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复合区间作图中标记筛选的效率及其影响因素研究

高效地筛选标记 ,是复合区间作图方法定位QTLs的基础。筛选出的主效标记和互作标记 ,除了用作控制背景遗传效应外 ,在定位具有上位性效应的QTLs时 ,还将用于构筑两维搜索区间。因而 ,标记筛选的效率将直接影响QTL定位的功效和精度。通过对不同方法筛选标记的效率进行模拟研究 ,发现回归方法明显优于随机效应预测方法 ,同归方法中又以前向选择法简单有效。普通遗传力和基因型×环境互作遗传力的增加都能提高标记筛选效率 ,前者对主效应较大的QTLs影响明显 ,后者对主效应较小的QTLs作用较大。过多过密的标记会降低标记筛选效率 ,其中密度增加对标记筛选的负作用更为突出。为了缓解标记筛选效率制约QTL定位功效的缺陷 ,可以用多环境下筛选出的标记共同构建两维搜索区间     

作物数量性状基因座定位及分析研究进展

近年来,分子数量遗传学的快速发展,使作物复杂数量性状尤其是经济性状的QTL研究取得了巨大进展,大大促进了复杂数量性状的遗传改良和分子操纵。本文从分子标记连锁图谱的构建、QTL定位力方法及效应分析、精细定位和QTL验证及应用等方面综述了二十多年来作物QTL的研究进展,讨论当前QTL研究中存在的问题,并展望了作物QTL研究的发展前景。     

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。     

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。     

利用回交导入系群体定位大豆蛋白质含量与脂肪含量QTL

对大豆的蛋白质含量和脂肪含量进行QTL定位,可为其分子标记辅助育种提供依据。以回交导入系群体中黄13×中黄20的BC2F5的100个家系为材料,分析群体的SSR标记多态性,采用近红外光谱分析技术测定群体蛋白质含量和脂肪含量。构建了一张涵盖大豆20个连锁群、总长为948.01 c M、平均遗传距离为8.78 c M、包含108个SSR标记的大豆遗传连锁图谱。共检测到与蛋白质含量相关的QTL 5个,与脂肪含量相关的QTL 9个,其中Satt445~Sat_303连续2年被检测到与脂肪含量相关,Satt445~Sat_303与Satt543~Satt574均被检测到与蛋白质含量和脂肪含量相关,Sat_389~Satt590、Satt238~Satt388及Satt685~Sat_381均与脂肪含量相关。     

不同作图群体对显隐性分子标记位点的作图效率

根据位点组合和位点的有效性,发展了一种对使用３种不同的作用图群体作图显隐性分子标记的作图效率评价方法,应用该方法所评价的结果是,双单倍体（ＤＨ）群体的作图效率最高,自交群体（Ｆ２群体）与回交群体的作图效率相同,因此使用双单倍体群体作图不仅所用费用低,而且作图速度快,但只有在极少数植物中能获得双单倍体群体,对于那些不能获得双单倍体的动植物物种而言,可使用自交群体或回交群体作图。如果作高密度的分子标记     

小麦幼苗耐热性的QTL定位分析

以小麦DH群体(‘旱选10号’ב鲁麦14’)为材料,在高温(热胁迫)及常温(对照)两种条件下考察小麦幼苗的根干重、苗干重、幼苗生物量、叶片叶绿素含量、叶绿素荧光参数及其耐热指数,并应用基于混合线性模型的复合区间作图法分析幼苗性状及其耐热指数QTL的数量、染色体分布及表达情况,以及QTL与环境的互作效应。结果显示:(1)亲本‘旱选10号’的耐热性明显优于‘鲁麦14’,且杂交后代的耐热性出现超亲分离。(2)控制幼苗耐热相关性状的QTL位点在染色体2D、6B、3A、4A、5A和7A上分布较多,而控制幼苗性状耐热指数的QTL在染色体6A、6B、3A、2D、5A和7A上分布较多,QTL位点在染色体上的分布有区域化的趋势。(3)控制幼苗性状的单个加性QTL和上位性QTL解释的表型变异分别平均为2.48%和2.65%;而控制耐热指数的单个加性QTL和上位性QTL解释的表型变异分别平均为8.84%和1.98%。(4)在热胁迫和对照条件下共检测到与幼苗性状及其耐热指数有关的加性效应QTL 13个和上位性效应QTL 28对,分布在除4D和6D以外的19条染色体上。研究表明,控制幼苗性状的QTL以上位性效应为主,而其耐热指数的QTL以加性效应为主。     

应用重组自交系群体定位大豆抗虫QTL

以大豆组合科丰1号×南农1138-2衍生的重组自交系(RIL)群体为材料构建遗传连锁图谱, 利用软件 Cartographer V.2.5 采用复合区间作图法检测定位大豆抗虫QTL。以斜纹夜蛾幼虫重为抗性指标, 检测到 1 个与抗虫性有关的 QTL, 位于G20-O连锁群上, 其端距离为31.91 cM, 加性效应估计值为0.0408, 对性状变异的解释率为 11.74％; 以蛹重为抗性指标, 检测到 2 个与抗虫性有关的 QTL, 分别位于G8-D1b+W和G17-L连锁群上, 其端距离分别为 14.71 cM和0.01 cM, 加性效应估计值分别为-0.0139和0.0103, 对性状变异的解释率分别为 11.30％和6.36％。     

QTL形态标记定位的一种数学方法

根据家蚕中位于Z染色体上的伴性遗传的双形态标记和假定与其有连锁关系的一个具有一对主基因差异的数量性状在测交世代中,所作的理论分布,本文建立了QTL形态标记定位的数学方法,即频数分布面积法,并给出了相应的检测一对主基因在测交世代中的同分离比例及其与形态标记是否有连锁关系的X2统计量．这种定位方法亦适应于非伴性遗传方式的QTL形态标记定位．与单标记定位的极大似然方法相比,我们的方法所作的双标记定位能显示QTL与形态标记发生重组的交叉干步作用,并且定位结果不受作用于数量性状的环境效应所影响．     

利用商品猪群定位猪耳型QTL

由 19头杂种公猪 [皮特兰× (皮特兰×汉普夏 ) ]、5 2头杂种母猪 [Leicoma× (大约克×长白 ) ]及其 332头后代组成的商品群作为参考家系 ,选择 172个微卫星标记和 3个 1类标记 (RYR1、PRKAG3、PIT1)对参考家系的个体进行遗传标记分型 ,构建了猪的整个基因组微卫星连锁图谱。按照线性评分的方法 (竖耳 :1分 ;垂耳 :- 1分 ;半垂耳 :0分 )测定猪的耳型表型值。耳型测定值的平均值为 0 .2 3,标准差为 0 .82。应用最小二乘回归方法对耳型的QTL进行定位 ,结果仅在 6号染色体的末端 (Sw1881和Sw32 2 )之间以 1%基因组显著性水平检测出 1个耳型基因位点 ,而在其他染色体上即使 10 %染色体显著性水平上也没有发现QTL。     

Improving QTL Mapping Resolution Based on Genotypic Sampling—a Case Using a RIL Population

The QTL mapping results were compared with the genotypically selected and random samples of the same size on the base of a RIL population. The results demonstrated that there were no obvious differences in the trait distribution and marker segregation distortion between the genotypically selected and random samples with the same population size. However, a significant increase in QTL detection power, sensitivity, specificity, and QTL resolution in the genotypically selected samples were observed. Moreover, the highly significant effect was detected in small size of genotypically selected samples. In QTL mapping, phenotyping is a more sensitive limiting factor than genotyping so that the selection of samples could be an attractive strategy for increasing genome-wide QTL mapping resolution. The efficient selection of samples should be more helpful for QTL maker assistant selection, fine mapping, and QTL cloning.     

我国农作物QTL定位研究的现状和进展

植物大多数重要的经济性状都是数量性状,研究植物数量性状遗传对植物育种具有十分重要的意义。近十几年来,分子生物学特别是分子标记技术的飞速发展,许多植物高饱和的分子连锁图谱的构建,使得我们可以更深入地进行数量性状的研究。本文综述了近年来我国在农作物数量性状遗传研究方面的进展及发展动态。列举了我国利用DNA分子标记进行数量性状QTL定位的47个实例。总结了控制数量性状的QTL的数目、类别、效应及其互作和QTL与环境间的互作关系。讨论了当前在该研究领域中存在的问题,展望了该领域的发展前景。     

远交群体动态性状基因定位的似然分析Ⅰ.理论方法

受动物遗传育种中用来估计动态性状育种值的随机回归测定日模型思想的启发 ,将关于时间 (测定日期 )的Legendre多项式镶嵌在遗传模型的每个遗传效应中 ,以刻画QTL对动态性状变化过程的作用 ,从而建立起动态性状基因定位的数学模型。利用远交设计群体 ,阐述了动态性状基因定位的似然分析原理 ,推导了定位参数似然估计的EM法两步求解过程。结合动态性状遗传分析的特点和普通数量性状基因定位研究进展 ,还提出了有关动态性状基因定位进一步研究的设想     

Mapping QTL for Grain Yield and Plant Traits in a Tropical Maize Population

The vast majority of reported QTL mapping for maize (Zea mays L.) traits are from temperate germplasm and, also, QTL by environment interaction (QTL × E) has not been thoroughly evaluated and analyzed in most of these papers. The maize growing areas in tropical regions are more prone to environmental variability than in temperate areas, and, therefore, genotype by environment interaction is of great concern for maize breeders. The objectives of this study were to map QTL and to test their interaction with environments for several traits in a tropical maize population. Two-hundred and fifty-six F_2:3 families evaluated in five environments, a genetic map with 139 microsatellites markers, and the multiple-environment joint analysis (mCIM) were used to map QTL and to test QTL × E interaction. Sixteen, eight, six, six, nine, and two QTL were mapped for grain yield, ears per plant, plant lodging, plant height, ear height, and number of leaves, respectively. Most of these QTL interacted significantly with environments, most of them displayed overdominance for all traits, and genetic correlated traits had a low number of QTL mapped in the same genomic regions. Few of the QTL mapped had already been reported in both temperate and tropical germplasm. The low number of stable QTL across environments imposes additional challenges to design marker-assisted selection in tropical areas, unless the breeding programs could be directed towards specific target areas.     

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.     

一个基于熵的精细定位数量性状位点的指数

针对数量性状位点的精细定位,本文采用群体的极端样本,利用稠密的标记位点,通过比较标记的熵和条件熵,给出了一个基于熵的指数。该指数是标记基因和性状位点间连锁不平衡系数的函数,它不依赖于标记基因的频率。该指数对应我们之前提出的数量性状位点精细定位的哈迪-温伯格不平衡(HWD)指数,但在精细定位数量性状位点时,本文提出的指数的效能要高于哈迪-温伯格不平衡(HWD)指数。通过计算机模拟,文章调查了不同遗传参数下该指数的性质。模拟结果表明该指数用作精细定位是有效的。     

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.