Construction of Genetic Linkage Map of Bread Wheat （Triticum aestivum L.） Using an Intervarietal Cross and QTL Map for Spike Related Traits

Most often a genetic linkage map is prepared using populations obtained from two highly diverse genotypes. However, the markers from such a map may not be useful in a breeding program as these markers may not be polymorphic among the varieties used in breeding. For the past nine years, intraspecific maps have been gaining importance and such maps based on Swiss （PaiUard et al., 2003）, Japanese （Suenaga et al., 2005）, Australian （Chaimcrs et al., 2001） wheat varieties arc available. A map based on Indian wheat varieties however has not been reported. We constructed a genetic linkage map based on a cross between two Indian bread wheat （Triticum aestivum L.） varieties, Sonalika and Kalyansona. One hundred and fifty F2 individuals were analyzed for arbitrarilyprimed polymerase Chain reaction （AP-PCR）, random amplified polymorphic DNA （RAPD）, inter simple sequence repeats （ISSR）, Sequence Tagged Microsatelhte Sites （STMS）, Amplified Fragment Length Polymorphism （AFLP） markers, seed storage proteins and known genes. A linkage map was constructed consisting of 236 markers and spanning a distance of 3 639 cM with 1 211.2 cM for A genome, 1 669.2 cM for B genome, 192.4 cM for D genome and 566.2 cM for unassigned groups,     

Microsatellite markers associated with lint percentage trait in cotton, Gossypium hirsutum

Molecular markers associated with fiber development traits have the potential to play a key role in understanding of cotton fiber development. Seventeen SSRs out of 304 markers tested from MGHES (EST-SSR), JESPR (genomic SSR), and TMB (BAC-derived SSR) collections showed significant linkage associations (using a Kurskal-Wallis non-parametric test) with lint percentage QTL in a set of recombinant inbred cotton lines (RILs) segregating for lint percentage. The permutation test of these potential markers associated with lint percentage QTL(s) determined that 12 SSR markers have stable estimates, exceeding empirically chosen threshold significance values at or above α = 0.01. Interval mapping demonstrated that 9 SSRs with stable critical LOD threshold values at α = 0.01 have significant QTL effect. Multiple QTL-mapping (MQM) revealed that at least, two highly significant fiber development QTLs exist around regions TMB0471 and MGHES–31 (explained about 23–59% of the phenotypic variation of lint percentage) and around markers MGHES–31 and TMB0366 (accounted for 5.4–12.5% phenotypic variation of lint percentage). These markers, in particular fiber-specific EST-SSRs, might be the possible ‘candidate’ loci contributing for fiber development in cotton. BAC-derived SSRs associated with fiber trait are the possible markers that are useful for the identification of physical genomic contigs that contain fiber development genes. Several lint percentage trait associated SSR markers have been located to chromosomes 12, 18, 23, and 26 using deletion analysis in aneuploid chromosome substitution lines. Outcomes of the work may prove useful in understanding and revealing the molecular basis of the fiber development, and the utilization of these markers for development of superior cotton cultivars through marker-assisted selection (MAS) programs. I. Y. Abdurakhmonov and S. Saha contributed equally to the work     

重组近交系群体定位绿豆抗绿豆象基因

绿豆象是对绿豆危害最严重的仓储害虫,检测和利用抗绿豆象基因是控制该害虫最经济有效的方法。利用高感豆象绿豆栽培种Berken和高抗豆象绿豆野生种ACC41亚种内杂交得到的重组近交系（RIL）群体及据此构建的1个包含79个RFLP分子标记的遗传图谱,通过对3个种植环境条件下收获的RIL群体进行2年的室内人工接虫鉴定,评价其抗绿豆象表现。QTL作图结果表明,在第9连锁群mgM213~VrCS161标记之间的1个QTL在2年3点试验中均被检测到,贡献率在74.05%~79.27%,是抗绿豆象主效基因。该QTL的加性效应值为负,表明来自父本ACC41的这个位点的等位基因可以提高绿豆对绿豆象的抗性。本研究结果对开展绿豆抗绿豆象育种以及抗绿豆象基因的精细定位和克隆有重要意义。     

基于高分辨率熔解曲线技术快速筛选黄瓜SNP1

为筛选用以黄瓜纯度检验和品种鉴定的SNP位点,以17个黄瓜杂交品种及其34份亲本为SNP筛选群体,利用高分辨率熔解曲线(high resolution melting,HRM)技术筛选SNP位点.本研究通过HRM分析和焦磷酸测序确认,获得CLA0(TT/--)、CLA1(C/T)和CLA6(A/G)三个SNP位点,群体...     

利用双向回交导入系定位水稻苗期耐亚铁毒和锌毒的QTL

铁和锌是水稻生长必需的微量元素,也是重金属污染元素。在低洼或酸性土壤中,水稻容易遭受亚铁和锌毒害,抑制水稻生长,造成生物量和产量下降。为探讨水稻苗期耐亚铁毒、锌毒的遗传机制,利用优质粳稻品种Lemont和高产籼稻品种特青为亲本构建的高代双向回交导入系和308个在染色体上均匀分布的SNP标记剖析耐亚铁毒、锌毒相关的QTL。从双向导入系共检测到42个影响耐亚铁毒、锌毒相关性状如苗高、苗干重、根干重以及胁迫与对照相对值的QTL,多数位点增强亚铁毒、锌毒抗性的有利等位基因来自Lemont。其中同时在2个背景下表达的QTL有4个,占定位QTL总数的9.52%,说明大多数QTL的表达具有明显的遗传背景效应。同一遗传背景下同时影响耐亚铁毒和锌毒的QTL有9个,其中QSdw5在2个背景中均被检测到,其效应大小和方向一致,说明水稻苗期耐亚铁毒、锌毒之间存在遗传重叠位点。因此,通过分子标记辅助选择从Lemont中导入或聚合有利的遗传重叠区域,可以提高特青对亚铁毒、锌毒的抗性水平。     

SNPS在大豆等作物遗传及改良中的应用

单核苷酸多态性(singlenucleotidepolymorphism,SNP)是指DNA序列上的单个碱基变异,它具有分布广、多态信息量大、易于检测和统计分析等优点,被称为继RFLP和微卫星标记后的第三代基因遗传标记。单核苷酸多态性是等位基因间序列差异最为普遍的类型,可作为一种高通量的遗传标记。已建立PCR扩增目标序列及其产物测序和电子SNP(eSNP)等多种发现和检测SNP的方法。大豆等作物也已开展了SNP分析。一些栽培作物种质的多样件不断减少,其结果连锁不平衡(linkagedise鄄quilibrium,LD)增加,这有利于目的基因座上SNP单元型(haplotype)与表型的相关性分析。SNP已在作物基因作图及其整合、分子标记辅助育种和功能基因组学等领域展示了广泛的应用价值。     

利用DH群体动态检测水稻抗褐飞虱数量性状基因位点

利用籼粳交珍汕97／武育粳2号F1花培获得的190个双单倍体群体(doubled—haploid population,DH系)及其构建的179个SSR分子标记遗传图谱,通过对DH系群体苗期重复接虫试验和2个不同时期对褐飞虱危害程度进行动态调查,并应用Mapmaker／exp Version3．0和Windows QTL Cartographer V2．0对水稻抗褐飞虱数量性状基因位点(quantative trait locus,QTL)进行动态检测和遗传效应分析。结果表明,在苗期对褐飞虱抗性的检测中,共检测到6个抗性QTL,分别位于第2、3、4、8和10染色体上,各QTL的LOD值分别为2．22-4．64,贡献率为5．04％～13．73％,第3染色体和第4染色体上各有1个OTL的加性效应为正值,表明来自于亲本武育粳2号的这2个位点的等位基因可以提高水稻对褐飞虱的抗性,其余4个QTL的加性效应均为负值,表明来自于亲本珍汕97的这些位点的等位基因可以提高水稻对褐飞虱的抗性。     

水稻基部伸长节间性状与倒伏相关性分析及QTL定位

利用珍汕97B/密阳46 RILs群体及其构建的连锁图谱,对水稻株高和基部Ⅰ、Ⅱ伸长节间性状与稻株抗倒伏能力进行相关分析,并对基部Ⅰ、Ⅱ伸长节间性状进行QTL定位,共检测到加性效应QTLs 16个、加性´加性互作33对。估算了每个QTL的加性效应值和每对加加互作的上位性效应值,比较了QTLs的基因组分布。在第1染色体短臂和第     

Dissection of genetic effects of quantitative trait loci (QTL) in transgenic cotton

When alien DNA inserts into the cotton genome in a multicopy manner, several quantitative trait loci (QTLs) in the cotton genome are disrupted; these are called dQTL in this study. A transgenic mutant line is near-isogenic to its recipient, which is divergent for the dQTL from the remaining QTLs. Therefore, a set of data from a transgenic QTL line mutated by Agrobacterium-mediated transformation (30074), its recipient and their F1 hybrids, and three elite lines were analyzed under a modified additive-dominance model with genotype × environment interactions in three different environments to separate the genetic effects due to dQTL from whole-genome effects. Our result showed that dQTL had significant additive effects on lint percentage, boll weight, and boll number per square meter, while it had little genetic association with fiber traits, seed cotton yield, and lint yield. The dQTL in 30074 significantly increased lint percentage and boll number, while significantly decreasing boll weight, having little effect on fibre traits, while those from the recipient and three elite lines showed significant genetic effects on lint percentage. In addition, the remaining QTL other than dQTL had significant additive effects on seed cotton yield, fruiting branch number, uniformity index, micronaire, and short fibre index, and significant dominance effects on seed cotton yield, lint yield, and boll number per square meter. The additive and dominance effects under homozygous and heterozygous conditions for each line are also predicted in this study.     

水稻抽穗期控制候选基因的SNP/InDel多态性分析

抽穗期是水稻的一种重要农艺性状,但其遗传控制机理还不清楚。本文利用数据库搜索的方法,搜索并分析了30个与拟南芥光周期调控途径花期控制基因同源的水稻基因。利用水稻多态性数据库,得到了发生在这些基因以及Hd6、Ehd1、OsSOCl上的820个SNP及191个InDel位点。分析表明,这些位点不是平均分布到每个基因上。SNP／InDel含量较高的基因存在SNP／InDel的热点片段。14．1％的SNP位点,及7％的InDel位点分布在基因的编码区。本文也分析了侯选基因侧翼5Kb范围内的SNP／InDel多态性位点。按照侧翼多态性位点数目的分布可以将侯选基因分成4种类型,即两侧保守型、两侧热点型、单侧热点型、一侧热点一侧保守型。     

水稻抽穗期数量性状基因的定位及遗传效应分析

本研究利用特早抽穗粳稻品种石狩白毛和籼稻品种明恢63杂交的F2分离群体共116株,构建了含88个共显性分子标记的连锁图谱,对水稻(Oryza sativA L．)抽穗期进行基因定位。利用2种分析软件MAPMAKER／QTL和QTLMapper进行分析,共检测到3个抽穗期的数量性状基因座(QTLs)。2个软件共同发现第7染色体上RM214与A5106标记区间内存在1个主效QTL Hd7a（Hd7c),来自明恢63的这个位点的等位基因可使抽穗期延迟。其余2个QTLs分别位于第7、9染色体上。同时检测到有5对位点间存在上位性作用,但相对贡献率较小,表明上位性效应也是影响抽穗期的遗传基础。     

稻米胚重相关性状QTL分析

利用米粒胚重差异显著的广东早籼品种窄叶青8号（ZYQ8）和北方粳稻品种京系17（JX17）,以及由它们构建的加倍单倍体（DH）群体127个株系,对糙米重、胚乳重、胚重和胚重率4个性状进行了评价与QTL分析。4个性状表现型均为连续分布,且都存在一定数量的双向超亲遗传类型,受多基因控制。共检测到13个影响4个性状的QTL,分布于     

玉米穗行数主效位点qKRN5.04精细定位与遗传效应解析

穗行数是影响玉米产量的重要因素之一,其遗传机制解析和关键基因精细定位对开展分子育种具有重要的意义。本研究以穗行数仅有4行的\"四路糯系选\"和多穗行自交系\"农531\"(18~22行)为亲本,构建了高代回交群体和次级定位群体(四路糯选系为供体亲本,农531为轮回亲本)。通过对不同类型试验群体的多环境表型鉴定和基因型鉴定,利用完备区间作图法(ICIM)进行穗行数主效QTL定位分析,将穗行数主效位点q KRN5.04定位到第5染色体136.3~140.0 Mb的区间之内;遗传效应分析发现,该位点在不同环境条件下最大可解释的表型变异为21.76%,效应值为0.80~1.76行。通过次级分离群体重组事件分析可将其进一步定位到~300 kb区间内。本研究结果不仅为分子标记辅助选择提供了实用的In Del标记,而且为玉米穗行数主效位点q KRN5.04的图位克隆和候选基因挖掘奠定了重要的基础。     

水稻叶鞘相关性状的遗传分析

本研究以典型釉粳交(春江06/台中本地1号)的F1代花药加倍的DH群体为材料,系统考察了DH群体及其双亲在抽穗期的叶鞘长、叶鞘重、叶鞘厚等性状上的分布情况.结果表明,叶鞘长度的遗传力较高,受环境的影响相对较小,叶鞘长与叶鞘厚及叶鞘干重间大多呈极显著的正相关.QTL分析共检测到31个与叶鞘有关的QTL,分布于水稻第1、第...     

棉花数量性状遗传与QTL定位研究进展

棉花许多重要的性状多为数量性状。现代分子生物技术的发展为植物数量性状基因的定位、分离等研究提供了条件。从数量性状基因座(QTL)作图群体类型及其特点,QTL定位方法,QTL精细定位、克隆、利用等方面进行了综述,并对今后QTL研究进行了展望。     

利用重组自交系群体检测水稻耐铝毒数量性状基因座

利用Kinmaze / DV85 81个重组自交家系(RIL)作图群体,采用苗期单营养液水培鉴定方法,以相对根伸长量（RRE）作为耐铝毒性状的表型值,分析亲本和重组自交系群体对铝毒的耐性表现。利用Windows QTL Cartographer 1.13a软件共检测到5个耐铝毒QTLs,分别位于第1、5、8、9和11染色体上,各个QTL的贡献率在8.64%～18.60%之间,其     

A major QTL associated with preharvest sprouting in rapeseed (<Emphasis Type="Italic">Brassica napus</Emphasis> L.)

Preharvest sprouting (PHS) is one of the most important factors affecting the cereal production worldwide, in regions characterized by rainfall and high humidity during harvest season. It is sometimes a problem in rapeseed (Brassica napus L.), especially in production of commercial F₁ hybrids. To detect quantitative trait loci (QTL) controlling PHS, a F₂ population consisting of 269 F_2:3 lines was created from the cross between a PHS-tolerant line (117AB) and a PHS-susceptible line (7,605). A linkage map was constructed using 35 Simple Sequence Repeat markers and 242 Amplified Fragment Length Polymorphism markers. PHS was measured as a percentage of sprouted seeds on the mother plant, 7 days after physiological maturity. Five putative QTLs for PHS were detected and located on LG2 (N11) and LG7 (N3), respectively. Phenotypic variance explained by each QTL ranged from 4.11 to 50.78% and the five putative QTLs explained about 75.63% of the total phenotypic variance. A major QTL was identified on LG2 (N11) flanked by P3C4180 and C6C13160, which explained 50.78% of the total phenotypic variance. Meanwhile, we detected four significant epistatic interactions with a total contribution of 17.16% of the total phenotypic variance.