玉米营养品质性状的QTL定位

以玉米自交系201×698-3的233个F2:3家系为作图群体,利用SSR分子标记构建遗传图谱.采用随机区组设计,分别在四川雅安和德阳进行田间试验,人工套袋自交种子供性状考查,利用区间作图法进行QTL定位分析.构建了具有134对SSR标记的玉米遗传图谱,覆盖整个基因组1831.4cM,平均图距13.67cM.从16个营养品质性状中共检侧到35个QTL,其中影响蛋白质、淀粉和油份含量的有6个QTL,分别位于第1、2、4和8染色体上,单个性状的QTL为1～3个,每个QTL的作用可解释表型变异的8.1%～21.0%;控制赖氨酸等13种氨基酸含量的有29个QTL,分别位于第1、2、4、8、9和10染色体上,单个性状的QTL为1～5个,每个QTL的作用可解释表型变异的3.5%～30.1%.在本群体的营养品质性状QTL中,超显性效应起主导作用,其次为完全显性效应.     

小麦株高性状的QTL定位分析

以国际小麦作图组织的重组自交系群体W7984×Opata85为材料,在两种不同试验环境(2009年天津东丽区、2009年天津西青区姚村)下,分析其亲本及114个株系群体的株高,并利用QTL作图软件WinQTLCart2.5和区间作图及复合区间作图方法,对控制小麦株高性状的QTL进行定位.共检测到4个与小麦株高相关的QT...     

小麦根系性状对磷胁迫响应QTL定位

土壤缺磷是限制作物产量提高的主要的非生物胁迫之一,培育磷高效基因型是解决这一问题的有效途径.根系对作物的正常生长发育,尤其是营养吸收和磷效率的提高至关重要,所以定位不同磷胁迫条件下根系及其胁迫反应相关数量性状位点(QTL)将有助于磷高效品种的培育.本实验以小麦重组近交系群体(农大3338×Altgold)为材料,结合高密度遗传图谱,在正常供磷、低磷处理和磷饥饿处理条件下共检测到与小麦根系性状(根长、根数和根干重)及其磷胁迫响应有关的QTL位点30个(LOD>2.0),分布在14条染色体上,其中单个位点对表型贡献率大于10%的有5个,在染色体7B上共检测到7个位点.分析发现,根系性状QTL和磷胁迫响应QTL是由不同位点所控制的.此外,控制幼苗根系相关性状的QTL的增效等位基因分散于双亲中,聚合这些来自双亲的增效等位基因,将有可能选育出磷效率明显提高的小麦品种.     

植物数量性状基因座（QTL）的作图与克隆

从QTL定位方法以及QTL精细定位和克隆等方面对植物数量性状遗传进行了综述。     

水稻垩白性状的遗传研究进展

垩白性状是稻米最重要的品质性状之一,阐明水稻垩白性状的遗传机理十分重要.主要对水稻垩白的遗传基础、垩白数量性状位点(Quantitative trait loci,QTL)的定位及其垩白性状相关基因的分离与克隆进行了综述,并提出了相应的遗传改良措施,以期为优质水稻育种提供参考和借鉴.     

叶绿素含量相关QTL定位研究进展

叶绿素含量属于数量性状,利用QTL定位是对其性状进行分析的重要方法之一,有利于加快作物高光效育种进程。本文综述了QTL研究的主要策略和近年来叶绿素含量初步定位和精细定位工作研究进展以及存在的问题,并对未来的研究方向提出建议,以期为今后深入开展叶绿素QTL定位研究及相关基因克隆研究和利用提供参考。     

玉米产量相关性状的QTL定位与剖析

玉米因其自身具有高产潜质而成为了当今世界最重要的粮食作物之一.玉米产量是复杂的数量性状,由许多主/微效基因控制,易受各种环境因素影响.果穗是玉米的主要收获器官,籽粒性状是玉米品质的重要体现,因此发掘玉米穗部性状和籽粒性状相关QTL对玉米的遗传改良,培育优质高产的玉米具有重要意义.本研究白刺包谷(P2)和妻染黄(P13)为亲本构建了包含152个家系的F2∶3作图群体,选择在两亲本间具有多态性的176个微卫星标记构建遗传图谱,对产量相关性状进行了单环境的QTL定位与分析.最终定位到了14个QTL,分布在除9号染色体外的其余9条染色体上,单个QTL可解释的表型变异率为4.9％～18.8％.值得注意的是,在6号染色体上的百粒重和穗行数的一致性QTL(qHKW06-1和qERN06-1)与8号染色体上的穗行数QTL(qERN08-1)是本研究中特有的,其中qERN08-1解释了12.4％的表型变异率.     

复合性状的QTL定位模型构建

【目的】传统复合性状的QTL(quantitative trait locus)定位方法仅仅利用两个或几个构成性状的计算值作为表型值,未考虑复合性状的生物学内涵,从而影响定位的准确性。因此,发展适合于复合性状的QTL定位模型,对于深入解析控制复合性状的遗传结构,进而提高基因定位准确性显得越来越重要。【方法】针对全基因组重测序数据,构建了一个复合性状QTL定位模型(composite traits mapping model, CTM),利用CTM对复合性状进行分解,把分解后的组分以二元或多元正态分布形式整合到QTL作图的框架内。【结果】应用CTM分析杨树材积生长数据,可成功定位到大量与杨树材积生长相关的基因,并与传统方法进行了比较,定位出较多的显著位点,表现出较好的性能。计算机模拟试验表明,所构建的CTM模型在定位复合性状QTL中具有较高的效力,在达到一定的样本数量和遗传力条件下,CTM模型具有较强的效力,样本量和遗传力的增加都能够增加参数估计的精度。【结论】CTM模型有助于复合性状遗传结构的解析,促进林木分子标记辅助育种的开展。     

玉米穗部性状的QTL定位

以玉米自交系L26和095组配的Fz世代为定位群体,采用SSR分子标记技术构建了包括98个位点的连锁图谱,结合F2穗部性状的鉴定结果,利用复合区间作图法对秃尖长等8个穗部性状进行基因定位,共检出21个QTL.其中穗长检测到3个QTL;穗粗、穗行数分别检测到2个QTL;行粒数检测到3个QTL;轴粗检测到2个QTL;200粒质量检测到3个QTL;穗粒质量检测到6个QTL;秃尖长没有检测到QTL.检出的21个QTL中,有10个QTL的解释变异率超过了20%,表现为主效QTL效应.研究还发现,穗部性状QTL在玉米10条染色体上分布不均匀,且成簇分布.该试验中检测到的21个QTL中,有10个影响不同性状的QTL位于3个染色体区域.各个QTL位点上起增、减效作用的等位基因在亲本间分布不均匀.     

玉米产量相关性状的QTL定位与剖析（英文）

玉米因其自身具有高产潜质而成为了当今世界最重要的粮食作物之一.玉米产量是复杂的数量性状,由许多主/微效基因控制,易受各种环境因素影响.果穗是玉米的主要收获器官,籽粒性状是玉米品质的重要体现,因此发掘玉米穗部性状和籽粒性状相关QTL对玉米的遗传改良,培育优质高产的玉米具有重要意义.本研究白刺包谷(P2)和妻染黄(P13)为亲本构建了包含152个家系的F_(2∶3)作图群体,选择在两亲本间具有多态性的176个微卫星标记构建遗传图谱,对产量相关性状进行了单环境的QTL定位与分析.最终定位到了14个QTL,分布在除9号染色体外的其余9条染色体上,单个QTL可解释的表型变异率为4.9%~18.8%.值得注意的是,在6号染色体上的百粒重和穗行数的一致性QTL(qHKW06-1和qERN06-1)与8号染色体上的穗行数QTL(qERN08-1)是本研究中特有的,其中qERN08-1解释了12.4%的表型变异率.     

Statistical method for mapping QTLs for complex traits based on two backcross populations

Most important agronomic and quality traits of crops are quantitative in nature.The genetic variations in such traits are usually controlled by sets of genes called quantitative trait loci (QTLs),and the interactions between QTLs and the environment.It is crucial to understand the genetic architecture of complex traits to design efficient strategies for plant breeding.In the present study,a new experimental design and the corresponding statistical method are presented for QTL mapping.The proposed mapping population is composed of double backcross populations derived from backcrossing both homozygous parents to DH (double haploid) or RI (recombinant inbreeding) lines separately.Such an immortal mapping population allows for across-environment replications,and can be used to estimate dominance effects,epistatic effects,and QTL-environment interactions,remedying the drawbacks of a single backcross population.In this method,the mixed linear model approach is used to estimate the positions of QTLs and their various effects including the QTL additive,dominance,and epistatic effects,and QTL-environment interaction effects (QE).Monte Carlo simulations were conducted to investigate the performance of the proposed method and to assess the accuracy and efficiency of its estimations.The results showed that the proposed method could estimate the positions and the genetic effects of QTLs with high efficiency.     

A new statistical method for mapping QTLs underlying endosperm traits

Genetic expression for an endosperm trait in seeds of cereal crops may be controlled simultaneously by the triploid endosperm genotypes and the diploid maternal genotypes. However, current statistical methods for mapping quantitative trait loci （QTLs） underlying endosperm traits have not been effective in dealing with the putative maternal genetic effects. Combining the quantitative genetic model for diploid maternal traits with triploid endosperm traits, here we propose a new statistical method for mapping QTLs controlling endosperm traits with maternal genetic effects. This method applies the data set of both DNA molecular marker genotypes of each plant in segregation population and the quantitative observations of single endosperms in each plant to map QTL. The maximum likelihood method implemented via the expectation-maximization algorithm was used to the estimate parameters of a putative QTL. Since this method involves the maternal effect that may contribute to endosperm traits, it might be more congruent with the genetics of endosperm traits and more helpful to increasing the precision of QTL mapping. The simulation results show the proposed method provides accurate estimates of the QTL effects and locations with high statistical power.     

Influence of outliers on QTL mapping for complex traits

A method was proposed for the detection of outliers and influential observations in the framework of a mixed linear model, prior to the quantitative trait locus （QTL） mapping analysis. We investigated the impact of outliers on QTL mapping for complex traits in a mouse BXD population, and observed that the dropping of outliers could provide the evidence of additional QTL and epistatic loci affecting the 1 stBrain-OB and the 2ndBrain-OB in a cross of the abovementioned population. The results could also reveal a remarkable increase in estimating heritabilities of QTL in the absence of outliers. In addition, simulations were conducted to investigate the detection powers and false discovery rates （FDRs） of QTLs in the presence and absence of outliers. The results suggested that the presence of a small proportion of outliers could increase the FDR and hence decrease the detection power of QTLs. A drastic increase could be obtained in the estimates of standard errors for position, additive and additive× environment interaction effects of QTLs in the presence of outliers.     

Dissecting anxiety-related QTLs in mice by univariate and multivariate mapping

To detect genes underlying anxiety-related traits in mice,we performed univariate and multivariate QTL mapping analyses of phenotypes obtained from 71 mice of the BXD recombinant inbred (RI) strains (n=528 mice) and their parental strains (C57BL/6J and DBA/2J).Separate and joint mapping analyses were carried out using a linkage map composed of 506 simple sequence repeats (SSRs).The main QTL effects,interactions between pairs of QTLs (epistasis),and their environmental interactions were estimated.The results showed that anxiety-related traits were influenced by multiple QTLs (five main effect QTLs and three epistatic QTLs).Ten potential anxiety-related candidate genes within the QTL intervals on chromosomes 5,13 and 15 were identified.Some of these genes have been reported previously to be associated with the anxiety response.Based on our results,it is suggested that the multivariate QTL mapping approach improves the statistical power for detecting QTL and the precision of parameter estimation.Moreover,multivariate mapping can also detect pleiotropic QTL effects.     

Molecular dissection of plant height QTLs using recombinant inbred lines from hybrids between common wheat （Triticum aestivum L.） and spelt wheat （Triticum spelta L.）

In wheat, plant height is an important agronomic trait, and a number of quantitative trait loci (QTLs) controlling plant height have been located. In this study, using the conditional and unconditional QTL mapping methods, combined with data from five different growth stages over two years of field trials, the developmental behavior for plant height in wheat was dissected. Nine unconditional QTLs and 8 conditional QTLs were identified, of which 6 were detected by both methods. None of the 11 QTLs was detected at all of the 5 investigated developmental stages, but 7 QTLs were detected at certain stages in both years. Further analysis identified 9 unconditional QTLs at different stages, which could explain the phenotypic variation from 4.81% to 17.35%. It was noteworthy that one major QTL designated QHt-4B-2, which was located on chromosome 4B, was detected on May 18 and 25 in both years, and its genetic contributions to plant height ranged from 13.42% to 16.13%. Moreover, of the 8 conditional QTLs identified, six were detected in both years, in the order of QHt-3B→QHt-4B-1→QHt-4B-2→QHt-4D→QHt-5A and QHt-2B expressed at the same developmental stage. The results indicate that QTL expression during plant height development is selective and in a temporal order.     

Quantitative trait loci （QTLs） for quality traits related to protein and starch in wheat

Quality traits in wheat （Triticum aestirum L.） were studied by quantitative trait locus （QTL） analysis in a recombinant inbred line （RIL） population, a set of 131 lines derived from Chuan 35050 × Shannong 483 cross （ChSh）. Grains from RILs were assayed for 21 quality traits related to protein and starch. A total of 35 putative QTLs for 19 traits with a single QTL explaining 7.99-40.52% of phenotypic variations were detected on 10 chromosomes, 1D, 2A, 2D, 3B, 3D, 5A, 6A, 6B, 6D, and 7B. The additive effects of 30 QTLs were positive, contributed by Chuan 35050, the remaining 5 QTLs were negative with the additive effect contributed by Shannong 483. For protein traits, 15 QTLs were obtained and most of them were located on chromosomes 1 D, 3B and 6D, while 20 QTLs for starch traits were detected and most of them were located on chromosomes 3D, 6B and 7B. Only 7 QTLs for protein and starch traits were co-located in three regions on chromosomes 1D, 2A and 2D. These protein and starch trait QTLs showed a distinct distribution pattern in certain regions and chromosomes. Twenty-two QTLs were clustered in 6 regions of 5 chromosomes. Two QTL clusters for protein traits were located on chromosomes 1D and 3B, respectively, three clusters for starch traits on chromosomes 3D, 6B and 7B, and one cluster including protein and starch traits on chromosome 1D.     

Dynamic analysis of QTL for plant height at different developmental stages in maize （Zea mays L.）

Plantheightisoneofimportantagronomictraitsinmaizebreeding.Inthepastfewyears,toincreasetheplantingdensityandpreventplantsfromlodging,studiesonthegeneticmechanismofplantheightweregivengreatattentionto.Sincethe1990s,molecularmarkershaveprovidedapowerfultooltostudythetraitofplantheightatthemolecularlevel[1—3].Butmostofresearchforplantheightonlyfocusedondataatmaturestage.Tillnow,about70genesorQTLshavebeenlocated[4].Moreover,somegeneshavebeenevencloned[5—7].Duringthevege-tativegrowthperiod,plant…     

Comparative mapping of rice root traits in seedlings grown in nutrient or non-nutrient solution

The component and amount of nutrient in the growth medium are the major factors affecting root growth.For the systematic dissection of root gene expression,evaluation of nutrient and non-nutrient solutions was conducted for their effect on root traits and quantitative trait loci(QTL)mapping.Three rice root parameters,maximum root length(MRL),root dry weight(RDW),and root/ shoot ratio of dry weight(RSR),were characterized within a double haploid(DH)population from a cross of ZYQ8(indica)and JX17(japonica).The value of the three root traits in two parents all decreased under the nutrient condition compared to those under the nonnutrient condition,of which RSR decreased up to 2.6-fold on average.In the DH population,more than 70 % lines in MRL,94 % lines in RDW,and all the lines in RSR were scored lower.In total,eight QTLs were identified in nutrient system(5 from JX17 alleles and 3 from ZYQ8 alleles)while five QTLs were detected in non-nutrient system(4 from JX17 alleles and 1 from ZYQ8 alleles).Of them,one QTL for RSR was shared by both culturing systems,seven QTLs were specific in nutrient system and the other four QTLs were specific in non-nutrient system.All 13 QTLs were distributed over 7 rice chromosomes-2,3,4,5,6,9 and 10,respectively.     

Methods for mapping QTLs underlying endosperm traits based on random hybridization design

Cereal grains are mainly composed of endosperms, which are humans’ staple food containing rich nutri- tious substances such as starch, protein and lipid. Many endosperm traits are related to grain yield and quality. Therefore, studying the genetic basis …