分子标记在食用蕈菌遗传育种中的应用*

分子标记是以个体间遗传物质内核苷酸序列变异为基础的遗传标记,是DNA水平遗传变异的直接反映。与其它几种遗传标记——形态标记、同工酶标记、细胞标记相比,分子标记具有很多优越性：大多数分子标记共显性遗传,对隐性的农艺性状的选择十分便利;基因组变异极其丰富,分子标记的数量几乎是无限的;在生物发育的不同阶段,不同组织的DNA都可用于标记分析;分子标记直接揭示来自DNA的变异;表现为中性,不影响目标性状的表达,与不良性状无必然的连锁。 随着分子生物学技术的发展,目前已经开发了几十种基于DNA多态性的分子标记,如RF…     

遗传标记的发展

遗传标记 (Genetic Markers)是基因型特殊的易于识别的表现形式。它一般具有较强的多态性、表现的共显性、不影响重要的农艺性状和经济方便、易于观察记载等优点。它在遗传学的建立和发展过程中起着重要作用。随着遗传学的发展 ,遗传标记也在不断的发展。从遗传学的建立到现在 ,遗传标记的发展主要经历了 4个阶段 ,表现出了 4种类型。1　形态标记 (Morphological Markers)形态标记是指生物的外部特征特性。包括质量性状作遗传标记和数量性状作遗传标记 ,例如人的肤色、作物的株高、种子的粒形和动物的体重等。它是最早被使用和研究的一类…     

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.     

RIL群体区间分子标记-QTL定位的相关方法

分析了RIL群体中以分子区间标记进行QTL定位的相关方法．通过对分子标记赋值可获得与数量性状表型值的简单相关系数．然后,在此基础上进行连锁检验．此外．在特定情况下利用R值,可以估计数量性状座位(QTL)和分子标记位点(ML)间的重组值．     

玉米抗甘蔗花叶病毒分子标记开发

由甘蔗花叶病毒引起的玉米矮花叶病是我国黄淮海地区玉米生产的重要病害,开发抗矮花叶病基因分子标记是开展抗病分子标记辅助育种的基础。本文基于玉米6．00-6．01区域的“一致性抗甘蔗花叶病毒QTL区间”寻找抗病基因的功能保守域,依据序列多态性开发出抗病分子标记InDel-130和InDel-110,在已知抗性的102份玉米自交系中进行验证。通过分析标记抗病带型和感病带型中的抗病和感病自交系数目,卡平方测验表明标记InDel-130在供试自交系中与抗病性的表现独立无关．而标记InDel-110与甘蔗花叶病毒抗性高度相关,为共显性标记,可用于玉米抗甘蔗花叶病毒种质筛选和分子标记辅助育种。     

几种基于PCR的分子标记在甘薯遗传多样性研究中的应用

遗传多样性是甘薯品种遗传改良的基础。由于分子标记具有数量极大、不受环境及基因表达与否的限制、多为共显性、不影响生物性状表现等优点,现已在甘薯遗传多样性研究中得到广泛应用。本文比较了RAPD、AFLP、SSR、ISSR和SRAP等几种基于PCR的分子标记方法,分别从遗传差异和亲缘聚类分析两方面,对它们在甘薯遗传多样性研究中的应用进行了综述。对比分析表明ISSR是一种共显性、成本较低、重复性好、多态性较高且非常有发展前途的分子标记,并已经被广泛应用到甘薯遗传多样性、物种亲缘关系、系统分类和辅助育种研究中。     

甜瓜遗传图谱的构建及果实与种子QTL分析

以遗传性状差异较大的甜瓜材料日本安农二号与新疆哈密瓜K413杂交产生的143个F2单株为作图群体,以AFLP与SSR分子标记为主构建了包含12个连锁群、142个遗传标记位点的甜瓜遗传图谱,其中包括121个AFLP标记、16个SSR标记、3个STS标记、2个性状标记,连锁群总长度为1 014.2 cM。应用复合区间作图法对甜瓜果实的大小、长宽比、糖度、硬度以及甜瓜种子的长、宽、形状、重量等性状进行遗传定位与分析。基因定位结果显示控制果肉颜色的基因位于C9连锁群AFLP分子标记NDAA与NCFA之间。其他性状表现为数量性状控制,共检测到25个数量性状基因座,不同性状基因座位有重叠分布的特点。其中C5连锁群标记NCA-N73C区间检测到QTLs Sl5.1、Sw5.1和Swt5.1分别控制种子长、宽和千粒重,分别可解释表型变异的17%、19%和23%。该区域包含的来自母本安农二号的基因位点对甜瓜种子的长、宽、千粒重均有明显的抑制作用;位于C8连锁群标记N73A与NFDA间的QTL通过影响种子的宽度从而影响种子的形状与重量;同样位于C8连锁群的果实长宽比QTL Fs8.1在F2和F3中均检测到,分别解释表型变异的25%和19%,表现为部分显性,来自安农二号的等位基因抑制甜瓜果实伸长,生成圆形甜瓜;还发现控制甜瓜果实心糖、边糖、果实硬度的QTL各一个。     

用分子标记研究禾本科赖草属植物可溶性碳水化合物、色素积累和生长特性之间的遗传相关性

在低温条件下 ,冷季草的可溶性碳水化合物通常与紫色素的积累、生长的缓慢性及对寒冷的适应性具有相关性。Vrn_1基因对一年生冬性禾本科物种的春化、生长和可溶性碳水化合物的积累有重要作用。本研究以Ley muscinereus×L .triticoides的杂种F1开放授粉获得的F2 群体为材料 ,用 2 0 4个未定位的AFLP分子标记和几个基因组特定的与vrn_1相连锁的DNA标记检测了控制可溶性碳水化合物的积累、紫色素的积累和生长特性等几个数量性状的QTL。根据生长特性和适应性可将Leymuscinereus和L .triticoides区分开来。研究表明可溶性碳水化合物与紫色素的积累呈正相关 ,而且有关基因对这两种性状具有多效性。与之相类似 ,分蘖、叶发育、叶生长、草被剪后的再生长和地下茎的蔓延性这些性状之间也呈正相关 ,控制这些性状的基因具有多效性。但是可溶性碳水化合物的积累与生长的缓慢性无相关性。有几个分子标记包括与vrn_Xm1邻近的一个分子标记对叶可溶性碳水化合物的浓度和低温生长具有正效应。而与vrn_Ns1邻近的一个DNA标记对分蘖具有更加特别的效应。我们推测vrn_1对多年生赖草低温下叶可溶性碳水化合物的积累及生长习性具有数量效应。发现几个DNA标记对可溶性碳水化合物的积累及多个生长特性有较强的作用。研究结果暗示     

玉米抗甘蔗花叶病毒分子标记开发

由甘蔗花叶病毒引起的玉米矮花叶病是我国黄淮海地区玉米生产的重要病害,开发抗矮花叶病基因分子标记是开展抗病分子标记辅助育种的基础。本文基于玉米6.00-6.01区域的“一致性抗甘蔗花叶病毒QTL区间”寻找抗病基因的功能保守域,依据序列多态性开发出抗病分子标记InDel-130和InDel-110,在已知抗性的102份玉米自交系中进行验证。通过分析标记抗病带型和感病带型中的抗病和感病自交系数目,卡平方测验表明标记InDel-130在供试自交系中与抗病性的表现独立无关,而标记InDel-110与甘蔗花叶病毒抗性高度相关,为共显性标记,可用于玉米抗甘蔗花叶病毒种质筛选和分子标记辅助育种。     

水稻耐淹涝性状的遗传分析和SSR标记的研究

淹涝胁迫对水稻生产造成了严重影响, 发掘可应用于耐淹涝辅助选择的分子标记(MAS), 将有助于水稻耐淹涝性状的遗传改良。应用耐淹涝材料FR13A和淹涝敏感材料IR39595-503-2-1-2为亲本做正反交获得F₁和F₂代群体。对正反交的F₁群体的耐淹涝性状进行遗传分析, 发现正反交的F₁代群体在耐淹涝性状上没有显著差异, 说明耐淹涝性状是核基因控制。从两次淹涝处理中F₂代群体的分离情况来看, 来源于FR13A的耐淹特性表现出数量-质量性状遗传的特点。当淹涝胁迫压力比较轻时表现为数量性状遗传, 具有微效多基因的作用。当淹涝胁迫压力增大时, 表现为主效基因控制的质量性状。在SSR分析中, 187对SSR引物中有73对引物在两亲本间有明显的差异, 差异率为39%。用这73对差异引物, 对F2群体进行多态筛选, 结果筛选到一个与耐淹涝性状连锁的标记RM219, 验证了耐淹涝性状确实由主效基因Sub1控制, 因此, RM219在水稻耐淹涝育种中具有利用价值。     

Interactions between markers can be caused by the dominance effect of quantitative trait loci

F₂ populations are commonly used in genetic studies of animals and plants. For simplicity, most quantitative trait locus or loci (QTL) mapping methods have been developed on the basis of populations having two distinct genotypes at each polymorphic marker or gene locus. In this study, we demonstrate that dominance can cause the interactions between markers and propose an inclusive linear model that includes marker variables and marker interactions so as to completely control both additive and dominance effects of QTL. The proposed linear model is the theoretical basis for inclusive composite-interval QTL mapping (ICIM) for F₂ populations, which consists of two steps: first, the best regression model is selected by stepwise regression, which approximately identifies markers and marker interactions explaining both additive and dominance variations; second, the interval mapping approach is applied to the phenotypic values adjusted by the regression model selected in the first step. Due to the limited mapping population size, the large number of variables, and multicollinearity between variables, coefficients in the inclusive linear model cannot be accurately determined in the first step. Interval mapping is necessary in the second step to fine tune the QTL to their true positions. The efficiency of including marker interactions in mapping additive and dominance QTL was demonstrated by extensive simulations using three QTL distribution models with two population sizes and an actual rice F₂ population.     

High-resolution association mapping of quantitative trait loci: a population-based approach

In this article, population-based regression models are proposed for high-resolution linkage disequilibrium mapping of quantitative trait loci (QTL). Two regression models, the "genotype effect model" and the "additive effect model," are proposed to model the association between the markers and the trait locus. The marker can be either diallelic or multiallelic. If only one marker is used, the method is similar to a classical setting by Nielsen and Weir, and the additive effect model is equivalent to the haplotype trend regression (HTR) method by Zaykin et al. If two/multiple marker data with phase ambiguity are used in the analysis, the proposed models can be used to analyze the data directly. By analytical formulas, we show that the genotype effect model can be used to model the additive and dominance effects simultaneously; the additive effect model takes care of the additive effect only. On the basis of the two models, F-test statistics are proposed to test association between the QTL and markers. By a simulation study, we show that the two models have reasonable type I error rates for a data set of moderate sample size. The noncentrality parameter approximations of F-test statistics are derived to make power calculation and comparison. By a simulation study, it is found that the noncentrality parameter approximations of F-test statistics work very well. Using the noncentrality parameter approximations, we compare the power of the two models with that of the HTR. In addition, a simulation study is performed to make a comparison on the basis of the haplotype frequencies of 10 SNPs of angiotensin-1 converting enzyme (ACE) genes.     

Estimates of marker-associated QTL effects in Monte Carlo backcross generations using multiple regression

Summary The decision of whether or not to use QTLassociated markers in breeding programs needs further information about the magnitude of the additive and dominance effects that can be estimated. The objectives of this paper are (1) to apply some of the Moreno-Gonzalez (1993) genetic models to backcross simulation data generated by the Monte Carlo method, and (2) to get simulation information about the number of testing progenies and mapping density in relation to the magnitude of gene effect estimates. Results of the Monte Carlo study show that the stepwise regression analysis was able to detect relatively small additive and dominance effects when the QTL are independently segregating. When testing selfed families derived from backcross individuals, dominance effects had a larger error standard deviation and were estimated at a lower frequency. Linked QTL require a higher marker mapping density on the genome and a larger number of progenies to detect small genetic effects. Reduction of the environmental error variance by evaluating selfed backcross families in replicate experiments increased the power of the test. Expressions of the number of progenies for detecting significant additive effects were developed for some genetic situations. The ratio of the within-backcross genetic variance to the square of a gene effect estimate is a function of the number of progenies, the heritability of the trait, the marker map density and the portion of the genetic variance explained by the model. Different values (from 0 to 1) assigned to (relative position of the QTL in the marker segment) did not cause a large shift in the residual mean square of the model.     

Genetic analysis of tolerance to low-phosphorus stress in maize using restriction fragment length polymorphisms

Summary An understanding of the genetic nature underlying tolerance to low-phosphorus (low-P) stress could aid in the efficient development of tolerant plant strains. The objective of this study was to identify the number of loci in a maize (Zea mays L.) population segregating for tolerance to low-P stress, their approximate location, and the magnitude of their effect.Seventy-seven restriction fragment length polymorphisms (RFLPs) were identified and scored in a maize F₂ population derived from a cross between line NY821 and line H99. The F₂ individuals were self-pollinated to produce F₃ families. Ninety F₃ families were grown in a sand-alumina system, which simulated diffusion-limited, low-P soil conditions. The F₃ families were evaluated for vegetative growth in a controlled-environment experiment. To identify quantitative trait loci (QTLs) underlying tolerance to low-P stress, the mean phenotypic performances of the F₃ families were contrasted based on genotypic classification at each of 77 RFLP marker loci.Six RFLP marker loci were significantly associated with performance under low-P stress (P<0.01). One marker locus accounted for 25% of the total phenotypic variation. Additive gene action was predominant for all of the QTLs identified. Significant marker loci were located on four separate chromosomes representing five unlinked genomic regions. Two marker loci were associated with an additive by additive epistatic interaction. A multiple regression model including three marker loci and the significant epistatic interaction accounted for 46% of the total phenotypic variation. Heterozygosity per se was not predictive of phenotypic performance.     

QTL analysis: a simple ‘marker-regression’ approach

A method to locate quantitative trait loci (QTL) on a chromosome and to estimate their additive and dominance effects is described. It applies to generations derived from an F₁ by selfing or backcrossing and to doubled haploid lines, given that marker genotype information (RFLP, RAPD, etc.) and quantitative trait data are available. The method involves regressing the additive difference between marker genotype means at a locus against a function of the recombination frequency between that locus and a putative QTL. A QTL is located, as by other regression methods, at that point where the residual mean square is minimised. The estimates of location and gene effects are consistent and as reliable as conventional flanking-marker methods. Further applications include the ability to test for the presence of two, or more, linked QTL and to compare different crosses for the presence of common QTL. Furthermore, the technique is straightforward and may be programmed using standard pc-based statistical software.     

Multiple regression for molecular-marker,quantitative trait data from large F2 populations

Molecular marker-quantitative trait associations are important for breeders to recognize and understand to allow application in selection. This work was done to provide simple, intuitive explanations of trait-marker regression for large samples from an F₂ and to examine the properties of the regression estimators. Beginning with a(- 1,0,1) coding of marker classes and expected frequencies in the F₂, expected values, variances, and covariances of marker variables were calculated. Simple linear regression and regression of trait values on two markers were computed. The sum of coefficient estimates for the flanking-marker regression is asymptotically unbiased for an included additive effect with complete interference, and is only slightly biased with no interference and moderately close (15 cM) marker spacing. The variance of the sum of regression coefficients is much more stable for small recombination distances than variances of individual coefficients. Multiple regression of trait variables on coded marker variables can be interpreted as the product of the inverse of the marker correlation matrix R and the vector a of simple linear regression estimators for each marker. For no interference, elements of the correlation matrix R can be written as products of correlations between adjacent markers. The inverse of R is displayed and used to illustrate the solution vector. Only markers immediately flanking trait loci are expected to have non-zero values and, with at least two marker loci between each trait locus, the solution vector is expected to be the sum of solutions for each trait locus. Results of this work should allow breeders to test for intervals in which trait loci are located and to better interpret results of the trait-marker regression.     

DNA分子标记在小麦抗条锈性遗传研究中的应用

综述了近年来DNA分子标记在小麦抗条锈性遗传研究中的应用现状和潜力。内容涉及DNA分子标记在基因标记,基因克隆,遗传图谱构建和辅助选择育种等方面的应用,并列举了代表性实例,展望了DNA分子标记技术在小麦抗条锈病研究上的前景。     

The covariance between relatives conditional on genetic markers

The development of molecular genotyping techniques makes it possible to analyze quantitative traits on the basis of individual loci. With marker information, the classical theory of estimating the genetic covariance between relatives can be reformulated to improve the accuracy of estimation. In this study, an algorithm was derived for computing the conditional covariance between relatives given genetic markers. Procedures for calculating the conditional relationship coefficients for additive, dominance, additive by additive, additive by dominance, dominance by additive and dominance by dominance effects were developed. The relationship coefficients were computed based on conditional QTL allelic transmission probabilities, which were inferred from the marker allelic transmission probabilities. An example data set with pedigree and linked markers was used to demonstrate the methods developed. Although this study dealt with two QTLs coupled with linked markers, the same principle can be readily extended to the situation of multiple QTL. The treatment of missing marker information and unknown linkage phase between markers for calculating the covariance between relatives was discussed.     

水稻亚铁胁迫诱导ADH的基因定位及其遗传分析

籼稻品种ＩＲ６４与粳稻品种Ａｚｕｃｅｎａ及其ＤＨ群体１３５个系用于进行Ｆｅ＾２＋胁迫（２５０ｍｇ／Ｌ,ｐＨ４．５）及对照实验,对处理及对照条件下的ＡＤＨ进行基因定位及遗传分析,结果表明,在Ｆｅ＾２＋胁迫条件下,ＡＤＨ酶的活性大大提高,群体在Ｆｅ＾２＋胁迫条件下,表现低值的超亲现象,分布偏向ＩＲ６４,单标记分析和最大似然区间作图结果表明,Ｆｅ＾２＋胁迫条件下,１１号染色体上紧密连锁的３个标记位点ＲＧ     

Bayesian analysis of linkage between genetic markers and quantitative trait loci. I. Prior knowledge

Summary Prior information on gene effects at individual quantitative trait loci (QTL) and on recombination rates between marker loci and QTL is derived. The prior distribution of QTL gene effects is assumed to be exponential with major effects less likely than minor ones. The prior probability of linkage between a marker and another single locus is a function of the number and length of chromosomes, and of the map function relating recombination rate to genetic distance among loci. The prior probability of linkage between a marker locus and a quantitative trait depends additionally on the number of detectable QTL, which may be determined from total additive genetic variance and minimum detectable QTL effect. The use of this prior information should improve linkage tests and estimates of QTL effects.