雄性不交换条件下F2群体高密度分子区间标记QTL的精确作图

提出雄性不交换条件下F2群体间标记定位QTL的相关方法，研究高密度分子标记存在强烈交叉干涉时，QTL的精确定位方法。     

具交叉干涉的三点测交区间标记定位（QTL）的相关方法

本文提出在有交叉干涉条件下,在测交群体中对区间标记座位赋值后求其与待定位的数量性状表型值间的简单相关系数R,以此进行连锁测验,并且在一定条件下用R值求出该数量性状座位（QTL）与各标记座位（ML）间的重组值。     

性状遗传力与QTL方差对标记辅助选择效果的影响

在采用动物模型标记辅助最佳线性无偏预测方法对个体育种值进行估计的基础上,模拟了在一个闭锁群体内连续对单个性状选择10个世代的情形,并系统地比较了性状遗传力和QTL方差对标记辅助选择所获得的遗传进展、QTL增效基因频率和群体近交系数变化的影响。结果表明：在对高遗传力和QTL方差较小的性状实施标记辅助选择时,可望获得更大的遗传进展;遗传力越高,QTL方差越大,则QTL增效基因频率的上升速度越快;遗传力较高时,群体近交系数上升的速度较为缓慢,而QTL方差对群体近交系数上升速度的影响则不甚明显。结合前人关于标记辅助选择相对效率的研究结果,可以认为：当选择性状的遗传力和QTL方差为中等水平时,标记辅助选择可望获得理想的效果。     

植物QTL分析的理论研究进展

数量性状的表型是由数量性状基因座 ( Quantitative trait locus,QTL)和环境效应共同作用的结果。传统的数量遗传学采用统计学的方法由一级统计量和二级统计量描述处理 QTL的复合作用 ,估计各种遗传参数 (例如遗传力、遗传相关、遗传进度、有效因子数等 ) ,用于指导遗传育种实践。然而 ,在传统的数量遗传学分析中 ,往往假设数量性状受微效多基因控制 ,这些基因具有相同的并且是较微小的效应 ,所估计的遗传参数反映的是数量性状多基因系统的整体特征 ,其理论方法不能用于追踪研究和描述单个数量性状基因的作用。近年来 ,由于分子生物学技…     

基于性状—标记回归的QTL区间测验方法

本提两种基于性状－标记回归的QTL区间测验方法,分别称为TMRIT－I和TMRIT－II。前采用似然比统计量进行显性测验,与基于最小二乘的简化复合区间定位法（sCIM)等价,但计算机上明显简单快捷;后则采用一种“伪似然比”统计量进行显性测验,不仅进一步简化计算,而且明显提高统计功效,二皆可通过排列测验估计显阈值,给出了一个模拟例子。     

多基因抗性的QTL作图及其在作物持久性抗病育种上的应用

QTL作图已成为解析生物复杂性状遗传基础和基因座之间互作机制的一种有效的研究工具。多基因抗性没有明显的生理小种特异性,一般表现为数量性状。多基因抗性的QTL作图在植物持久性抗病育种中有重要的应用价值,有助于分离到广谱性抗病基因。从作图群体(F1、F2、DH、RIL、BIL和NIL)构建、抗性表型测定和标记辅助育种等方面论述了多基因抗性QTL作图的最新研究进展。     

双单倍体群体中区间分子标记定位(QTL) 的相关方法

本文提出对区间标记座位赋值后求此赋值与待定位的数量性状表型值间的简单相关系数C,以此进行连锁测验,并且在一定条件下还能用C值继而求出该数量性状座位（QTL）与各标记座位（ML）间的重组值．     

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…     

基于性状-标记回归的QTL区间测验方法

本文提出了两种基于性状-标记回归的QTL区间测验方法,分别称为TMRIT-I和TMRIT-II。前者采用似然比统计量进行显著性测验,与基于最小二乘的简化复合区间定位法（sCIM）等价,但计算上明显简单快捷;后者则采用一种“伪似然比”统计量进行显著性测验,不仅进一步简化计算,而且明显提高统计功效。二者皆可通过排列测验估计显著阈值。给出了一个模拟例子。 Abstract：Two methods of interval test of quantitative trait loci (QTLs) based on trait-marker regression are proposed, named as TMRIT-I and TMRIT-II, respectively. The former uses likelihood ratio statistic for significance test, equivalent to the method of simplified composite interval mapping (sCIM) based on least squares, but much simpler and quicker in calculation. The latter uses a ‘pseudo-likelihood ratio’ statistic for significance test, not only simplifying calculation further, but also significantly increasing statistical power. For both methods, significance threshold can be estimated by permutation tests. A simulated example is given.     

孙女设计中标记密度对QTL定位精确性的影响

采用蒙特卡罗方法分析了在孙女设计中不同的嫩体结构、性状遗传力、ＱＴＬ效应大小和ＱＴＬ在染色体上的位置中个因素不同水平组合下４种标记密度（标记间隔５ｃＭ,１０ｃＭ,２０ｃＭ、５０ｃＭ对ＱＴＬ定位精确性（以均方误ＭＳＥ为衡量指标）的影响,并从经济学角度探讨了应用于标记辅助选（ＭＡＳ）的ＱＴＬ定位的最佳标记密度。结果表明,一般说来,在各因素水平都较低时,ＭＳＥ随标记密度加大而下降的相对幅度也较 小,反之     

油葵苗期抗旱相关性状的QTL定位及候选基因筛选

干旱是限制向日葵生长发育的重要因素之一。为探究向日葵苗期抗旱性分子机制,该研究以向日葵K55与K58杂交构建的150个F₇重组自交系群体为材料,对其在正常浇水和干旱胁迫两种水分处理条件下的叶片相对电导率、叶绿素含量、叶面积、叶片相对含水量、根长进行表型测定,利用前期建立的SNP、SSR分子标记遗传连锁图谱,通过复合区间作图法对5个抗旱相关的性状进行QTL定位。结果表明：(1)共定位到向日葵QTL位点11个,其中正常浇水条件下5个,干旱胁迫条件下6个,表型贡献率为0.768%~7.547%,且5号连锁群上定位到的QTL位点最多(3个)。(2)QTL置信区间内共筛选到62个与干旱相关的候选基因,包括位于qLA 8 1上的rna23019、rna23004、rna22661、rna22193、rna23294、rna22783和位于qCC 13 1上的rna40140,这些基因可作为后续基因克隆及功能研究的重点候选基因。该研究结果为向日葵抗旱性研究及其遗传改良奠定了基础。     

植物QTL定位方法的研究进展

本文系统地介绍了QTL定位的单一标记分析法、区间作图法以及复合区间作图法、混合显性模型的分析方法,概述了一些主要定位方法的分析原理、存在的主要优缺点。单一标记分析法可以采用方差分析、回归分析或似然比检验的方法分析。区间作图法和复合区间作图法是基于两个相邻标记的QTL定位方法,可采用回归分析或最大似然法分析。复合区间作图法在模型中包括了与其他QTL连锁的标记,可以提高作图的精度和效率。混合线性模型的QTL定位方法可以包括复杂的遗传效应及QTL与环境的互作效应,具有更广阔的应用前景。 Abstract:QTL mapping methods are reviewed for single-marker mapping,interval mapping,composite interval mapping,and mixed-model based method.Statistical approaches along with their properties are discussed for the mapping methods.ANOVA,regression method and likelihood ratio test can be applied in single-marker mapping.Interval mapping and composite interval mapping can be conducted,based on two interval markers,by regression method and maximum likelihood method.Since markers linked with other QTLs are include in the model,composite interval mapping is more precision and powerful.Mapping QTL by mixed-model approaches is more applicable when complicated QTL effects as well as QTL by environment interaction are analyzed.     

Mapping multiple QTL of different effects: comparison of a simple sequential testing strategy and multiple QTL mapping

The aim of this study was to explore, by computer simulation, the mapping of QTLs in a realistic but complex situation of many (linked) QTLs with different effects, and to compare two QTL mapping methods. A novel method to dissect genetic variation on multiple chromosomes using molecular markers in backcross and F₂ populations derived from inbred lines was suggested, and its properties tested using simulations. The rationale for this sequential testing method was to explicitly test for alternative genetic models. The method consists of a series of four basic statistical tests to decide whether variance was due to a single QTL, two QTLs, multiple QTLs, or polygenes, starting with a test to detect genetic variance associated with a particular chromosome. The method was able to distinguish between different QTL configurations, in that the probability to `detect' the correct model was high, varying from 0.75 to 1. For example, for a backcross population of 200 and an overall heritability of 50%, in 78% of replicates a polygenic model was detected when that was the underlying true model. To test the method for multiple chromosomes, QTLs were simulated on 10 chromosomes, following a geometric series of allele effects, assuming positive alleles were in coupling in the founder lines For these simulations, the sequential testing method was compared to the established Multiple QTL Mapping (MQM) method. For a backcross population of 400 individuals, power to detect genetic variance was low with both methods when the heritability was 0.40. For example, the power to detect genetic variation on a chromosome on which 6 QTLs explained 12.6% of the genetic variance, was less than 60% for both methods. For a large heritability (0.90), the power of MQM to detect genetic variance and to dissect QTL configurations was generally better, due to the simultaneous fitting of markers on all chromosomes. It is concluded that when testing different QTL configurations on a single chromosome using the sequential testing procedure, regions of other chromosomes which explain a significant amount of variation should be fitted in the model of analysis. This study reinforces the need for large experiments in plants and other species if the aim of a genome scan is to dissect quantitative genetic variation.     

单核苷酸多态性及其在鸡QTL定位上的应用

单核苷酸多态性是指DNA序列上的单个碱基变异,它具有分布广、多态信息含量大、易于检测和统计分析等优点,能较好用于基因图谱构建和数量性状QTL定位研究,被称为继RFLP和微卫星标记之后的第3代基因遗传标记。本文综述了单核苷酸多态性的性质及检测技术、利用候选基因SNP进行鸡QTL定位研究的现状,并对未来SNP的应用前景进行了展望。Abstract:Single nucleotide polymorphism (SNP) refers to the change of single nucleotide in DNA sequence.Because of its high density in genomes and easy in detection and analysis statistically,SNP can be used in genetic linkage map construction and QTL mapping.Here,the characters and detecting technology of SNP,as well as the status and foreground of the use of candidate gene SNP in chicken QTL mapping are introduced.     

玉米抗南方锈病基因的QTL定位

为发掘新的抗南方锈病基因资源,本研究以感病自交系黄早四为母本、抗病自交系W456为父本,构建F2群体并开展抗病基因定位研究。采用人工接种鉴定的方法对两个亲本、F1、F2群体及对照材料进行表型鉴定和遗传分析。利用均匀覆盖10条染色体的200个SSR标记,分析240个F2单株的基因型并构建含有200个SSR位点的遗传连锁图,连锁图总长度3331 cM,标记间平均距离16.6 cM。使用QTL IciMapping V4.1软件中的完备区间作图法对抗病QTL进行分析,共检测到6个控制南方锈病的QTL:qSCR3、qSCR7、qSCR8-1、qSCR8-2、qSCR9和qSCR10,邻近标记分别为umc2105和umc1729、umc1066和bnlg2271、umc1904和umc1984、umc1984和bnlg1651、umc1957和bnlg1401、umc2034和umc1291,分别位于3、7、8、9和10号染色体上,其中8号染色体上有两个位点,标记区间长度在5～19 cM之间。单个QTL的表型贡献率在2.61%～24.19%之间,可以解释表型总变异的62.3%,其中3个QTL贡献率大于10%,位于10号染色体上的qSCR10贡献率最大,可解释表型变异的24.19%。通过对目标区间标记加密,将该位点的定位区间进一步缩小到2.51 cM内,与两侧标记的距离分别是2.15 cM和0.36 cM。初步定位得到10号染色体上存在抗南方锈病的主效QTL,可为抗病品种的培育提供参考。     

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.     

猪的肉质性状基因定位研究进展

随着生活水平的提高,猪肉的肉质问题已引起消费者和生产者的重视。遗传 因素是改善肉质品质的关键。对猪的肉质性状进行数量性状位点(QTL)定位是当前国际畜禽遗传育种的一个热点。本文较全面地介绍了有关肉质基因定位的情况,包括基本明确的主效基因:氟烷基因和酸肉基因;肌内脂肪的统计分析、候选基因及基因扫描定位结果:以及肌纤维、嫩度、多汁性、失水率、pH值和肉色的定位。同时论讨了肉质检验和基因定位面临的问题。 Abstract:People tend to be interested in meat quality when the standand of living has been increased.Location of quantitative trait loci(QTL)affecting meat quality were performed in many countries.The objective of this review is to provide a detailed introduction of meat quality mapping in pig.It included halothane gene,acid meat gene,intramuscular fat,firmness,fruice,drop loss,pH value and meat color.It also discussed the problems of meat quality testing and QTL mapping.     

Mapping of QTL associated with waterlogging tolerance during the seedling stage in maize

BACKGROUND AND AIMS: Soil waterlogging is a major environmental stress that suppresses maize (Zea mays) growth and yield. To identify quantitative trait loci (QTL) associated with waterlogging tolerance at the maize seedling stage, a F2 population consisting of 288 F(2:3) lines was created from a cross between two maize genotypes, 'HZ32' (waterlogging-tolerant) and 'K12' (waterlogging-sensitive). METHODS: The F2 population was genotyped and a base-map of 1710.5 cM length was constructed with an average marker space of 11.5 cM based on 177 SSR (simple sequence repeat) markers. QTL associated with root length, root dry weight, plant height, shoot dry weight, total dry weight and waterlogging tolerance coefficient were identified via composite interval mapping (CIM) under waterlogging and control conditions in 2004 (EXP.1) and 2005 (EXP.2), respectively. KEY RESULTS AND CONCLUSIONS: Twenty-five and thirty-four QTL were detected in EXP.1 and EXP.2, respectively. The effects of each QTL were moderate, ranging from 3.9 to 37.3 %. Several major QTL determining shoot dry weight, root dry weight, total dry weight, plant height and their waterlogging tolerance coefficient each mapped on chromosomes 4 and 9. These QTL were detected consistently in both experiments. Secondary QTL influencing tolerance were also identified and located on chromosomes 1, 2, 3, 6, 7 and 10. These QTL were specific to particular traits or environments. Although the detected regions need to be mapped more precisely, the findings and QTL found in this study may provide useful information for marker-assisted selection (MAS) and further genetic studies on maize waterlogging tolerance.