人类混血群体连锁不平衡的遗传学模型与基因定位

人类混血群体可以说是混合群体的一种特例．在无选择、无突变、无限随机交配群体的假定前提下,研究了亲本群体的基因频率对混血群体及其衍生后代群体连锁不平衡结构的影响,导出了各群体连锁不平衡值的表达式,建立了一个估计基因间重组率的简便方法;同时, 采用估算分子标记与QTL之间连锁不平衡系数的统计分析方法,分析了人类混血群体及其衍生后代群体QTL检测与估计的关系,建立了该关系的系列理论公式．研究结果表明,本方法不仅适用于人类疾病(包括复杂遗传疾病)基因定位,而且适合于人类正常基因的定位,同时也适用于人类普通多基因性状的QTL分析．     

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。     

与偏分离位点连锁的QTL作图的统计方法

提出了一种统计方法,可以估计与偏分离位点连锁的QTL的位置和效应。该方法利用回交群体中呈现偏分离的分子标记,首先用最大似然法对偏分离位点与标记位点之间的重组率和配子存活率进行估计,然后用区间作图法估计加性-显性模型下QTL的位置和效应参数。该方法可用于对常规作图研究中表现偏分离的标记进行分析,以帮助我们发现新的偏分离基因（或不育基因）和数量性状位点。     

谷子SSR分子图谱的构建及主要农艺性状QTL定位

试验拟对谷子重要农艺性状进行数量性状位点QTL分析。以表型差异较大的沈3/晋谷20F2作图群体为材料,观测其株高、穗长等性状,选用SSR做分子标记,利用完备区间作图法(BASTEN C J)进行QTL分析。结果显示,表型数据在作图群体中呈现连续分布,表现为多基因控制的数量性状,被整合的54个SSR标记构建10个连锁群,LOD阈值设置为2.0,检测到与株高相关的主效QTL2个,联合贡献率45.9637%,穗长主效QTL1个,贡献率14.9647%,与穗重、粒重相关的主效QTL为同一位点,贡献率分别为11.9601%和10.1879%。有6组QTL位点之间存在基因互作效应,大小范围为-0.4986-16.6407,对性状的贡献率在2.2716％至6.7478％之间。谷子表型控制复杂,相关QTL的检测受环境影响较大,不同连锁群QTL间互作明显。     

水稻QTL分析的研究进展

水稻许多重要的性状是由多基因控制的数量性状,经典的数量遗传学只能把数量性状作为一个整体进行研究。近年来．高密度分子标记连锁图的构建和有效的生物统计学方法的发展使人们对数量性状遗传基础的研究出现了革命性的变化。通过对不同群体内的个体或品系的分子标记基因型和表型数据的共分离分析,能对QTL进行检测和定位。本文对QTL定位的原理和方法进行了介绍,从QTL的数目和效应、上位性效应、QTL基因型与环境的互作、相关性状的QTL以及个体发育不同阶段的QTL等方面对水稻QTL分析的研究进展进行了综述。水稻基因组测序计划已经完成,本文还对基因组时代水稻QTL精细定位和克隆的方法进行了探讨,对QTL分析在水稻育种中的应用前景进行了展望。     

不同QTL增效基因初始频率下标记辅助选择的效果

采用随机模拟方法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。在假定选择性状受一个位于常染色体上的QTL和多基因共同控制的情况下,采用动物模型标记辅助最佳线性无偏预测方法估计个体育种值并据此进行种畜的选留,并在此基础上系统地比较了QTL增效基因初始频率对标记辅助选择效果的影响。结果表明：当群体中QTL增效基因的初始频率较低时,选择所获得的QTL基因型值的进展会更大,标记辅助选择在单位时间内可获得较大的遗传进展;此时,尽管QTL增效基因在群体中固定所需的世代数会更长一些,但其频率上升的速度却更快。而QTL增效基因初始频率的高低对群体近交增量的影响不是很大。     

水稻化感材料控制稗草的基因定位研究

利用中156／谷梅2号建立的重组自交系(RILs)所构建的包括168个DNA标记,全长为1447．9cM。基本覆盖水稻基因组12条染色体的连锁图,用差时播种共培法的改进方法对134个该群体的株系及其亲本对无芒稗进行了化感作用评价,用无芒稗的植株干重作为表型定位水稻化感控制稗草的基因,用QTL Mapper 1．01b软件进行区间作图,检测到1个与化感作用有关的主效应QTL。该QTL位于第7条染色体上,解释了32．30％的表型变化;检测到6对上位QTL,解释了47．83％的无芒稗干重抑制的变化,主效应和上位效应QTL共解释了80．13％的表型变化。     

RIL群体区间分子标记-QTL定位的相关方法

分析了RIL群体中以分子区间标记进行QTL定位的相关方法．通过对分子标记赋值可获得与数量性状表型值的简单相关系数．然后,在此基础上进行连锁检验．此外．在特定情况下利用R值,可以估计数量性状座位(QTL)和分子标记位点(ML)间的重组值．     

长期人工选择对群体遗传结构的影响——一项计算机模拟研究 Ⅱ.长期选择对群体遗传方差的作用

本文给出了显性与超显性模型下加性方差的分剖公式,为研究选择作用下基因间关系的变化提供了有力的方法。并模拟研究了群体大小、连锁强度与遗传力水平对遗传方差变化的影响。小群体中遗传方差在世代间波动很大;大群体中则稳定下降、波动较小。选择作用下平衡加性方差下降很快,特别是高遗传力性状。紧密连锁在小群体中一方面降低选择反应,一方面维持了更多的加性方差,从而使得预测长期选择反应甚为困难。     

小麦品种冀麦24抗麦红吸浆虫QTL分析

麦红吸浆虫是影响小麦产量和品质的重要害虫,研究小麦对吸浆虫抗性的遗传及其连锁分子标记对于提高抗虫品种的选择效率具有重要意义。本研究以小麦感虫品系6218与抗虫品种冀麦24产生的重组近交系(RIL)群体为材料,利用SSR标记和人工虫圃对冀麦24的抗虫性遗传进行了研究。结果表明:6218与冀麦24的抗性差异显著,RIL群体在2年2点的鉴定中抗性稳定;所构建的遗传连锁图谱包含112个SSR位点,形成26个连锁群,图谱全长835.7 cM,标记间平均距离为7.5 cM。利用QTL IciMapping的完备区间作图法,在4A染色体上检测到1个加性效应位点(QSm.hbau-4A),该位点在2个鉴定年度的贡献率分别为9.67%、10.57%。该抗性QTL及其连锁SSR标记的发掘,将有助于提高小麦抗吸浆虫育种的选择效率。     

Changes in Variance Components of Flanking Marker Genotypes Under Varying Selection Intensities

Selection is practically ubiquitous during marker-QTL linkage analysis with an experimental population. Thus, it is necessary to investigate the impacts of selection upon linkage analyses in order to obtain unbiased estimates of QTL position and effect. In this article, by exploiting flanking markers through the widely applied half-sib design, we have developed the structures of three variance components, i.e., variance component between marker genotypes, polygenic variance component and recombinant variance component within marker genotypes. Changes in these variance components under varying selection intensities were investigated in this study to formulate the effects of selection on various variance components. Results showed clearly that all variance components presented were quite sensitive to changes in selection intensity. As selection intensity increased, all variance components declined by differing extents in a quadratic fashion. Comparatively speaking, the variance between marker genotypes decreased most drastically, followed by the polygenic variance within marker genotypes and then the recombinant variance within marker genotypes, which suggested a decrease of power for QTL linkage analysis. Therefore, steps should be taken to avoid as much as possible the presence of selection in real populations, so as to further eliminate the negative effects of selection on QTL linkage analysis.     

Ghost QTL and hotspots in experimental crosses: novel approach for modeling polygenic effects

Ghost quantitative trait loci (QTL) are the false discoveries in QTL mapping, that arise due to the “accumulation” of the polygenic effects, uniformly distributed over the genome. The locations on the chromosome that are strongly correlated with the total of the polygenic effects depend on a specific sample correlation structure determined by the genotypes at all loci. The problem is particularly severe when the same genotypes are used to study multiple QTL, e.g. using recombinant inbred lines or studying the expression QTL. In this case, the ghost QTL phenomenon can lead to false hotspots, where multiple QTL show apparent linkage to the same locus. We illustrate the problem using the classic backcross design and suggest that it can be solved by the application of the extended mixed effect model, where the random effects are allowed to have a nonzero mean. We provide formulas for estimating the thresholds for the corresponding t-test statistics and use them in the stepwise selection strategy, which allows for a simultaneous detection of several QTL. Extensive simulation studies illustrate that our approach eliminates ghost QTL/false hotspots, while preserving a high power of true QTL detection.     

Fine mapping of a quantitative trait locus for twinning rate using combined linkage and linkage disequilibrium mapping

A novel and robust method for the fine-scale mapping of genes affecting complex traits, which combines linkage and linkage-disequilibrium information, is proposed. Linkage information refers to recombinations within the marker-genotyped generations and linkage disequilibrium to historical recombinations before genotyping started. The identity-by-descent (IBD) probabilities at the quantitative trait locus (QTL) between first generation haplotypes were obtained from the similarity of the marker alleles surrounding the QTL, whereas IBD probabilities at the QTL between later generation haplotypes were obtained by using the markers to trace the inheritance of the QTL. The variance explained by the QTL is estimated by residual maximum likelihood using the correlation structure defined by the IBD probabilities. Unlinked background genes were accounted for by fitting a polygenic variance component. The method was used to fine map a QTL for twinning rate in cattle, previously mapped on chromosome 5 by linkage analysis. The data consisted of large half-sib families, but the method could also handle more complex pedigrees. The likelihood of the putative QTL was very small along most of the chromosome, except for a sharp likelihood peak in the ninth marker bracket, which positioned the QTL within a region <1 cM in the middle part of bovine chromosome 5. The method was expected to be robust against multiple genes affecting the trait, multiple mutations at the QTL, and relatively low marker density.     

A Monte Carlo method for Bayesian analysis of linkage between single markers and quantitative trait loci. I. Methodology

A Bayesian approach to the statistical mapping of Quantitative Trait Loci (QTLs) using single markers was implemented via Markov Chain Monte Carlo (MCMC) algorithms for parameter estimation and hypothesis testing. Parameter estimators were marginal posterior means computed using a Gibbs sampler with data augmentation. Variables sampled included the augmented data (marker-QTL genotypes, polygenic effects), an indicator variable for linkage, and the parameters (allele frequency, QTL substitution effect, recombination rate, polygenic and residual variances). Several MCMC algorithms were derived for computing Bayesian tests of linkage, which consisted of the marginal posterior probability of linkage and the marginal likelihood of the QTL variance associated with the marker.     

QTL detection with bidirectional and unidirectional selective genotyping: marker-based and trait-based analyses

Selective genotyping of one or both phenotypic extremes of a population can be used to detect linkage between markers and quantitative trait loci (QTL) in situations in which full-population genotyping is too costly or not feasible, or where the objective is to rapidly screen large numbers of potential donors for useful alleles with large effects. Data may be subjected to 'trait-based' analysis, in which marker allele frequencies are compared between classes of progeny defined based on trait values, or to 'marker-based' analysis, in which trait means are compared between progeny classes defined based on marker genotypes. Here, bidirectional and unidirectional selective genotyping were simulated, using population sizes and selection intensities relevant to cereal breeding. Control of Type I error was usually adequate with marker-based analysis of variance or trait-based testing using the normal approximation of the binomial distribution. Bidirectional selective genotyping was more powerful than unidirectional. Trait-based analysis and marker-based analysis of variance were about equally powerful. With genotyping of the best 30 out of 500 lines (6%), a QTL explaining 15% of the phenotypic variance could be detected with a power of 0.8 when tests were conducted at a marker 10 cM from the QTL. With bidirectional selective genotyping, QTL with smaller effects and (or) QTL farther from the nearest marker could be detected. Similar QTL detection approaches were applied to data from a population of 436 recombinant inbred rice lines segregating for a large-effect QTL affecting grain yield under drought stress. That QTL was reliably detected by genotyping as few as 20 selected lines (4.5%). In experimental populations, selective genotyping can reduce costs of QTL detection, allowing larger numbers of potential donors to be screened for useful alleles with effects across different backgrounds. In plant breeding programs, selective genotyping can make it possible to detect QTL using even a limited number of progeny that have been retained after selection.     

QTL mapping under truncation selection in homozygous lines derived from biparental crosses

In plant breeding, a large number of progenies that will be discarded later in the breeding process must be phenotyped and marker genotyped for conducting QTL analysis. In many cases, phenotypic preselection of lines could be useful. However, in QTL analyses even moderate preselection can have a significant effect on the power of QTL detection and estimation of effects of the target traits. In this study, we provide exact formulas for quantifying the change of allele frequencies within marker classes, expectations of marker contrasts and the variance of the marker contrasts under truncation selection, for the general case of two QTL affecting the target trait and a correlated trait. We focused on homozygous lines derived at random from biparental crosses. The effects of linkage between the marker and the QTL under selection as well as the effect of selection on a correlated trait can be quantified with the given formulas. Theoretical results clearly show that depending on the magnitude of QTL effects, high selection intensities can lead to a dramatic reduction in power of QTL detection and that approximations based on the infinitesimal model deviate substantially from exact solutions. The presented formulas are valuable for choosing appropriate selection intensity when performing QTL mapping experiments on the data on phenotypically preselected traits and enable the calculation and bias correction of the effects of QTL under selection. Application of our theory to experimental data revealed that selection-induced bias of QTL effects can be successfully corrected.     

A comparison of bivariate and univariate QTL mapping in livestock populations

This study presents a multivariate, variance component-based QTL mapping model implemented via restricted maximum likelihood (REML). The method was applied to investigate bivariate and univariate QTL mapping analyses, using simulated data. Specifically, we report results on the statistical power to detect a QTL and on the precision of parameter estimates using univariate and bivariate approaches. The model and methodology were also applied to study the effectiveness of partitioning the overall genetic correlation between two traits into a component due to many genes of small effect, and one due to the QTL. It is shown that when the QTL has a pleiotropic effect on two traits, a bivariate analysis leads to a higher statistical power of detecting the QTL and to a more precise estimate of the QTL''s map position, in particular in the case when the QTL has a small effect on the trait. The increase in power is most marked in cases where the contributions of the QTL and of the polygenic components to the genetic correlation have opposite signs. The bivariate REML analysis can successfully partition the two components contributing to the genetic correlation between traits.     

Robust variance-components approach for assessing genetic linkage in pedigrees.

To assess evidence for genetic linkage from pedigrees, I developed a limited variance-components approach. In this method, variability among trait observations from individuals within pedigrees is expressed in terms of fixed effects from covariates and effects due to an unobservable trait-affecting major locus, random polygenic effects, and residual nongenetic variance. The effect attributable to a locus linked to a marker is a function of the additive and dominance components of variance of the locus, the recombination fraction, and the proportion of genes identical by descent at the marker locus for each pair of sibs. For unlinked loci, the polygenic variance component depends only on the relationship between the relative pair. Parameters can be estimated by either maximum-likelihood methods or quasi-likelihood methods. The forms of quasi-likelihood estimators are provided. Hypothesis tests derived from the maximum-likelihood approach are constructed by appeal to asymptotic theory. A simulation study showed that the size of likelihood-ratio tests was appropriate but that the monogenic component of variance was generally underestimated by the likelihood approach.     

Power of QTL detection by either fixed or random models in half-sib designs

The aim of this study was to compare the variance component approach for QTL linkage mapping in half-sib designs to the simple regression method. Empirical power was determined by Monte Carlo simulation in granddaughter designs. The factors studied (base values in parentheses) included the number of sires (5) and sons per sire (80), ratio of QTL variance to total genetic variance (λ = 0.1), marker spacing (10 cM), and QTL allele frequency (0.5). A single bi-allelic QTL and six equally spaced markers with six alleles each were simulated. Empirical power using the regression method was 0.80, 0.92 and 0.98 for 5, 10, and 20 sires, respectively, versus 0.88, 0.98 and 0.99 using the variance component method. Power was 0.74, 0.80, 0.93, and 0.95 using regression versus 0.77, 0.88, 0.94, and 0.97 using the variance component method for QTL variance ratios (λ) of 0.05, 0.1, 0.2, and 0.3, respectively. Power was 0.79, 0.85, 0.80 and 0.87 using regression versus 0.80, 0.86, 0.88, and 0.85 using the variance component method for QTL allele frequencies of 0.1, 0.3, 0.5, and 0.8, respectively. The log₁₀ of type I error profiles were quite flat at close marker spacing (1 cM), confirming the inability to fine-map QTL by linkage analysis in half-sib designs. The variance component method showed slightly more potential than the regression method in QTL mapping.     

Estimation of Effects of Quantitative Trait Loci in Large Complex Pedigrees

A method was derived to estimate effects of quantitative trait loci (QTL) using incomplete genotype information in large outbreeding populations with complex pedigrees. The method accounts for background genes by estimating polygenic effects. The basic equations used are very similar to the usual linear mixed model equations for polygenic models, and segregation analysis was used to estimate the probabilities of the QTL genotypes for each animal. Method R was used to estimate the polygenic heritability simultaneously with the QTL effects. Also, initial allele frequencies were estimated. The method was tested in a simulated data set of 10,000 animals evenly distributed over 10 generations, where 0, 400 or 10,000 animals were genotyped for a candidate gene. In the absence of selection, the bias of the QTL estimates was <2%. Selection biased the estimate of the Aa genotype slightly, when zero animals were genotyped. Estimates of the polygenic heritability were 0.251 and 0.257, in absence and presence of selection, respectively, while the simulated value was 0.25. Although not tested in this study, marker information could be accommodated by adjusting the transmission probabilities of the genotypes from parent to offspring according to the marker information. This renders a QTL mapping study in large multi-generation pedigrees possible.