基于BC_1F_（1：2）种子的胚乳QTL区间定位

提出了基于分子标记基因型信息来自BC_1F_1母体植株,胚乳性状表型值来自BC_1F_（1：2）单粒种子胚乳的试验设计的胚乳QTL定位的区间作图方法.同时,用计算机全面模拟以验证该模型的可行性,模拟结果表明,只要群体足够大,该模型能有效地进行胚乳性状QTL定位并能估计出胚乳QTL的各种遗传效应和母体效应.     

基于F3种子的胚乳性状QTL区间定位

文章提出了包括胚乳效应和母体效应的胚乳性状QTL定位的统计方法,该方法的实验设计是分子标记基因型信息来自F2母体植株和F3种子胚(或植株),胚乳性状表型值来自F3单粒种子胚乳,称之为两步等级设计。同时,用计算机全面模拟以验证该模型的可行性,模拟结果表明,只要群体足够大,该模型能较有效地进行胚乳性状QTL定位并精确地估计出胚乳QTL的各种遗传效应和母体效应。     

QTL定位的研究方法

QTL 定位就是采用类似单基因定位的方法将QTL定位在遗传图谱上 ,确定 QTL与遗传标记间的距离 (以重组率表示 ) [1]。根据标记数目的不同 ,可分为单标记、双标记和多标记几种方法。根据统计分析方法的不同 ,可分为方差与均值分析法、回归及相关分析法、矩估计及最大似然法等。根据标记区间数可分为零区间作图、单区间作图和多区间作图。此外 ,还有将不同方法结合起来的综合分析方法 ,如 QTL复合区间作图 (CIM)、多区间作图 (MIM)、多 QTL作图、多性状作图 (MTM)等等。建立在标记与数量性状之间相互关联基础上的关联分析方法主要有…     

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。       

数量性状基因座的动态定位策略

分子标记辅助数量性状基因（QTL）定位和效应分析技术为深入研究数量性状的遗传基础提供了一个有力手段．但目前的QTL定位策略是静态的,只估计各QTL在某观察时刻的累积效应,无法了解QTL的表达动态．本文提出一种新的QTL定位策略,称为“动态定位”,能够揭示QTI表达的动态过程,并能极大地提高QTL定位的统计功效.     

QTL×环境互作对标记辅助选择响应的影响

基因型×环境互作是植物数量性状的普通属性和遗传育种改良的关注重点.采用Monte Carlo模拟方法研究了基因型×环境互作对标记辅助选择(Marker-assisted selection,简称MAS)响应的影响,揭示了育种上利用QTL(Quantitafivetrait locus,简称QTL)应当同时考虑其环境互作效应.存在基因型×环境互作下,MAS比普通表型选择更有效.特别以选育广适应性的品种为目标,MAS的优越性更明显.基于单个环境QTLs的MAS,QTL×环境互作效应通常降低了一般选择响应,一般选择响应累积量的降低程度与改良性状的QTL×环境互作效应大小相关.基于多个环境QTLs的MAS,不但产生较高的一般选择响应,而且获得的一般选择响应不受其QTL×环境互作效应大小的影响.但在某一特定环境下获得的总体选择响应仅与改良性状的总遗传率大小有关,普通遗传率和基因型与环境互作遗传率的相对变化对其影响很小.还比较研究了单地和穿梭选择对MAS遗传响应的影响.植物育种者应谨慎将某一环境的QTL信息用于实施另一环境的育种研究.     

水稻QTL分析的研究进展

水稻许多重要的性状是由多基因控制的数量性状,经典的数量遗传学只能把数量性状作为一个整体进行研究。近年来．高密度分子标记连锁图的构建和有效的生物统计学方法的发展使人们对数量性状遗传基础的研究出现了革命性的变化。通过对不同群体内的个体或品系的分子标记基因型和表型数据的共分离分析,能对QTL进行检测和定位。本文对QTL定位的原理和方法进行了介绍,从QTL的数目和效应、上位性效应、QTL基因型与环境的互作、相关性状的QTL以及个体发育不同阶段的QTL等方面对水稻QTL分析的研究进展进行了综述。水稻基因组测序计划已经完成,本文还对基因组时代水稻QTL精细定位和克隆的方法进行了探讨,对QTL分析在水稻育种中的应用前景进行了展望。     

豌豆遗传图谱构建及QTL定位研究进展

豌豆的许多性状是多基因控制的数量性状,QTL定位就是以分子标记技术为工具、以遗传连锁图谱为基础、利用分子标记与QTL之间的连锁关系确定控制数量性状的基因在基因组中的位置.本文对QTL定位原理、方法进行了简单介绍;对豌豆遗传图谱构建及主要性状,如产量、品质、抗病性等QTL定位、遗传效应分析等方面的研究进行综述;对目前基于QTL豌豆分子标记育种存在的问题、应用前景进行了探讨.     

水稻生物学产量及其构成性状的QTL定位

QTL的加性效应、加性×加性上位性效应及它们与环境的互作效应是数量性状的重要遗传分量.利用IR64/Azucena的125个DH品系为群体,分析了水稻生物学产量及其两个构成性状干草产量和谷粒产量的遗传组成.用基于混合模型的复合区间作图(MCIM)方法进行QTL定位.检测到12个位点有加性主效应,27个位点涉及双位点互作,18个位点存在环境互作.结果表明水稻生物学产量和它的两个构成性状普遍存在上位性效应和QE互作效应.此外,还探讨了性状间相关的遗传基础.发现4个QTLs和一对上位性QTLs可能与生物学产量与干草产量之间的正相关有关.3个QTL可能与干草产量与谷粒产量之间的负相关有关.这些结果可能部分地解释了这3个性状相关的遗传原因.通过对水稻生物学产量及其两个构成性状所定位QTL的分析,加深了对数量性状QTL的认识.首先,QTL的上位性效应和QE互作效应是普遍存在的;其次,QTL的多效性或紧密连锁可能是遗传相关的原因,当QTL对两个性状作用的方向相同时可导致正向遗传相关,反之则为负向遗传相关,当有些QTL表现为同向作用而另一些QTL表现为反向作用时,则可削弱性状间的遗传相关性;第三,复合性状的QTL效应可分解为其组成性状的QTL效应,如果QTL对各组成性状的效应方向相反而相互抵消,可使复合性状的QTL效应不易被检测;第四,加性效应的QTL常参预构成上位性效应,而具有上位性效应的QTL并非都有加性主效应,表明忽略上位性的QTL定位方法会降低检测QTL的功效;最后,鉴别不同类型的QTL效应有利于指导育种实践,选择主效QTL适用于多环境,QE互作QTL适用于特定环境,对上位性QTL应强调选择基因组合而并非单个基因.     

鲤鱼体长性状的QTL定位及其遗传效应分析

以大头鲤/荷包红鲤抗寒品系的重组自交系群体及其遗传连锁图谱, 利用Windows Map Manager 2.0的标记回归法进行QTL单标记定位分析和复合区间作图法进行QTL区间检测, 通过置换实验(1 000次重复)确定连锁群显著性水平阈值。在体长性状的标记回归研究中, 共7个标记达到显著水平(P<0.01), 对性状的贡献率为14.00%～27.00%, 其中3个标记达到极显著水平(P<0.001)。HLJ534, HLJ319, HLJ370座位可能与影响鲤鱼体长性状的主效基因连锁。在体长性状的QTL区间定位研究中, 共6个QTL达到连锁群显著水平(P=0.05), 对性状的贡献率为11.33.%～23.12%, 其中2个达到连锁群极显著性水平(P=0.01), 它们的加性效应方向并不一致。HLJ190-HLJ497区间和HLJ479-HLJ483区间是影响鲤鱼体长性状的主效QTL区间。     

Mapping quantitative trait loci underlying triploid endosperm traits

Endosperm, which is derived from two polar nuclei fusing with one sperm, is a triploid tissue in cereals. Endosperm tissue determines the grain quality of cereals. Improving grain quality is one of the important breeding objectives in cereals. However, current statistical methods for mapping quantitative trait loci (QTL) under diploid genetic control have not been effective for dealing with endosperm traits because of the complexity of their triploid inheritance. In this paper, we derive for the first time the conditional probabilities of F(3) endosperm QTL genotypes given different flanking marker genotypes in F(2) plants. Using these probabilities, we develop a multiple linear regression method implemented via the iteratively reweighted least-squares (IRWLS) algorithm and a maximum likelihood method (ML) implemented via the expectation-maximization (EM) algorithm to map QTL underlying endosperm traits. We use the mean value of endosperm traits of F(3) seeds as the dependent variable and the expectations of genotypic indicators for additive and dominance effect of a putative QTL flanked by a pair of markers as independent variables for IRWLS mapping. However, if an endosperm trait is measured quantitatively using a single endosperm sample, the ML mapping method can be used to separate the two dominance effects. Efficiency of the methods is verified through extensive Monte Carlo simulation studies. Results of simulation show that the proposed methods provide accurate estimates of both the QTL effects and locations with very high statistical power. With these methods, we are now ready to map endosperm traits, as we can for regular quantitative trait under diploid control.     

Statistical methods for dissecting triploid endosperm traits using molecular markers. An autogamous model

The endosperm, a result of double fertilization in flowering plants, is a triploid tissue whose genetic composition is more complex than diploid tissue. We present a new maximum-likelihood-based statistical method for mapping quantitative trait loci (QTL) underlying endosperm traits in an autogamous plant. Genetic mapping of quantitative endosperm traits is qualitatively different from traits for other plant organs because the endosperm displays complicated trisomic inheritance and represents a younger generation than its mother plant. Our endosperm mapping method is based on two different experimental designs: (1) a one-stage design in which marker information is derived from the maternal genome and (2) a two-stage hierarchical design in which marker information is derived from both the maternal and offspring genomes (embryos). Under the one-stage design, the position and additive effect of a putative QTL can be well estimated, but the estimates of the dominant and epistatic effects are upward biased and imprecise. The two-stage hierarchical design, which extracts more genetic information from the material, typically improves the accuracy and precision of the dominant and epistatic effects for an endosperm trait. We discuss the effects on the estimation of QTL parameters of different sampling strategies under the two-stage hierarchical design. Our method will be broadly useful in mapping endosperm traits for many agriculturally important crop plants and also make it possible to study the genetic significance of double fertilization in the evolution of higher plants.     

Multiple-interval mapping for quantitative trait loci controlling endosperm traits

Endosperm traits are trisomic inheritant and are of great economic importance because they are usually directly related to grain quality. Mapping for quantitative trait loci (QTL) underlying endosperm traits can provide an efficient way to genetically improve grain quality. As the traditional QTL mapping methods (diploid methods) are usually designed for traits under diploid control, they are not the ideal approaches to map endosperm traits because they ignore the triploid nature of endosperm. In this article, a statistical method considering the triploid nature of endosperm (triploid method) is developed on the basis of multiple-interval mapping (MIM) to map for the underlying QTL. The proposed triploid MIM method is derived to broadly use the marker information either from only the maternal plants or from both the maternal plants and their embryos in the backcross and F2 populations for mapping endosperm traits. Due to the use of multiple intervals simultaneously to take multiple QTL into account, the triploid MIM method can provide better detection power and estimation precision, and as shown in this article it is capable of analyzing and searching for epistatic QTL directly as compared to the traditional diploid methods and current triploid methods using only one (or two) interval(s). Several important issues in endosperm trait mapping, such as the relation and differences between the diploid and triploid methods, variance components of genetic variation, and the problems if effects are present and ignored, are also addressed. Simulations are performed to further explore these issues, to investigate the relative efficiency of different experimental designs, and to evaluate the performance of the proposed and current methods in mapping endosperm traits. The MIM-based triploid method can provide a powerful tool to estimate the genetic architecture of endosperm traits and to assist the marker-assisted selection for the improvement of grain quality in crop science. The triploid MIM FORTRAN program for mapping endosperm traits is available on the worldwide web (http://www.stat.sinica.edu.tw/chkao/).     

A mixture model approach to the mapping of QTL controlling endosperm traits with bulked samples

Endosperm traits are of triploid inheritance and have become a focus of breeding effort for their close relations with the grain quality. Current methods for mapping quantitative trait loci (QTL) underlying endosperm traits are restricted to the use of the phenotypes of single grain samples as input data set, which are often not available in practice due to the small size of the cereal seeds. This paper proposed a statistical model for one specially tailored mapping strategy, where the marker genotypes are obtained from the maternal plants in the segregation population and the phenotypic responses are replaced by the trait means of composite endosperm samples pooled from each plant. It should therefore be more practical and have wide applicability in mapping endosperm traits. The method was implemented by fitting the phenotypic means of endosperms into a Gaussian mixture model. Both the exact and approximate Expectation-Maximization algorithms were proposed to estimate the model parameters. The presence of the QTL was determined by likelihood ratio test statistics. Statistical power and other properties of the new method were investigated and compared to the current single-seed method under a variety of scenarios through simulation studies. The simulations suggest a reasonable sample size should be used to ensure reliable results. The proposed method was also applied to a simulated genome data for further evaluation. As an illustration, a real data of maize was analyzed to find the loci responsible for the popping expansion volume.     

A simple regression method for mapping quantitative trait loci in line crosses using flanking markers

The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F(2) population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two lined loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analysis, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.     

Assessing genetic potential in germplasm collections of crop plants by marker-trait association: a case study for potatoes with quantitative variation of resistance to late blight and maturity type

Genetic diversity of crop plants resulting from breeding and selection is preserved in gene banks. Utilization of such materials for further crop improvement depends on knowledge of agronomic performance and useful traits, which is usually obtained by phenotypic evaluation. Associations between DNA markers and agronomic characters in collections of crop plants would (i) allow assessment of the genetic potential of specific genotypes prior to phenotypic evaluation, (ii) identify superior trait alleles in germplasm collections, (iii) facilitate high resolution QTL mapping and (iv) validate candidate genes responsible for quantitative agronomic characters. The feasibility of association mapping was tested in a gene bank collection of 600 potato cultivars bred between 1850 and 1990 in different countries. The cultivars were genotyped with five DNA markers linked to previously mapped QTL for resistance to late blight and plant maturity. Specific DNA fragments were tested for association with these quantitative characters based on passport evaluation data. Highly significant association with QTL for resistance to late blight and plant maturity was detected with PCR markers specific for R1, a major gene for resistance to late blight, and anonymous PCR markers flanking the R1 locus at 0.2 Centimorgan genetic distance. The marker alleles associated with increased resistance and later plant maturity were traced to an introgression from the wild species S. demissum. These DNA markers are the first marker that are diagnostic for quantitative agronomic characters in a large collection of cultivars.     

Mapping quantitative trait loci using molecular marker linkage maps

Summary High-density restriction fragment length polymorphism (RFLP) and allozyme linkage maps have been developed in several plant species. These maps make it technically feasible to map quantitative trait loci (QTL) using methods based on flanking marker genetic models. In this paper, we describe flanking marker models for doubled haploid (DH), recombinant inbred (RI), backcross (BC), F₁ testcross (F₁TC), DH testcross (DHTC), recombinant inbred testcross (RITC), F₂, and F₃ progeny. These models are functions of the means of quantitative trait locus genotypes and recombination frequencies between marker and quantitative trait loci. In addition to the genetic models, we describe maximum likelihood methods for estimating these parameters using linear, nonlinear, and univariate or multivariate normal distribution mixture models. We defined recombination frequency estimators for backcross and F₂ progeny group genetic models using the parameters of linear models. In addition, we found a genetically unbiased estimator of the QTL heterozygote mean using a linear function of marker means. In nonlinear models, recombination frequencies are estimated less efficiently than the means of quantitative trait locus genotypes. Recombination frequency estimation efficiency decreases as the distance between markers decreases, because the number of progeny in recombinant marker classes decreases. Mean estimation efficiency is nearly equal for these methods.