作物数量性状(QTL)基因研究进展

从作物数量性状基因座QTL(quantitative trait locus)作图群体类型及特点,QTL定位的原理和方法,作物QTL研究现状,以及QTL精细定位、克隆、利用等方面进行了综述。对作物QTL分子标记辅助选择育种进行了探讨,并对目前QTL定位中存在的问题和今后QTL的研究方向提出了一些思考。     

作物数量性状基因研究进展

分子生物技术的发展对作物数量性状基因（QTL）研究提供了条件,不同的定位群体各有其特点,相继出现的QTL定位也逐步完善。大量的研究揭示了QTL的基本特征,剖析了重要农艺4性状的遗传基础,给作物遗传改良带来了新的策略,不断深入的研究已经完成了特定的QTL的精细定位和克隆。本从QTL的定位群体,定位方法,研究现状,精细定位与克隆,以及QTL利用等方面对作物数量性状基因的研究进行了综述。     

作物数量性状基因研究进展

分子生物技术的发展对作物数量性状基因(QTL)研究提供了条件,不同的定位群体各有其特点,相继出现的QTL定位方法也逐步完善.大量的研究揭示了QTL的基本特征,剖析了重要农艺性状的遗传基础,给作物遗传改良带来了新的策略,不断深入的研究已经完成了特定QTL的精细定位和克隆.本文从QTL的定位群体,定位方法,研究现状,精细定位与克隆,以及QTL利用等方面对作物数量性状基因的研究进行了综述。 Abstract:With the rapid development of molecular biotechnology,QTL analyses were executed for a lot of important agronomic traits in many crops.Different experimental populations and mapping methods had their own advantages in QTL analysis.Amounts of studies paid attention to locate the QTLs for important traits,and others tried to disect the genetic bases using molecular markers.Near isogenic lines were the best populations for QTL fine mapping and positional cloning,A few studies had been reported their results on materials with improvement traits using marker-assisted selection.This paper summarizes the recent progress on QTL mapping populations and methods,the status of QTL locating,QTL fine mapping and positional cloning,and QTL.application in breeding.     

Multiple-Interval Mapping for Quantitative Trait Loci With a Spike in the Trait Distribution

For phenotypic distributions where many individuals share a common value—such as survival time following a pathogenic infection—a spike occurs at that common value. This spike affects quantitative trait loci (QTL) mapping methodologies and causes standard approaches to perform suboptimally. In this article, we develop a multiple-interval mapping (MIM) procedure based on mixture generalized linear models (GLIMs). An extended Bayesian information criterion (EBIC) is used for model selection. To demonstrate its utility, this new approach is compared to single-QTL models that appropriately handle the phenotypic distribution. The method is applied to data from Listeria infection as well as data from simulation studies. Compared to the single-QTL model, the findings demonstrate that the MIM procedure greatly improves the efficiency in terms of positive selection rate and false discovery rate. The method developed has been implemented using functions in R and is freely available to download and use.     

Mapping Environment-Specific Quantitative Trait Loci

Environment-specific quantitative trait loci (QTL) refer to QTL that express differently in different environments, a phenomenon called QTL-by-environment (Q × E) interaction. Q × E interaction is a difficult problem extended from traditional QTL mapping. The mixture model maximum-likelihood method is commonly adopted for interval mapping of QTL, but the method is not optimal in handling QTL interacting with environments. We partitioned QTL effects into main and interaction effects. The main effects are represented by the means of QTL effects in all environments and the interaction effects are represented by the variances of the QTL effects across environments. We used the Markov chain Monte Carlo (MCMC) implemented Bayesian method to estimate both the main and the interaction effects. The residual error covariance matrix was modeled using the factor analytic covariance structure. A simulation study showed that the factor analytic structure is robust and can handle other structures as special cases. The method was also applied to Q × E interaction mapping for the yield trait of barley. Eight markers showed significant main effects and 18 markers showed significant Q × E interaction. The 18 interacting markers were distributed across all seven chromosomes of the entire genome. Only 1 marker had both the main and the Q × E interaction effects. Each of the other markers had either a main effect or a Q × E interaction effect but not both.GENOTYPE-BY-ENVIRONMENT (G × E) interaction is a very important phenomenon in quantitative genetics. With the advanced molecular technology and statistical methods for quantitative trait loci (QTL) mapping (Lander and Botstein 1989; Jansen 1993; Zeng 1994), G × E interaction analysis has shifted to QTL-by-environment (Q × E) interaction. In the early stage of QTL mapping, almost all statistical methods were developed in a single environment (Paterson et al. 1991; Stuber et al. 1992). Data from different environments were analyzed separately and the conclusions were drawn from the separate analyses of QTL across environments. These methods do not consider the correlation of data under different environments and thus may not extract maximum information from the data. Composite interval mapping for multiple traits can be used for Q × E interaction if different traits are treated as the same trait measured in different environments (Jiang and Zeng 1995). This multivariate composite interval mapping approach makes good use of all data simultaneously and increases statistical power of QTL detection and accuracy of the estimated QTL positions. However, the number of parameters of this method increases dramatically as the number of environments increases. Therefore, the method may not be applied when the number of environments is large. Several other models have been proposed to solve the problem of a large number of environments (Jansen et al. 1995; Beavis and Keim 1996; Romagosa et al. 1996). These methods were based on some special situations and assumptions. One typical assumption was independent errors or constant variances across environments. These assumptions are often violated in real QTL mapping experiments.Earlier investigators realized the problem and adopted the mixed-model methodology to solve the problem (Piepho 2000; Boer et al. 2007). Under the mixed-model framework, people can choose which model effects are random and which are fixed. The mixed-model methodology is very flexible, leading to an easy way to model genetic and environmental correlation between environments using a suitable error structure. Piepho (2000) proposed a mixed model to detect QTL main effect across environments. Similar to the composite interval mapping analysis, his model incorporated one putative QTL and a few cofactors. The Q × E effects in the model were assumed to be random, which greatly reduced the number of estimated parameters. However, the fact that only one QTL is included in the model means that Piepho''s (2000) model remains a single-QTL model rather than a multivariate model. Boer et al. (2007) proposed a step-by-step mixed-model approach to detecting QTL main effects, Q × E interaction effects, and QTL responses to specific environmental covariates. In the final step, Boer et al. (2007) rewrote the model to include all QTL in a multiple-QTL model and reestimated their effects.In this study, we extended the Bayesian shrinkage method (Xu 2003) to map Q × E interaction effects of QTL. In the original study (Xu 2003), we treated each marker as a putative QTL and used the shrinkage method to simultaneously estimate marker effects of the entire genome. In the multiple-environment case, we can still use this approach to simultaneously evaluate marker effects under multiple environments but we can further partition the marker effects into main and Q × E interaction effects. For any particular marker, the mean of the marker effects represents the main effect and the variance of the marker effects represents the Q × E interaction effect for that marker. Under the Bayesian framework, we assigned a normal prior with zero mean and an unknown variance to each marker main effect and a multivariate normal prior with zero vector mean and homogeneous diagonal variance–covariance matrix to the Q × E interaction effects of each maker. In multiple environments, the structure of the error terms might be very complicated since we need to consider the correlation of the same genotype under different environments. In our analysis, we used different variance–covariance structures to model the error terms. The simplest case was the homogeneous diagonal matrix, and the most complex choice was an unstructured matrix. We also used a heterogeneous diagonal matrix whose parameters are somewhere between the two models. Finally, we considered several factor analytic models. The reason to use the factor analytic structure is that it can separate genetic effects into common effects and environment-specific effects. In addition, the factor analytic structure is parsimonious and thus can substantially reduce the computational burden of the mixed-model analyses.     

Precision Mapping of Quantitative Trait Loci

Adequate separation of effects of possible multiple linked quantitative trait loci (QTLs) on mapping QTLs is the key to increasing the precision of QTL mapping. A new method of QTL mapping is proposed and analyzed in this paper by combining interval mapping with multiple regression. The basis of the proposed method is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. This is achieved by fitting other genetic markers in the statistical model as a control when performing interval mapping. Compared with the current QTL mapping method (i.e., the interval mapping method which uses a pair or two pairs of markers for mapping QTLs), this method has several advantages. (1) By confining the test to one region at a time, it reduces a multiple dimensional search problem (for multiple QTLs) to a one dimensional search problem. (2) By conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved. (3) By selectively and simultaneously using other markers in the analysis, the efficiency of QTL mapping can be also improved. The behavior of the test statistic under the null hypothesis and appropriate critical value of the test statistic for an overall test in a genome are discussed and analyzed. A simulation study of QTL mapping is also presented which illustrates the utility, properties, advantages and disadvantages of the method.     

Mapping Quantitative Trait Loci for Complex Binary Diseases Using Line Crosses

A composite interval gene mapping procedure for complex binary disease traits is proposed in this paper. The binary trait of interest is assumed to be controlled by an underlying liability that is normally distributed. The liability is treated as a typical quantitative character and thus described by the usual quantitative genetics model. Translation from the liability into a binary (disease) phenotype is through the physiological threshold model. Logistic regression analysis is employed to estimate the effects and locations of putative quantitative trait loci (our terminology for a single quantitative trait locus is QTL while multiple loci are referred to as QTLs). Simulation studies show that properties of this mapping procedure mimic those of the composite interval mapping for normally distributed data. Potential utilization of the QTL mapping procedure for resolving alternative genetic models (e.g., single- or two-trait-locus model) is discussed.     

林木数量性状基因定位中的若干问题

随着ＤＮＡ分子标记技术的迅速发展,ＱＴＬ定位已成为当前生物学研究领域的前沿。迄今已对许多种动、植物定位了许多重要性状的ＱＴＬ。这些研究促进了遗传学的发展,并将作为育种的新策略应用。与作物相比,林木ＱＴＬ定位有其特性。本文详细讨论了林木的生物学特性对ＱＴＬ定位的影响、ＱＴＬ定位的系谱设计和统计分析方法。     

Mixed Effects Models for Quantitative Trait Loci Mapping With Inbred Strains

Fixed effects models have dominated the statistical analysis of genetic crosses between inbred strains. In spite of their popularity, the traditional models ignore polygenic background and must be tailored to each specific cross. We reexamine the role of random effect models in gene mapping with inbred strains. The biggest difficulty in implementing random effect models is the lack of a coherent way of calculating trait covariances between relatives. The standard model for outbred populations is based on premises of genetic equilibrium that simply do not apply to crosses between inbred strains since every animal in a strain is genetically identical and completely homozygous. We fill this theoretical gap by introducing novel combinatorial entities called strain coefficients. With an appropriate theory, it is possible to reformulate QTL mapping and QTL association analysis as an application of mixed models involving both fixed and random effects. After developing this theory, our first example compares the mixed effects model to a standard fixed effects model using simulated advanced intercross line (AIL) data. Our second example deals with hormone data. Here multivariate traits and parameter identifiability questions arise. Our final example involves random mating among eight strains and vividly demonstrates the versatility of our models.     

油菜油分、蛋白质和硫苷含量相关性分析及QTL定位

为定位与油分、蛋白质和硫苷含量等品质性状相关的数量性状位点（QTL）,以2个含油量较高的甘蓝型油菜（Brassica napus）品系8908B和R1为研究材料,配置正反交组合。在正反交F2代群体中,含油量和蛋白质含量都存在极显著的负相关,相关系数分别为-0．68和-0．81,含油量和硫苷含量相关性不显著：蛋白质含量和硫苷含量在正交群体中相关性不显著,但在反交群体中存在显著负相关（相关系数r=-0．45）。利用正交F2代群体中的118个单株,构建了包含121个标记的遗传连锁图谱,图谱长1298．7cM,有21个连锁群（LGs）。采用复合区间作图法,在连锁图上定位了2个与含油量有关的QTL,分别位于LG8和LG10,其贡献率分别为4．8％和13．7％,增效基因都来源于R1;定位了2个与蛋白质含量有关的QTL：pr01和pr02,分别位于LG1和LG3,其贡献率分别为15．2％和14．1％,位点pr07由8908B提供增效基因,pro2则由R1提供增效基因：定位了4个与硫苷含量有关的QTL,其中LG20上有2个,LG4和LG8上各1个,它们的贡献率在1．9％-25．4％之间,除LG20上glu7的增效基因来自R1外,其余3个QTL位点均由8908B提供增效基因。     

油菜油分、蛋白质和硫苷含量相关性分析及QTL 定位

为定位与油分、蛋白质和硫苷含量等品质性状相关的数量性状位点(QTL), 以2个含油量较高的甘蓝型油菜(Brassica napus)品系8908B和R1为研究材料, 配置正反交组合。在正反交F₂代群体中, 含油量和蛋白质含量都存在极显著的负相关, 相关系数分别为-0.68和-0.81, 含油量和硫苷含量相关性不显著; 蛋白质含量和硫苷含量在正交群体中相关性不显著, 但在反交群体中存在显著负相关(相关系数r =-0.45)。利用正交F₂代群体中的118个单株, 构建了包含121个标记的遗传连锁图谱, 图谱长1 298.7 cM, 有21个连锁群(LGs)。采用复合区间作图法, 在连锁图上定位了2个与含油量有关的QTL, 分别位于LG8和LG10, 其贡献率分别为4.8%和13.7%, 增效基因都来源于R1; 定位了2个与蛋白质含量有关的QTL: pro1 和 pro2, 分别位于LG1和LG3, 其贡献率分别为15.2%和14.1%, 位点pro1由8908B提供增效基因, pro2则由R1提供增效基因; 定位了4个与硫苷含量有关的QTL, 其中LG20上有2个, LG4和LG8上各1个, 它们的贡献率在1.9%-25.4%之间, 除LG20上glu1的增效基因来自R1外, 其余3个QTL位点均由8908B提供增效基因。     

Interval Mapping of Multiple Quantitative Trait Loci

The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in segregating generations derived from crosses between inbred lines. The efficiency of detecting and the accuracy of mapping multiple QTLs by using genetic markers are much increased by employing multiple QTL models instead of the single QTL models (and no QTL models) used in interval mapping. However, the computational work involved with multiple QTL models is considerable when the number of QTLs is large. In this paper it is proposed to combine multiple linear regression methods with conventional interval mapping. This is achieved by fitting one QTL at a time in a given interval and simultaneously using (part of) the markers as cofactors to eliminate the effects of additional QTLs. It is shown that the proposed method combines the easy computation of the single QTL interval mapping method with much of the efficiency and accuracy of multiple QTL models.     

构建数量性状基因图谱的几种统计方法

以线性数学模型为线索,概述了用于构建数量性状基因图谱的几种主要统计方法,包括方差分析法、标记回归法、区间作图法、复合区间作图法、Jansen的复合区间作图法、双侧标记回归法以及新近发展的多区间作图法和多亲本作图法等.讨论了各种方法的优缺点. Abstract:Statistical methods for mapping QTLs were summarized, including one marker analysis, arker regression analysis,interval mapping (IM),composite interval mapping (CIM),Jansen's composite interval mapping, flanking marker regression analysis,multiple interval mapping (MIM) and multiple families mapping.Their advantages and disadvantages were discussed.     

Multiple Trait Analysis of Genetic Mapping for Quantitative Trait Loci

We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.     

Interval Mapping of Quantitative Trait Loci Employing Correlated Trait Complexes

An approach to increase the resolution power of interval mapping of quantitative trait (QT) loci is proposed, based on analysis of correlated trait complexes. For a given set of QTs, the broad sense heritability attributed to a QT locus (QTL) (say, A/ a) is an increasing function of the number of traits. Thus, for some traits x and y, H(xy)(2) (A/ a) >/= H(x)(2) (A/ a). The last inequality holds even if y does not depend on A/ a at all, but x and y are correlated within the groups AA, Aa and aa due to nongenetic factors and segregation of genes from other chromosomes. A simple relationship connects H(2) (both in single trait and two-trait analysis) with the expected LOD value, ELOD = -1/2N log(1 - H(2)). Thus, situations could exist that from the inequality H(xy)(2) (A/ a) >/= H(x)(2) (A/ a) a higher resolution is provided by the two-trait analysis as compared to the single-trait analysis, in spite of the increased number of parameters. Employing LOD-score procedure to simulated backcross data, we showed that the resolution power of the QTL mapping model can be elevated if correlation between QTs is taken into account. The method allows us to test numerous biologically important hypotheses concerning manifold effects of genomic segments on the defined trait complex (means, variances and correlations).     

作物数量性状基因图位克隆研究进展

对数量性状基因(QTL)的鉴定和克隆不仅有利于从分子水平上阐明作物重要农艺性状的形成机理,而且对于有效开展这些性状的分子育种,进一步提高作物增产潜力具有重要意义.近年来作物QTL图位克隆取得了重要突破,一批QTL被成功克隆,而模式植物基因组研究的快速发展则为作物QTL图位克隆技术带来了新的策略和方法.本文就相关研究的主要进展和发展趋势进行了综述.     

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.     

A Nonparametric Approach for Mapping Quantitative Trait Loci

Genetic mapping of quantitative trait loci (QTLs) is performed typically by using a parametric approach, based on the assumption that the phenotype follows a normal distribution. Many traits of interest, however, are not normally distributed. In this paper, we present a nonparametric approach to QTL mapping applicable to any phenotypic distribution. The method is based on a statistic Z(w), which generalizes the nonparametric Wilcoxon rank-sum test to the situation of whole-genome search by interval mapping. We determine the appropriate significance level for the statistic Z(w), by showing that its asymptotic null distribution follows an Ornstein-Uhlenbeck process. These results provide a robust, distribution-free method for mapping QTLs.