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．     

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。     

雄性不交换条件下F2群体区间标记——QTL定位的相关方法（英文）

提出了雄性不交换条件下F2群体区间标记定位QTL的相关方法,并且对其适用的条件进行了讨论,通过对分子区间标记进行赋值,计算在无交叉干涉条件下分子标记与表型值的简单相关系数,并在此基础上进行连锁检验,在特定条件下可以估计数量性状座位（QTL）与分子标记座位间的连锁值。     

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．     

利用最大似然法进行水稻产量性状基因的分子作图

本研究根据对估计标记－数量性状基因座位（ＱＴＬ）之间重组率的两种分析方法（矩量法和最大似然法）、两种方差模型（ＱＴＬ基因型之间的方差同质和异质模型）的分析,揭示了ＬＯＤ值在标记－ＱＴＬ连锁检测上所得结果的相关性高于重组率估计值的相关性。采用最大似然法和异质方差模型,估计了水稻产量构成有关的ＱＴＬ与分布于１１对染色体上的５１个限制性片段长度多态性（ＲＦＬＰ）标记之间的重组率,并对似然比（以ＬＯＤ值表示）进行Ｘ￣２检验,发现７个存在显著连锁关系的标记－性状组合,其平均重组率为１０．０％。这些标记分布于第１、５、６、８和１１等５对染色体上,涉及７个ＲＦＬＰ标记和３个产量构成性状,即每穗颖花数（ＲＧ５７３、ＲＺ６１７、ＲＧ１０３）、单株穗数（ＲＧ６４Ｂ）和每穗实粒数（ＲＧ１０１、ＲＧ２４４、ＲＧ６５３）。     

应用贝叶斯理论推断DNA分子标记基因型

引入贝叶斯理论用以从DNA分子标记的表现型（电泳谱带）推断其基因型（DNA来源）。结果表明,根据标记座位独立贫富而确定的遗传信息不完全标记的基因型概率,与根据邻近的遗传信息完全标记的基因型和有关重组率算得的相应贝叶斯概率,通常都有很大的差异,所以在进行数量性状基因定位和标记辅助选择等工作前前,应当计算每一个体基因组上所有遗传信息不完全座位的有关基因型的贝叶斯概率,文中列出计算未知基因型的贝叶斯概率的详细过程,也讨论了贝叶斯概率的若干推广应用。     

应用贝叶斯理论推断DNA分子标记基因型

引入贝叶斯理论用以从DNA分子标记的表现型（电泳谱带）推断其基因型（DNA来源）。结果表明,根据标记座位独立贫富而确定的遗传信息不完全标记的基因型概率,与根据令近的遗传信息完全标记的基因型和有关重组率算得的相应贝叶斯概率,通常都有很大的差异,所以在进行数量性状基因定位和标记辅助选择等工作之前,应当计算每一个体基因组上所有遗传信息不完全座位的有关基因型的贝叶斯概率。文中列出计算未知基因型的贝叶期概率的详细过程,也讨论了贝叶斯概率的若干推广应用。     

利用分子标记和数量性状基因型值构建作物核心种质库的研究

提出了一种基于分子标记数据及数量性状基因型值构建作物种质资源核心种质库的方法.采用包括基因型与环境互作的遗传模型及相应的混合线性模型统计分析方法,无偏预测各材料的基因型值,分别用基因型值和分子标记数据计算个体间的相似系数,加权得到最终的相似距离.采用不加权类平均法（UPGMA）进行系统聚类,用多次聚类随机取样法构建核心种质库.以水稻DH群体111个基因型8个农艺性状、175个分子标记位点的数据为实例,按四种抽样比率（25%,20%,15%,10%）构建了四个核心种质库,比较了核心种质库与整个群体的分子标记多样性及数量性状的遗传变异,评价了所用方法的有效性。     

利用分子标记定位水稻野败型核质互作雄性不育恢复基因

以籼稻恢复系圭６３０与粳型广亲和品种０２４２８的Ｆ１代花药培养,获得８１个双单倍体（ＤＨ）,构建了有２３３个ＲＦＬＰ标记的分子图谱。用籼稻野败型不育系珍汕９７Ａ测定各ＤＨ系的恢复性,并将恢复性作为数量性状进行ＱＴＬ的区间作图分析,鉴别出８个基因座位,其中有２个基因座位,Ｒｆｉ－３和尾Ｒｆｉ－４,单个ＱＴＬ的基因贡献值分别是４９．６％和３５．４％,对育性恢复起主要作用,定为主效基因座位,位于第三和四染色体上,其它６个基因座位对育性恢复亦有一定的影响。表明野败型雄性不育恢复性是受主效基因和微效基因共同控制的性状。     

家蚕分子连锁图谱的构建及分子标记育种研究进展

国际蚕分子育种计划（ＩｎｔｅｒｎａｔｉｏｎａｌＳｉｌｋｗｏｒｍＰｒｏｊｅｃｔ）是ＲｈｏｄｅＩｓｌａｎｄ大学的Ｍ．Ｒ．Ｇｏｌｄｓｍｉｔｈ在１９９１年提出来的,也叫基因标记育种计划〔１〕。该计划主要包括两步工作：第一步是制作蚕的分子基因图,第二步是数量性状定位分析（简称ＱＴＬ分析,也叫数量性状定位作图：ＱＴＬＭａｐｐｉｎｇ）。最后利用分子标记直接在ＤＮＡ水平进行重要经济性状（数量性状）的选择、固定,即实施“分子育种”,用很短时间育成兼具高抗、强健、丝多、质优、易繁等特性的新蚕品种,本文对此进行简要介…     

The anomalous effects of biased mutation

A model is presented in which alleles at a number of loci combine to influence the value of a quantitative trait that is subject to stabilizing selection. Mutations can occur to alleles at the loci under consideration. Some of these mutations will tend to increase the value of the trait, while others will tend to decrease it. In contrast to most previous models, we allow the mean effect of mutations to be nonzero. This means that, on average, mutations can have a bias, such that they tend to either increase or decrease the value of the trait. We find, unsurprisingly, that biased mutation moves the equilibrium mean value of the quantitative trait in the direction of the bias. What is more surprising is the behavior of the deviation of the equilibrium mean value of the trait from its optimal value. This has a nonmonotonic dependence on the degree of bias, so that increasing the degree of bias can actually bring the mean phenotype closer to the optimal phenotype. Furthermore, there is a definite maximum to the extent to which biased mutation can cause a difference between the mean phenotype and the optimum. For plausible parameter values, this maximum-possible difference is small. Typically, quantitative-genetics models assume an unconstrained model of mutation, where the expected difference in effect between a parental allele and a mutant allele is independent of the current state of the parental allele. Our results show that models of this sort can easily lead to biologically implausible consequences when mutations are biased. In particular, unconstrained mutation typically leads to a continual increase or decrease in the mean allelic effects at all trait-controlling loci. Thus at each of these loci, the mean allelic effect eventually becomes extreme. This suggests that some of the models of mutation most commonly used in quantitative genetics should be modified so as to introduce genetic constraints.     

A stochastic expectation and maximization algorithm for detecting quantitative trait-associated genes

MOTIVATION: Most biological traits may be correlated with the underlying gene expression patterns that are partially determined by DNA sequence variation. The correlations between gene expressions and quantitative traits are essential for understanding the functions of genes and dissecting gene regulatory networks. RESULTS: In the present study, we adopted a novel statistical method, called the stochastic expectation and maximization (SEM) algorithm, to analyze the associations between gene expression levels and quantitative trait values and identify genetic loci controlling the gene expression variations. In the first step, gene expression levels measured from microarray experiments were assigned to two different clusters based on the strengths of their association with the phenotypes of a quantitative trait under investigation. In the second step, genes associated with the trait were mapped to genetic loci of the genome. Because gene expressions are quantitative, the genetic loci controlling the expression traits are called expression quantitative trait loci. We applied the same SEM algorithm to a real dataset collected from a barley genetic experiment with both quantitative traits and gene expression traits. For the first time, we identified genes associated with eight agronomy traits of barley. These genes were then mapped to seven chromosomes of the barley genome. The SEM algorithm and the result of the barley data analysis are useful to scientists in the areas of bioinformatics and plant breeding. Availability and implementation: The R program for the SEM algorithm can be downloaded from our website: http://www.statgen.ucr.edu.     

R/qtlcharts: Interactive Graphics for Quantitative Trait Locus Mapping

Every data visualization can be improved with some level of interactivity. Interactive graphics hold particular promise for the exploration of high-dimensional data. R/qtlcharts is an R package to create interactive graphics for experiments to map quantitative trait loci (QTL) (genetic loci that influence quantitative traits). R/qtlcharts serves as a companion to the R/qtl package, providing interactive versions of R/qtl’s static graphs, as well as additional interactive graphs for the exploration of high-dimensional genotype and phenotype data.     

Moving from QTL experimental results to the utilization of QTL in breeding programmes

Results from quantitative trait loci studies cannot be readily implemented into breeding schemes through marker assisted selection because of uncertainty about whether the quantitative trait loci identified are real and whether the identified quantitative trait loci are segregating in the breeding population. The present paper outlines and discusses strategies to reduce uncertainty in the results from quantitative trait loci studies. One strategy to confirm results from quantitative trait loci studies is to combine P -values from many quantitative trait loci experiments, while another is to establish a confirmation study. The power of a confirmation study must be high to ensure that the postulated quantitative trait loci can be verified. In the calculation of the experimental power, there are many issues that have to be addressed: size of the quantitative trait loci to be detected, significance level required, experimental design and expected heterozygosity for the design. To ensure marker assisted selection can be quickly implemented once quantitative trait loci are confirmed, DNA samples should be retained from daughters, and the sires and dams of elite sires.     

Improved power of sib-pair linkage analysis using measures of complex trait dynamics

The influence of epistasis on a quantitative trait can reduce the power of linkage analysis to identify the underlying loci. In the present study, we simulated a complex trait derived from a dynamic one-locus gene expression system with epistasis arising from feedback regulation and tested the power of sib-pair linkage analysis methods for detecting the underlying quantitative trait locus (QTL). Using this simple genetic architecture, we demonstrate that the power of sib-pair linkage analysis can be greatly improved if measures of complex trait dynamics are considered.     

Concordance of murine quantitative trait loci for salt-induced hypertension with rat and human loci

To investigate the genetic control of salt-induced hypertension, we performed a quantitative trait locus analysis on male mice from a reciprocal backcross between the salt-sensitive C57BL/6J and the normotensive A/J inbred mouse strains after they were provided with water containing 1% salt for 2 weeks. Genome-wide scans performed on these mice and analyzed with a combination of conventional marker-based regressions and a novel simultaneous search for pairs revealed six significant quantitative trait loci associated with salt-induced blood pressure, two of which were interacting loci. These six loci, named Bpq1-6 for blood pressure quantitative trait loci, mapped to D1Mit334, D1Mit14, D4Mit164, D5Mit31, D6Mit15, and D15Mit13. Furthermore, five of these six loci were concordant with hypertension loci in rats, and four were concordant with hypertension loci in humans, suggesting that quantitative trait loci mapping in model organisms can be used to guide the search for human blood pressure genes.     

Efficiency of triple test cross for detecting epistasis with marker information

The triple test cross (TTC) is an experimental design for detecting epistasis and estimating the components of genetic variance for quantitative traits. In this paper, we extend the analysis to include molecular information. The statistical power of the mating design was assessed under a model assuming that a finite number of loci affect the trait in question. Formulae are developed for the analysis with or without marker information relating to the recombination fraction between loci, the genetical properties of quantitative trait controlled by the quantitative trait loci (QTL), the linkage phases of the parents and population size. Application of these formulae showed that the recombination fraction between genes and the magnitude and the types of epistasis have important interactions in their effects on power. The results demonstrate that the TTC may have increased power to detect epistasis when marker information is present. However, the simulation experiments show that the standard deviation of the estimated expected mean square was higher with one marker than that with two, whereas the corresponding value without marker information was the lowest. In addition, we demonstrate that the relative position of QTL and markers and the number of markers can both affect the power of epistatic detection.     

Quantitative trait prediction based on genetic marker-array data, a simulation study

MOTIVATION: Using simulation studies for quantitative trait loci (QTL), we evaluate the prediction quality of regression models that include as covariates single-nucleotide polymorphism (SNP) genetic markers which did not achieve genome-wide significance in the original genome-wide association study, but were among the SNPs with the smallest P-value for the selected association test. We compare the results of such regression models to the standard approach which is to include only SNPs that achieve genome-wide significance. Using mean square prediction error as the model metric, our simulation results suggest that by using the coefficient of determination (R(2)) value as a guideline to increase or reduce the number of SNPs included in the regression model, we can achieve better prediction quality than the standard approach. However, important parameters such as trait heritability, the approximate number of QTLs, etc. have to be determined from previous studies or have to be estimated accurately.