High density genetic map and quantitative trait loci(QTLs) associated with petal number and flower diameter identified in tetraploid rose

Rose is one of the most important ornamental and economic plants in the world. Modern rose cultivars are primarily tetraploid, and during meiosis, they may exhibit double reduction or preferential chromosome pairing. Therefore, the construction of a high density genetic map of tetraploid rose is both challenging and instructive. In this study, a tetraploid rose population was used to conduct a genetic analysis using genome sequencing. A total of 17 382 single nucleotide polymorphism(SNP) markers were selected from 2 308 042 detected SNPs. Combined with 440 previously developed simple sequence repeats(SSR) and amplified fragment length polymorphism(AFLP) markers, a marker dosage of 6 885 high quality markers was successfully assigned by GATK software in the tetraploid model. These markers were used in the construction of a high density genetic map, containing the expected seven linkage groups with 6 842 markers, a total map length of 1 158.9 c M, and an average inter-marker distance of 0.18 c M. Quantitative trait locus(QTL) analysis was subsequently performed to characterize the genetic architecture of petal number and flower diameter. One major QTL(qpnum-3-1) was detected for petal number in three consecutive years, which explained 20.18–22.11% of the variation in petal number. Four QTLs were detected for flower diameter; the main locus, qfdia-2-2, was identified in two consecutive years. Our results will benefit the molecular marker-assisted breeding of modern rose cultivars. In addition, this study provides a guide for the genetic and QTL analysis of autotetraploid plants using sequencing-based genotyping methods.     

Identification of QTLs for Starch Content in Sweetpotato （Ipomoea batatas （L.） Lam.）

Sweetpotato （Ipomoea batatas （L.） Lam.） breeding is challenging due to its genetic complexity. In the present study, interval mapping （IM） and multiple quantitative trait locus （QTL） model （MQM） analysis were used to identify QTLs for starch content with a mapping population consisting of 202 F1 individuals of a cross between Xushu 18, a cultivar susceptible to stem nematodes, with high yield and moderate starch, and Xu 781, which is resistant to stem nematodes, has low yield and high starch content. Six QTLs for starch content were mapped on six linkage groups of the Xu 781 map, explaining 9.1-38.8% of the variation. Especially, one of them, DMFN 4, accounted for 38.8% of starch content variation, which is the QTL that explains the highest phenotypic variation detected to date in sweetpotato. All of the six QTLs had a positive effect on the variation of the starch content, which indicated the inheritance derived from the parent Xu 781. Two QTLs for starch content were detected on two linkage groups of the Xushu 18 map, explaining 14.3 and 16.1% of the variation, respectively. They had a negative effect on the variation, indicating the inheritance derived from Xu 781. Seven of eight QTLs were co-localized with a single marker. This is the first report on the development of QTLs co-localized with a single marker in sweetpotato. These QTLs and their co-localized markers may be used in marker-assisted breeding for the starch content of sweetpotato.     

Identification of QTLs for Starch Content in Sweetpotato(Ipomoea batatas(L.) Lam.)

Sweetpotato（Ipomoea batatas（L.）Lam.）breeding is challenging due to its genetic complexity.In the present study,interval mapping（IM）and multiple quantitative trait locus（QTL）model（MQM）analysis were used to identify QTLs for starch content with a mapping population consisting of 202 F1 individuals of a cross between Xushu 18,a cultivar susceptible to stem nematodes,with high yield and moderate starch,and Xu 781,which is resistant to stem nematodes,has low yield and high starch content.Six QTLs for starch content were mapped on six linkage groups of the Xu 781 map,explaining 9.1-38.8%of the variation.Especially,one of them,DMFN₄,accounted for 38.8%of starch content variation,which is the QTL that explains the highest phenotypic variation detected to date in sweetpotato.All of the six QTLs had a positive effect on the variation of the starch content,which indicated the inheritance derived from the parent Xu 781.Two QTLs for starch content were detected on two linkage groups of the Xushu 18 map,explaining 14.3 and 16.1%of the variation,respectively.They had a negative effect on the variation,indicating the inheritance derived from Xu 781.Seven of eight QTLs were co-localized with a single marker.This is the first report on the development of QTLs co-localized with a single marker in sweetpotato.These QTLs and their co-localized markers may be used in marker-assisted breeding for the starch content of sweetpotato.     

QTL Mapping of Chlorophyll Contents in Rice

The objectives of this study were to investigate the genetic factors controlling the chlorophyll content of rice leaf using QTL analysis. A linkage map consisting of 207 DNA markers was constructed by using 247 recombinant inbred lines (RILs) derived from an indica-indica rice cross of Zhenshan97B×Milyang 46. In 2002 and 2003, the contents of chlorophyll a and b of the parents and the 247 RILs were measured on the top first leaf, top second leaf, and top third leaf, respectively. The software QTLMapper 1.6 was used to detect quantitative trait loci (QTLs), additive by environment (AE) interactions, and epistatic by environment (AAE) interactions. A total of eight QTLs in four intervals were detected to have significant additive effects on chlorophyll a and b contents at different leaf positions, with 1.96-9.77% of phenotypic variation explained by a single QTL, and two QTLs with significant AE interactions were detected. Epistasis analysis detected nine significant additive-by-additive interactions on chlorophyll a and b contents, and one pair of QTLs with significant AAE interactions was detected. On comparison with QTLs for yield traits detected in the same population, it was found in many cases that the QTLs for chlorophyll a and b contents and those for yield traits were located in the same chromosome intervals.     

Construction of Genetic Linkage Map Based on SSR Markers in Peanut （Arachis hypogaea L.）

Molecular genetic maps of crop species can be used in a variety of ways in breeding and genomic research such as identification and mapping of genes and quantitative trait loci （QTLs） for morphological, physiological and economic traits of crop species. However, a comprehensive genetic linkage map for cultivated peanut has not yet been developed due to the extremely low frequency of DNA polymorphism in cultivated peanut. In this study, 142 recombinant inbred lines （RILs） derived from a cross between Yueyou 13 and Zhenzhuhei were used as mapping population in peanut （Arachis hypogaea L.）. A total 652 pairs of genomic-SSR primer and 392 pairs of EST-SSR primer were used to detect the polymorphisms between the two parents. 141 SSR primer pairs, 127 genomic-SSR and 14 EST-SSR ones, which can be used to detect polymorphisms between the two parents, were selected to analyze the RILs population. Thus, a linkage genetic map which consists of 131 SSR loci in 20 linkage groups, with a coverage of 679 cM and an average of 6.12 cM of inter-maker distance was constructed. The putative functions of 12 EST-SSR markers located on the map were analyzed. Eleven showed homology to gene sequences deposited in GenBank. This is the first report of construction of a comprehensive genetic map with SSR markers in peanut （Arachis hypogaea L.）. The map presented here will provide a genetic framework for mapping the qualitative and quantitative trait in peanut.     

Identification of QTLs Underlying Folate Content in Milled Rice

Understanding the genetic mechanism underlying folate biosynthesis and accumulation in rice would be beneficial for breeding high folate content varieties as a cost-effective approach to addressing widespread folate deficiency in developing countries. In this study, the inheritance of rice grain folate content was investigated in the Lemont/Teqing recombinant inbred lines and the Koshihikari/Kasalath//Koshihikari backcross inbred lines. 264 F12 recombinant inbred lines（RILs） and 182 BC1F10 backcross inbred lines（BILs） with their parents planted in randomized complete blocks with two replicates in 2010, and RILs harvested in 2008 were used for QTL detection using inclusive composite interval mapping（ICIM） method. In the RIL population, two QTLs, denoted by qQTF-3-1 and qQTF-3-2（QTF, quantitative total folate）, explaining 7.8% and 11.1-15.8% of the folate content variation were detected in one or two years, respectively. In the BIL population, a QTL, denoted by qQTF-3-3, was detected, explaining 25.3% of the variation in folate content. All the positive alleles for higher folate content were from the high-folate parents, i.e., Teqing and Kasalath. The known putative folate biosynthesis genes do not underlie the QTLs detected in this study and therefore may be novel loci affecting folate content in milled rice. QTLs identified in this study have potential value for marker assisted breeding for high-folate rice variety.     

QTL Mapping of Isoflavone,Oil and Protein Contents in Soybean （Glycine max L. Merr.）

Soybean （Glycine max L. Merr.） is the world＇s foremost source of edible plant oil and proteins, meantime, the biologically active secondary metabolites such as saponins and isoflavones are benefit to human health. The objective of this study was to identify quantitative trait loci （QTL） and epistatic interactions associated with isoflavone, protein, and oil contents in soybean seeds. An F13 recombinant inbred line （RIL） comprising 474 lines was derived from a cross between Jindou 23 and Huibuzhi cultivars. SSR technique was employed for mapping of the QTLs. The QTLs for isoflavone, protein, and oil contents were analyzed and 23 QTLs were detected based on the constructed linkage map. Six QTLs for isoflavone content were localized in linkage groups J, N, D2, and G, eleven QTLs for oil content were localized in the linkage groups A1, A2, B2, C2, and D2, and six QTLs for protein content were localized in linkage groups B2, C2, G, and H1. The correlative analysis demonstrated that the isoflavone content had significant correlation with protein content, while significantly negative correlations was existed between oil and protein content, and significantly positive correlations was existed between protein and oil content. All these findings have laid an important basis for the marker assisted breeding in soybean. The phenotypic correlations of quantitative traits may be resulted from the correlation of the QTL controlling those traits.     

Identification of QTLs Related to Bolting in Brassica rapa ssp. pekinensis （syn. Brassica campestris ssp. pekinensis）

A genetic linkage map of Brassica rapa ssp. pekinensis was constructed with 186 AFLP （amplified fragment length polymorphism） markers by using a doubled-haploid （DH） population with 183 individuals. The individuals were derived from F1 which was developed by crossing a bolting resistant DH line Y-177-12 and an easy bolting DH line Y195-93a. AFLPs were generated by the use of restriction enzymes EcoR Ⅰ and Mse Ⅰ . The segregation of each marker and linkage was analyzed by using JoinMap version 3.0. Mapped markers were aligned in ten linkage groups which covered 887.8 cM with an average marker interval of 4.47 cM. Markers showing skewed segregation ratio were clustered in six LGs. Quantitative trait loci （QTL） were mapped for bolting resistance by using MAPQTL 4.0 package. Four QTLs explaining from 7.0 to 9.4% of the total variation were detected, all of them increase bolting resistance. These mapped QTLs could be used to develop a marker assisted selection programme for bolting resistance breeding.     

QTL Mapping for Dough Mixing Characteristics in a Recombinant Inbred Population Derived from a Waxy×Strong Gluten Wheat （Triticum aestivum L.）

Protein and starch are the most important traits in determining processing quality in wheat. In order to understand the genetic basis of the influence of Waxy protein (Wx) and high molecular weight gluten subunit (HMW-GS) on processing quality, 256 recombinant inbred lines (RILs) derived from the cross of waxy wheat Nuomai 1 and Gaocheng 8901 were used as mapping population. DArT (diversity arrays technology), SSR (simple sequence repeat), HMW-GS, and Wx markers were used to construct the molecular genetic linkage map. QTLs for mixing peak time (MPT), mixing peak value (MPV), mixing peak width (MPW), and mixing peak integral (MPI) of Mixograph parameters were evaluated in three different environments. The genetic map comprised 498 markers, including 479 DArT, 14 SSR, 2 HMW-GS, and 3 Wx protein markers, covering 4 229.7 cM with an average distance of 9.77 cM. These markers were identified on 21 chromosomes. Eighteen additive QTLs were detected in three different environments, which were distributed on chromosomes 1A, 1B, 1D, 4A, 6A, and 7D. QMPT-1D.1 and QMPT-1D.2 were close to the Glu-D1 marker accounting for 35.2, 22.22 and 36.57% of the phenotypic variance in three environments, respectively. QMPV-1D and QMPV-4A were detected in all environments, and QMPV-4A was the nearest to Wx-B1. One minor QTL, QMPI-1A, was detected under three environments with the genetic distances of 0.9 cM from the nearest marker Glu-A1, explaining from 5.31 to 6.67% of the phenotypic variance. Three pairs of epistatic QTLs were identified on chromosomes 2D and 4A. Therefore, this genetic map is very important and useful for quality trait related QTL mapping in wheat. In addition, the finding of several major QTLs, based on the genetic analyses, further suggested the importance of Glu-1 loci on dough mixing characteristics.     

QTL Analysis of the Oil Content and the Hull Content in Brassica napus L.

The QTLs of the oil content and the hull content were analyzed in Brassica napus L. By constructing the linkage map. The F26 RIL population with 188 lines, derived from the cross of GH06 × P147, was used as the mapping population. The SRAP, SSR, AFLP, and TRAP markers were used to construct the linkage map, and the composite interval mapping (CIM) to identify the quantitative trait loci associated with the oil content and the hull content. 300 markers were integrated into 19 linkage groups, covering 1 248.5 cM in total. Seven QTLs were found to be responsible for the oil content with the single contribution to phenotypic variance ranging from 3.73 to 10.46%; four QTLs were found for the hull content with the single contribution to phenotypic variance ranging from 4.89 to 6.84%. The yellow-seeded Brassica napus L. Has the advantage of higher oil content and the hull content has a significant effect on the oil content. In addition, the SRAP marker is good for detecting QTL.     

QTLs for Sugar Content of Stalk in Sweet Sorghum (Sorghum bicolor L.Moench)

High sugar content of sorghum stalk is an important factor in the sorghum silage production. To identify the genomic regions controlling sugar content and to develop molecular markers linked to sugar content in sweet sorghum, we used an Early Folger, and a normal inbred line, N32B, for genetic linkage mapping and quantitative trait locus (QTL) analysis. We constructed a genetic linkage map spanning 983.5 cM based on a total of 327 markers comprising 31 restriction fragment length polymorphism (RFLP) markers, 254 amplified fragment length polymorphism (AFLP) markers, and 42 simple sequence repeat (SSR) markers. In the 20 linkage groups detected, 98.2% of markers aligned to the 10 linkage groups of sorghum.Variations in sugar content at different growth stages and among internodes suggested that the sugar content of middle internodes is stable and suitable for measuring at early dough stage. The broad sense heritability (hB2) of sugar content was 0.64 and 0.62 estimated from the data of F3 families and each parent in 2003 and 2004. We identified one and two QTLs accounting for 22.2 to 25.0% of phenotypic variance using simple interval mapping method in 2003 and 2004, respectively.These two QTLs showed a negative additive effect, and over-dominance effect. A QTL on LG-D was detected in both two years. Above results will be help us to understand the genetic mechanism of sugar content in sorghum and the QTL detected in this study might be useful in the improvement of sugar content by marker-assisted selection.     

Construction of a genetic linkage map and QTL analysis for some leaf traits in pear (Pyrus L.)

The major incompatibility barriers to specific inbred lines and the long generation duration in Pyrus L. may hinder the Pyrus breeding process. A genetic linkage map provides the foundation for quantitative trait loci (QTL) mapping and molecular marker-assisted breeding. In this study, we constructed a genetic map with 145 F₁ populations from a cross of two cultivars, Yali and Jingbaili, using AFLP and SSR markers. The map consisted of 18 linkage groups which included 402 genetic markers and covered 1395.9 cM, with an average genetic distance of 3.8 cM. The interval mapping was used to identify quantitative trait loci associated with four leaf agronomic traits in the F₁ population. The results indicated that four QTLs were associated with leaf length, two QTLs with leaf width, two with leaf length/leaf width, and three with petiole length. The eleven QTLs were associated with 9.9%–48.5% of the phenotypic variation in different traits. It is considered that the map covers almost the whole genome, and molecular markers will be greatly helpful to the related breeding.     

基于EST-SSR标记甘薯连锁图谱构建及淀粉性状的QTL定位

【目的】利用分子标记技术,构建甘薯遗传连锁图谱,并分析甘薯淀粉含量性状的QTL位点,为高淀粉含量甘薯种质资源利用及甘薯分子标记辅助育种提供理论和实践依据。【方法】以高淀粉含量品种万薯5号为母本、低淀粉含量品种商丘52-7为父本建立杂交群体,利用EST-SSR标记,采用"双假测交"策略和运用Join Map4.0软件,分别构建双亲遗传连锁图谱,并结合F1(2012、2013年)群体表型数据采用区间作图法对淀粉含量性状进行QTL检测。【结果】利用1 679对EST-SSR引物筛选出的1 045对多态性引物检测F1群体的标记基因型,获得了1 418个标记位点。分别对上述获得的父母本多态性标记进行遗传连锁分析,在LOD≥5.0情况下,分别构建父母本的连锁遗传图谱。采用642个标记的多态性位点构建母本连锁群74个,其中,215个标记位点位于连锁图谱上,占标记多态性位点总数的33.5%。每个连锁群上有2—11个标记位点,连锁群长度在0.6—129.4 cM,图谱总长为3 826.07 c M,标记间平均距离为17.80 c M。属于父本的776个标记位点构建了80个连锁群,共有252个标记位点构建在连锁图谱上,占标记总数的32.5%,每个连锁群上有2—24个标记位点,连锁群长度在2.0—156.8 c M,图谱总长为3 955.0 cM,标记间平均距离为15.7 c M。以F1杂交群体构建的遗传连锁图谱,结合2012年、2013年2个环境,利用QTL作图软件MapQTL5.0,采用区间作图法进行分析,共检测到17个与淀粉含量性状相关的QTL,贡献率在8.4%—40.5%。其中qWsc-1、qWsc-2、qWsc-3 3个QTL位于母本万薯5号连锁群上,且在2年环境中均可检测到;14个QTL位于父本商丘52-7连锁群上,qSsc-1、qSsc-2、qSsc-3、qSsc-4、qSsc-8、qSsc-10、qSsc-11、qSsc-12是在2个环境均检测到的QTL。qSsc-5、qSsc-6、qSsc-7、qSsc-9、qSsc-13、qSsc-14是只在1个环境检测到的QTL。标记GDAAS0603在双亲中和2个环境中均同时检测到,这些环境稳定QTL可用于分子标记辅助选择。【结论】分别构建了亲本EST-SSR分子标记连锁群图谱,丰富了构建甘薯图谱的标记类型,定位了17个与淀粉含量相关的QTL位点。     

G.41×新疆野苹果树体生长性状的QTL精细定位及候选基因预测

【目的】矮化砧木可以诱导地上部接穗品种矮化,实现苹果的丰产和稳产。因此,深入挖掘调控苹果树体生长的基因,可以改善苹果树形,加强苹果园地的管理方便性。【方法】以矮化苹果砧木‘G.41’和乔化苹果砧木新疆野苹果(Malus sieversii)为亲本构建的188株F1代分离群体为试材,并于每个分离群体植株上嫁接‘富士冠军’。基于SSR标记技术,采用Join Map 4.0作图软件构建苹果遗传连锁图,应用Map QTL 5.0作图软件,结合后代群体的表型数据,对接穗高度和接穗横截面积生长性状进行初步QTL定位。结合苹果基因组序列信息,利用Primer5.0软件进行新SSR标记开发,并对初步定位区域进行精细定位及候选基因预测。【结果】该研究从361对SSR引物中筛选出108对在亲本之间表现出多态的SSR引物,多态率为29.9%。其中95对引物用于遗传连锁图谱构建,并在5号连锁群上初步检测出4个与植株生长性状相关的QTL位点,与接穗高度、接穗横截面积性状紧密连锁的两个标记,为L05024和Hi09b04。根据初步定位区域的序列信息,设计了新的SSR引物24对,其中在亲本间表现出多态的有10对,利用该10对引物对5号连锁群重新分析。构建了包含21个SSR标记、总长为86.0 c M的高密度遗传图谱,其平均遗传距离为7.52 c M。通过对接穗高度与接穗横截面积生长相关性状的QTL分析,在SSR标记L05024和Hi09b04之间找到与其相关的QTL位点,其表型贡献率分别为19.2%和51.7%。将该QTL位点与基因组序列对比,发现其物理距离为543 kb,并且该QTL区间包含16个候选基因。推测其中MDP0000323212为与植株生长密切相关的基因。【结论】本研究将砧木诱导矮化基因定位于苹果第5号染色体543 kb区段内,侧翼标记分别为L05024和Hi09b04,其物理距离为4.048—4.591 Mb,并筛选到可能参与调控植株生长的候选基因MDP0000323212。     

鲜食甜玉米南方锈病抗性QTL定位分析

为挖掘新的抗南方锈病基因资源,本研究以甜玉米组合M5×M114的216个F2单株为遗传作图群体,应用BSA方法从500对SSR引物中筛选出2对在F2代抗病和感病DNA池间具有多态性的引物,分别位于4和9号染色体上;在4和9号染色体上重新设计100对SSR引物,构建了包含33个标记位点总长为241.2cM的连锁遗传图,各个标记间的平均距离为7.53cM。结合F2单株对南方锈病的抗性表现,用复合区间作图法在4和9号染色体上共检测到7个显著的南方锈病抗性QTLs,其中:4个QTLs位于4号染色体上,可解释12.1%、7.8%、18.2%和14.9%表型变异;3个位于9号染色体上,分别解释17.0%、13.3%与19.2%的表型变异。研究结果可为抗南方锈病的精细定位、主效基因克隆和抗南方锈病鲜食甜玉米品种选育提供理论依据。     

利用大白菜×芜菁F2群体定位抗根肿病QTL及上位性互作分析

【目的】挖掘和分析芸薹种抗根肿病基因及基因间的互作关系,为芸薹种抗根肿病育种提供理论依据。【方法】以大白菜自交系‘BJN’为母本,芜菁自交系‘Siloga’为父本进行杂交获得F₁。F₁单株自交获得由140个单株构成的F₂群体,用于遗传图谱构建。95个F₂单株及其F₃家系用于抗根肿病（CR）性状的QTL定位和上位性互作分析。利用1 214个分子标记和前人开发的与7个CR连锁的22个标记进行亲本间多态性筛选。根据作图群体的多态性标记基因型,采用JoinMap 4.0作图软件构建遗传连锁图谱。以采自甘蓝型油菜栽培地的根肿菌对2个亲本及其95个F_2：3家系进行根肿病抗性鉴定。根据F₃植株的发病等级,计算F₂单株的平均发病指数（DI）。利用Windows QTL Cartographer 2.5软件,采用复合区间作图法检测抗根肿病QTL。利用基于混合线型模型的QTL Network 2.0软件进行互作关系分析。SPSS 18.0.0软件用于分析F₂群体中QTL连锁标记的基因型与其相应个体平均DI值间的相关性。双因素方差分析方法分析QTL连锁标记的交互作用。通过单因素方差分析,采用最小显著差异法和q检验（SNK）多重比较QTL紧密连锁标记（sau_um026和BrID90197)组合成的9种基因型在根肿病抗性上的差异。【结果】抗病性鉴定表明‘Siloga’对根肿菌表现为抗病,而‘BJN’表现为感病。F₂群体中根肿病发病指数呈偏正态分布,表明根肿病抗性表现为由主效基因存在的多基因控制的数量性状。利用检测到的261个多态性标记构建出一个包含222个标记和10条连锁群的芸薹种遗传图谱。该图谱总长度为1 152.6 cM,定位了与3个CR连锁的5个标记,覆盖了大白菜参考基因组的88.6%。通过QTL定位共检测到源于‘Siloga’的2个QTL位点,分别位于A3连锁群的主效QTL（qPbBa3.1）和A8连锁群的微效QTL（qPbBa8.1）。qPbBa3.1和qPbBa8.1的贡献率分别为19.02%和7.82%。qPbBa3.1区域内包含有抗根肿病基因CRa或CRb,而qPbBa8.1与Crr1毗邻。此外,检测到qPbBa3.1和qPbBa8.1间的上位性互作,互作效应为加性×加性,贡献率为6.58%。双因素方差分析表明,sau_um026与BrID90197间存在极显著差异（P=7.22×10^-5）,进一步验证了qPbBa3.1和qPbBa8.1间的互作效应。单因素方差分析表明,sau_um026和BrID90197的9种基因型间存在显著性差异（P=9.45×10^-10）。多重比较结果表明,qPbBa3.1为抗病亲本‘Siloga’基因型的个体抗病性显著高于其他基因型,杂合基因型的高于感病亲本‘BJN’基因型,含有qPbBa8.1的个体抗病性得到加强。【结论】芜菁自交系‘Siloga’根肿病抗性受主效qPbBa3.1,微效qPbBa8.1,qPbBa3.1和qPbBa8.1间的加性×加性上位性互作效应的影响。     

油菜株高QTL定位、整合和候选基因鉴定

【目的】通过对油菜株高进行多环境QTL定位并与已报道的油菜株高QTL和植物株高基因分别进行整合和比对分析,揭示油菜株高的遗传结构和候选基因并为其分子改良提供依据。【方法】以油菜优良品种中双11(测序)和No.73290(重测序)衍生的含184个单株的Bna ZNF2群体为试验材料。首先,对Bna ZNF2群体进行基因型分析,利用Joinmap 4.0软件构建了一张含803个分子标记的高密度遗传图谱。其次,对F2:3和F2:4家系进行连续两年(2010—2011)两点(武汉和西宁)田间试验和表型鉴定。然后,利用Bna ZNF2群体的基因型数据和F2:3以及F2:4家系的株高表型数据,采用Win QTLCart 2.5软件的复合区间作图法进行QTL检测。最后,利用元分析的方法采用Bio Mercator软件对不同环境中检测到的株高QTL进行整合。【结果】对两年两点环境下分别检测到的株高QTL进行整合总共得到5个株高QTL的位点:q PH.A2-1、q PH.A2-2、q PH.C2-1、q PH.C3-1和q PH.C3-2,分布于A2、C2和C3染色体上,解释2.6%—55.6%的表型方差。其中,q PH.A2-1和q PH.A2-2只在武汉检测到,而q PH.C2-1、q PH.C3-1和q PH.C3-2只在西宁检测到。位于C2连锁群的主效QTL-q PH.C2-1只在西宁被重复检测到,而且LOD值、加性效应和贡献率(分别为23.4、-16.0和55.6%)均高于前人报道,是目前发现的效应最大的一个油菜株高QTL。基于油菜基因组物理图谱对本研究和已报道的油菜株高QTL和植物株高基因分别进行整合和比对分析,获得了一个由183个QTL和287个候选基因组成的相对完整的油菜株高遗传结构图。其中,有18个株高QTL簇能在不同研究中被共同检测到,分布在A1、A2、A3、A6、A7、A9、C6和C7染色体上。另外,本研究定位到的5个油菜株高QTL的物理位置和已报道的油菜株高QTL均不重叠,因而是新的株高QTL位点。其中,q PH.A2-2、q PH.C3-1和q PH.C3-2物理区间内总共找到了15个株高同源基因,而11个在2个亲本中存在序列变异,被选作候选基因进行进一步研究。【结论】QTL定位和整合获得5个油菜株高QTL,均为首次报道而且都只在武汉或西宁被检测到。其中位于C2连锁群的主效QTL效应值超过以往报道,表现出极强的QTL与环境的互作。通过与已报道的油菜株高QTL和植物株高基因分别进行整合和比对分析,较为全面地揭示了油菜株高的遗传结构和候选基因,生物信息学分析还鉴定到11个位于本研究定位到的3个株高QTL区间内的候选基因。