萍乡显性核不育水稻育性相关基因的定位和遗传分析

萍乡显性核不育水稻(Pingxiang Dominant Genic Male Sterile Rice,PDGMSR)是在水稻中首次发现的显性核不育材料,其育性由两对显性基因互作控制,一对是萍乡显性核不育基因Ms-p,另一对是显性上位恢复基因(dominant epistatic fertility restorer gene,Rfe)。两者共同存在时显性上位恢复基因能抑制不育基因的表达,从而使育性表现可育。本实验用一个对萍乡显性核不育水稻有恢复能力的水稻品种E823与萍乡显性核不育水稻配制杂交组合,将(萍乡核不育水稻/E823)F2作为定位群体,根据F3株系的育性分离,选择育性分离株系对应F2单株(基因型为Ms-pMs-pRefrfe和Ms-pms-pRferfe)构建可育池,用对应F2株系中的不育单株(基因型为Ms-pMs-prferfe或Ms-pms-prferfe)构建不育池,将显性上位恢复基因Rfe定位在水稻10染色体RM311和RM3152一侧,遗传距离分别为7.9cM和3.6cM。根据已有的Ms-p的定位结果,合成10染色体部分微卫星引物,对不育单株进行分析,发现RM171和RM6745位于Ms-p的两侧,距离分别为0.3cM和3.0cM。根据10染色体的测序结果,将Ms-p界定在约730kb的范围内,并构建了Ms-p的电子重叠群。植物显性核不育的育性恢复机理存在“复等位基因”和“显性上位互作”两种假说,贺浩华等用经典的遗传学方法证明了萍乡显性核不育水稻育性恢复的遗传机理属于“显性上位互作”。理论上认为,确定其遗传机理最为有效的方法是基因定位,如果不育基因和恢复基因位于同一位点,则其遗传机理属于“复等位基因”,否则为“显性上位互作”。本实验将不育基因和恢复基因定位在水稻10染色体不同的位点,用基因定位的方法证实了萍乡显性核不育水稻育性恢复的遗传机理属于“显性上位互作”。     

水稻株高上位性效应和ＱＥ互作效应的ＱＴＬ遗传研究

利用基因混合模型的ＱＴＬ定位方法研究了由籼稻品种ＩＲ６４和粳稻品种Ａzucena杂交衍生的ＤＨ群体在４个环境中的ＱＴＬ上位性效应和环境互作效应,结果表明,上位性是数量性状的重要遗传基础,并揭示了上位性的几个重要特点,所有的ＱＴＬ都参与了上位性效应的形成,６４％的ＱＴＬ还具有本身的加性效应,因此传统方法对ＱＴＬ加性效应的估算会由于上位性的影响而有偏,其他３６％的ＱＴＬ没有本身的加性效应,却参与了４８％的上位性互作用,这些位点可能通过诱发和修饰其他位点而起作用,上位性的特点还包括,经常发现了一个ＱＴＬ与多个ＱＴＬ发生互作;大效应的ＱＴＬ也参与上位性互作;上位性互作受环境影响,ＱＴＬ与环境的互效应比ＱＴＬ的主效应更多地被检测到,表明数量性状基因的表达易受环境影响。     

多QTL定位的压缩估计方法

本文综述了多标记分析和多QTL定位的压缩估计方法。对于前者,Xu（Genetics,2003,163：789—801）首先提出了Bayesian压缩估计方法。其关键在于让每个效应有一个特定的方差参数,而该方差又服从一定的先验分布,以致能从资料中估计之。由此,能够同时估计大量分子标记基因座的遗传效应,即使大多数标记的效应是可忽略的。然而,对于上位性遗传模型,其运算时间还是过长。为此,笔者将上述思想嵌入极大似然法,提出了惩罚最大似然方法。模拟研究显示：该方法能处理变量个数大于样本容量10倍左右的线性遗传模型。对于后者,本文详细介绍了基于固定区间和可变区间的Bayesian压缩估计方法。固定区间方法可处理中等密度的分子标记资料;可变区间方法则可分析高密度分子标记资料,甚至是上位性遗传模型。对于上位性检测,已介绍的惩罚最大似然方法和可变区间Bayesian压缩估计方法可供利用。应当指出,压缩估计方法在今后的eQTL和QTN定位以及基因互作网络分析等研究中也是有应用价值的。     

大白菜显性不育基因向白菜型油菜上转育的遗传研究

利用大白菜的显性核不育基因向白菜型油菜上转育,成功地育成了白菜油菜３种不育系。其配合力测定的总趋势,甲型不育〉全不育〉乙型不育,经遗传分析,选育,找到了上位显性核互作恢复基因（Ｍｓ）,它对核不育基因（Ｓｐ）具有上位恢复作用,育性是２对显性核不育基因（Ｓｐ）和上位显性核互作恢复基因（Ｍｓ）控制的。并弄清了其相应的遗传模式。     

影响微生物互作的重要基因研究进展：以大肠杆菌为例

微生物在自然界中广泛存在,微生物间的相互作用对群落结构和功能有重要影响。目前已经对微生物相互作用的机制给予了很大的关注,通过高通量测序技术和统计学分析方法的结合可以定位获得影响菌株互作的重要基因。为了深入研究微生物相互作用的遗传机制,本文以大肠杆菌(Escherichia coli)为例,综述了与大肠杆菌运动性、耐药性、营养物质吸收和代谢调节相关的基因在互作条件下发挥的作用,并从这几个方面分别阐述了大肠杆菌互作遗传机制。总之,这些基因在大肠杆菌与其他微生物互作中发挥重要作用,同时增强了对细菌互作机制的理解,为今后研究更复杂的微生物群落互作遗传机制奠定了理论基础。     

稻瘟菌无毒基因研究进展

稻瘟菌是引起水稻稻瘟病的病原物。水稻与稻瘟菌间存在广泛而特异的相互作用,是研究寄主与病原物互作的重要模式系统。本文对稻瘟菌与水稻互作最重要的激发子―无毒基因的研究现状进行了概括,讨论了无毒基因的定位、克隆方法以及已克隆无毒基因的功能及进化研究,同时对今后无毒基因研究的重要方向进行了探讨,为深入理解无毒基因的功能及与水稻可能的互作关系奠定了基础。     

籼型光敏核不育水稻育性可转换性的基因定位和基因互作研究

本研究以来源于农垦58S的灿型光敏核不育系培矮64S（短日条件下育性难转换）和8902S（短日条件下育性蝗转换）及其F1,F2群体为材料,通过短日不同光温和不同生态条件4种处理,利用RFLP分子标记研究了影响光敏偿育水稻在短日条件下的育性可转换性的遗传,基因定位和基因互作,主要结果表明：影响光敏不育水稻的育性可转换性表现为微效基因的作用,定位了7个控制光敏核不育水稻的育性可转换性QTL,即S2,S3a,S3b,S5,S8和S10,揭示了基因互作真实存在于光敏核不育水稻中,基因互作形式和互作类型对光敏核不育水稻的育性可转换性的影响表现多种多样,不同类型的基因互作所解释的遗传变异处于2．15％－10．07％之间。     

水稻稻瘟病抗性基因的克隆、育种利用及稻瘟菌无毒基因研究进展

稻瘟病是世界上影响水稻(Oryza sativa)粮食生产的主要病害之一, 抗病基因的发掘与利用是抗病育种的基础和核心。随着寄主水稻和病原菌稻瘟病菌(Magnaporthe oryzae)基因组测序和基因注释的完成, 水稻和稻瘟病菌的互作体系成为研究植物与真菌互作的模式系统。该文对稻瘟病抗病基因的遗传、定位、克隆及育种利用进行概述, 并通过生物信息学分析方法, 探讨了水稻全基因组中NBS-LRR类抗病基因在水稻12条染色体上的分布情况, 同时对稻瘟病菌无毒基因的鉴定及无毒蛋白与抗病蛋白的互作进行初步分析。最后对稻瘟病抗病基因研究存在的问题进行分析并展望了未来的研究方向, 以期为水稻抗稻瘟病育种发展和抗病机制的深入理解提供参考。     

籼型光敏核不育水稻育性可转换性的基因定位和基因互作研究

本研究以来源于农垦58S的籼型光敏核不育系培矮64S(短日条件下育性难转换)和8902S(短日条件下育性易转换)及其F１、F２群体为材料,通过短日不同光温和不同生态条件4种处理,利用RFLP分子标记研究了影响光敏核不育水稻在短日条件下的育性可转换性的遗传、基因定位和基因互作,主要结果表明：影响光敏核不育水稻的育性可转换性表现为微效基因的作用,定位了7个控制光敏核不育水稻的育性可转换性QTL,即S2、S3a、S3b、S5、S8和S10。揭示了基因互作真实存在于光敏核不育水稻中,基因互作形式和互作类型对光敏核不育水稻的育性可转换性的影响表现多种多样,不同类型的基因互作所解释的遗传变异处于2.15%～10.07%之间。     

单位点孢子体-配子体互作模型中不育基因的位置和效应的估计方法

水稻籼粳亚种间杂交F1通常表现为高度不育,这种不育性的一种遗传学解释称为单位点孢子体-配子体互作模型.为了研究这种不育性,提出了一种统计方法,可以估计单位点孢子体-配子体互作模型中不育基因位点的位置和效应.该方法利用回交群体中呈现异常分离的标记位点,用最大似然法对不育基因与标记位点之间的重组率和雌配子存活率进行估计.由于所依据的是非连续变异的遗传标记的分离,而不是连续分布的配子育性指标,因此可以避免由育性直接估计所带来的重组率结果的不稳定.     

Modeling epistasis of quantitative trait loci using Cockerham's model

We use the orthogonal contrast scales proposed by Cockerham to construct a genetic model, called Cockerham's model, for studying epistasis between genes. The properties of Cockerham's model in modeling and mapping epistatic genes under linkage equilibrium and disequilibrium are investigated and discussed. Because of its orthogonal property, Cockerham's model has several advantages in partitioning genetic variance into components, interpreting and estimating gene effects, and application to quantitative trait loci (QTL) mapping when compared to other models, and thus it can facilitate the study of epistasis between genes and be readily used in QTL mapping. The issues of QTL mapping with epistasis are also addressed. Real and simulated examples are used to illustrate Cockerham's model, compare different models, and map for epistatic QTL. Finally, we extend Cockerham's model to multiple loci and discuss its applications to QTL mapping.     

Predicting epistasis: an experimental test of metabolic control theory with bacterial transcription and translation

Epistatic interactions between mutations are thought to play a crucial role in a number of evolutionary processes, including adaptation and sex. Evidence for epistasis is abundant, but tests of general theoretical models that can predict epistasis are lacking. In this study, I test the ability of metabolic control theory to predict epistasis using a novel experimental approach that combines phenotypic and genetic perturbations of enzymes involved in gene expression and protein synthesis in the bacterium Pseudomonas aeruginosa. These experiments provide experimental support for two key predictions of metabolic control theory: (i) epistasis between genes involved in the same pathway is antagonistic; (ii) epistasis becomes increasingly antagonistic as mutational severity increases. Metabolic control theory is a general theory that applies to any set of genes that are involved in the same linear processing chain, not just metabolic pathways, and I argue that this theory is likely to have important implications for predicting epistasis between functionally coupled genes, such as those involved in antibiotic resistance. Finally, this study highlights the fact that phenotypic manipulations of gene activity provide a powerful method for studying epistasis that complements existing genetic methods.     

Multiple-interval mapping for ordinal traits

Many statistical methods have been developed to map multiple quantitative trait loci (QTL) in experimental cross populations. Among these methods, multiple-interval mapping (MIM) can map QTL with epistasis simultaneously. However, the previous implementation of MIM is for continuously distributed traits. In this study we extend MIM to ordinal traits on the basis of a threshold model. The method inherits the properties and advantages of MIM and can fit a model of multiple QTL effects and epistasis on the underlying liability score. We study a number of statistical issues associated with the method, such as the efficiency and stability of maximization and model selection. We also use computer simulation to study the performance of the method and compare it to other alternative approaches. The method has been implemented in QTL Cartographer to facilitate its general usage for QTL mapping data analysis on binary and ordinal traits.     

Statistical power for detecting epistasis QTL effects under the F-2 design

Epistasis refers to gene interaction effect involving two or more genes. Statistical methods for mapping quantitative trait loci (QTL) with epistasis effects have become available recently. However, little is known about the statistical power and sample size requirements for mapping epistatic QTL using genetic markers. In this study, we developed analytical formulae to calculate the statistical power and sample requirement for detecting each epistasis effect under the F-2 design based on crossing inbred lines. Assuming two unlinked interactive QTL and the same absolute value for all epistasis effects, the heritability of additive × additive (a × a) effect is twice as large as that of additive × dominance (a × d) or dominance × additive (d × a) effect, and is four times as large as that of dominance × dominance (d × d) effect. Consequently, among the four types of epistasis effects involving two loci, ''a × a'' effect is the easiest to detect whereas ''d × d'' effect is the most difficult to detect. The statistical power for detecting ''a × a'' effect is similar to that for detecting dominance effect of a single QTL. The sample size requirements for detecting ''a × d'', ''d × a'' and ''d × d'' are highly sensitive to increased distance between the markers and the interacting QTLs. Therefore, using dense marker coverage is critical to detecting those effects.     

An E-MAP of the ER

Genome-wide phenotype mapping has the potential to provide insights into gene function on an unprecedented scale. In this issue of Cell, Schuldiner et al.(2005) show that saturation epistasis analysis of genes implicated in the early secretory pathway in yeast provides a new strategy for uncovering functional relationships using high-throughput genomic data.     

The role of pleiotropy vs signaller-receiver gene epistasis in life history trade-offs: dissecting the genomic architecture of organismal design in social systems

Traditional life history theory ignores trade-offs due to social interactions, yet social systems expand the set of possible trade-offs affecting a species evolution--by introducing asymmetric interactions between the sexes, age classes and invasion of alternative strategies. We outline principles for understanding gene epistasis due to signaller-receiver dynamics, gene interactions between individuals, and impacts on life history trade-offs. Signaller-receiver epistases create trade-offs among multiple correlated traits that affect fitness, and generate multiple fitness optima conditional on frequency of alternative strategies. In such cases, fitness epistasis generated by selection can maintain linkage disequilibrium, even among physically unlinked loci. In reviewing genetic methods for studying life history trade-offs, we conclude that current artificial selection or gene manipulation experiments focus on pleiotropy. Multi-trait selection experiments, multi-gene engineering methods or multiple endocrine manipulations can test for epistasis and circumvent these limitations. In nature, gene mapping in field pedigrees is required to study social gene epistases and associated trade-offs. Moreover, analyses of correlational selection and frequency-dependent selection are necessary to study epistatic social system trade-offs, which can be achieved with group-structured versions of Price's (1970) equation.     

Mapping the genetic architecture of complex traits in experimental populations

SUMMARY: Understanding how interactions among set of genes affect diverse phenotypes is having a greater impact on biomedical research, agriculture and evolutionary biology. Mapping and characterizing the isolated effects of single quantitative trait locus (QTL) is a first step, but we also need to assemble networks of QTLs and define non-additive interactions (epistasis) together with a host of potential environmental modulators. In this article, we present a full-QTL model with which to explore the genetic architecture of complex trait in multiple environments. Our model includes the effects of multiple QTLs, epistasis, QTL-by-environment interactions and epistasis-by-environment interactions. A new mapping strategy, including marker interval selection, detection of marker interval interactions and genome scans, is used to evaluate putative locations of multiple QTLs and their interactions. All the mapping procedures are performed in the framework of mixed linear model that are flexible to model environmental factors regardless of fix or random effects being assumed. An F-statistic based on Henderson method III is used for hypothesis tests. This method is less computationally greedy than corresponding likelihood ratio test. In each of the mapping procedures, permutation testing is exploited to control for genome-wide false positive rate, and model selection is used to reduce ghost peaks in F-statistic profile. Parameters of the full-QTL model are estimated using a Bayesian method via Gibbs sampling. Monte Carlo simulations help define the reliability and efficiency of the method. Two real-world phenotypes (BXD mouse olfactory bulb weight data and rice yield data) are used as exemplars to demonstrate our methods. AVAILABILITY: A software package is freely available at http://ibi.zju.edu.cn/software/qtlnetwork     

Estimation of the Recombination Fraction by the Maximum Likelihood Method in Mapping Interacting Genes Relative to Marker Loci

For mapping nonlinked interacting genes relative to marker loci, the recombination fractions can be calculated by using the log-likelihood functions were derived that permit estimation of recombinant fractions by solving the ML equations on the basis of F₂ data at various types of interaction. In some cases, the recombinant fraction estimates are obtained in the analytical form while in others they are numerically calculated from concrete experimental data. With the same type of epistasis the log-functions were shown to differ depending on the functional role (suppression or epistasis) of the mapped gene. Methods for testing the correspondence of the model and the recombination fraction estimates to the experimental data are discussed. In ambiguous cases, analysis of the linked marker behavior makes it possible to differentiate gene interaction from distorted single-locus segregation, which at some forms of interaction imitate phenotypic ratios.     

Genetic mapping of sterile genes with epistasis in backcross designs

The mapping of sterile genes is an essential issue, which should be solved for the investigation of sterility mechanism in wide hybridization of plants. However, the methods formerly developed cannot address the problem of mapping sterile loci with epistasis. In this study, we developed a new method to map sterile genes with epistasis in wide hybridizations of plants using a backcross design. The maximum likelihood method was used to estimate the parameters of recombination fractions and effects of sterile genes, and the convergent results of these parameters were obtained using the expectation maximization (EM) algorithm. The application and efficiency of this method were tested and demonstrated by a set of simulated data and real data analysis. Results from the simulation experiments showed that the method works well for simultaneously estimating the positions and effects of sterile genes, as well as the epistasis between sterile genes. A real data set of a backcross (BC) population from an interspecific hybrid between cultivated rice, Oryza sativa, and its wild African relative, Oryza longistaminata, was analyzed using the new method. Five sterile genes were detected on the chromosomes of 1, 3, 6, 8 and 10, and significant epistatic effects were found among the four pairs of sterile genes.     

Estimation of the recombination fraction by the maximum likelihood method in mapping interacting genes relative to marker loci

For mapping nonlinked interacting genes relative to marker loci, the recombination fractions can be calculated by using the log-likelihood functions were derived that permit estimation of recombinant fractions by solving the ML equations on the basis of F2 data at various types of interaction. In some cases, the recombinant fraction estimates are obtained in the analytical form while in others they are numerically calculated from concrete experimental data. With the same type of epistasis the log-functions were shown to differ depending on the functional role (suppression or epistasis) of the mapped gene. Methods for testing the correspondence of the model and the recombination fraction estimates to the experimental data are discussed. In ambiguous cases, analysis of the linked marker behavior makes it possible to differentiate gene interaction from distorted single-locus segregation, which at some forms of interaction imitate phenotypic ratios.