并结合该群体农艺性状(株高,穗长,穗颈节长,小穗数,穗粒数,单株有效穗数,千粒重,单株重)的表现型,利用QTL作图软件ICIMapping 4.1进行了QTL定位。结果表明,共检测到20个QTL,其中4个为株高QTL,分布于2A、3B、5B染色体上,可解释表型变异的5.4%~10.8%;4个为穗长QTL,分布于2D、3B、5B染色体上,可解释表型变异的1.4%-8.8%;3个为穗颈节长QTL,分布于1A和5A染色体上,可解释表型变异的4.6%~12.2%;2个为穗粒数QTL,分布于3D和5A染色体上,可解释表型变异的18.9%~29.8%;1个为单株有效穗数QTL,分布于2A染色体上,可解释表型变异10.2%;5个为千粒重QTL,分布于1B、5A、5B、5D和7B染色体上,可解释表型变异的8.9%~10.9%;1个为位于7B染色体上的单株重QTL,可解释表型变异的6.1%。同时,在5B和7B染色体上存在控制多个性状的同一QTL位点。利用生物信息学的方法,筛选到1个千粒重相关的候选基因。以上结果可为人工合成小麦农艺性状QTL精细定位、分子标记辅助选择育种和基因克隆奠定基础。     

利用极端材料定位水稻粒形性状数量基因位点

利用极端大粒材料GSL156(千粒重71.9 g)与特小粒材料川七(千粒重12.1 g,轮回亲本)杂交、回交获得的BC₂F₂ 216个个体为作图群体,在北京进行稻谷粒长、粒宽、粒厚、长宽比、千粒重等粒形性状的鉴定。采用单标记分析和复合区间作图法,利用SSR标记对粒形性状进行数量性状基因座检测。结果表明,上述粒形性状在BC₂F₂群体均呈正态连续分布,表现为由多基因控制的数量性状;共检测到与粒形性状相关的QTL 28个,分布于第1、2、3、4、5、6和12染色体上。其中qGL3-2、qGL3-3、qGT12-1、qGT2-1、qGT5-1、qGW1-1、qGW12-1、qGW2-1、qGW5-1、qRLW3-1、qTGW12-1、qTGW2-1、qTGW3-3和qTGW5-1对表型变异的贡献率分别为13.70%、52.51%、21.13%、18.79%、20.92%、14.59%、18.33%、30.03%、20.05%、24.53%、13.47%、11.43%、21.30%和15.68%,为主效QTL。其中,第3染色体上检测出来的QTL最多。在所有检测到的28个QTL中,6个QTL的增效等位基因来源于小粒亲本川七,而其余QTL的增效等位基因均来源于大粒亲本GSL156,基因作用方式主要表现为加性或部分显性。第3染色体RM7580~RM8208区间是分别与粒宽、长宽比和千粒重相关的3个主效QTL的共同标记区间,第2染色体的RM7636~RM5812区间、第5染色体的RM3351~RM26区间和第12号染色体的RM1103~RM17区间是分别与粒宽、粒厚和千粒重相关的3个主效QTL的共同标记区间,这些区间对粒形贡献率较大,为进一步精细定位或克隆这些新的粒重或粒形QTL奠定了基础。同时大粒亲本对稻谷粒长、粒宽、粒厚和千粒重等性状的增效作用显著。     

大豆重组自交系群体荚粒性状的QTL分析

利用大豆重组自交系soy01群体中的255个家系进行2年田间试验，采用两种作图方法，寻找一粒荚、四粒荚、每荚粒数等5个荚粒性状稳定的QTL。结果表明，利用区间作图法，2年共找到24个荚粒性状QTL，解释的遗传变异为5%~80%；利用复合区间作图法，2年共找到27个荚粒性状QTL，解释的遗传变异为4%~73%。利用复合区间作图法，2年找到2个重复出现、稳定的四粒荚QTL和2个每荚粒数QTL，为大豆荚粒性状QTL的精细定位和分子标记辅助育种提供了基础和依据。     

小麦籽粒产量及穗部相关性状的QTL定位

由小麦品种花培3号和豫麦57杂交获得DH群体168个株系,种植于3个环境中,利用305个SSR标记对籽粒产量和穗部相关性状(穗长、穗粒数、总小穗数、可育小穗数、小穗着生密度、千粒重和粒径)进行了QTL定位。利用基于混合线性模型的QTLNetwork 2.0软件,共检测到27个加性效应和13对上位效应位点,其中 8个加性效应位点具有环境互作效应。相关性高的性状间有一些共同的QTL位点,表现出一因多效或紧密连锁效应。5D染色体区段Xwmc215–Xgdm63,检测到控制籽粒产量、穗粒数、总小穗数、可育小穗数和小穗着生密度5个性状的QTL位点,各位点的遗传贡献率较大且遗传效应方向相同,增效等位基因均来源于豫麦57,适用于分子标记辅助育种和聚合育种。控制千粒重与穗粒数的QTL位于染色体不同区段,有利于实现穗粒数与粒重的遗传重组。     

小麦穗部性状和株高的QTL定位

穗粒数是小麦的重要产量性状之一,减少基部不育小穗数是提高小麦穗粒数的重要途径。利用EMS诱变小麦品系山农1186获得基部2-4个小穗不育突变体M7652。本研究利用M7652与山农1186回交获得的BC3F2群体及其衍生的BC3F2:3株系(MS)群体,M7652与泰农18杂交获得的F2群体及其衍生的F2:3株系(MT)群体为材料,进行遗传作图和QTL定位。通过SSR标记检测构建了分别覆盖38.9 c M、73.1 c M的两个4B染色体的遗传连锁图。对两个群体的穗部性状和株高进行QTL分析表明,MS回交群体在3个环境下都检测到6个QTL,包括5个控制穗部性状和1个株高性状的QTL位点,其贡献率为18.22%~47.23%,且位于染色体的相同区段,形成一个QTL簇;MT群体在1个环境中检测到4个控制穗部性状、1个控制株高的QTL位点,其贡献率为1.51%~39.15%,形成一个QTL簇。利用MS和MT群体检测到的QTL簇在染色体上的位置相同,都覆盖swes24标记,这个区域与增加小麦穗粒数、降低株高有关,为小麦穗粒数、株高的精细定位、基因克隆奠定了基础。     

小麦D基因组产量性状QTL定位

粗山羊草是普通小麦的D染色体组供体,为了寻找粗山羊草中对小麦产量性状遗传改良有益的基因,通过对四倍体硬粒小麦与粗山羊草杂交合成的双二倍体Am6-1和普通小麦品种Ph85-16的回交一代进行产量性状变异特点分析,发现粗山羊草的D组染色体对小麦的产量性状具有显著影响,千粒重、穗长、穗粒数和每穗小穗数明显高于Ph85-16;同时利用130对SSR引物对几个与产量性状相关的QTL位点进行了定位,初步寻找到4个主效QTL,它们分别为与穗长相关的QSl．sdau-5D,其贡献率为31．58％,与株高相关的QPh．sdau-1D,其贡献率为25．38％,与穗粒数相关的QGs．sdau-5D,其贡献率为44.65％,与千粒重相关的QTgw．sdau-3D,其贡献率为61．62％。     

影响水稻穗部性状及籽粒碾磨品质的QTL及其环境互作分析

利用优质恢复系测258为轮回亲本与粳型糯稻新品系IR75862杂交创制的BC₁F₇回交导入系群体,在广西南宁和海南三亚定位了产量相关性状(二次枝梗数、穗总粒数、穗实粒数、粒重和穗重)、粒型(粒长、宽、厚)和碾磨品质(糙米率、精米率和整精米率)的主效QTL并剖析其环境互作效应。双亲在穗实粒数、千粒重、粒长和粒宽及整精米率等性状上存在显著差异。各产量相关性状间呈极显著正相关,而与千粒重和粒长呈极显著负相关。多数产量及粒型相关性状与3种碾磨品质相关不显著。在南宁和三亚环境下检测到影响产量相关性状、粒型及碾磨品质的主效QTL共计57个,包括二次枝梗数的6个,穗实粒数4个,穗总粒数、粒重和穗重各5个,粒长9个,粒宽7个,粒厚1个,糙米率4个,精米率5个和整精米率6个,分布在除第11染色体外的所有染色体上。多数影响枝梗数、穗粒数和粒重的QTL成簇分布,而且与影响BR、MR和HR的QTL分布在不同染色体区域。在第2、第3、第4、第5和第6染色体上鉴定出影响穗粒数、粒重、粒型及碾磨品质的重要QTL,这些QTL在以往不同遗传背景和环境下被多次检测到。在第8染色体RM152~RM310区间鉴定到1个影响粒长和粒宽的新的QTL,能同步增加粒宽和粒长。鉴定出的这些稳定表达的QTL具有标记辅助选择育种的应用价值。整精米率是受环境影响最大的性状,其QTL的环境互作效应明显。对QTL的环境互作效应特点及其在品种标记辅助改良中的作用进行了深入探讨。     

玉米数量性状基因定位

以48-2×5003的166个F2:3 家系作为定位群体,采用13×13a-简单格子方设计,在成都、雅安分别调查了株高、穗位高、穗长、秃尖长、穗行数、行粒数、穗粗、轴粗、粒深、300粒重、出籽率、小区产量等12个经济性状,采用区间作图法对其进行QTL定位分析,共检测出59个QTL,每个性状检测出3～8个QTL,单个QTL的作用可解释表型变异     

基于玉米BC2F2群体的穗部性状QTL分析

以农系110为受体、农系531为供体材料,采用回交法构建了样本容量为95株的BC2F2回交群体,选取126个均匀分布在10条染色体上的多态性SSR标记进行6个穗部性状QTL分析.结果表明,受体农系110的穗长、穗粗、穗行数、行粒数、百粒重和穗粒重6个穗部性状表型值分别增加了9.4%,9.6%,4.1%,2.7%,0.35%和4.4%;构建一张含126个SSR标记的玉米分子标记连锁遗传图谱,总长度为2 317.4 cM,平均区间长18.4 cM;采用复合区间作图法,定位了19个与玉米穗部性状有关的QTL,其中穗长QTL 4个,穗粗QTL 2个,行粒数QTL 1个,穗行数QTL 3个,百粒重QTL 4个,穗粒重QTL 5个.     

QTL mapping of yield-related traits in the wheat germplasm 3228

The new wheat germplasm 3228, a putative derivative of tetraploid Agropyron cristatum Z559 and the common wheat Fukuhokomugi, has superior features in yield-related traits, particularly in spike morphological traits, such as large spike and superior grain number. To identify favorable alleles of these traits in 3228, 237 F_2:3 families were developed from the cross 3228/Jing 4839. A genetic map was constructed using 179 polymorphic SSR and EST-SSR markers. A total of 76 QTL controlling spike number per plant (SNP), spike length (SL), spikelet number per spike (SNS), floret number per spikelet (FNS), grain number per spike (GNS) and thousand-grain weight (TGW) were detected on 16 chromosomes. Each QTL explained 1.24–27.01% of the phenotypic variation, and 9 QTL (28.95%) were detected in two or all environments. Additive effects of 45 QTL were positive with 3228 alleles increasing the QTL effects, 31 QTL had negative effects indicating positive contributions from Jing 4839. Three important clusters involving all traits were located on chromosomes 5A, 6A and 4B, and several co-located QTL were also found. Most of the QTL detected on the three chromosome regions could contribute to the use of 3228 in breeding for grain yield improvement.     

Identification of microsatellite markers linked with yield components under drought stress at terminal growth stages in durum wheat

Grain yield and yield components are the main important traits involved in durum wheat (Triticum turgidum L.) improvement programs. The purpose of this research was to identify quantitative trait loci (QTL) associated with yield components such as 1000 grain weight (TGW), grain weight per spike (GWS), number of grains per spike (GNS), spike number per m² (SN), spike weight (SW), spike harvest index (SHI) and harvest index (HI) using microsatellite markers. Populations of F₃ and F₄ lines derived from 151 F₂ individuals developed from a cross between Oste-Gata, a drought tolerant, and Massara-1, a drought susceptible durum wheat genotypes, were used. The populations were evaluated under four environmental conditions including two irrigation regimes of drought stress at terminal growth stages and normal field conditions in two growing seasons. Two hundred microsatellite markers reported for A and B genomes of bread wheat were used for parental polymorphism analysis and 30 polymorphic markers were applied to genotype 151 F_2:3 families. QTL analysis was performed using genome-wide single marker regression analysis (SMA) and composite interval mapping (CIM). The results of SMA revealed that about 20% of the phenotypic variation of harvest index and TGW could be explained by Xcfd22-7B and Xcfa2114-6A markers in different environmental conditions. Similarly, Xgwm181-3B, Xwmc405-7B and Xgwm148-3B and marker Xwmc166-7B were found to be associated with SHI and GWS, respectively. A total of 20 minor and major QTL were detected; five for TGW, two for GWS, two for GNS, three for SN, five for HI, two for SHI and one for SW. The mapped QTL associated with ten markers. Moreover, some of these QTL were prominent and stable under drought stress and non drought stress environments and explained up to 49.5% of the phenotypic variation.     

Identification and validation of a yield-enhancing QTL cluster in rice (Oryza sativa L.)

Improvement of rice grain yield (YD) is an important goal in rice breeding. YD is determined by its related traits such as spikelet fertility (SF), 1,000-grain weight (TGW), and the number of spikelets per panicle (SPP). We previously mapped quantitative trait loci (QTLs) for SPP and TGW using the recombinant inbred lines (RILs) derived from the crosses between Minghui 63 and Teqing. In this study, four QTLs for SF and four QTLs for YD were detected in the RILs. Comparison of the locations of QTLs for these three yield-related traits identified one QTL cluster in the interval between RM3400 and RM3646 on chromosome 3. The QTL cluster contained three QTLs, SPP3a, SF3 and TGW3a, but no YD QTL was located there. To validate the QTL cluster, a BC₄F₂ population was obtained, in which SPP3a, SF3 and TGW3a were simultaneously mapped to the same region. SPP3a, SF3 and TGW3a explained 36.3, 29.5 and 59.0 % of phenotype variance with additive effect of 16.4 spikelets, 6 % SF and 1.8 g grain weight, respectively. In the BC₄F₂ population, though the region has opposite effects on TGW and SPP/SF, a YD QTL YD3 identified in this cluster region can increase 4.6 g grains per plant, which suggests this QTL cluster is a yield-enhancing QTL cluster and can be targeted to improve rice yield by marker aided selection.     

不同生态环境条件下小麦籽粒灌浆速率及千粒重QTL分析

以142个和尚麦/豫8679的F7:8重组自交系及其亲本为试验材料, 分析了籽粒平均灌浆速率、最高灌浆速率及千粒重在北京(2006, 2007)、安徽合肥(2007)和四川成都(2007)4个生态环境下的性状表现, 并利用已构建的含有170个SSR标记和2个EST标记的遗传图谱, 对这3个性状进行了QTL定位分析。共检测到54个QTLs, 涉及小麦1A、1B、2A、2D、3A、3B、3D、4A、4D、5A、5B、6D 和7D染色体。其中, 17个与平均灌浆速率相关, 可解释表型变异的7.17%~20.83%; 16个与最高灌浆速率相关, 可解释表型变异的6.31%~15.95%; 21个与千粒重相关, 可解释表型变异的4.36%~16.80%。另外, 在1A、1B、2A、3B、4D、6D和7D染色体上发现10个涉及“一因多效”或紧密连锁位点的基因组区段, 有助于了解籽粒灌浆和籽粒产量相关性状的遗传基础。     

Evaluation of the effects of five QTL regions on Fusarium head blight resistance and agronomic traits in spring wheat (Triticum aestivum L.)

Fusarium head blight (FHB) is an important disease of wheat (Triticum aestivum L.). The aim of this study was to determine the effects of quantitative trait locus (QTL) regions for resistance to FHB and estimate their effects on reducing FHB damage to wheat in Hokkaido, northern Japan. We examined 233 F₁-derived doubled-haploid (DH) lines from a cross between ‘Kukeiharu 14’ and ‘Sumai 3’ to determine their reaction to FHB during two seasons under field conditions. The DH lines were genotyped at five known FHB-resistance QTL regions (on chromosomes 3BS, 5AS, 6BS, 2DL and 4BS) by using SSR markers. ‘Sumai 3’ alleles at the QTLs at 3BS and 5AS effectively reduced FHB damage in the environment of Hokkaido, indicating that these QTLs will be useful for breeding spring wheat cultivars suitable for Hokkaido. Some of the QTL regions influenced agronomic traits: ‘Sumai 3’ alleles at the 4BS and 5AS QTLs significantly increased stem length and spike length, that at the 2DL QTL significantly decreased grain weight, and that at the 6BS QTL significantly delayed heading, indicating pleiotropic or linkage effects between these agronomic traits and FHB resistance.     

利用高密度SNP 遗传图谱定位小麦穗部性状基因

小麦穗部性状之间相关性密切, 其中穗粒数和千粒重是重要的产量构成要素, 挖掘与穗部性状相关联的基因位点对分子标记辅助育种及解释基因效应具有重要意义。本研究以RIL群体(山农01-35×藁城9411) 173个F_8:9株系为材料, 利用90 k小麦SNP基因芯片、DArT芯片技术及传统的分子标记技术构建的高密度遗传图谱, 在5个环境下进行穗部相关性状QTL定位。检测到位于1B、4B、5B、6A染色体上7个控制千粒重的加性QTL, 解释表型变异率6.00%~36.30%, 加性效应均来自大粒母本山农01-35; 检测到8个控制穗长的加性QTL, 解释表型变异率14.34%~25.44%; 3个控制穗粒数的加性QTL; 5个控制可育小穗数的加性QTL; 3个控制不育小穗数的加性QTL, 贡献率为8.70%~37.70%; 4个控制总小穗数的加性QTL; 6个控制小穗密度的加性QTL。通过基因型与环境互作分析, 检测到32个加性QTL, 解释表型变异率0.05%~1.05%。在4B染色体区段EX_C101685–RAC875_C27536检测到控制粒重、穗长、穗粒数、可育小穗数、不育小穗数、总小穗数的一因多效QTL,其贡献率为5.40%~37.70%, 该位点在多个环境中被检测到, 是稳定主效QTL。在6A染色体wPt-0959-TaGw2-CAPS区间上检测到控制粒重、总小穗数的QTL。研究结果为穗部性状的分子标记开发、基因精细定位和功能基因克隆奠定了基础。     

Simple sequence repeat markers reveal multiple loci governing grain-size variations in a japonica rice (Oryza sativa L.) mutant induced by cosmic radiation during space flight

Quantitative trait locus (QTL) for grain size traits that include grain length (GL), grain width (GW), grain thickness (GT) as well as thousand grain weight (TGW) were identified using F₂ population derived from a cross between a japonica cultivar Nongken58 and its large grain-sized mutant, ‘Dali’, which was selected in SP₂ generation of plants from Nongken58 seeds exposed to cosmic radiation upon space-flight, and then advanced it over eight successive generations by bagging the panicles to ensure self pollination. ‘Dali’ had similar GW and GT but 4.8 mm longer in GL, and 18.1 g heavier in TGW than those of Nongken58. Seven main-effect QTLs (M-QTLs) were identified for the grain size and weight traits. Among them, three M-QTLs, QGs3a and QGs3b for both GL and TGW, and QGs5 for GW, GT and TGW, which had strong additive effects on grain shape and grain weight, were validated in the two F₂ plant-derived F₃ populations. The three M-QTLs were found to be non-allelic to the cloned genes GS3, GL3.1, qSW5 and QGs5 by comparative mapping. However, there was only one pair of digenic epistasis involving QGs3b for TGW detected in this population. Interestingly, homozygous ‘Dali’ alleles at the QGs3a, QGs3b and QGs5 showed significant increase in the grain size and weight, suggesting these novel alleles of ‘Dali’ at the above three loci may be a very useful for marker-assisted improvement of grain quality for japonica cultivars.     

Quantitative trait loci for grain-quality traits across a rice F2 population and backcross inbred lines

Improving grain-quality is an important goal in rice breeding programs. One vital step is to find major quantitative trait loci (QTLs) for quality related traits and then investigate the relationships among them. We crossed ‘N22’, an indica variety with good appearance but low grain weight, to a japonica variety, ‘Nanjing35’, with superior grain yield but poor appearance. This enabled us to construct an F₂ population and a set of backcross inbred lines (BILs) for QTL-mapping for the traits related grain appearance. In all, 37 QTLs were identified for grain length (GL), grain width (GW), grain thickness (GT), thousand-grain weight (TGW), and the percentage of grains with chalkiness (PGWC). Of these, 17 QTLs detected from 184 plants in the F₂ population explained 4.97–27.26 % of the phenotypic variance, another 20 QTLs were identified using BILs from 2009 to 2010. Quantitative trait loci for major effects were detected in different populations and across years. A new QTL hot spot (marker interval RM504–RM520) was found on Chromosome 3, which harbored QTLs for GL, GW, GT, and TGW. Among our five examined traits, grain shape was significantly correlated with TGW and PGWC. The PGWC values of two heavier grains BILs, L93, and L145 are much lower than Nanjiing35, the analysis of genotype showed that this greater weight may due to the locus for GL occurring within RM504–RM520 on Chromosome 3. Therefore, those two lines will allow us to develop a long-grain high-yield rice variety with less chalkiness.     

Comparison of quantitative trait loci for 1,000-grain weight and spikelets per panicle across three connected rice populations

The ability to detect quantitative trait loci (QTLs) in a bi-allelic population is often limited. The power of QTL detection and identification of the most beneficial allele at each QTL could be greatly improved by comparing QTLs among different populations derived from connecting multi-parents. In this study, three sets of connected recombinant inbred lines (RILs) derived from the crosses between Zhenshan 97 and Minghui 63 (PZM), Zhenshan 97 and Teqing (PZT), and Minghui 63 and Teqing (PMT), respectively, were used. QTL analyses for the number of spikelets per panicle (SPP) and 1,000-grain weight (TGW) were performed in PZT, and five SPP QTLs on chromosomes 1, 6, and 7 and two TGW QTLs on chromosome 1 were detected. QTL for SPP was also identified in PMT, and six QTLs were detected on chromosomes 1, 2, 3, 6, and 7 in this population. In an earlier study, we identified five SPP QTLs and four TGW QTLs in PMT and nine TGW QTLs in PZM. Comparison of the QTL mapping results of these two studies showed that one QTL was common to the three populations, 11 QTLs were detected in two populations, and six QTLs were found in only one population. Comparison of genetic effect and the action direction of the QTLs detected in the three populations showed that additive effects of QTLs estimated in different populations were also expressed additively among three parental alleles. Additive effects of SPP7a estimated in three near-isogenic line F₂ populations supported this finding. Based on these results, we suggest that pyramiding the most beneficial alleles among the three parents could efficiently improve rice yield.