大豆遗传图谱的构建和若干农艺性状的QTL定位分析

大豆许多重要农艺性状都是由微效多基因控制的数量性状,对这些数量性状进行QTL定位是大豆数量性状遗传研究领域的一个重要内容.本研究利用栽培大豆科新3号为父本、中黄20为母本杂交得到含192个单株的F2分离群体,构建了含122 个SSR标记、覆盖1719.6cM、由33个连锁群组成的连锁遗传图谱.利用复合区间作图法,对该群体的株高、主茎节数、单株粒重和蛋白质含量等农艺性状的调查数据进行QTL分析,共找到两个株高QTL,贡献率分别为9.15%和6.08%;两个主茎节数QTL,贡献率分别为10. 1%和8.6%;一个蛋白质含量QTL,贡献率为9.8%;一个单株粒重QTL,贡献率为11.4% .通过遗传作图共找到与所定位的4个农艺性状QTL连锁的6个SSR标记,这些标记可以应用于大豆种质资源的分子标记辅助选择,从而为大豆分子标记辅助育种提供理论依据.     

鲤饲料转化率性状的QTL 定位及遗传效应分析

数量性状(QTL)定位是实现分子标记辅助育种、基因选择和定位、培育新品种及加快性状遗传研究进展的重要手段。饲料转化率是鲤鱼的重要经济性状和遗传改良的主要目标, 而通过QTL 定位获得与饲料转化率性状紧密连锁的分子标记以及相关基因是遗传育种的重要工具。研究利用SNP、SSR、EST-SSR 等分子标记构建鲤鱼(Cyprinus carpio L.)遗传连锁图谱并对重要经济性状进行QTL 定位。选用174 个SSR 标记、41 个EST-SSR 标记、345 个SNP 标记对德国镜鲤F2 代群体68 个个体进行基因型检测, 用JoinMap4.0 软件包构建鲤鱼遗传连锁图谱。再用MapQTL5.0 的区间作图法(Interval mapping, IM)和多QTL 区间定位法(MQMMapping, MQM)对饲料转化率性状进行QTL 区间检测, 通过置换实验(1000 次重复)确定连锁群显著性水平阈值。结果显示, 在对饲料转化率性状的多QTL 区间定位中, 共检测到15 个QTLs 区间, 分布在9 个连锁群上, 解释表型变异范围为17.70%—52.20%, 解释表型变异最大的QTLs 区间在第48 连锁群上, 为52.20%。HLJE314-SNP0919(LG25)区间标记覆盖的图距最小, 为0.164 cM; 最大的是HLJ1439-HLJ1438(LG39)区间,覆盖图距为24.922 cM。其中区间HLJ1439-HLJ1438、HLJ922 -SNP0711 解释表型变异均超过50.00%, 可能是影响饲料转化率性状的主效QTLs 区间。与饲料转化率相关的15 个QTLs 的加性效应方向并不一致, 有3个区间具有负向加性效应, 平均为?0.027; 12 个正向加性效应, 平均值为0.06。研究检测出的与鲤鱼饲料转化率性状相关的QTL 位点可为鲤鱼分子标记辅助育种和更进一步的QTL 精细定位打下基础。       

水稻粒长QTL定位与主效基因的遗传分析

该研究利用短粒普通野生稻矮杆突变体和长粒栽培稻品种KJ01组配杂交组合F_1,构建分离群体F_2;并对该群体粒长进行性状遗传分析,利用平均分布于水稻的12条染色体上的132对多态分子标记对该群体进行QTL定位及主效QTLs遗传分析,为进一步克隆新的主效粒长基因奠定基础,并为水稻粒形育种提供理论依据。结果表明:(1)所构建的水稻杂交组合分离群体F_2的粒长性状为多基因控制的数量性状。(2)对543株F_2分离群体进行QTL连锁分析,构建了控制水稻粒长的连锁遗传图谱,总长为1 713.94 cM,共检测出24个QTLs,只有3个表现为加性遗传效应,其余位点均表现为遗传负效应。(3)检测到的3个主效QTLs分别位于3号染色体的分子标记PSM379～RID24455、RID24455～RM15689和RM571～RM16238之间,且三者对表型的贡献率分别为54.85%、31.02%和7.62%。(4)在标记PSM379～RID24455之间已克隆到的粒长基因为该研究新发现的主效QTL位点。     

油菜油分、蛋白质和硫苷含量相关性分析及QTL 定位

为定位与油分、蛋白质和硫苷含量等品质性状相关的数量性状位点(QTL), 以2个含油量较高的甘蓝型油菜(Brassica napus)品系8908B和R1为研究材料, 配置正反交组合。在正反交F₂代群体中, 含油量和蛋白质含量都存在极显著的负相关, 相关系数分别为-0.68和-0.81, 含油量和硫苷含量相关性不显著; 蛋白质含量和硫苷含量在正交群体中相关性不显著, 但在反交群体中存在显著负相关(相关系数r =-0.45)。利用正交F₂代群体中的118个单株, 构建了包含121个标记的遗传连锁图谱, 图谱长1 298.7 cM, 有21个连锁群(LGs)。采用复合区间作图法, 在连锁图上定位了2个与含油量有关的QTL, 分别位于LG8和LG10, 其贡献率分别为4.8%和13.7%, 增效基因都来源于R1; 定位了2个与蛋白质含量有关的QTL: pro1 和 pro2, 分别位于LG1和LG3, 其贡献率分别为15.2%和14.1%, 位点pro1由8908B提供增效基因, pro2则由R1提供增效基因; 定位了4个与硫苷含量有关的QTL, 其中LG20上有2个, LG4和LG8上各1个, 它们的贡献率在1.9%-25.4%之间, 除LG20上glu1的增效基因来自R1外, 其余3个QTL位点均由8908B提供增效基因。     

油菜油分、蛋白质和硫苷含量相关性分析及QTL定位

为定位与油分、蛋白质和硫苷含量等品质性状相关的数量性状位点（QTL）,以2个含油量较高的甘蓝型油菜（Brassica napus）品系8908B和R1为研究材料,配置正反交组合。在正反交F2代群体中,含油量和蛋白质含量都存在极显著的负相关,相关系数分别为-0．68和-0．81,含油量和硫苷含量相关性不显著：蛋白质含量和硫苷含量在正交群体中相关性不显著,但在反交群体中存在显著负相关（相关系数r=-0．45）。利用正交F2代群体中的118个单株,构建了包含121个标记的遗传连锁图谱,图谱长1298．7cM,有21个连锁群（LGs）。采用复合区间作图法,在连锁图上定位了2个与含油量有关的QTL,分别位于LG8和LG10,其贡献率分别为4．8％和13．7％,增效基因都来源于R1;定位了2个与蛋白质含量有关的QTL：pr01和pr02,分别位于LG1和LG3,其贡献率分别为15．2％和14．1％,位点pr07由8908B提供增效基因,pro2则由R1提供增效基因：定位了4个与硫苷含量有关的QTL,其中LG20上有2个,LG4和LG8上各1个,它们的贡献率在1．9％-25．4％之间,除LG20上glu7的增效基因来自R1外,其余3个QTL位点均由8908B提供增效基因。     

小麦籽粒品质性状的QTL分析

利用小麦中国春(母本)和兰考大粒(父本)F2群体构建了169个标记的分子遗传图谱,将F2∶3家系分别种植于3个环境中,利用基于完备区间混合模型的单环境作图模型和多环境作图模型对小麦籽粒容重、硬度、蛋白含量和结合水含量性状进行了QTL分析。结果显示:(1)两种作图模型下,检测到容重的6个共同QTL(QTW-6B-6、QTW-7B-6、QTW-7B-9、QTW-5D-2、QTW-6D-1、QTW-6D-4),单环境模型下遗传贡献率为1.99%~6.57%,多环境模型下遗传贡献率为3.66%~20.07%,其中QM TW-7B-9、QM TW-6D-1和QM TW-6D-4在多环境模型中表现为主效QTL。(2)检测到硬度的3个共同QTL(QHD-4A-5、QHD-7A-1和QHD-7B-9),单环境模型下的遗传贡献率为6.00%~6.95%,多环境模型中遗传贡献率为5.43%~9.64%。(3)检测到蛋白含量1个共同QTL(QPR-6D-1),单环境模型下的遗传贡献率为5.39%,多环境模型中遗传贡献率为10.06%,表现为主效QTL。(4)检测到籽粒结合水含量1个共同QTL(QMO-1B-4),单环境模型下的遗传贡献率为39.20%,多环境模型下的遗传贡献率为75.01%,均表现为主效QTL。(5)1B染色体上存在同时控制籽粒容重、硬度、蛋白和结合水含量的QTL,说明1B染色体对小麦品质的影响可能很大。研究表明,小麦容重、硬度、蛋白含量、结合水含量的遗传主要受加性效应控制。该研究初步定位的一些重要QTL可为进一步精细定位、基因挖掘和育种早代品质性状分子标记辅助选择提供依据。     

裸燕麦子粒性状的QTL研究

以六倍体裸燕麦578(大粒品种)和三分三(小粒品种)为亲本进行杂交,构建包含202个家系的F2遗传作图群体。由172个SSR标记构建出包含21个连锁群的遗传连锁图谱。采用复合区间作图对子粒性状进行QTL定位,共检测到17个控制子粒长度、宽度、千粒重的QTL位点。其中,6个与子粒长度相关的QTL位点表型的贡献率为0.70%~12.83%,5个与子粒宽度相关的QTL位点表型的贡献率为0.77%~12.92%,6个与子粒千粒重相关的QTL位点表型的贡献率为0.58%~10.64%。在这些QTLs中有4个的贡献率达到了10%以上,分别是与子粒长有关的qGL-2(12.83%)、与子粒宽有关的qGW-5(12.92%)以及与千粒重有关的qTGW-3(10.64%)和qTGW-4(10.05%),被认为是主效基因所在位点。而且qGL-2和qTGW-4位于连锁群的相同位置上。还发现第3号连锁群上AM1089~AM1512区段分别与子粒长度、宽度和千粒重相关,同时3号连锁群AM86-2~AM1044区间分别与子粒长度和千粒重相关,而位于第21号连锁群AM3217~AM965区段分别与子粒宽度和千粒重相关。这一研究为燕麦子粒性状的深入研究和相关标记开发以及分子辅助选择研究奠定了基础。     

小麦旗叶长、宽及面积的QTL分析

以小麦品种‘小偃81’和‘西农1376’构建的含236个家系的自交重组系(RIL)群体(F2:7、F2:8代)为研究材料,采用完全随机区组设计,连续2年在陕西杨陵、河南驻马店和山东济南于灌浆期(花后20d)随机取每个株系10株测量旗叶长、宽,并利用172个SSR标记构建了遗传连锁图谱,通过基于完备区间作图法的QTL IciMapping V3.2软件,对控制小麦旗叶长、宽和面积的数量性状位点(QTL)进行了加性效应分析。结果发现:(1)9个旗叶长QTLs位于1A、4A、3B、5D和7D染色体上,单个QTL可解释5.10%~16.44%的表型变异;10个旗叶宽QTLs位于1A、3A、5A、7A、3B和5D染色体上,单个QTL可解释4.63%~14.24%的表型变异;12个旗叶面积QTLs位于1A、4A、3B、2D和5D染色体上,单个QTL可解释4.25%~22.67%的表型变异。(2)控制小麦旗叶长、宽和面积的QTLs存在差异,同一QTL在不同性状中的遗传贡献率也不同。(3)同一性状在同一年份,不同地点和在不同年份,相同地点下检测到的QTLs有的相同,但有的差异明显。(4)有些控制不同性状的QTLs在染色体的同一标记区间,表现一因多效。研究表明:位于1A和5D染色体上的2个加性QTLs都同时控制旗叶长、宽和面积,且前者为主效基因,后者遗传贡献率也较大,可用于标记辅助育种和分子聚合育种。     

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。     

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。     

Mapping of Quantitative Trait Loci for Butter Content and Hardness in Cocoa Beans (Theobroma cacao L.)

Cocoa butter is an important raw material for the chocolate, pharmaceutical, and cosmetic industries. The butter content and quality in cocoa beans are genetically controlled characteristics, and affect its commercial value and industrial applicability. In the present work, an F₂ population derived from the cross between the ICS-1 and Scavina-6 cocoa clones was used for molecular mapping. A linkage map was constructed based on amplified fragment length polymorphism, random amplified polymorphic DNA, and simple sequence repeat markers, resulting in a total of 273 markers, distributed in 14 linkage groups (LGs). Phenotyping of butter content was performed after ether extraction and butter hardness was determined by sweeping differential calorimetry. One quantitative trait locus (QTL) associated to butter content was mapped at linkage group 9 (LG9) and two QTLs for butter hardness were identified at linkage groups 9 and 7 (LG9 and LG7). The two QTLs mapped at the LG9 explained 51.0% and 28.8% of the phenotypic variation for butter content and hardness, respectively. These QTLs were concentrated in the same map region, suggesting a close genetic linkage or pleiotropic effect. The QTLs identified may be useful in further marker-assisted selection breeding programs aimed at cocoa butter quality improvement.     

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.     

Identification of QTL for agronomic traits of importance for olive breeding

Development of methodologies for early selection is one of the most important goals of olive breeding programs at present. In this context, the identification of molecular markers associated with beneficial alleles could allow the development of marker-assisted selection (MAS) strategies in olive breeding programs. Fruit-related and plant vigor traits, which are of key importance for olive selection and breeding, were analyzed during two seasons in a progeny derived from the cross ‘Picual’ × ‘Arbequina.’ Quantitative trait loci (QTL) analyses were performed using MapQTL 4.0. A total of 22 putative QTLs were identified in the map of ‘Arbequina.’ QTLs clustered in linkage groups (LG) 1, 10 and 17. QTLs for oil-related traits located in LG 1 and 10 explained around 20–30 % of the phenotypic variability depending on the season and the trait. QTL for moisture-related traits were detected in LG 1, 10 and 17, and QTLs for the ratio pulp to stone were identified in LG 10 and 17 explaining around 15–20 %. Interaction between QTLs for the same trait was investigated. The significance of these results was discussed considering the co-localization of QTLs and Pearson correlations among traits. Five additional QTLs were detected in the map of ‘Picual.’ Four of them clustered in LG 17 indicating the presence of a QTL for fruit weight explaining around 12.7–15.2 % of the variability. Additionally, a QTL for trunk diameter was detected in LG 14 explaining 16 % of the variation. These results represent an important step toward the application of MAS in olive breeding programs.     

Integration genetic linkage map construction and several potential QTLs mapping of Chinese shrimp (Fenneropenaeus chinensis) based on three types of molecular markers

In this study, totally 54 selected polymorphic SSR loci of Chinese shrimp (Fenneropenaeus chinensis), in addition with the previous linkage map of AFLP and RAPD markers, were used in consolidated linkage maps that composed of SSR, AFLP and RAPD markers of female and male construction, respectively. The female linkage map contained 236 segregating markers, which were linked in 44 linkage groups, and the genome coverage was 63.98%. The male linkage map contained 255 segregating markers, which were linked in 50 linkage groups, covering 63.40% of F. chinensis genome. There were nine economically important traits and phenotype characters of F. chinensis were involved in QTL mapping using multiple-QTL mapping strategy. Five potential QTLs associated with standard length (q-standardl-01), with cephalothorax length (q-cephal-01), with cephaloghorax width (q-cephaw-01), with the first segment length (q-firsel-01) and with anti-WSSV (q-antiWSSV-01) were detected on female LG1 and male LG44 respectively with LOD> 2.5. The QTL q-firsel-01 was at 73.603 cM of female LG1. Q-antiWSSV-01 was at 0 cM of male LG44. The variance explained of these five QTLs was from 19.7-33.5% and additive value was from -15.9175 to 7.3675. The closest markers to these QTL were all SSR, which suggested SSR marker was superior to AFLP and RAPD in the QTL mapping.     

Genetic mapping of hop (Humulus lupulus L.) applied to the detection of QTLs for alpha-acid content.

The map locations and effects of quantitative trait loci (QTLs) were estimated for alpha-acid content in hop (Humulus lupulus L.) using amplified fragment length polymorphism (AFLP) and microsatellite marker (simple sequence repeat (SSR)) genetic linkage maps constructed from a double pseudotestcross. The mapping population consisted of 111 progeny from a cross between the German hop cultivar 'Magnum', which exhibits high levels of alpha-acids, and a wild Slovene male hop, 2/1. The progeny segregated quantitatively for alpha-acid content determined in 2002, 2003, and 2004. The maternal map consisted of 96 markers mapped on 14 linkage groups defining 661.90 cM of total map distance. The paternal map included 70 markers assigned to 12 linkage groups covering 445.90 cM of hop genome. QTL analysis indicated 4 putative QTLs (alpha1, alpha2, alpha3, and alpha4) on linkage groups (LGs) 03, 01, 09, and 03 of the female map, respectively. QTLs explained 11.9%-24.8% of the phenotypic variance. The most promising QTL to be used in marker-assisted selection is alpha2, the peak of which colocated exactly with the AFLP marker. Three chalcone synthase-like genes (chs2, chs3, and chs4) involved in hop bitter acid synthesis mapped together on LG04 of the female map. Saturation of the maps, particularly the putative QTL regions, will be carried out using SSR markers, and the stability of the QTLs will be tested in the coming years.     

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.     

A complete genetic linkage map and QTL analyses for bast fibre quality traits, yield and yield components in jute (Corchorus olitorius L.)

We report the first complete microsatellite genetic map of jute (Corchorus olitorius L.; 2n = 2 × = 14) using an F₆ recombinant inbred population. Of the 403 microsatellite markers screened, 82 were mapped on the seven linkage groups (LGs) that covered a total genetic distance of 799.9 cM, with an average marker interval of 10.7 cM. LG5 had the longest and LG7 the shortest genetic lengths, whereas LG1 had the maximum and LG7 the minimum number of markers. Segregation distortion of microsatellite loci was high (61%), with the majority of them (76%) skewed towards the female parent. Genomewide non-parametric single-marker analysis in combination with multiple quantitative trait loci (QTL)-models (MQM) mapping detected 26 definitive QTLs for bast fibre quality, yield and yield-related traits. These were unevenly distributed on six LGs, as co-localized clusters, at genomic sectors marked by 15 microsatellite loci. LG1 was the QTL-richest map sector, with the densest co-localized clusters of QTLs governing fibre yield, yield-related traits and tensile strength. Expectedly, favorable QTLs were derived from the desirable parents, except for nearly all of those for fibre fineness, which might be due to the creation of new gene combinations. Our results will be a good starting point for further genome analyses in jute.