旱稻导入系碾磨品质和垩白粒率的QTL定位及其与土壤水分的互作分析

以优质水稻品种越富为遗传背景,具有旱稻品种IRAT109导入片段的271份导入系为材料,在水、旱田2个土壤水分环境下调查糙米率、精米率、整精米率和垩白粒率4个品质性状,研究旱田栽培对稻米品质性状的影响,进行QTL定位及基因型与环境的互作分析。结果表明,整精米率和垩白粒率易受土壤水分环境的影响,糙米率和精米率相对稳定。适当水分胁迫能提高稻米的整精米率,减少垩白粒率。利用混合线性模型,水、旱田条件下共检测到4个品质性状的10个加性QTL和2对上位性互作QTL,分别位于第3、4、7、8和9染色体。3个加性QTL (qMR9、qHMR7和qHMR9)和一对上位性互作QTL (qHMR3~qHMR9)的贡献率大于10%。7个QTL与前人研究结果相一致。第4染色体RM1112~RM1272和第9染色体RM1189~RM410是QTL集中分布的区域。根据不同性状对干旱胁迫的反应特点,分别选择水、旱田条件下贡献率大、稳定的QTL或者具有旱田特异性的QTL,进行标记辅助聚合育种是培育抗旱、优质稻的一个有效途径。     

多种环境下大豆单株粒重QTL的定位与互作分析

定位大豆单株粒重QTL、分析QTL间的上位效应及QTL与环境互作效应, 有利于大豆单株粒重遗传机理的深入研究。利用147个F_2:14~F_2:18 RIL群体, 5年2点多环境下以CIM和MIM方法同时定位大豆单株粒重QTL, 检测到17个控制单株粒重的QTL, 分别位于D1a、B1、B2、C2、F、G和A1连锁群上, 贡献率为6.0%~47.9%;用2种方法同时检测到3个QTL, 即qSWPP-DIa-3、qSWPP-F-1和qSWPP-D1a-5, 贡献率为6.3%~38.3%;2年以上同时检测到4个QTL, 即qSWPP-DIa-1、qSWPP-DIa-2、qSWPP-B1-1和qSWPP-G-1, 贡献率为8.1%~47.9%;利用QTLMapper分析QE互作效应和QTL间上位效应, 7种环境下的数据联合分析得到1个QE互作QTL和4对上位效应QTL, 贡献率和加性效应都较小。在分子标记辅助育种中应该同时考虑主效QTL及各微效QTL之间的互作。     

稻米垩白性状的QTL检测、上位性及环境效应分析

水稻的垩白性状是当前限制中国稻米品质提升的最主要因素。研究垩白形成机理及遗传特性,将有利于提高育种中垩白性状的改良效率。本课题组先前构建了广陆矮4号/佳辐占重组自交系(GJ RIL)及遗传图谱。本研究连续2年在上杭县和龙海市两地共种植6季GJ RILs,据各季垩白性状表型数据进行遗传分析,结合遗传图谱进行QTL定位、上位性分析和环境效应分析。遗传分析发现垩白粒率和垩白度呈偏态分布,推测垩白性状受主效基因与微效基因共同影响。QTL定位中,垩白粒率获得3个QTLs,qPGWC2、qPGWC4和qPGWC5,遗传贡献率分别为2.84%、3.74%和14.09%;垩白度获得3个QTLs,qDEC1、qDEC4和qDEC5,遗传贡献率分别为2.96%、4.88%和7.79%。上位性分析中,垩白粒率和垩白度分别获得7对和5对上位性QTLs,贡献率为0.23%~3.55%。RM307~RM518区间内同时检测到垩白粒率和垩白度的QTLs,并参与了垩白粒率和垩白度的上位性互作。RM598~RM5140区间内也同时检测到垩白粒率和垩白度的QTLs,也参与了垩白度的上位性互作。环境效应分析发现,垩白度的3个QTLs及eqDEC10和eqDEC9这对上位性QTLs均与2010年早季龙海种植环境发生显著或极显著的互作效应。     

籼型恢复系导入群体株高的遗传剖析

以将大面积应用的籼型恢复系蜀恢527和明恢86为轮回亲本,以另外3个水稻品种作为导入亲本,在回交种植的BC2F2世代,按照产量综合性状较优的原则,在6个群体选择单株,得到6个籼型恢复系的产量选择导入群体。通过对6个群体在合肥和海南试验点的田间株高性状的单双向方差分析,总共检测到0.01显著水平的12个主效QTL和145对上位互作,主效QTL和上位互作在不同群体之间表现出了遗传稳定性和群体和环境的特异性。一些位点通过上位互作和遗传连锁相互联系起来,构成了控制株高的协同互作的网络,又发现主效QTL与很多的参与上位互作位点紧密连锁或者直接参与了很多的上位互作,由此推断主效QTL是由于位点上位互作效应使之凸显出来。最后提出了改良水稻的株高提高水稻产量的策略。     

多环境下水稻DH群体剑叶长度的QTL分析

种植由籼稻品种和粳稻品种杂交衍生的DH群体,连续4年测定剑叶长度,运用基于混合模型的复合区间作图法,定位其QTL及上位性互作,估算遗传主效应和环境互作效应。结果表明,全部18个QTL都参与了上位性的形成,其中3个没有自身的遗传效应,但参与了3对上位性互作,这是传统方法不能发现的。另外,一个QTL可与多个QTL发生互作,这可能预示着存在更高阶互作。QTL与上位性互作可以具有不受环境影响而稳定表达的效应,以及与环境的互作效应。有些QTL与环境的互作效应可以在多环境下被检测到,但却不具有主效应,这种QTL可能易受环境因子的影响。QTL与环境的互作效应为随机效应,一个QTL或一对上位性与环境的互作效应总和理论上应等于零,否则会影响对遗传效应的估算,因此多环境下估算的遗传效应更可靠。     

大豆叶片性状和叶绿素含量QTL间的上位性和环境互作效应

以丰产性好、抗旱力强的栽培大豆晋豆23为母本,山西农家品种半野生大豆灰布支黑豆为父本杂交衍生的447个RIL作为供试群体。将亲本及447个家系分别于2011、2012和2013年采用随机试验种植,按照标准测量叶长、叶宽和叶柄长3个性状,并于2012年8月1日和8月8日和2013年8月2日和8月9日各测量1次叶绿素含量。采用QTLNETwork 2.0混合线性模型分析方法和主基因+多基因混合遗传分离分析法,对大豆叶片性状和叶绿素含量进行遗传分析和QTL间的上位性和环境互作效应研究。结果表明,叶长受2对加性-加性×加性上位性混合主基因控制,叶宽受3对等效主基因控制,叶柄长受4对加性-加性×加性上位性主基因控制,叶绿素含量受4对加性主基因控制;检测到10个与叶长、叶宽、叶柄长和叶绿素含量相关的QTL,分别位于A1、A2、C2、H_1、L和O染色体。其中2个叶长QTL分别位于C2和L染色体,是2对加性×加性上位互作效应及环境互作效应QTL;3个叶宽加性与环境互作QTL分别位于A2、C2和O染色体;2个叶柄长QTL分别位于L和O染色体;3个叶绿素含量QTL分别位于A1、C2和H_1染色体。叶片性状和叶绿素含量的遗传机制较复杂,加性效应、加性×加性上位互作效应及环境互作效应是大豆叶片性状和叶绿素含量的重要遗传基础。建议大豆分子标记辅助育种中,一方面要考虑起主要作用的QTL,另一方面要注重上位性QTL的影响,这对于性状的遗传和稳定表达具有积极的意义。     

大豆籽粒硬实加性和上位性QTL定位

硬实是植物种子的普遍特性, 是影响大豆种子发芽率、生存能力及储存期的重要数量性状, 同时影响着大豆的加工品质。本实验通过对大豆籽粒硬实性状的加性和上位性互作QTL (quantitative trait locus)分析, 明确控制大豆籽粒硬实的重要位点及效应, 旨在为进一步解析硬实性状复杂的遗传机制提供理论依据。以冀豆12和地方品种黑豆(ZDD03651)杂交构建的包含186个家系的F_6:8和F_6:9重组自交系群体为材料, 采用WinQTL Cartographer V. 2.5的复合区间作图法(composite interval mapping, CIM)定位不同年份的籽粒硬实性状相关的加性QTL, 同时采用IciMapping 4.1软件中的完备区间作图法(inclusive composite interval mapping, ICIM)检测籽粒硬实性状的加性及上位性QTL。共检测到3个籽粒硬实性状相关的加性QTL, 分别位于第2、第6和第14染色体, 遗传贡献率范围为5.54%~12.94%。同时检测到4对上位性互作QTL, 分别位于第2、第6、第9、第12和第14染色体, 可解释的表型变异率为2.53%~3.47%。同时检测到籽粒硬实性状加性及上位性互作QTL, 且上位性互作多发生在主效QTL间或主效QTL与非主效QTL间, 表明上位性互作效应在大豆籽粒硬实性状的遗传基础中具有重要的作用。     

大豆产量及主要农艺性状QTL的上位性互作和环境互作分析

以栽培大豆晋豆23为母本,半野生大豆灰布支黑豆ZDD2315为父本杂交衍生的F2:15和F2:16的447个RIL家系为遗传群体,绘制SSR遗传图谱,采用混合线性模型方法,对2年大豆小区产量及主要农艺性状进行加性QTL、加性×加性上位互作及环境互作分析。结果检测到9个与小区产量、茎粗、有效分枝、主茎节数、株高、结荚高度相关的QTL,分别位于J_2、I、M连锁群上,其中小区产量、茎粗、株高、有效分枝和主茎节数QTL的加性效应为正值,说明增加这些性状的等位基因来源于母本晋豆23。同时,检测到7对影响小区产量、茎粗、株高和结荚高度的加性×加性上位互作效应及环境互作效应的QTL,共发现14个与环境存在互作的QTL。上位效应和QE互作效应对大豆小区产量及主要农艺性状的遗传影响较大。大豆分子标记辅助育种中,既要考虑起主要作用的QTL,又要注重上位性QTL,才有利于性状的稳定表达和遗传。     

玉米穗长上位效应和Q×E互作效应分析

以掖478×丹340的500个F2单株为作图群体,利用混合线性模型的复合区间作图法对397个F2： 3家系在5个生态环境下进行穗长的QTL定位分析.共检测到16个穗长QTL,单个QTL所解释的表型变异在0.15%～6.24%,累计贡献率为47.8%.在16个QTL中有10个与环境发生互作,占62.5%,贡献率在0.48%～3.78%之间.上位性互作检测到4对QTL,未检测到上位性QTL与环境互作.表明穗长受微效多基因的控制,易与环境发生互作,上位性互作在其遗传中起一定作用.     

水稻抽穗期上位效应和QE互作效应的分析

抽穗期是水稻的重要农艺性状,深入了解其遗传效应对水稻育种实践具有重要现实意义。本研究利用基于明恢86×佳辐占、广陆矮×佳辐占两个重组自交系构建的SSR遗传图谱,应用混合线性模型方法对2003年晚季和2005年早季获得的两季水稻抽穗期数据进行QTL定位,并作加性效应、加性×加性上位互作效应及环境互作效应分析。两个群体共检测到10个控制抽穗期的QTL,分别位于1、2、3、6、7和10号染色体上,仅qHD10(广佳重组自交系中为qHD10-1)在两个群体中同时检测到,另检测到11对具有上位效应的互作位点,其中有5个是加性效应显著的QTL。环境互作检测中,发现明佳重组自交系的qHD10和广佳重组自交系的qHD7与环境存在显著互作,贡献率分别为0.34%和2.32%。本研究表明:两群体的抽穗期性状的遗传受环境因素影响较小,特别是明佳组合,较适合作为分子辅助育种的研究材料。     

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.     

水稻抗纹枯病QTL表达的遗传背景及环境效应

利用水稻纹枯病菌强致病菌系RH-9人工接种Lemont导入到特青背景的213个近等基因导入系(TQ-ILs)群体和特青导入到Lemont背景的195个近等基因导入系(LT-ILs)群体,定位和分析了水稻抗纹枯病数量性状座位(quantitative trait loci, QTL)及其表达的环境与遗传背景效应。亲本Lemont对RH-9表现为高度感病,特青表现为中等抗病。人工接种后TQ-ILs群体的相对病斑高度(病斑高度与株高比)呈连续正态分布,LT-IL群体则明显偏向感病亲本Lemont。在不同年份和遗传背景下检测到影响纹枯病相对病斑高度的主效QTL 10个和互作QTL 13个,其中2006年在TQ-IL群体定位到的6个主效QTL在2007年均得到验证,表明这些QTL具有较好年度间的重复性。QSh4是唯一在双向导入系背景下表达的QTL,该位点特青等位基因降低相对病斑高度,提高抗性水平。在TQ-ILs群体中定位到位于第10染色体RM216~RM311区间的QSb10a与在LT-IL群体中定位到的位于相邻区间RM222~RM216的QSb10b的基因作用方向不同,推断这两个QTL存在紧密连锁关系。绝大多数在TQ-IL群体中表达的主效及互作QTL在LT-ILs群体中不表达,表明水稻抗纹枯病QTL具有明显的遗传背景效应。通过比较作图,本研究定位到的其中8个QTL在以往不同群体中同样被检测到,这些主效QTL对通过分子标记辅助选择(marker-assisted selection, MAS)培育水稻抗纹枯病育种可能具有应用价值。研究指出,标记辅助选择在不同遗传背景中能稳定表达的QTL或通过聚合不同抗病QTL是进一步提高水稻纹枯病抗性水平的一个有效途径。     

Comparison and analysis of main effects,epistatic effects,and QTL × environment interactions of QTLs for agronomic traits using DH and RILs populations in rice

Two genetic linkage maps based on doubled haploid (DH) and recombinant inbred lines (RILs) populations, derived from the same indica-japonica cross ‘Samgang × Nagdong’, were constructed to analyze the quantitative trait loci (QTLs) affecting agronomic traits in rice. The segregations of agronomic traits in RILs population showed larger variations than those in DH population. A total of 10 and 12 QTLs were identified on six chromosomes using DH population and seven chromosomes using RILs population, respectively. Three stable QTLs including pl9.1, ph1.1, and gwp11.1 were detected through different years. The percentages of phenotypic variation explained by individual QTLs ranged from 8 to 18% in the DH population and 9 to 33% in the RILs population. Twenty-three epistatic QTLs were identified in the DH population, while 21 epistatic QTLs were detected in the RILs population. Epistatic interactions played an important role in controlling the agronomic traits genetically. Four significant main-effect QTLs were involved in the digenic interactions. Significant interactions between QTLs and environments (QE) were identified in two populations. The QTLs affecting grain weight per panicle (GWP) were more sensitive to the environmental changes. The comparison and QTLs analysis between two populations across different years should help rice breeders to comprehend the genetic mechanisms of quantitative traits and improve breeding programs in marker-assisted selection (MAS).     

Molecular Marker-Assisted Dissection of Quantitative Trait Loci for seven Morphological Traits in Rice (Oryza Sativa L.)

Summary The genetic dissection of morphological traits can helpful to evaluate their potential values as markers for rice genetic improvement. In this study, a RI population derived from a cross from Zhenshan97 and IRAT109 was used to dissect the genetic bases of seven morphological traits such as leaf sheath color (LSC), grain apiculus color (GAC), grain hairiness density (GHD), grain awn length (GAL), ratio of leaf length to width (RLW), leaf erectness (LER) and natural leaf rolling status (NLR). Totally, 26 main-effect QTLs and 22 epistatic QTLs were detected. Of them, 11 main-effect and 3 epistatic QTLs expressed environmental interactions. GAC controlled by a single gene could be regarded as the most useful marker. LSC controlled by two major interacted main-effect QTLs, but with no environmental interaction, is suitable to become morphological marker. LSC will be a very efficient morphological marker for identification of hybrid plants at rice seedling stage when the two major QTLs are introduced into male sterile line and restorer line separately. GHD controlled by a major QTL and a few minor QTLs with comparative low QEIs could also be used as marker. The traits GAL, NLR, RLW and LER, which were controlled by a number of minor effect QTLs and affected by environmental conditions could not be used as marker. But the QTLs with large effects, such as nrl8, can be targeted for corresponding trait improvement through marker-aided selection in rice breeding.     

Dynamic analyses of rice blast resistance for the assessment of genetic and environmental effects

A doubled haploid population was employed to characterize the dynamic changes of the genetic components involved in rice blast resistance, including main-effect quantitative trait loci (QTLs), epistatic QTLs and QTL-by-environment interactions. The study was carried out at three different developmental stages of rice, using natural infection tests over 2 years. The number of main-effect QTLs, epistatic QTLs and their environmental interactions differed across the various measuring stages. One QTL ( d12 ) on chromosome 12 was detected at all stages, whereas most QTLs were active only at one or two stages in the population. These findings suggest that the unstable expression of most QTLs identified for blast resistance was influenced by the developmental status of the plants, epistatic effects between different loci and the environments in which they were grown. These findings demonstrate the complexity of expression of rice blast resistance and have important implications for durable resistance-breeding and map-based cloning of quantitative traits.     

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.     

Identification and multiple comparisons of QTL and epistatic interaction conferring high yield under boron and phosphorus deprivation in Brassica napus

Boron (B) and phosphorus (P) are two essential nutrients for plants. To unravel the genetic basis of B and P efficiency in Brassica napus, quantitative trait locus (QTL) and epistatic interaction analysis for yield and yield-related traits under contrast B and P conditions were performed using two mapping populations across various environments. Main effect QTLs were detected by QTLNetwork and QTL Icimapping (ICIM), and were compared with our previously reported main effect QTLs identified by QTLCartographer. Epistatic QTLs were identified by QTLNetwork, ICIM and Genotype matrix mapping (GMM), and multiple comparisons of main effect QTLs and epistatic QTLs were conducted. For the two mapping populations, 51 main effect QTLs were identified by QTLNetwork, 106 by ICIM. Among them, 35 main effect QTLs were simultaneously identified by three programs. Moreover, 578, 18 and 62 epistatic QTLs were identified by GMM, QTLNetwork and ICIM, respectively. Interestingly, a total of 235 epistatic QTLs identified by GMM were associated with 50 main effect QTLs identified by three programs. However, only nine epistatic QTLs identified by QTLNetwork and ICIM were involved in main effect QTLs. Twenty-two main effect QTLs in the BERIL population overlapped with 20 main effect QTLs for the same traits in the BQDH population, but no main effect QTLs were detected both under P and B stress environments, indicating the genetic differences in B and P homeostasis in B. napus. By in silico mapping, 29 candidate genes were located in the consensus QTL intervals. This study suggested the availability of dissecting genetic basis for complex traits under B/P deficiency by analyzing main effect QTLs and epistatic QTLs using multiple programs across different environments. The robust main effect QTLs and epistatic QTLs associated could be useful in breeding B and P efficient cultivars of B. napus.