遗传基因组学(Genetical genomics)的研究进展

遗传基因组学(geneticalgenomics)是将微阵列技术和数量性状座位(QTL)分析结合起来,全基因组水平上定位基因表达的QTL(eQTL).它为研究复杂性状的分子机理和调控网络提供全新的手段.遗传基因组这个概念和研究策略在2001年由Janson和Nap首先提出,到目前为止,遗传基因组学已应用于酵母、老鼠、人以及玉米等植物.研究结果表明:基因表达水平的差异是可遗传的复杂性状;eQTL可以分为顺式作用eQTL和反式作用eQTL,顺式作用eQTL就是某个基因的eQTL定位到该基因所在的基因组区域,表明可能是该基因本身的差别引起mRNA水平的差别,反式作用就是eQTL定位到其他基因组区域,表明其他基因的差别控制该基因mRNA水平的差异.将eQTL结果、基因功能注解以及多种统计分析方法相结合,不仅能更准确地鉴别控制复杂性状及其相关基因表达的候选基因,而且能构建相应的基因调控网络.     

生物信息学与eQTL作图:问题和展望

阐述生物信息学在基因表达QTL作图中的应用。主要对后基因组时代的研究的新领域——遗传基因组学的概念的提出、研究方向和发展进行论述,同时,就eQTL作图的统计学策略、生物信息学软件工具以及遗传网络构建等方面进行讨论,并对今后遗传基因组学的发展前景进行分析。     

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…     

基于选择牵连效应的标记/性状关联分析方法简介

许多重要农艺性状如产量、品质、抗逆性等多表现为数量性状, 是由多个基因和环境共同作用的结果, 对其遗传基础的研究比较困难。近年发展起来的以选择牵连效应分析为基础, 通过标记/性状之间的关联分析方法为这些性状的作图和遗传解析提供了新的手段, 也为作物的分子设计育种提供了新的思路, 其与QTL作图结果互相验证、互相补充, 必将促进数量遗传学、应用基因组学和育种学的发展。文章对关联分析的思路、方法、优缺点及应用时应注意的问题进行了比较系统的介绍。     

植物数量性状基因的定位与克隆

作物的许多重要农艺性状属于数量性状,鉴定和发掘控制数量性状的基因及其优异的等位变异,并使之快速应用于育种实践是新时期作物科学家和育种学家所面临的重大课题。本文从QTL作图、QTL的精细定位与图位克隆、QTL近等基因系和染色体片断代换系的建立以及基于LD的关联分析等方面对植物数量性状的研究进展进行了讨论,提出了以植物基因组学技术为平台,将QTL作图与关联分析方法相结合,是进行数量性状遗传机理研究同时服务于作物育种实践的有效途径。     

植物数量性状基因的定位与克隆

作物的许多重要农艺性状属于数量性状, 鉴定和发掘控制数量性状的基因及其优异的等位变异, 并使之快速应用于育种实践是新时期作物科学家和育种学家所面临的重大课题。本文从QTL作图、QTL的精细定位与图位克隆、QTL近等基因系和染色体片断代换系的建立以及基于LD的关联分析等方面对植物数量性状的研究进展进行了讨论, 提出了以植物基因组学技术为平台, 将QTL作图与关联分析方法相结合, 是进行数量性状遗传机理研究同时服务于作物育种实践的有效途径。     

植物数量性状变异的分子基础与QTL克隆研究进展

探讨数量性状变异规律以便对其进行遗传操纵一直是植物遗传学的一个重要领域。DNA分子标记和QTL作图技术的发展以及拟南芥和水稻全基因组测序的完成极大地促进了植物数量性状分子基础的研究。现已克隆了拟南芥ED1、水稻Hdl、玉米Tb1、番茄fw2．2和Brii9-2-5等控制目标数量性状的基因。数量性状表型变异不仅源于多个数量性状基因（QTL）的分离．而且还受到内外环境的修饰。QTL等位基因变异与孟德尔基因变异具有类似的分子基础,即基因表达或蛋白质功能发生改变。通过分析已克隆的植物QTL的变异特征及分子基础,讨论了植物QTL克隆技术策略,并对QTL研究所面临的挑战和应用前景进行了展望。     

植物数量性状基因定位研究概述

植物重要的性状多为数量性状。长期以来,人类一直寻求解释植物数量性状的遗传规律以便对其进行遗传操纵。现代分子生物技术的发展为植物数量性状基因的定位、分离等研究提供了条件。本文从数量性状基因座(QTL)作图群体类型及其特点,QTL定位方法,植物QTL研究现状,以及QTL精细定位、克隆、利用等方面进行了综述,并对今后植物QTL研究进行了展望。     

植物数量性状基因定位研究概述

植物重要的性状多为数量性状。长期以来,人类一直寻求解释植物数量性状的遗传规律以便对其进行遗传操纵。现代分子生物技术的发展为植物数量性状基因的定位、分离等研究提供了条件。本文从数量性状基因座(QTL)作图群体类型及其特点,QTL定位方法,植物QTL研究现状,以及QTL精细定位、克隆、利用等方面进行了综述,并对今后植物QTL研究进行了展望。     

数量性状由表型变异到基因发现的研究进展

分子生物学的快速发展为研究数量性状的遗传基础提供了更为有效的途径。我们可以沿着由表型变异去发现基因之路, 更准确地剖析数量性状的遗传基础; 尤其是对作物的许多重要的数量性状进行的QTL研究越来越受到重视。文章对数量遗传发展, QTL作图群体和方法的发展, QTL定位和QTG(quantitative traits genes)的鉴别方面的现状进行了综述。     

A genetical genomics approach to genome scans increases power for QTL mapping

We describe a method for integrating gene expression information into genome scans and show that this can substantially increase the statistical power of QTL mapping. The method has three stages. First, standard clustering methods identify small (size 5-20) groups of genes with similar expression patterns. Second, each gene group is tested for a causative genetic locus shared with the clinical trait of interest. This is done using an EM algorithm approach that treats genotype at the putative causative locus as an unobserved variable and combines expression information from all of the genes in the group to infer genotype information at the locus. Finally, expression QTL (eQTL) are mapped for each gene group that shares a causative locus with the clinical trait. Such eQTL are candidates for the causative locus. Simulation results show that this method has far superior power to standard QTL mapping techniques in many circumstances. We applied this method to existing data on mouse obesity. Our method identified 27 putative body weight QTL, whereas standard QTL mapping produced only one. Furthermore, most gene groups with body weight QTL included cis genes, so candidate genes could be immediately identified. Eleven body weight QTL produced 16 candidate genes that have been previously associated with body weight or body weight-related traits, thus validating our method. In addition, 15 of the 16 other loci produced 32 candidate genes that have not been associated with body weight. Thus, this method shows great promise for finding new causative loci for complex traits.     

Advanced technologies for genomic analysis in farm animals and its application for QTL mapping

Rapid progress in farm animal breeding has been made in the last few decades. Advanced technologies for genomic analysis in molecular genetics have led to the identification of genes or markers associated with genes that affect economic traits. Molecular markers, large-insert libraries and RH panels have been used to build the genetic linkage maps, physical maps and comparative maps in different farm animals. Moreover, EST sequencing, genome sequencing and SNPs maps are helping us to understand how genomes function in various organisms and further areas will be studied by DNA microarray technologies and proteomics methods. Because most economically important traits in farm animals are controlled by multiple genes and the environment, the main goal of genome research in farm animals is to map and characterize genes determining QTL. There are two main strategies to identify trait loci, candidate gene association tests and genome scan approaches. In recent years, some new concepts, such as RNAi, miRNA and eQTL, have been introduced into farm animal research, especially for QTL mapping and finding QTN. Several genes that influence important traits have already been identified or are close to being identified, and some of them have been applied in farm animal breeding programs by marker-assisted selection.     

Characterization of Expression Quantitative Trait Loci in Pedigrees from Colombia and Costa Rica Ascertained for Bipolar Disorder

The observation that variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits has made the genome-wide characterization of gene expression an important tool in genetic mapping studies of such traits. As part of a study to identify genetic loci contributing to bipolar disorder and other quantitative traits in members of 26 pedigrees from Costa Rica and Colombia, we measured gene expression in lymphoblastoid cell lines derived from 786 pedigree members. The study design enabled us to comprehensively reconstruct the genetic regulatory network in these families, provide estimates of heritability, identify eQTL, evaluate missing heritability for the eQTL, and quantify the number of different alleles contributing to any given locus. In the eQTL analysis, we utilize a recently proposed hierarchical multiple testing strategy which controls error rates regarding the discovery of functional variants. Our results elucidate the heritability and regulation of gene expression in this unique Latin American study population and identify a set of regulatory SNPs which may be relevant in future investigations of complex disease in this population. Since our subjects belong to extended families, we are able to compare traditional kinship-based estimates with those from more recent methods that depend only on genotype information.