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。     

谷子SSR分子图谱的构建及主要农艺性状QTL定位

试验拟对谷子重要农艺性状进行数量性状位点QTL分析。以表型差异较大的沈3/晋谷20F2作图群体为材料,观测其株高、穗长等性状,选用SSR做分子标记,利用完备区间作图法(BASTEN C J)进行QTL分析。结果显示,表型数据在作图群体中呈现连续分布,表现为多基因控制的数量性状,被整合的54个SSR标记构建10个连锁群,LOD阈值设置为2.0,检测到与株高相关的主效QTL2个,联合贡献率45.9637%,穗长主效QTL1个,贡献率14.9647%,与穗重、粒重相关的主效QTL为同一位点,贡献率分别为11.9601%和10.1879%。有6组QTL位点之间存在基因互作效应,大小范围为-0.4986-16.6407,对性状的贡献率在2.2716％至6.7478％之间。谷子表型控制复杂,相关QTL的检测受环境影响较大,不同连锁群QTL间互作明显。     

大豆重要农艺性状的QTL分析

应用栽培大豆科丰1号（♀）和南农1138-2（♂）杂交得到的F9代重组自交系（RILs）群体（201个家系）,构建了含302遗传标记、覆盖2363.8cM、由22个连锁群组成的遗传连锁图谱。采用区间作图法,对该群体的主要农艺性状的调查数据进行QTL分析,表明与开花期、成熟期、株高、主茎节数、每节荚数、倒状性、种子重、产量、蛋白质和含油量等10个重要农艺性状连锁的QTL位点34个,每个数量性状的遗传变异是由多个QTL位点决定的。与产量有关的农艺性状的一些QTL集中在几个连锁群上。     

梨分子遗传图谱构建及生长性状的QTL分析

利用鸭梨和京白梨杂交得到的F1(145株)实生苗为作图群体,通过对AFLP和SSR两种分子标记的遗传连锁分析,应用Joinmap 3.0作图软件,368个AFLP标记、34个SSR标记构建了分属18个连锁群的梨分子遗传连锁图谱,各连锁群的LOD值在4.0～7.0范围之间,图谱总长度覆盖梨基因组1395.9cM,平均图距为3.8cM.采用区间作图法,对该群体与生长性状相关的调查数据进行QTL分析,检测到与新梢生长量、新梢茎粗、节间长度、节间数量、树干径、树高及皮孔密度7个农艺性状连锁的QTL位点35个,其中主效QTL位点11个(LOD≥3.5).与生长性状相关的农艺性状QTL位点多集中在LG16连锁群上.     

亚麻主要农艺与品质相关性状的QTL定位

为了全面了解亚麻产量和品质相关性状的遗传基础,为亚麻基因克隆和分子标记辅助育种提供理论依据,在已构建SNP连锁遗传图谱的基础上,以LH-89为父本,R43为母本构建F2:3家系QTL定位群体,用R/QTL软件采用复合区间作图法对13个农艺和品质性状进行QTL定位。结果表明:(1)该研究共检测出35个QTL位点,与粗脂肪及其组成成分相关的QTL有20个,与农艺性状相关的QTL有15个;其中:亚油酸和粗脂肪各5个,亚麻酸、千粒重各4个,棕榈酸、株高、工艺长度各3个,硬脂酸、分枝数各2个,单株果数、果粒数、单株粒重、油酸各1个。(2)共有18个QTL的表型贡献率超10%(主效基因),其中农艺性状定位8个主效基因,品质性状定位10个主效基因。     

甘蓝型油菜千粒重性状的QTL初步定位研究

千粒重是油菜重要的产量相关性状之一,构建油菜遗传连锁图谱是研究其产量性状基因的前提。本研究利用小孢子培养技术,选育出了甘蓝型油菜大粒品系(G-42)和小粒品系(7-9)的纯合DH系DH-G-42和DH-7-9,其千粒重分别为6.24 g和2.42 g,二者比值达2.58。以DH-G-42为母本、DH-7-9为父本,构建了含190个单株的F2遗传作图群体,利用SSR和SRAP标记技术绘制遗传连锁图谱,该图谱共包含20个连锁群,涉及128个SSR标记和100个SRAP标记,图谱总长1546.6cM,标记间平均图距为6.78cM。本研究共检测到3个与千粒重性状相关的QTL,分别位于A9和C1连锁群,其中qSW-A9-1和qSW-A9-2贡献率分别达到10.98%和27.45%,均可视为控制粒重的主效QTL。本研究为后续进行油菜千粒重性状QTL的精细定位分析、分子标记辅助选择育种及新基因的克隆等奠定了基础。     

测序水稻品种SSR遗传连锁图谱的构建及其农艺性状基因座分析

利用测序水稻品种"Nipponbare（粳）/广陆矮4号（籼）"杂交F2群体90个单株为作图群体,构建含148个SSR标记的水稻遗传连锁图谱,覆盖基因组全长1737.81cM,标记间平均距11.90cM。利用该图谱及Excel2000和Mapmaker/QTL1.1b软件对分蘖数、穗数、穗长、主穗长、株高、剑叶长等六个农艺性状间的相互关系和基因位点进行分析,结果在LOD>2.2和P<0.005的条件下共检测到28个QTLs,它们分布在水稻所有染色体上,单个QTL对性状的分子贡献率11.1%-42.9%,其中大于20%有10个,并对选用已测序材料为亲本构建图谱来探讨水稻农艺性状的分子基础及其育种意义进行了讨论。     

南瓜遗传连锁图谱的构建及粒宽性状的QTL定位

以印度南瓜纯系大粒材料‘0515-1’和小粒材料‘0460-1-1’为亲本,获得193个南瓜F2单株群体,应用AFLP和SSR分子标记技术进行多态性筛选,构建了含84个标记位点的遗传连锁图谱。结果表明,整个图谱包含12个连锁群,全长683.50cM,标记平均间距为8.13cM。采用复合区间定位分析,共检测到控制南瓜籽粒宽度的4个数量性状位点(QTL),分别位于3个连锁群上,各QTL的贡献率在2.87%～29.68%之间。     

大粒裸燕麦(Avena nuda L.)遗传连锁图谱的构建

以“元莜麦”和“555”杂交得到的281个F2单株为作图群体,利用20对AFLP引物、3对SSR引物和1个穗型性状构建了一张大粒裸燕麦遗传连锁图。该图谱全长1544.8cM,包含19个连锁群,其上分布有92个AFLP标记、3个SSR标记和1个穗型形态标记,不同连锁群标记数为2-14个,长度在23.7-276.3cM之间,平均长度为81.3cM,标记间平均距离为20.1cM。穗型标记分离比符合3：1,11个AFLP标记表现为偏分离,偏分离比为11.5%。该图谱符合遗传连锁框架图的要求,为今后大粒裸燕麦的QTL定位、分子标记辅助育种和比较基因组学等研究奠定基础。     

绿豆产量相关农艺性状的QTL定位

绿豆育种的目标性状大多是受多基因控制的数量性状,表现型受环境影响很大。为深入分析绿豆复杂数量性状的遗传特征,本试验以绿豆Berken/ACC41 F10重组近交系群体为作图群体,利用该群体已经构建的包含79个RFLP标记的分子连锁图谱对北京和广西2个种植环境下考察的11个绿豆产量相关农艺性状进行QTL定位。结果表明,2个环境下共检测到产量相关性状QTL 63个（其中北京25个,广西38个）,分布于除第13连锁群以外的12条连锁群。大部分QTL只在单一环境下被检测到,说明产量相关QTLs与环境之间存在明显的互作。2个环境均能检测到的QTL仅有6个,分别为控制荚长、百粒重、生育期的QTLs,这6个在不同生态环境下同时发挥效应的QTLs对于绿豆分子标记辅助育种具有特殊的意义。研究还发现2个QTLs富集区域和若干成束分布的QTLs,它们可能是发掘通用QTL的候选位点。     

Identification of genomic regions determining flower and pod numbers development in soybean （Glycine max L.）

Flower and pod numbers per plant are important agronomic traits underlying soybean yield.So far quantitative trait loci (QTL) detected for flower and pod-related traits have mainly focused on the final stage,and might therefore have ignored genetic effects expressed during a specific developmental stage.Here,dynamic expressions of QTL for flower and pod numbers were identified using 152 recombinant inbred lines (RILs) and a linkage map of 306 markers.Wide genetic variation was found among RILs;17 unconditional and 18 conditional QTL were detected for the two traits at different developmental stages over two years.Some QTL were detected only at one stage and others across two or more stages,indicating that soybean flower and pod numbers development may be governed by time-dependent gene expression.Three main QTL (qfn-Chr18-2,qfn-Chr20-1,and qfn-Chr19) were detected for flower number,and two main QTL (qpn-Chr11 and qpn-Chr20) were detected for pod number.The phenotypic variation explained by them ranged from 6.1% to 34.7%.The markers linked to these QTL could be used in marker-assisted selection for increasing soybean flower and pod numbers,with the ultimate aim of increasing soybean yield.Comparison of the QTL regions for flower and pod numbers traits with the related genes reported previously showed that seven and four related genes were located in the QTL regions of qfn-Chr11 and qfn-Chr19,respectively.Tbese results provide a basis for fine mapping and cloning of flower and pod development-related genes.     

Quantitative trait locus analysis of multiple agronomic traits in the model legume Lotus japonicus.

The first quantitative trait locus (QTL) analysis of multiple agronomic traits in the model legume Lotus japonicus was performed with a population of recombinant inbred lines derived from Miyakojima MG-20 x Gifu B-129. Thirteen agronomic traits were evaluated in 2004 and 2005: traits of vegetative parts (plant height, stem thickness, leaf length, leaf width, plant regrowth, plant shape, and stem color), flowering traits (flowering time and degree), and pod and seed traits (pod length, pod width, seeds per pod, and seed mass). A total of 40 QTLs were detected that explained 5%-69% of total variation. The QTL that explained the most variation was that for stem color, which was detected in the same region of chromosome 2 in both years. Some QTLs were colocated, especially those for pod and seed traits. Seed mass QTLs were located at 5 locations that mapped to the corresponding genomic positions of equivalent QTLs in soybean, pea, chickpea, and mung bean. This study provides fundamental information for breeding of agronomically important legume crops.     

QTL analysis of agronomic traits in recombinant inbred lines of sunflower under partial irrigation

The objective of the present research was to map QTLs associated with agronomic traits such as days from sowing to flowering, plant height, yield and leaf-related traits in a population of recombinant inbred lines (RILs) of sunflower (Helianthus annuus). Two field experiments were conducted with well-irrigated and partially irrigated conditions in randomized complete block design with three replications. A map with 304 AFLP and 191 SSR markers with a mean density of 1 marker per 3.7 cM was used to identify QTLs related to the studied traits. The difference among RILs was significant for all studied traits in both conditions. Three to seven QTLs were found for each studied trait in both conditions. The percentage of phenotypic variance (R ²) explained by QTLs ranged from 4 to 49%. Three to six QTLs were found for each yield-related trait in both conditions. The most important QTL for grain yield per plant on linkage group 13 (GYP-P-13-1) under partial-irrigated condition controls 49% of phenotypic variance (R ²). The most important QTL for 1,000-grain weight (TGW-P-11-1) was identified on linkage group 11. Favorable alleles for this QTL come from RHA266. The major QTL for days from sowing to flowering (DSF-P-14-1) were observed on linkage group 14 and explained 38% of the phenotypic variance. The positive alleles for this QTL come from RHA266. The major QTL for HD (HD-P-13-1) was also identified on linkage group 13 and explained 37% of the phenotypic variance. Both parents (PAC2 and RHA266) contributed to QTLs controlling leaf-related traits in both conditions. Common QTL for leaf area at flowering (LAF-P-12-1, LAF-W-12-1) was detected in linkage group 12. The results emphasise the importance of the role of linkage groups 2, 10 and 13 for studied traits. Genomic regions on the linkage groups 9 and 12 are specific for QTLs of leaf-related traits in sunflower.     

Identification of QTLs for eight agronomically important traits using an ultra-high-density map based on SNPs generated from high-throughput sequencing in sorghum under contrasting photoperiods

The productivity of sorghum is mainly determined by agronomically important traits. The genetic bases of these traits have historically been dissected and analysed through quantitative trait locus (QTL) mapping based on linkage maps with low-throughput molecular markers, which is one of the factors that hinder precise and complete information about the numbers and locations of the genes or QTLs controlling the traits. In this study, an ultra-high-density linkage map based on high-quality single nucleotide polymorphisms (SNPs) generated from low-coverage sequences (~0.07 genome sequence) in a sorghum recombinant inbred line (RIL) population was constructed through new sequencing technology. This map consisted of 3418 bin markers and spanned 1591.4 cM of genome size with an average distance of 0.5 cM between adjacent bins. QTL analysis was performed and a total of 57 major QTLs were detected for eight agronomically important traits under two contrasting photoperiods. The phenotypic variation explained by individual QTLs varied from 3.40% to 33.82%. The high accuracy and quality of this map was evidenced by the finding that genes underlying two cloned QTLs, Dw3 for plant height (chromosome 7) and Ma1 for flowering time (chromosome 6), were localized to the correct genomic regions. The close associations between two genomic regions on chromosomes 6 and 7 with multiple traits suggested the existence of pleiotropy or tight linkage. Several major QTLs for heading date, plant height, numbers of nodes, stem diameter, panicle neck length, and flag leaf width were detected consistently under both photoperiods, providing useful information for understanding the genetic mechanisms of the agronomically important traits responsible for the change of photoperiod.     

QTL mapping of ten agronomic traits on the soybean (<Emphasis Type="Italic">Glycine max</Emphasis> L. Merr.) genetic map and their association with EST markers

A set of 184 recombinant inbred lines (RILs) derived from soybean vars. Kefeng No.1 × Nannong 1138-2 was used to construct a genetic linkage map. The two parents exhibit contrasting characteristics for most of the traits that were mapped. Using restricted fragment length polymorphisms (RFLPs), simple sequence repeats (SSRs) and expressed sequence tags (ESTs), we mapped 452 markers onto 21 linkage groups and covered 3,595.9 cM of the soybean genome. All of the linkage groups except linkage group F were consistent with those of the consensus map of Cregan et al. (1999). Linkage group F was divided into two linkage groups, F1 and F2. The map consisted of 189 RFLPs, 219 SSRs, 40 ESTs, three R gene loci and one phenotype marker. Ten agronomic traits—days to flowering, days to maturity, plant height, number of nodes on main stem, lodging, number of pods per node, protein content, oil content, 100-seed weight, and plot yield—were studied. Using winqtlcart, we detected 63 quantitative trait loci (QTLs) that had LOD>3 for nine of the agronomic traits (only exception being seed oil content) and mapped these on 12 linkage groups. Most of the QTLs were clustered, especially on groups B1 and C2. Some QTLs were mapped to the same loci. This pleiotropism was common for most of the QTLs, and one QTL could influence at most five traits. Seven EST markers were found to be linked closely with or located at the same loci as the QTLs. EST marker GmKF059a, encoding a repressor protein and mapped on group C2, accounted for about 20% of the total variation of days to flowering, plant height, lodging and nodes on the main stem, respectively.Communicated by H.F. LinskensW.-K. Zhang, Y.-J. Wang and G.-Z. Luo contributed equally to this investigation.     

Identification of quantitative trait loci for agronomically important traits and their association with genic-microsatellite markers in sorghum

The identification of quantitative trait loci (QTLs) affecting agronomically important traits enable to understand their underlying genetic mechanisms and genetic basis of their complex interactions. The aim of the present study was to detect QTLs for 12 agronomic traits related to staygreen, plant early development, grain yield and its components, and some growth characters by analyzing replicated phenotypic datasets from three crop seasons, using the population of 168 F₇ RILs of the cross 296B × IS18551. In addition, we report mapping of a subset of genic-microsatellite markers. A linkage map was constructed with 152 marker loci comprising 149 microsatellites (100 genomic- and 49 genic-microsatellites) and three morphological markers. QTL analysis was performed by using MQM approach. Forty-nine QTLs were detected, across environments or in individual environments, with 1–9 QTLs for each trait. Individual QTL accounted for 5.2–50.4% of phenotypic variance. Several genomic regions affected multiple traits, suggesting the phenomenon of pleiotropy or tight linkage. Stable QTLs were identified for studied traits across different environments, and genetic backgrounds by comparing the QTLs in the study with previously reported QTLs in sorghum. Of the 49 mapped genic-markers, 18 were detected associating either closely or exactly as the QTL positions of agronomic traits. EST marker Dsenhsbm19, coding for a key regulator (EIL-1) of ethylene biosynthesis, was identified co-located with the QTLs for plant early development and staygreen trait, a probable candidate gene for these traits. Similarly, such exact co-locations between EST markers and QTLs were observed in four other instances. Collectively, the QTLs/markers identified in the study are likely candidates for improving the sorghum performance through MAS and map-based gene isolations.     

Mapping QTL for grain yield and other agronomic traits in post-rainy sorghum [Sorghum bicolor (L.) Moench]

Sorghum, a cereal of economic importance ensures food and fodder security for millions of rural families in the semi-arid tropics. The objective of the present study was to identify and validate quantitative trait loci (QTL) for grain yield and other agronomic traits using replicated phenotypic data sets from three post-rainy dry sorghum crop seasons involving a mapping population with 245 F₉ recombinant inbred lines derived from a cross of M35-1 × B35. A genetic linkage map was constructed with 237 markers consisting of 174 genomic, 60 genic and 3 morphological markers. The QTL analysis for 11 traits following composite interval mapping identified 91 QTL with 5–12 QTL for each trait. QTL detected in the population individually explained phenotypic variation between 2.5 and 30.3 % for a given trait and six major genomic regions with QTL effect on multiple traits were identified. Stable QTL across seasons were identified. Of the 60 genic markers mapped, 21 were found at QTL peak or tightly linked with QTL. A gene-based marker XnhsbSFCILP67 (Sb03g028240) on SBI-03, encoding indole-3-acetic acid-amido synthetase GH3.5, was found to be involved in QTL for seven traits. The QTL-linked markers identified for 11 agronomic traits may assist in fine mapping, map-based gene isolation and also for improving post-rainy sorghum through marker-assisted breeding.     

Identification of genomic regions that affect grain-mould incidence and other traits of agronomic importance in sorghum

Grain-mould is a major problem in grain sorghum utilization as mouldy grain has a reduced quality due to the deterioration of the endosperm and reduced embryo viability. Here, our objective was to use genome mapping to improve knowledge of genetic variation and co-variation for grain-mould incidence and other inter-related agronomic traits. Grain-mould incidence, kernel-milling hardness, grain density, plant height, panicle peduncle length, foliar-disease incidence, and plant color were measured on 125 F₅ genotypes derived from a cross of Sureño and RTx430. Quantitative trait loci were detected by means of 130 mapped markers (44 microsatellites, 85 AFLPs, one morphological-trait locus) distributed among ten linkage groups covering 970 cM. One to five QTLs affected each trait, with the exception of grain density for which no QTLs were detected. Grain-mould incidence was affected by five QTLs each accounting for between 10 and 23% of the phenotypic variance. The effects and relative positions of QTLs for grain-mould incidence were in accordance with the QTL distribution of several inter-related agronomic traits (e.g., plant height, peduncle length) and with the correlation between these phenotypic traits and grain-mould incidence. The detection of QTLs for grain-mould incidence was dependent on the environment, which is consistent with heritibility estimates that show strong environmental and genotype × environment effects. Several genomic regions affected multiple traits including one region that affected grain-mould incidence, plant height, panicle peduncle length, and grain-milling hardness, and a second region that influenced grain-mould (in four environments) and plant height. One genomic region, which harbors loci for plant color, influenced the severity of foliar disease symptoms and the incidence of grain-mould in one environment. Collectively QTLs detected in the present population explained between 10% and 55% of the phenotypic variance observed for a given trait.