稻米粒形的QTL定位及上位性和QE互作分析

利用'广陆矮4号'×'佳辐占'水稻重组自交系构建了SSR标记的遗传图谱.联合2007年和2008年获得的两组稻米粒长(GL)、粒宽(GW)、长宽比(L/W)数据应用混合线性模型方法进行QTL定位,并作加性效应、加性×加性上位互作效应以及加性QTL、上位性QTL与环境的互作效应分析.结果显示;(1)在加性效应分析中两个群体共检测到4个控制粒长的QTL,4个控制粒宽的QTL,5个控制长宽比的QTL,贡献率分别为13.81%、15.36%和 16.29%.(2)在上位互作效应分析中两个群体共检测到2对控制粒长的互作QTL,1对控制粒宽的互作QTL,3对控制长宽比的互作QTL,贡献率分别为5.77%、2.59%和7.42%.(3)环境互作检测中,发现共有13个加性QTL和4对QTL的加性×加性上位性与环境产生了互作效应.结果表明,上位性效应和加性效应都影响稻米粒形遗传,QE互作效应也对粒形有着显著的影响.     

水稻叶片叶绿素和过氧化氢含量的QTL检测及上位性分析

研究水稻叶片叶绿素和过氧化氢含量的遗传规律,对探讨光合代谢产物遗传规律和开展高产育种具有重要指导意义。利用由日本晴／Kasalath∥日本晴的杂交组合衍生的98个回交重组自交家系(BC1F9)所组成的BIL(backcross inbred lines)群体,在第1、2、3和10染色体上分别检测出5个与叶绿素含量相关的QTL和2个影响剑叶过氧化氢含量的QTL,其中位于第1染色体的RFLP标记C86和C813之间的q-Chll对叶绿素含量的影响最为显著,对表型变异的贡献率达22％,其增效基因来自粳稻品种日本晴;同时在该区间检测到1个与剑叶过氧化氢含量相关的QTL:q-H2O2I,对过氧化氢含量的减效基因来自日本晴品种。上位性分析结果显示影响叶绿素含量及过氧化氢含量的非等位QTL之间存在显著的上位性效应。具有上位性效应的QTL分布于第2、6、11和12染色体上,未检测到与q-Chll或q-H2O2I互作的位点。暗示日本晴品种的RFLP标记C86和C813之间存在1个能够提高叶绿素含量,同时又能降低过氧化氢含量的主效QTL,其加性效应显著而不存在上位性效应。     

水稻柱头外露率QTLs定位及其互作分析

以协青早B/密阳46所构建的RIL群体及其相应分子遗传图谱,设置海南和杭州两地遗传试验,应用基于混合线性模型检测QTL主效应、上位性效应和G×E互作效应的遗传分析方法,对水稻柱头外露率(%)进行QTL联合分析．结果表明,该性状明显表现出海南较高(21．83%)而杭州较低(8．35%)的趋势．试验检测到1个主效应QTL(qSE6-1),其LOD值高达28．16,对性状表型的贡献率为14．14%,增效等位基因来自于母本,加性效应为5．10%,不存在显著的GE互作．试验还检测到3对显著的加性×加性双基因互作,上位性互作性效应和贡献率相对较小,且与环境不存在显著的互作．     

芥菜型油菜每角籽粒数QTL 的上位性互作和环境互作分析

【目的】为揭示芥菜型油菜及芸薹属作物每角籽粒数形成的分子机理,提高和改良芥菜型油菜产量和育种工作奠定基础。【方法】研究以包含221个芥菜型油菜株系的重组自交系(recombinant inbred line, RIL)群体为材料,在5个环境条件下对每角籽粒数性状进行加性QTL、加性×加性上位互作及环境互作分析。【结果】(1)共检测到7个与每角籽粒数相关的加性QTL,主要分布在芥菜型油菜A02、A03、A05、A08、B02和B03等染色体上,其加性效应分布在(-11.642 4)～4.524 6之间,其中qSS2-71的加性效应和遗传率均最大,分别达到-11.642 4和14.44%,其余6个加性QTL的加性效应和遗传率均较小;(2)检测到7对影响每角籽粒数的加性×加性QTL上位互作效应及其与环境的互作效应,上位性QTL互作效应值分布在(-4.930 8)～4.193 6之间,7对上位性QTL与不同环境互作的遗传力均接近0;(3)每角籽粒数性状的广义遗传率为80.98%,狭义遗传率为30.98%。【结论】综合分析,芥菜型油菜每角籽粒数受一定环境影响,但控制该性状的加性效应受环境影响较小...     

基于单片段代换系的水稻穗长QTL加性及其上位性效应

穗长是影响水稻（Oryza sativa）产量的重要因子之一,研究水稻穗长QTL间的上位性效应对于发掘水稻产量潜力具有重要意义。该研究以16个单片段代换系（single segment substitution lines,SSSLs）和15个双片段聚合系（double segment pyramiding lines,DSPLs）为材料研究了水稻穗长QTL的加性及上位性效应。以P〈0.01为阈值,共检测到6个穗长QTL和9对基因互作座位。其中2个（Pl-2和Pl-10）是尚未报道的穗长QTL。穗长QTL互作后,一些互作对的上位性效应与单个QTL的作用方式及效应大小各不相同,预示着基因聚合后会产生不同的互作效应。该研究结果对于通过分子聚合育种手段改良穗长具有重要意义。     

水稻籽粒淀粉分支酶活性的遗传分析

用由247个株系组成的珍汕97B/密阳46重组自交系群体及其含207个分子标记的连锁图谱,在2002年和2003年分别测定亲本和重组自交系群体开花后10 d和20 d籽粒的淀粉分支酶的活性,检测到3个控制开花后10 d Q酶活性的主效应QTL(qnantitative trait loci),联合贡献率为10%,其中qQ10-6与环境发生显著的互作;分别检测到5对和2对染色体区间对开花后10 d、20 d Q酶活性的影响具有加性×加性上位性作用,其中开花后10 d的3对染色体区间具有显著的上位性×环境互作效应.由此可见,水稻籽粒Q酶活性相关基因的表达,受到环境因子的极大影响.     

正常与水分胁迫下水稻叶片叶绿素含量的QTL分析

随着分子标记技术的发展,利用不同的遗传群体对叶绿素的分子遗传机理进行了一些探索,定位了一些控制叶绿素含量的数量性状基因座(Quantitative trait loci, QTL)。该研究着眼于当前干旱严重影响农业生产的形势,以水稻重组自交系‘珍汕97B’בIRAT109’ F₉代群体195个株系为材料,在正常与水分胁迫环境下研究叶片叶绿素含量与光合速率的变化及相关性,定位不同水分条件下影响叶绿素含量的QTL,为阐明干旱环境下水稻叶绿素含量的分子遗传机理、分子标记辅助育种和节水抗旱稻培育提供理论基础和依据。研究表明叶绿素含量与光合速率在正常供水下呈极显著正相关(r=0.185 7^**),但在干旱下则表现无关(r=0.076 6)。QTL定位共检测到13个影响叶绿素含量的主效QTL,分别位于第1、2、3、4、5、6、10染色体:其中在干旱处理下检测到6个,其联合贡献率为47.39%;在正常供水下检测到7个,联合贡献率达56.19%。检测到显著互作效应位点16对:其中干旱处理下有4对显著互作,联合贡献率为18.57%;正常供水下有12对显著互作,联合贡献率达38.49%。     

大豆小区产量及其相关性状QTL间的上位性和环境互作效应

以晋豆23栽培大豆(Glycine max)为母本、灰布支黑豆(ZDD2315,半野生大豆)为父本衍生出447个RIL群体,通过构建SSR遗传图谱及利用混合线性模型分析方法,对2年大豆小区产量及主要植物学性状进行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。结果显示,共检测到12个与小区产量、单株粒重、单株茎重、单株粒茎比、有效分枝、主茎节数、株高和结荚高度相关的QTL,分别位于A1、A2、H_1、I、J_2和M连锁群上。其中小区产量、株高、单株粒重、有效分枝和主茎节数均表现为遗传正效应,即增加其性状的等位基因来源于母本晋豆23。同时,检测到11对影响小区产量、单株粒重、单株茎重、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,发现22个QTL与环境存在互作。实验结果表明,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很大。进行大豆分子标记辅助育种时,既要考虑效应起主要作用的QTL,又要注重上位性QTL,这样有利于性状的稳定表达和遗传。     

水稻生物学产量及其构成性状的QTL定位

QTL的加性效应、加性×加性上位性效应及它们与环境的互作效应是数量性状的重要遗传分量.利用IR64/Azucena的125个DH品系为群体,分析了水稻生物学产量及其两个构成性状干草产量和谷粒产量的遗传组成.用基于混合模型的复合区间作图(MCIM)方法进行QTL定位.检测到12个位点有加性主效应,27个位点涉及双位点互作,18个位点存在环境互作.结果表明水稻生物学产量和它的两个构成性状普遍存在上位性效应和QE互作效应.此外,还探讨了性状间相关的遗传基础.发现4个QTLs和一对上位性QTLs可能与生物学产量与干草产量之间的正相关有关.3个QTL可能与干草产量与谷粒产量之间的负相关有关.这些结果可能部分地解释了这3个性状相关的遗传原因.通过对水稻生物学产量及其两个构成性状所定位QTL的分析,加深了对数量性状QTL的认识.首先,QTL的上位性效应和QE互作效应是普遍存在的;其次,QTL的多效性或紧密连锁可能是遗传相关的原因,当QTL对两个性状作用的方向相同时可导致正向遗传相关,反之则为负向遗传相关,当有些QTL表现为同向作用而另一些QTL表现为反向作用时,则可削弱性状间的遗传相关性;第三,复合性状的QTL效应可分解为其组成性状的QTL效应,如果QTL对各组成性状的效应方向相反而相互抵消,可使复合性状的QTL效应不易被检测;第四,加性效应的QTL常参预构成上位性效应,而具有上位性效应的QTL并非都有加性主效应,表明忽略上位性的QTL定位方法会降低检测QTL的功效;最后,鉴别不同类型的QTL效应有利于指导育种实践,选择主效QTL适用于多环境,QE互作QTL适用于特定环境,对上位性QTL应强调选择基因组合而并非单个基因.     

基于单片段代换系的水稻穗长QTL加性及其上位性效应

穗长是影响水稻(Oryza sativa)产量的重要因子之一, 研究水稻穗长QTL间的上位性效应对于发掘水稻产量潜力具有重要意义。该研究以16个单片段代换系(single segment substitution lines, SSSLs)和15个双片段聚合系(double segment pyramiding lines, DSPLs)为材料研究了水稻穗长QTL的加性及上位性效应。以P<0.01为阈值, 共检测到6个穗长QTL和9对基因互作座位。其中2个(Pl-2和Pl-10)是尚未报道的穗长QTL。穗长QTL互作后, 一些互作对的上位性效应与单个QTL的作用方式及效应大小各不相同, 预示着基因聚合后会产生不同的互作效应。该研究结果对于通过分子聚合育种手段改良穗长具有重要意义。     

Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches

A new methodology based on mixed linear models was developed for mapping QTLs with digenic epistasis and QTL×environment (QE) interactions. Reliable estimates of QTL main effects (additive and epistasis effects) can be obtained by the maximum-likelihood estimation method, while QE interaction effects (additive×environment interaction and epistasis×environment interaction) can be predicted by the-best-linear-unbiased-prediction (BLUP) method. Likelihood ratio and t statistics were combined for testing hypotheses about QTL effects and QE interactions. Monte Carlo simulations were conducted for evaluating the unbiasedness, accuracy, and power for parameter estimation in QTL mapping. The results indicated that the mixed-model approaches could provide unbiased estimates for both positions and effects of QTLs, as well as unbiased predicted values for QE interactions. Additionally, the mixed-model approaches also showed high accuracy and power in mapping QTLs with epistatic effects and QE interactions. Based on the models and the methodology, a computer software program (QTLMapper version 1.0) was developed, which is suitable for interval mapping of QTLs with additive, additive×additive epistasis, and their environment interactions. Received: 23 October 1998 / Accepted: 11 May 1999     

QTL for fibre-related traits in grain × sweet sorghum as a tool for the enhancement of sorghum as a biomass crop

Compared to maize and temperate grasses, sorghum has received less attention in terms of improving cell wall components. The objectives of this study were to identify quantitative trait loci (QTL) with main effects, epistatic and pleiotropic effects along with QTL × environment (QE) interactions controlling fibre-related traits in sorghum. Neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), cellulose, hemicellulose, fresh leaf mass, stripped stalk mass, dry stalk mass, fresh biomass and dry biomass were analysed from a population of 188 grain × sweet sorghum recombinant inbred lines. A genetic map consisting of 157 DNA markers was constructed, and QTL were detected using composite interval mapping (CIM). CIM detected more than 5 additive QTL per trait explaining 7.1–24.7% of the phenotypic variation. Abundant co-localization of these QTL was observed across all chromosomes, and the highest cluster was identified on chromosome 6. Searching for candidate genes using the confidence interval of our QTL clusters reveals that these clusters might comprise a set of genes that are tightly linked. Some QTL showed multiple effects; however, the allele for each trait was favouring the parent with the increasing effect. QE interactions were observed for QTL showing multiple effects. Additive × additive interaction was observed for 7 out of 10 traits, indicating the importance of epistatic analysis. However, the phenotypic variation explained by digenic interactions was lower compared to the individual QTL. Our results indicate that various genetic components contribute to fibre-related traits and should be considered during the enhancement of sorghum for lignocellulosic biomass.     

Analysis on additive effects and additive-by-additive epistatic effects of QTLs for yield traits in a recombinant inbred line population of rice

A linkage map consisting of 158 DNA markers were constructed by using a recombinant inbred line (RIL) population derived from the indica-indica rice cross Zhenshan 97B 2 Milyang 46. Quantitative trait loci (QTLs) conditioning grain yield and five yield component traits were determined at the one-locus and two-locus levels, and genotype-by-environment (GE) interactions were analyzed. Thirty-one QTLs were detected to have significant additive effects for yield traits, of which 12 also exhibited significant epistatic effects. Sixteen significant additive-by-additive (AA) interactions were detected, of which nine occurred between QTLs with own additive effects (M_epQTLs), four occurred between QTLs showing epistatic effects only (epQTLs), and three occurred between M_epQTLs and epQTLs. Significant GE interactions were found for six QTLs with additive effects and one AA interaction. Generally, the contributions to the phenotypic variation were higher due to QTL main effects than to epistatic effects. The detection of additive effects and AA effects of a QTL interfered with each other, indicating that the detection of QTLs with main effects, as well as the magnitude and directions of the additive effects, might vary depending on their interactions with other loci.     

QTL mapping for test weight by using F<Subscript><Emphasis Type="Bold">2:3</Emphasis></Subscript> population in maize

Test weight is an important trait in maize breeding. Understanding the genetic mechanism of test weight is important for effective selection of maize test weight improvement. In this study, quantitative trait loci (QTL) for maize test weight were identified. In the years 2007 and 2008, a F_2:3 population along with the parents Chang7-2 and Zheng58 were planted in Zhengzhou, People’s Republic of China. Significant genotypic variation for maize test weight was observed in both years. Based on the genetic map containing 180 polymorphic SSR markers with an average linkage distance of 11.0 cM, QTL for maize test weight were analysed by mixed-model composite interval mapping. Five QTL, including four QTL with only additive effects, were identified on chromosomes 1, 2, 3, 4 and 5, and together explained 25.2% of the phenotypic variation. Seven pairs of epistatic interactions were also detected, involving 11 loci distributed on chromosomes 1, 2, 3, 4, 5 and 7, respectively, which totally contributed 18.2% of the phenotypic variation. However, no significant QTL × environment (Q×E) interaction and epistasis × environment interaction effects were detected. The results showed that besides the additive QTL, epistatic interactions also formed an important genetic basis for test weight in maize.     

Identification of QTL for maize resistance to common smut by using recombinant inbred lines developed from the Chinese hybrid Yuyu22

Common smut in maize, caused by Ustilago maydis, reduces grain yield greatly. Agronomic and chemical approaches to control such diseases are often impractical or ineffective. Resistance breeding could be an efficient approach to minimize the losses caused by common smut. In this study, quantitative trait loci (QTL) for resistance to common smut in maize were identified. In 2005, a recombinant inbred line (RIL) population along with the resistant (Zong 3) and susceptible (87-1) parents were planted in Beijing and Zhengzhou. Significant genotypic variation in resistance to common smut was observed at both locations after artificial inoculation by injecting inoculum into the whorl of plants with a modified hog vaccinator. Basing on a genetic map containing 246 polymorphic SSR markers with an average linkage distance of 9.11 cM, resistance QTL were analysed by composite interval mapping. Six additive-effect QTL associated with resistance to common smut were identified on chromosomes 3 (three QTL), 5 (one QTL) and 8 (two QTL), and explained 3.2% to 12.4% of the phenotypic variation. Among the 6 QTL, 4 showed significant QTL x environment (Q x E) interaction effects, which accounted for 1.2% to 2.5% of the phenotypic variation. Nine pairs of epistatic interactions were also detected, involving 18 loci distributed on all chromosomes except 2, 6 and 10, which contributed 0.8% to 3.0% of the observed phenotypic variation. However, no significant epistasis x environment interactions were detected. In total, additive QTL effects and Q x E interactions explained 38.8% and 8.0% of the phenotypic variation, respectively. Epistatic effects contributed 15% of the phenotypic variation. The results showed that besides the additive QTL, both epistasis and Q x E interactions formed an important genetic basis for the resistance to Ustilago maydis in maize.     

Mapping QTLs for physiological and biochemical traits related to grain yield under control and terminal heat stress conditions in bread wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

In order to detect genomic regions with different effects for some of the physiological and biochemical traits of wheat, four experiments were conducted at Research Farm of Agricultural and Natural Resources Research Center of Zabol in 2015–2016 and 2016–2017 growing seasons. The experiments were carried out using four alpha lattice designs with two replications under non-stress and terminal heat stress conditions. Plant materials used in this study included 167 recombinant inbred lines and their parents (‘SeriM82’ and ‘Babax’). Six traits including grain yield (GY), proline content (PRO), water soluble carbohydrates (WSC), maximum efficiency of photosystem II (Fv/Fm), cytoplasmic membrane stability (CMS) and chlorophyll content (CHL) were evaluated. Genetic linkage map consisted of 211 AFLP marker, 120 SSR marker and 144 DArT markers with 1864 cm length and 4.4 cm mean distance. QTL analysis was carried out using a mixed-model-based composite interval mapping (MCIM) method. By the combined analysis of normal phenotypic values, 27 additive QTLs and five pairs of epistatic effects were identified for studied traits, among which two additive and one epistatic QTL showed significant QTL?×?environment interactions. By the combined analysis of stress phenotypic values, a total of 26 QTLs with additive effects and 5 epistatic QTLs were detected, among which one additive and one epistatic QTL showed QTL?×?environment interactions. Six QTLs with major effects (QGY-2B, QGY-2D, QPro-5B, QWSC-4A, QFv/Fm-6A and QCMS-4B), which were common between two conditions could be useful for marker-assisted selection (MAS) in order to develop heat tolerant and high-performance wheat varieties.     

Genetic mapping of QTLs for sugar-related traits in a RIL population of Sorghum bicolor L. Moench

The productivity of sorghum is mainly determined by quantitative traits such as grain yield and stem sugar-related characteristics. Substantial crop improvement has been achieved by breeding in the last decades. Today, genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement. We have investigated QTL associated with the sugar components (Brix, glucose, sucrose, and total sugar content) and sugar-related agronomic traits (flowering date, plant height, stem diameter, tiller number per plant, fresh panicle weight, and estimated juice weight) in four different environments (two locations) using a population of 188 recombinant inbred lines (RILs) from a cross between grain (M71) and sweet sorghum (SS79). A genetic map with 157 AFLP, SSR, and EST-SSR markers was constructed, and several QTLs were detected using composite interval mapping (CIM). Further, additive × additive interaction and QTL × environmental interaction were estimated. CIM identified more than five additive QTLs in most traits explaining a range of 6.0–26.1% of the phenotypic variation. A total of 24 digenic epistatic locus pairs were identified in seven traits, supporting the hypothesis that QTL analysis without considering epistasis can result in biased estimates. QTLs showing multiple effects were identified, where the major QTL on SBI-06 was significantly associated with most of the traits, i.e., flowering date, plant height, Brix, sucrose, and sugar content. Four out of ten traits studied showed a significant QTL × environmental interaction. Our results are an important step toward marker-assisted selection for sugar-related traits and biofuel yield in sorghum.     

油用向日葵主要农艺性状的遗传效应及相关性研究

根据加性-显性与环境互作的遗传模型,对6个油用向日葵自交系及其配制的9个杂交组合在2个环境下的7个农艺性状表现进行遗传分析,揭示油用向日葵主要农艺性状遗传性质、规律以及主要农艺性状对含油率的贡献率。结果表明:株高、茎粗、盘径、百粒重、籽仁率和单盘粒重等6个遗传性状主要受加性和显性共同控制,结实率的遗传以加性、显性×环境互作效应为主,籽仁率、单盘粒重以加性、显性、显性×环境互作效应为主;性状间的各项遗传相关性多以加性遗传相关为主。百粒重的净效应对籽实含油率的加性遗传方差贡献率最高,结实率的净效应对籽实含油率的显性遗传方差贡献率最高,单盘粒重对籽实含油率的加性×环境互作遗传方差的贡献率最高。     

水稻籽粒锌含量的QTL 定位

锌元素的营养失衡已成为影响人类健康的最重要因素之一, 籽粒锌含量的QTL(quantitative trait loci)定位对研究富锌水稻的遗传育种具有重要的意义。以水稻(Oryza sativa L.)亲本奉新红米和明恢100杂交的145个株系的F2群体为实验材料, 利用92个SSR(simple sequence repeat)标记对水稻籽粒锌含量进行了QTL定位, 共检测到3个QTLs , 分别定位于第3、6和11染色体上, 对表型变异的贡献率分别为4.97%、12.75%和7.74%。其中位于第3染色体上的分子标记RM186和RM168之间的QZN3对表型变异的贡献率最大, 其增效等位基因来自亲本明恢100, 表现为部分显性。3个QTLs 的联合贡献率为25.46%, 具有基因累加效应。该研究结果有利于深入理解水稻锌含量的遗传基础, 为锌含量的QTL精细定位、基因克隆和分子标记辅助选择提供依据。     

Novel pleiotropic loci controlling panicle architecture across environments in japonica rice （Oryza sativa L.）

To identify quantitative trait loci (QTLs) controlling panicle architecture in japonica rice, a genetic map was constructed based on simple sequence repeat (SSR) markers and 254 recombinant inbred lines (RILs) derived from a cross between cultivars Xiushui 79 and C Bao. Seven panicle traits were investigated under three environments. Single marker analysis indicated that a total of 27 SSR markers were highly associated with panicle traits in all the three environments. Percentage of phenotypic variation explained by single locus varied from 2% to 35%. Based on the mixed linear model, a total of 40 additive QTLs for seven panicle traits were detected by composite interval mapping, explaining 1.2%-35% of phenotypic variation. Among the 9 QTLs with more than 10% of explained phenotypic variation, two QTLs were for the number of primary branches per panicle (NPB), two for panicle length (PL), two for spikelet density (SD), one for the number of secondary branches per panicle (NSB), one for secondary branch distribution density (SBD), and one for the number of spikelets per panicle (NS), respectively. qPLSD-9-1 and qPLSD-9-2 were novel pleiotropic loci, showing effects on PL and SD simultaneously. qPLSD-9-1 explained 34.7% of the phenotypic variation for PL and 25.4% of the phenotypic variation for SD, respec- tively. qPLSD-9-2 explained 34.9% and 24.4% of the phenotypic variation for PL and SD, respectively. The C Bao alleles at the both QTLs showed positive effects on PL, and the Xiushui 79 alleles at the both QTLs showed positive effects on SD. Genetic variation of panicle traits are mainly attributed to additive effects. QTL × environment interactions were not significant for additive QTLs and additive × additive QTL pairs.