QTL for grain yield and yield parameters in winter wheat(Triticum aestivum L.)

Field trials with a set of 108 doubled haploid lines(DHs) derived from a cross between the Chinese winter wheat cvs.CA9613 and H1488 were run at Beijing(China).Phenotypic data were recorded for major agronomic yield traits,i.e.grain weight per ear,grain number per ear and thousand grain weight(Tgw) in two field trials at Beijing.Based on the phenotypic data and a genetic map comprising 168 SSR markers,an analysis of quantitative trait loci(QTL) was carried out for yield and yield parameters using the composite interval mapping(CIM) approach.A total of 14 QTL were detected for these traits across two environments.Four of these QTL located on chromosomes 1A and 2B,respectively,exhibited pleiotropic effects.Loci showing pleiotropic effects will be very useful for understanding the homeologous relationships of QTL and designing an appropriate marker-assisted selection programme by multi-trait selection in order to accumulate("pyramide") favorable alleles at different loci.     

QTL Analysis of the Oil Content and the Hull Content in Brassica napus L.

The QTLs of the oil content and the hull content were analyzed in Brassica napus L. By constructing the linkage map. The F26 RIL population with 188 lines, derived from the cross of GH06 × P147, was used as the mapping population. The SRAP, SSR, AFLP, and TRAP markers were used to construct the linkage map, and the composite interval mapping (CIM) to identify the quantitative trait loci associated with the oil content and the hull content. 300 markers were integrated into 19 linkage groups, covering 1 248.5 cM in total. Seven QTLs were found to be responsible for the oil content with the single contribution to phenotypic variance ranging from 3.73 to 10.46%; four QTLs were found for the hull content with the single contribution to phenotypic variance ranging from 4.89 to 6.84%. The yellow-seeded Brassica napus L. Has the advantage of higher oil content and the hull content has a significant effect on the oil content. In addition, the SRAP marker is good for detecting QTL.     

Conditional QTL Mapping of Sedimentation Volume on Seven Quality Traits in Common Wheat

To evaluate the possible genetic interrelationships between flour components and the sedimentation volume（SD）,a doubled haploid（DH） population comprising 168 lines were used to identify the conditional quantitative trait loci（QTLs） for SD in three environments.Ten additive QTLs and 15 pairs of epistatic QTLs were detected for SD through unconditional and conditional QTL mapping.Three major additive QTLs were detected for SD conditioned on the seven quality traits.Two additive QTLs were found to be independent of these traits.Three additive QTLs were suppressed by three of the seven traits because of non-detection in unconditional mapping.Three pairs of epistatic QTLs were completely affected by the seven traits because of detection in unconditional mapping but no-detection in conditional mapping.Twelve pairs of epistatic QTLs were detected in conditional mapping.Our results indicated that conditional mapping could contribute to a better understanding of the interdependence of different and closely correlated traits at the QTL molecular level,especially some minor QTLs were found.The conditional mapping approach provides new insights that will make it possible to avoid the disadvantages of different traits by breeding through molecular design.     

QTL Mapping of Isoflavone,Oil and Protein Contents in Soybean （Glycine max L. Merr.）

Soybean （Glycine max L. Merr.） is the world＇s foremost source of edible plant oil and proteins, meantime, the biologically active secondary metabolites such as saponins and isoflavones are benefit to human health. The objective of this study was to identify quantitative trait loci （QTL） and epistatic interactions associated with isoflavone, protein, and oil contents in soybean seeds. An F13 recombinant inbred line （RIL） comprising 474 lines was derived from a cross between Jindou 23 and Huibuzhi cultivars. SSR technique was employed for mapping of the QTLs. The QTLs for isoflavone, protein, and oil contents were analyzed and 23 QTLs were detected based on the constructed linkage map. Six QTLs for isoflavone content were localized in linkage groups J, N, D2, and G, eleven QTLs for oil content were localized in the linkage groups A1, A2, B2, C2, and D2, and six QTLs for protein content were localized in linkage groups B2, C2, G, and H1. The correlative analysis demonstrated that the isoflavone content had significant correlation with protein content, while significantly negative correlations was existed between oil and protein content, and significantly positive correlations was existed between protein and oil content. All these findings have laid an important basis for the marker assisted breeding in soybean. The phenotypic correlations of quantitative traits may be resulted from the correlation of the QTL controlling those traits.     

Genetic Analysis and Molecular Mapping of Two SMV-Resistance Traits in Soybean： Adult-Plant Resistance and Resistance to Seed Coat Mottling

Soybean mosaic virus （SMV） could lead to adult-plant system diseases, and cause mottling of soybean seeds. Genetic analysis and molecular mapping were conducted using an F2 population and derived F3 families from two crosses of Dongnong 3C624 （susceptible）x Dongnong 8143 （resistant） and Dongnong 3C628 （susceptible）× Tie 6915 （resistant）. Simple sequence repeat （SSR） markers with bulked segregation analysis （BSA） were used to conduct genetic mapping of the resistance to SMV1 in the segregating populations. The results indicated that resistance to SMV1 in adultplants and the resistance to seed coat mottling in Dongnong 8143 and Tie 6915 was separately controlled by one single dominant gene. The two dominant genes were identified to be linked on the MLG F by Mendel＇s genetics and SSR genetic mapping. The order and distance of markers DPRSMV1 and DSRSMV1 were Sat 229-6.9 cM-DSRSMV1-4.6 cM-Sat_317-3.6 cM-DPRSMV1-5.2 cM-Satt335. The order and distance of markers TPRSMV1 and TSRSMV1 was Satt160-16.1 cM-TPRSMV1-7.3 cM-Satt516-2.0 cM-TSRSMV1-4.5 cM-Sat_133. This research provides the useful information for breeders to select the two types of SMV resistance simultaneously in soybean breeding through molecular marker-assisted selection （MAS）.     

Quantitative Trait Loci for Asian Corn Borer Resistance in Maize Population Mc37×Zi330

Asian corn borer, Ostrinia furnacalis （Guen6e）, is the most important pest of maize in China and Southeast Asia. The objective of this study was to understand the genetic basis for Asian corn borer resistance. The study included 162 F2：3 populations derived from Mc37 （resistant） × Zi330 （susceptible） and field data were collected in 2004 and 2005. A linkage map was constructed from 118 SSR markers. Combined quantitative trait loci （QTL） analysis combined across environments was performed by composite interval mapping method （CIM）. Four QTLs with additive effects were detected for resistance to Asian corn borer leaf feeding damage in chromosome bins 1.08, 2.04/05, 4.01, and 10.04. Three putative QTLs were detected for Asian corn borer stalk damage in chromosome bins 1.01, 2.05, and 9.01. Maize resistance to Asian corn borer and European corn borer, O. nubilalis （Hubner）, may share a common mechanism. Genes responsible for DIMBOA biosynthesis are in chromosome bin 4.01 and may increase the observed resistance to Asian corn borer leaf feeding.     

QTL Mapping of Chlorophyll Contents in Rice

The objectives of this study were to investigate the genetic factors controlling the chlorophyll content of rice leaf using QTL analysis. A linkage map consisting of 207 DNA markers was constructed by using 247 recombinant inbred lines (RILs) derived from an indica-indica rice cross of Zhenshan97B×Milyang 46. In 2002 and 2003, the contents of chlorophyll a and b of the parents and the 247 RILs were measured on the top first leaf, top second leaf, and top third leaf, respectively. The software QTLMapper 1.6 was used to detect quantitative trait loci (QTLs), additive by environment (AE) interactions, and epistatic by environment (AAE) interactions. A total of eight QTLs in four intervals were detected to have significant additive effects on chlorophyll a and b contents at different leaf positions, with 1.96-9.77% of phenotypic variation explained by a single QTL, and two QTLs with significant AE interactions were detected. Epistasis analysis detected nine significant additive-by-additive interactions on chlorophyll a and b contents, and one pair of QTLs with significant AAE interactions was detected. On comparison with QTLs for yield traits detected in the same population, it was found in many cases that the QTLs for chlorophyll a and b contents and those for yield traits were located in the same chromosome intervals.     

Research on the modeling method of soybean leaf’s structure simulation

Leaf is one of the most important organs of soybean.The modeling of soybean leaf structure is useful to research of leaf function.The paper discussed it from two aspects that were distilling method of leaf profile and establishing method of leaf simulation model.It put forward basic method of soybean leaf digital process,and successfully established simulation model of soybean leaf structure based on L-system.It also solved a critical problem in the process of establishing soybean growth simulation model.And the research had guiding significance to establishment of soybean plant model.     

Preliminary Studies on Thickness of Nondestructive Rice （Oryza sativa L.） Leaf Blade

Two varieties, Yuexinzhan and Guangchao 3, were used to study leaf thickness in rice in this experiment. The thickness of the leaf blade was measured by the nondestructive leaf thickness instrument, which was modified from the thickness instrument for steel objects （John Bull, England）. The contacting area between the leaf and the probe of the instrument was 0.5 cm^2. There was no significant difference between the thickness of steel materials measured by the nondestructive rice leaf thickness instrument and the micrometer. The correlation between the thickness of the rice leaf blade measured by the nondestructive rice leaf thickness instrument and the specific leaf weight （SLW） was significant （P 〈 0.05 or P〈 0.01）. The results also showed that the rice leaf thickness was uneven and asymmetric. The thickness and SLW of flag leaf tended to increase from the base to the tip of the leaf blade. The middle part of the second and third top leaf was the thickest, but no significant difference in thickness between the basal part and the fore part was found. Drawing a line on the main vein in the top three leaves, the left part was thinner than the right part. The thickness of the lower leaves （6/0-9/0） on the main culm tended to increase with increasing positions of the leaves in the early and middle stages, but the tendency was not the same for the higher leaves （10/0 upwards）, although the higher leaves （10/0 upward） were thicker than the lower leaves （9/0 or downward）. Furthermore, different CO2 concentrations （550±30, 460 ± 30 μmol mol^-1） in the growth boxes had no effect on the thickness of rice leaf blades. It can be concluded that the measurement of rice leaf thickness using the nondestructive rice leaf thickness instrument is simple, precise, and nondestructive.     

QTL Detection and Epistasis Analysis for Heading Date Using Single Segment Substitution Lines in Rice （Oryza sativa L.）

Heading date of rice is a key agronomic trait determining cultivated areas and seasons and affecting yield. In the present study, ifve primary single segment substitution lines with the same genetic background were used to detect quantitative trait loci （QTLs） for heading date in rice. Two QTLs, qHD3 and qHD6 on the short arm of chromosome 3 and the short arm of chromosome 6, respectively, were identiifed under natural long-day （NLD）. Nineteen secondary single segment substitution lines （SSSLs） and seven double segments pyramiding lines were designed to map the two QTLs and to evaluate their epistatic interaction between them. By overlapping mapping, qHD3 was mapped in a 791-kb interval between SSR markers RM3894 and RM569 and qHD6 in a 1 125-kb interval between RM587 and RM225. Results revealed the existence of epistatic interaction between qHD3 and qHD6 under natural long-day （NLD）. It was also found that qHD3 and qHD6 had signiifcant effects on plant height and yield traits, indicating that both of the QTLs have pleiotropic effects.     

夏大豆重组自交系群体遗传图谱构建及开花期QTL分析

【背景】开花期是大豆重要的生育期性状,不仅决定了大豆品种的适种范围,而且对大豆的产量和品质有重要影响。江淮地区是中国重要的大豆产区,目前对该地区夏大豆开花期性状遗传基础研究相对较少。【目的】利用2个夏大豆材料杂交衍生的重组自交系群体对开花期进行QTL定位,为分子标记辅助选择育种和基因克隆提供依据。【方法】以科丰35（KF35）和南农1138-2（NN1138-2）为亲本,构建了含91个家系（F_2:8）的重组自交系群体（NJK3N-RIL）,在6个环境下调查开花期性状数据。利用限制位点相关DNA测序（restriction-site associated DNA sequencing,RAD-seq）技术对群体亲本及家系材料进行SNP标记分型,并利用窗口滑动法进行bin标记划分。利用bin标记构建该群体的遗传图谱,结合多年多点的表型数据,使用QTL Network 2.2软件中的基于混合线性模型的复合区间作图法（mixed-model based composite interval mapping,MCIM）和Windows QTL Cartographer V2.5_011软件中的复合区间作图法（composite interval mapping,CIM）对开花期性状进行QTL分析。【结果】在大豆全基因组范围内共获得36 778个高质量SNP标记,被划分为1 733个bin标记。利用1 733个bin标记构建了一张覆盖大豆20条染色体遗传图谱,图谱长度为2 362.4 cM,标记间平均遗传距离为1.4 cM。利用MCIM法共检测到9个控制开花期的加性QTL、2对上位性QTL和1个环境互作QTL,3种效应累积贡献率分别为63.9%、4.6%和2.1%。利用CIM法共检测到10个控制开花期的QTL,其中qFT-8-1、qFT-11-1、qFT-15-1、qFT-16-1能在3个及以上环境检测到。综合2种分析方法,共检测到12个开花期QTL,其中qFT-8-1、qFT-11-1、qFT-15-1、qFT-16-1、qFT-16-2、qFT-20-1和qFT-20-2等能够被2种方法检测到。同时qFT-5-1、qFT-8-1、qFT-8-2、qFT-13-1、qFT-15-1和qFT-20-2等是本研究新检测到的开花期QTL。【结论】夏大豆开花期遗传构成复杂,但加性QTL效应占绝对优势,上位性互作及环境互作效应对开花期影响较小。qFT-8-1、qFT-11-1、qFT-15-1、qFT-16-1能够被2种方法在多个环境中检测到,是NJK3N-RIL群体中控制开花期的重要位点。     

多环境下玉米保绿相关性状遗传位点的挖掘

【目的】功能性保绿通常被认为是包括玉米在内的主要作物品种的理想性状。挖掘新的控制玉米保绿相关位点和候选基因,为玉米保绿遗传研究提供理论基础。【方法】以150份由许178和K12组配的重组自交系（recombinant inbred lines,RIL）群体为材料,通过Windows QTL Cartographer V2.5的复合区间作图法（composite interval mapping,CIM）对3个保绿相关性状（保绿度（visual stay green,VSG）、吐丝期绿叶数（green leaf number at silking stage,GLNS）和成熟期绿叶数（green leaf number at mature stage,GLNM））进行QTL定位。同时,以139份自然材料组成的关联群体为材料,基于混合线性模型（mixed linear model,MLM）,结合50 790个高质量SNP标记,对这3个性状进行全基因组关联分析（genome-wide association study,GWAS）。【结果】基于CIM,利用单环境下的表型值和最佳线性无偏估计值（best linear unbiased prediction,BLUP）对GLNM、GLNS和VSG进行定位,共检测到37个QTL,分布在除第10染色体以外的其他染色体上,LOD范围为2.58—11.36,表型贡献率为4.34%—22.40%。GLNM、GLNS和VSG性状分别检测到14、12和11个位点。其中,4个遗传稳定的QTL（qGLNS2-1、qVSG1-1、qVSG1-2和qVSG7-1）,在3个及以上不同单环境中同时被检测到。利用MLM对保绿相关性状进行全基因组关联分析,共检测到44个超过阈值线的显著SNP,根据SNP标记在B73参考基因组的物理位置,发现共有15个位点落在连锁分析定位到的QTL区间内。【结论】通过QTL定位和全基因组关联分析共同检测到4个遗传稳定的共定位遗传区段（对应的B73参考基因组V4版物理位置区间为第1染色体6.2—8.2 Mb、第2染色体209.1—221.4 Mb、第6染色体96.8—102.1 Mb、第7染色体4.9—11.4 Mb）,并挖掘到4个与光合作用和抗逆相关的候选基因（Zm00001d006119、Zm00001d018975、Zm00001d006535和Zm00001d036763）。     

大豆蛋白质和油分含量QTL定位及互作分析

【目的】定位大豆蛋白质和油分含量QTL及互作分析,为大豆品质性状QTL精细定位和分子辅助育种提供基础。【方法】以Charleston和东农594为亲本,构建了含147个株系的重组自交系,以F2:19-F2:20代重组自交系为试验材料,利用Windows QTL Cartographer V. 2.5软件的复合区间作图法和多重区间作图法,对该群体的蛋白质和油分含量进行QTL定位分析,并利用QTL Network 2.1软件分析QTL间的上位性效应及环境互作效应。【结果】采用CIM和MIM 2种算法在2011和2012年哈尔滨、红兴隆、佳木斯和牡丹江每年3个地点共6个种植环境下共定位了9个蛋白质和11个油分含量QTL。蛋白质含量QTL分布在6个连锁群,分别在A1、C2、D1a、G、H和O连锁群上,对表型效应的贡献率为5.3%-18.6%,在H连锁群上的qPro-H-1贡献率最大,为18.6%,在D1a连锁群上的qPro-D1a-2贡献率最小,为5.3%,在单种植环境下有5个蛋白质含量QTL被2种算法同时检测到,分别是qPro-O-1、qPro-A1-1、qPro-D1a-1、qPro-D1a-2和qPro-C2-2。油分含量QTL分布在8个连锁群,分别在A1、A2、B1、C2、D1a、E、L和M连锁群上,对表型效应的贡献率为7.1%-24.4%,在B1连锁群上的qOil-B1-2贡献率最大,为24.4%,在C2连锁上的qOil-C2-3贡献率最小,为7.1%,在单种植环境下有2个油分含量的QTL被2种算法同时检测到,分别为qOil-C2-1和qOil-M-1。另外,有2个油分含量QTL在2个以上种植环境重复检测到,为2011年哈尔滨和2011年红兴隆2个种植环境下同时检测出的qOil-A1-1,2011红兴隆、2011牡丹江和2012哈尔滨3个地点同时被检测出的qOil-B1-2。在互作效应分析中,共检测出3对蛋白质上位效应QTL和4对油分上位效应QTL,在蛋白质上位性分析中,上位效应值在0.2068-0.3124,贡献率在0.0227%-0.0265%,分布在A1、C2、D1和E连锁群上,其中,qPro-A1-3与qPro-C2-1效应值为负,其余2对效应值为正,连锁群A1,D1a均有2个QTL发生互作。在油分上位性分析中,上位效应值在0.0926-0.1682,贡献率在0.0294%-0.0754%,分布在A1、C2、I、J、N和O连锁群上,其中,qOil-C2-4与qOil-N-1效应值为负,其余3对效应值为正,在N连锁群的qOil-N-1同时与2个QTL发生互作,分别是C2连锁群上的qOil-C2-1和qOil-C2-4。在与环境互作中,qPro-D1a-3与qPro-E-1在2012年佳木斯地点没检测出,其余6对都检测出与环境的互作效应,贡献率分别为0.0001%-0.0378%,互作效应都较小,明显小于自身的加性效应。【结论】定位到9个蛋白质相关QTL和11个油分相关QTL,并发现3对蛋白质含量上位性效应QTL和4对油分含量上位性QTL。     

二倍体数量性状QTL定位方法

论述了数量性状基因（quantitative trait locus,QTL）定位方法的产生与发展,并总结了每种方法的优缺点,特别是对区间作图方法和复合区间作图方法进行了较详细的介绍。     

大豆γ-生育酚的混合遗传分析与QTL定位

【目的】通过对大豆γ-生育酚进行混合遗传和QTL定位分析,了解其遗传机制,定位其主效QTL,为高γ-生育酚含量大豆品种的选育奠定基础。【方法】以栽培大豆晋豆23为母本,以山西农家品种大豆灰布支黑豆为父本杂交衍生的重组自交系作为供试群体构建遗传图谱。图谱全长2 047.6 cM,平均图距8.8 cM,包括227个SSR标记,232个标记位点。重组自交系试验群体及亲本材料分别于2011年、2012年和2015年夏季在河南省农业科学院原阳试验基地种植,冬季在海南省三亚南繁试验基地种植。田间试验采取随机区组设计,2次重复。从6个环境中每个家系选取15.00 g籽粒饱满,大小一致的大豆种子,利用高效液相色谱法定性、定量测定样品中的γ-生育酚含量。采用主基因+多基因混合遗传分离分析法,对大豆γ-生育酚含量进行混合遗传分析;采用WinQTLCart 2.5复合区间作图法,对大豆γ-生育酚含量进行QTL定位分析。【结果】主基因+多基因混合遗传分析表明,γ-生育酚受2对重叠作用主基因×加性多基因控制。遗传基因分布在双亲中。三亚试验数据检测出2对主基因间上位性效应值为0.4010—0.5169和多基因的加性效应值为0.1797—0.2146,主基因遗传率为11.27%—13.05%,多基因遗传率为82.51%—86.55%,多基因效应大于主基因效应。原阳试验数据检测到2对主基因间上位性效应值为0.9646—1.8455,主基因遗传率为39.51%—58.96%,没有检测出多基因效应。采用WinQTLCart 2.5复合区间作图（CIM）共检测到9个影响γ-生育酚的QTL,分布于A1（Chr.5）、A2（Chr.8）、C1（Chr.4）、K（Chr.9）、M（Chr.7）和G（Chr.8）6条染色体中,单个QTL的贡献率为7.29%—29.55%。qγ-G-1同时在2011年原阳、2012年三亚、2015年三亚3个环境下检测到,且均定位在G（Chr.18）染色体Satt275—Satt038标记区间0.01 cM处,解释的表型变异分别为8.97%、8.12%和7.91%。qγ-A1-1同时在2011年原阳和2015年原阳2个环境下检测到,且均定位在A1（Chr.5）染色体Satt276—Satt364标记区间0.01 cM处,解释的表型变异分别为29.54%和28.23%。qγ-G-1和qγ-A1-1 2个QTL能够稳定遗传。【结论】γ-T最适遗传模型符合MX2-Duplicate-A,即2对重叠作用主基因×加性多基因模型。其遗传同时受到基因型、环境和上位性的影响。检测到γ-T的2个稳定主效QTL,Satt275—Satt038和Satt276—Satt364是共位标记区间。     

基于四交群体的玉米叶夹角和叶向值QTL定位分析

选育耐密紧凑株型是增加玉米单位面积产量的重要途径之一,而叶夹角和叶向值是衡量株型的重要参数。本研究选用叶夹角和叶向值存在差异的玉米自交系郑58、PH6WC、87-1和自330构建1个四交(郑58/豫87-1//PH6WC/自330)组合,以228个四交F1单株为作图群体,构建了1张含225个SSR位点,全长1 387.2cM的玉米分子标记遗传连锁图谱,标记间平均间距为6.19cM。基于四交群体应用区间作图法检测4个环境下的QTL,共检测到13个叶夹角相关QTL,分别位于第1、2、3、4、5、7和10染色体上,单个QTL可解释5.1%~20.0%的表型变异;检测到15个叶向值相关QTL,分别位于第1、2、4、5、7、8和9染色体上,单个QTL可解释5.4%~20.1%的表型变异。其中qLA-E2-2和qLA-E4-2落在同一标记区间umc1692-umc2297(bin 5.03),分别解释16.6%和13.2%的表型变异;qLO-E1-1、qLO-E3-2和qLA-E4-1落在同一标记区间umc1568-bnlg1953(bin1.02),分别解释10.1%、19.9%和12.3%的表型变异;qLO-E2-1和qLO-E3-1落在同一标记区间phi056-phi427913(bin 1.01),分别解释13.8%和10.0%的表型变异。这些多个环境共同检测到的QTL将为玉米耐密理想株型育种中叶夹角叶向值的分子标记辅助选择提供有益信息,加速耐密株型玉米品种的选育。     

甘蓝型油菜含油量及皮壳率的QTL分析

【目的】通过构建甘蓝型油菜遗传连锁图谱，对含油量及皮壳率进行QTL分析。【方法】以黄籽亲本GH06和黑籽亲本P174杂交得到的F2:6家系的188个株系为作图群体，利用SRAP、SSR、AFLP及TRAP四种标记构建遗传连锁图谱，在此基础上采用复合区间作图法（CIM）对含油量及皮壳率两个性状进行QTL分析。【结果】图谱包含19个连锁群、300个标记位点，总长为1 248.5 cM。共得到7个与含油量相关的QTL，单位点遗传贡献率在3.73%～10.46%之间；4个与皮壳率相关的QTL，单位点遗传贡献率在4.89%～6.84%之间。【结论】黄籽油菜具有高含油量的优势；皮壳率对含油量有显著影响；SRAP标记具有较好的检测QTL位点的能力。     

大豆硬实性QTL定位及上位性互作分析

试验以东农46和L-100杂交构建的包含127个家系F2:10、F2:11、F2:12代重组自交系群体为试验材料,结合三年五点以及4个浸种时间段,调查大豆硬实率和种子吸水量两个表型性状,采用QTL IciMapping 4.1完备区间作图法,作QTL定位和加性效应分析.结果共检测到26个与大豆硬实性相关QTLs,分布于10条染色体,贡献率5.29％～47.31％,有8个位点在两个性状QTL定位中发生标记区间重叠,分别为qHS-2-1和qWAS-2-1、qHS-2-2和qWAS-2-2、qHS-4-2和qWAS-4-1、qHS-13-1和qWAS-13-3,其中,qHS-2-2和qWAS-2-2贡献率平均值最大,>25％.共检测到51对加性×加性上位互作QTL,贡献率为1.01％～8.40％.     

玉米杂交种苏玉16农艺性状的QTL分析

采用强优势玉米杂交种苏玉16(JB×Y53)的两个亲本自交系,配置F2和相应F2∶3作图群体。利用154个SSR标记构建了分子标记连锁图谱,覆盖全基因组1 735.0 cM,标记间平均图距为11.3 cM。同时考察F2和F2∶3群体的株高、穗位高、抽穗期和散粉期等共10个重要农艺性状,采用联合F2和F2∶3群体的作图方法定位有关QTL。此外,采用QTL Cartographer V2.5软件分别对F2和F2∶3群体进行了有关QTL的重演性验证。结果表明,采用联合作图方法在调查的10个性状上共定位到93个QTL,采用QTL Cartographer V2.5软件共定位到96个QTL,其中56个能用两种方法重演验证。     

大豆对大豆花叶病毒Sa株系抗扩展特性的遗传分析

作物抗性遗传研究可为抗性育种提供理论基础.在溧水中子黄豆×南农493-1杂交组合的244个F2∶3家系中,随机取171个F2∶3家系为材料,用150对SSR分子标记,通过JoinMap30软件构建了包括70对分子标记的25个连锁群;采用平均病级和综合病情指数两种指标,用Win QTL Cartographer V25软件的多区间作图法进行QTL定位.结果表明:大豆对大豆花叶病毒Sa株系的抗扩展平均病级和综合病情指数均有4个QTL,其中在C2-b连锁群的satt422-satt640标记间和D2-a连锁群有共同的QTL,两性状的4个和5个互作QTL可分别解释表型变异的1514%和526%.这些结果为抗性性状的遗传剖析和标记辅助育种提供理论依据.